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Hermes Agent Masterclass — Full Tutorial was originally published in Towards Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

OpenClaw Agent Masterclass — Full Tutorial

Stories by Parvez Mohammed @ Techlatest.net on Medium — Fri, 12 Jun 2026 12:53:37 GMT

Everything you need to install, configure, and extend OpenClaw — the open-source personal AI assistant that runs on your machine and talks to you on the chat apps you already use.

Official home: openclaw.ai · Docs: docs.openclaw.ai · Source: github.com/openclaw/openclaw

This guide follows the product story on the homepage (install → gateway → memory → tools → skills → channels → automation), uses prose and lists only (no comparison tables), and ships terminal + diagram GIFs like our Hermes masterclass.

What you’ll have at the end

OpenClaw installed with the Gateway daemon running
Browser Control UI at http://127.0.0.1:18789/
At least one messaging channel (Telegram recommended for first test)
A configured workspace with SOUL.md and optional ClawHub skill
Understanding of cron, heartbeats, and multi-agent routing

Deploy on OpenClaw VM

Want to skip infrastructure setup?

Available with both CPU and GPU configurations for development, testing, and production workloads.

Introduction — the AI that actually does things

OpenClaw is built for a simple promise: message an assistant from your phone, and it does real work on your computer — email triage, calendar checks, shell commands, browser tasks, file edits, and custom workflows via skills.

Unlike a chat-only bot, OpenClaw is self-hosted. Your context, skills, and session history live on your hardware. You pick the model (Anthropic, OpenAI, Google, local Ollama, and more). You control which channels can reach the agent and who is on the allowlist.

Community feedback on openclaw.ai consistently highlights the same strengths: persistent memory, persona onboarding, proactive cron/heartbeats, and the ability to extend the system by chatting (skills, plugins, even prompt hot-reload).

Part 1 — How OpenClaw is structured

OpenClaw centers on one long-running process: the Gateway. It is the control plane for:

Chat channels — WhatsApp, Telegram, Discord, Slack, Signal, iMessage, Matrix, Teams, WebChat, and plugin channels
Agent runtime — tool use, sessions, memory, skills
Control UI — browser dashboard for chat, config, and diagnostics
Companion apps — macOS menu bar, Windows tray, iOS/Android nodes (camera, voice, Canvas)

Docs: Architecture · Gateway

The Gateway is the single source of truth for sessions and routing. CLI commands (openclaw agent, openclaw onboard) and the dashboard all talk to the same core.

Part 2 — Prerequisites

You need Node.js 24 (recommended) or Node 22.19+ for compatibility. OpenClaw fails on older Node versions — if you are stuck on Node 20, use the Node 22 helper from our OpenClaw + Gemma guide.

You also need:

macOS, Linux, Windows 10+, or WSL2
An API key from your chosen provider or a local Ollama install
~5 minutes for onboarding; more if you add WhatsApp or iMessage pairing

Check:

node -v    # v22.19+ or v24
which npm

Part 3 — Install

Three paths match openclaw.ai:

One-liner (macOS, Linux, WSL)

curl -fsSL https://openclaw.ai/install.sh | bash

The installer can pull Node and dependencies. On macOS, first run may prompt for Administrator access (Homebrew).

npm global

npm install -g openclaw@latest

Hackable / from source

curl -fsSL https://openclaw.ai/install.sh | bash -s -- --install-method git
git clone https://github.com/openclaw/openclaw.git
cd openclaw && corepack enable && pnpm install
pnpm openclaw onboard

Switch release channels later:

openclaw update --channel stable   # or dev

Companion apps (beta): native macOS (15+) and Windows tray apps from openclaw.ai — gateway control, chat, and node features without living in the terminal.

Part 4 — Onboard the Gateway

Run the guided wizard:

openclaw onboard --install-daemon

The wizard walks through:

Gateway bind and authentication
LLM provider and model (API key or Ollama)
Workspace path (default under ~/.openclaw/)
Channel setup (Telegram is the fastest smoke test)
Daemon install (launchd on macOS, systemd on Linux) so the Gateway survives reboots

Onboard wizard — animated

Verify:

openclaw doctor
openclaw gateway status

Part 5 — Open the Control UI

openclaw dashboard

Default URL: http://127.0.0.1:18789/

From the dashboard you can chat, inspect sessions, edit config, and diagnose channel connections. Remote access patterns (Tailscale, SSH tunnel) are documented under Remote access.

Dashboard — animated

CLI chat without the browser:

openclaw agent --message "What can you do on this machine?" --thinking low

Agent CLI message — animated

Part 6 — What lives on disk

After onboarding, OpenClaw owns a home directory. Knowing the layout makes debugging easier.

~/.openclaw/
├── openclaw.json          # Main config (channels, models, security)
├── workspace/             # Agent workspace
│   ├── AGENTS.md
│   ├── SOUL.md            # Persona / identity
│   ├── TOOLS.md
│   └── skills/            # Installed + custom skills
│       └── <name>/
│           └── SKILL.md
├── credentials/           # Channel tokens (permissions-sensitive)
├── sessions/              # Session metadata
└── …                      # Logs, cron output, plugin state

openclaw.json is the source of truth for non-secret settings. Secrets and tokens route to appropriate credential stores.

SOUL.md defines who the agent is — tone, boundaries, and behavior. It is the identity layer (similar in spirit to HermesSOUL.md, but living in the workspace).

skills/ is where procedural knowledge lives — bundled skills, ClawHub installs, and agent-authored skills.

Workspace layout — animated

Copy a starter soul from this guide:

cp guides/openclaw/examples/SOUL.md ~/.openclaw/workspace/SOUL.md

Part 7 — Capabilities (from the homepage)

OpenClaw advertises six pillars on openclaw.ai. Here is what each means in practice.

Runs on your machine. macOS, Windows, or Linux. Connect Anthropic, OpenAI, Google, or local models. Data stays on your infrastructure unless a tool explicitly calls an external API.

Any chat app. One Gateway serves many channels. DMs and group chats are supported; group behavior often uses mention rules so the bot does not reply to every message.

Persistent memory. The agent remembers preferences and context across sessions — your assistant becomes specific to you, not a generic chatbot.

Browser control. Navigate pages, fill forms, extract data. Useful for research, booking flows, and admin panels that have no API.

Full system access (configurable). Read/write files, run shell commands, execute scripts. You choose sandbox vs full access based on trust and host environment.

Skills and plugins. Install community skills from ClawHub, add channel plugins, or describe a new workflow in chat and let the agent draft a skill.

Part 8 — Connect messaging channels

Telegram is the quickest first channel: create a bot with @BotFather, paste the token during onboarding or in config.

WhatsApp and iMessage require additional pairing steps documented in the Channels hub.

Minimal allowlist snippet — merge into ~/.openclaw/openclaw.json (full example in examples/openclaw-channels.snippet.json):

{
  channels: {
    whatsapp: {
      allowFrom: ["+15555550123"],
      groups: { "*": { requireMention: true } },
    },
  },
  messages: { groupChat: { mentionPatterns: ["@openclaw"] } },
}

Restart after config changes:

openclaw gateway restart

Security: start restrictive — allowlist phone numbers and require mentions in groups. See Security.

Supported surfaces include WhatsApp, Telegram, Discord, Slack, Signal, iMessage, Google Chat, Matrix, Microsoft Teams, Zalo, WebChat, and plugin channels — 50+ integrations on the marketing site.

Part 9 — Skills and ClawHub

Skills are Markdown with YAML frontmatter — the agent loads descriptions cheaply and pulls full instructions when a task matches.

Install from ClawHub:

openclaw skills search calendar
openclaw skills install <skill-slug>

Browse clawhub.ai. Recent OpenClaw releases emphasize Skill Cards and security scanning (SkillSpector) for hub skills — see the Skill Workshop blog post.

The agent can also author skills from conversation — e.g. “build a skill that checks my WHOOP metrics” — matching patterns described in community shoutouts on the homepage.

Skill Workshop (2026): review and approve proposed skills before they change agent behavior — product direction toward safer self-modification.

Progressive loading keeps token use sane:

Catalog view — names and descriptions only
Full skill — load SKILL.md when triggered
References — optional deep files inside the skill folder

Team-private skills: host a Git repo and install via a slug, using the same pattern as Hermes Skills Hub taps.

Part 10 — Models and local inference

Set or switch models:

openclaw models list
openclaw models set anthropic/claude-sonnet-4
# or local:
openclaw models set ollama/gemma4:e2b

For a full local stack (Ollama + RAG skill), follow OpenClaw + Gemma + RAG.

Providers are swappable without rebuilding the Gateway — the agent runtime handles translation to supported API formats.

Part 11 — Proactive automation: cron and heartbeats

OpenClaw is designed to be proactive, not only reactive.

Cron jobs schedule isolated agent runs — daily briefings, inbox sweeps, reminders. Describe schedules in natural language or use cron syntax. Jobs persist in config and survive Gateway restarts.

Example prompt inside a chat session:

Every weekday at 8am, summarize my calendar and unread priority emails.
Deliver the summary here. Set this up as a recurring cron job.

List jobs:

openclaw cron list

Heartbeats are periodic check-ins — the agent may reach out when something needs attention (community reports surprise check-ins during heartbeats). Configure through workspace and gateway settings per docs.

Useful variants:

One-shot delay: /cron add 30m "Remind me to check the build"
Interval: /cron add "every 2h" "Check server status"
Attach a skill: run a job with --skill <name> so the agent loads a playbook first

Part 12 — Multi-agent routing

One Gateway can route multiple isolated agents — different workspaces, sessions, or senders. Useful for “work agent” vs “personal agent”, or separate Telegram bots.

Concepts:

Session isolation — conversations do not leak context across routes
Workspace per agent — distinct SOUL.mdSkills and tools
Sender-based routing — map channels or users to different agents

Docs: Multi-agent routing

Part 13 — Nodes, voice, and Canvas

Mobile nodes pair iOS/Android apps with the Gateway for camera capture, voice workflows, and Canvas (visual workspace). The macOS/Windows companion apps expose tray controls and local node mode.

Docs: Nodes

This is how users run “fix production from a dog walk” workflows — phone chat triggers agent execution on a home server or Mac mini.

Part 14 — OpenClaw vs Hermes (prose only)

Both are self-hosted, messaging-friendly agent runtimes. Neither is a hosted SaaS.

OpenClaw leads with the Gateway and channels — the product feels like “message your computer from WhatsApp.” Skills extend behavior; the community hub (ClawHub) is large; onboarding and Control UI are polished for personal assistants.

Hermes leads with the learning agent — runtime skill authoring, Curator maintenance, optional GEPA offline validation, and research-oriented tooling (MCP, profiles, training pipeline). See Hermes Agent Masterclass.

You can migrate between them: hermes claw migrate Import OpenClaw-style config into Hermes. Full side-by-side: Hermes vs OpenClaw.

Pick OpenClaw when channel UX, ClawHub, and dashboard-first setup matter most. Pick Hermes when the self-improving skill library and experiment loop matter most. Many operators run one primary runtime and borrow skills from the other ecosystem.

Part 15 — Troubleshooting

openclaw: command not found — reinstall globally or ensure npm global bin is on PATH.

Gateway will not start — runopenclaw doctor; check port 18789 conflicts.

Node version errors — upgrade to Node 22.19+ or 24.

Channel connected but no replies — verify allowlists, mention rules in groups, and bot token.

Model errors — confirm API key in config; test with openclaw agent --message hi.

Docs entry: Troubleshooting

Part 16 — Verify this guide

chmod +x guides/openclaw/scripts/verify-openclaw.sh
./guides/openclaw/scripts/verify-openclaw.sh

Official links

openclaw.ai — product home
docs.openclaw.ai — documentation
github.com/openclaw/openclaw — source
clawhub.ai — skill registry
Discord community

Summary

OpenClaw is a Gateway-first personal agent: install withopenclaw onboard, chat from the dashboard or your favorite messaging app, extend with skills and cron, and keep data on your machine. Start with Telegram and the Control UI, tighten security with allowlists, then add ClawHub skills and automation once the loop feels natural.

Thank you so much for reading

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Catch us on

LinkedIn: https://www.linkedin.com/in/techlatest-net/

OpenClaw Agent Masterclass — Full Tutorial was originally published in Towards Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

🎲 Weekly Post #118

indieblog.page daily random posts — Fri, 12 Jun 2026 10:37:00 GMT

This random indieblog.page link was picked on Friday, June 12th 2026. It was originally published on Saturday, April 11th 2026 at Maudlin Ruminations.

If you'd like to report any problems with this post or the blog, please include the following ID with your report: 0937ef9e9428b5420a17b32d6f0dfd4c-696396

Maura Labingi's Tea Set

TrickJarrett.com — Fri, 12 Jun 2026 10:02:35 GMT

Here is a good peek inside my head.

Growing up, I would listen to Car Talk on the radio with my mom. Car Talk was an advice show on NPR that was hosted by the Magliozzi brothers. Two Boston mechanics who answered questions from callers about car issues every week.

And one of the brothers had a phrase he used which has stuck with me. I don't remember if it was across multiple shows or just one which sticks in my memory, but I remember he said "Tara Lipinski's tutu!" I think this must have been around a Winter Olympics.

Jump forward to this week.

I was reading a discussion about a person discovering that Tolkien had made a note about translating the names in Lord of the Rings, and how Frodo's name is actually Maura Labingi. I have known this fact for years but it isn't something I really think about. But, this time, as I read the name my brain connected the scanning of Labingi with Lipinski.

That set me to thinking about the above quote from Car Talk, and I made myself chuckle by thinking about using the phrase "Maura Labingi's tutu" except I realized Frodo aka Maura would not wear a tutu, but he would definitely use a tea set.

And so, in an alternate universe where there are two hobbit brothers answering wagon questions at the Bird and Baby inn, one of them is surprised by a question and proclaims loudly "Maura Labingi's tea set!"

Steam nerfed popular upcoming. So what?

Alice GG — Fri, 12 Jun 2026 10:00:00 GMT

In the latest Steam client update, Valve unveiled major changes to its store’s home page. One of the main difference is that the “Popular Upcoming” section, which shows upcoming games now shows a lot fewer titles. This seems to worry indie developers who relied on this section as a major driver of visibility before launch. I personally think this is will not change much to how indie games are marketed and might even be a positive for many niche developers.

Popular upcoming is dead

Game development is more accessible than ever, and anyone can post their games on Steam and Epic. All those new developers needing to market their games has created a cottage industry of game marketing influencers. The strategy they teach is basically always a variant of:

Make a game (and a demo)
Get a lot of people to play the demo during Steam Next Fest
Get 6000+ wishlists
Appear in Popular Upcoming
???
Profit

Since everyone is running around with approximately the same strategy, it became a very crowded trade. With demos getting more and more polished, Next Fest began to have diminishing returns for most developers. In the same way Popular Upcoming was very impactful when maybe 1 or 2 games appeared in it every day, but when it started showing a dozen game a day, it stopped being a guarantee of success.

And let’s be honest, players have probably been paying less and less attention to this section of the store for a while. Unless they were really interested in finding out what every shovelware publisher has been working on.

Long live the Personal Calendar

In the same update, Valve introduced a new feature: the Personal Calendar. It’s a whole page which aims at making players discover games that are match their taste.

The recommendation algorithm seems to work somewhat decently. I do not think it is entirely based on tags, since it seems to recommend me a lot of “Open World Survival Craft” games, which is not something I usually play. It may be using wishlist behavior of similar players to make recommendations.

One of the major differences is that it shows a calendar for the next eight weeks, so games can have a much longer visibility window. If a player of your target audience didn’t browse Steam on the day before your launch, it would miss you on popular upcoming, while now they have six weeks to do that.

It also shows recently released games, in the past 7 days and past month. Contrary to the homepage “Popular New Releases” tab (ex “New & Trending”), which requires a lot of active players, this section seems accessible even to unknown games. Steam is currently recommending me Imago Season with (at the moment of writing) 0 review and only 2 concurrent players.

A new era for niche games?

Before this change, it was extremely difficult for developers to market niche games. I know it from first-hand experience, since I have no doubt Dice ‘n Goblins would have been easier to market if it was not the weirdest combination of ideas possible.

Because of this, a lot of new indie game developers were advised to stick to a popular genre. You love real-time strategy? Too bad, you’ll have to make an action roguelike instead.

This has caused a loss of creativity in the indie game world. With many developers working on genres that do not interest them, using recycled ideas and aesthetics.

I do believe that this new version of Steam might swing the pendulum in the other way. Now that players of niche games can get recommendation customized to their taste, making games for a non-mainstream audience can become viable again.

Agent-Native Epistemics: An Epistemology a Machine Can Run

june.kim — Fri, 12 Jun 2026 03:00:00 GMT

You Cannot Ring a Semiring

june.kim — Fri, 12 Jun 2026 03:00:00 GMT

Why are cached input tokens cheaper with AI services?

Xe Iaso's blog — Fri, 12 Jun 2026 03:00:00 GMT

TL;DR: the GPU doesn't have to math as hard

Taking Stock of the Seed Stage

Who is Nnamdi? — Fri, 12 Jun 2026 03:00:00 GMT

The total stock of seed-stage startups appears to be shrinking, despite the AI funding boom.

Nightly Rust development with devenv

Paperless — Fri, 12 Jun 2026 03:00:00 GMT

Creating a Nix shell for Rust development is easy if you’re prepared to either use only stable Rust or use Rustup to manage your Rust toolchain. But the latest version of Bevy was not compatible with stable Rust at the time of writing, and using Rustup would sacrifice one of the biggest advantages of Nix: having reproducibility managed by a single component. I’m pretty happy with what I ended up with, so here’s the gist of what you need.

Automated Archives for June, 11th 2026

TrickJarrett.com — Fri, 12 Jun 2026 02:45:00 GMT

This post was automatically generated.

Articles To Read

The following are articles that I saved today. Substance and quality will vary drastically.

The Heist State

Chess For the Day

Record: 5-2-4
Net Elo Change: +5

Games Played

Blog Posts On This Day

June 11, 2025 (1 post)
June 11, 2024 (1 post)
June 11, 2023 (6 posts)

Sunil Shenoy — Fri, 12 Jun 2026 00:52:22 GMT

Kwonny

I liked this print from the art collection by Kwonny. On my wish list to buy.

Hashtag Motorsport

LostFocus — Thu, 11 Jun 2026 23:25:20 GMT

I very much prefer the fediverse aka Mastodon (yes, I know) over the other text-based social networks. Especially over the one that used to be so important to me.But it does have it downsides and I usually notice them when I am trying to see who else besides the people I follow is talking about […]

Anthropic Cybersecurity Skills — Full Tutorial

Stories by Parvez Mohammed @ Techlatest.net on Medium — Thu, 11 Jun 2026 22:29:38 GMT

Give any AI agent the structured decision-making of a senior security analyst — not generic web search, but step-by-step playbooks mapped to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF.

Based on mukul975/Anthropic-Cybersecurity-Skills (754 skills · 26 domains · Apache 2.0).

Community project — not affiliated with Anthropic PBC.

What you’ll learn

What the library is and why it exists
How the agentskills.io standard enables progressive disclosure
All five framework mappings and how to use them in compliance workflows
Install on Claude Code, Cursor, Copilot, Codex CLI, Gemini CLI, Hermes, and MCP agents
Skill anatomy — frontmatter, Workflow, Verification, references, scripts
End-to-end examples: memory forensics, threat hunting, cloud IR
All 26 security domains and when to activate each
Contributing, responsible use, citation, and troubleshooting

Part 1 — The problem this solves
Part 2 — Library at a glance
Part 3 — Architecture and progressive disclosure
Part 4 — Five frameworks, one skill library
Part 5 — Quick start installation
Part 6 — Claude Code setup
Part 7 — Cursor setup
Part 8 — GitHub Copilot and Codex CLI
Part 9 — Gemini CLI and other platforms
Part 10 — Hermes Agent integration
Part 11 — Skill anatomy deep dive
Part 12 — How agents discover and execute skills
Part 13 — Walkthrough: credential theft in a memory dump
Part 14 — Walkthrough: hypothesis-driven threat hunting
Part 15 — Walkthrough: multi-cloud breach scoping
Part 16 — All 26 security domains
Part 17 — MITRE ATT&CK v19.1 coverage
Part 18 — Compliance and risk frameworks in practice
Part 19 — Casky Playground and GARS-2026
Part 20 — Contributing your own skill
Part 21 — Security, ethics, and authorized use
Part 22 — Troubleshooting
Part 23 — Citation and license

TL;DR

754 production-grade cybersecurity skills for AI agents — structured playbooks, not random scripts or payload dumps
Community project (mukul975/Anthropic-Cybersecurity-Skills) — not affiliated with Anthropic PBC · Apache 2.0
26 security domains — cloud, DFIR, threat hunting, web app, OT/ICS, red team, and more
5 framework mappings per skill — MITRE ATT&CK v19.1 · NIST CSF 2.0 · MITRE ATLAS · D3FEND · NIST AI RMF
Built on agentskills.io — YAML frontmatter for discovery + Markdown workflows for execution
Progressive disclosure — scan all 754 skills at ~30 tokens each, load only matching playbooks at ~500–2K tokens
One-line install: npx skills add mukul975/Anthropic-Cybersecurity-Skills
Works with Cursor, Claude Code, Copilot, Codex CLI, Gemini CLI, Hermes, and MCP agents
Tutorial includes animated GIFs — install steps, architecture, skill anatomy, DFIR walkthrough, domain + ATT&CK tables
Runnable scripts — inspect real SKILL.md files and walk through a credential-dump IR scenario
Closes the gap between “LLM that searches the web” and “agent that follows a senior analyst playbook.”

Note

BlackArch Linux
We also provide a ready-to-deploy BlackArch Linux VM that can be launched instantly on AWS, GCP, or Azure. No installation, setup, or dependency management required — just spin it up and start using a full arsenal of penetration testing and security auditing tools in minutes.

Kali GUI Linux
Our Kali GUI Linux VM comes fully pre-configured with a graphical interface, making it easy for both beginners and professionals to get started. Deploy directly on AWS, GCP, or Azure with zero setup — no installation hassles, just immediate access to a complete offensive security toolkit.

Browser-Based Kali Linux
We offer a browser-based Kali Linux environment that runs entirely in the cloud. Simply deploy and access it from your browser — no downloads, no local setup, no compatibility issues. Deploy directly on AWS, GCP, or Azure with zero setup — no installation hassles, just immediate access to a complete offensive security toolkit. Perfect for quick testing, learning, and remote security operations from anywhere.

ParrotOS Linux
Our ParrotOS Linux VM is optimized for security, privacy, and development workflows. Available for instant deployment on AWS, GCP, and Azure, it eliminates the need for manual installation — giving you a secure, ready-to-use environment in just a few clicks.

Part 1 — The problem this solves

The cybersecurity workforce gap hit 4.8 million unfilled roles globally in 2024 (ISC2). AI agents can help close that gap — but only if they have structured domain knowledge to work from.

Today’s agents can write code and search the web. They typically cannot:

Pick the right Volatility3 plugin for a suspicious memory dump
Know which Sigma rules catch Kerberoasting
Scope a cloud breach across AWS, Azure, and GCP with consistent playbooks
Map findings to ATT&CK techniques without hallucinating IDs

Existing security repos give you wordlists, payloads, or exploit code. None give an AI agent the decision workflow a senior analyst follows: prerequisites, step order, verification, and framework mapping.

Anthropic Cybersecurity Skills fills that gap: 754 skills, each a practitioner playbook in agentskills.io format — YAML frontmatter for discovery, Markdown body for execution, optional references/scripts/assets for depth.

Part 2 — Library at a glance

What it is not

Not an Anthropic official product
Not a script dump or payload collection
Not a replacement for authorization, legal scope, or human judgment

What it is

An AI-native knowledge base built for agent toolchains
Validated ATT&CK v19.1 mappings via mitreattack-python — zero revoked IDs
The only open-source skills library with unified five-framework coverage per skill

Part 3 — Architecture and progressive disclosure

Part 4 — Five frameworks, one skill library

No other open-source skills library maps every skill to all five frameworks. One skill, five compliance checkboxes.

Example — one skill, five mappings

Skill: analyzing-network-traffic-of-malware

Part 5 — Quick start installation

Option A — npx (recommended)

Works with any agentskills.io-compatible platform:

npx skills add mukul975/Anthropic-Cybersecurity-Skills

The installer registers skills in your agent’s configured skills directory.

Option B — Git clone

git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
cd Anthropic-Cybersecurity-Skills

Inspect skills/ — each subdirectory is one skill with SKILL.md at the root.

Option C — This guide’s helper script

cd guides/anthropic-cybersecurity-skills
chmod +x install-skills.sh verify-install.sh
./install-skills.sh
./verify-install.sh

Default clone path: ~/.cybersec-skills/Anthropic-Cybersecurity-Skills. Override:

export CYBERSEC_SKILLS_DIR=/opt/security-skills/Anthropic-Cybersecurity-Skills
./install-skills.sh

Part 6 — Claude Code setup

Claude Code — symlink skills to ~/.claude/skills/

Claude Code loads skills from .claude/skills/ (project) or ~/.claude/skills/ (global).

Global install (all projects)

SKILLS_SRC=~/.cybersec-skills/Anthropic-Cybersecurity-Skills/skills
mkdir -p ~/.claude/skills

# Symlink entire library (754 skills — high discovery surface)
ln -sf "${SKILLS_SRC}"/* ~/.claude/skills/

# Or copy a subset — e.g. DFIR only
cp -r "${SKILLS_SRC}"/performing-memory-forensics-with-volatility3 ~/.claude/skills/
cp -r "${SKILLS_SRC}"/hunting-for-credential-dumping-lsass ~/.claude/skills/

Project-scoped (one engagement)

mkdir -p .claude/skills
ln -sf ~/.cybersec-skills/Anthropic-Cybersecurity-Skills/skills/* .claude/skills/

Verify in Claude Code

Start a session and ask:

Use the performing-memory-forensics-with-volatility3 skill. List prerequisites and the first three Workflow steps only.

Claude should read SKILL.md and cite structured sections — not invent generic Volatility commands.

Part 7 — Cursor setup

Cursor — npx or manual symlink to ~/.cursor/skills/

Cursor discovers skills listed in agent configuration and from ~/.cursor/skills/ (user skills).

Install via npx

npx skills add mukul975/Anthropic-Cybersecurity-Skills

Follow Cursor-specific prompts if the installer detects your environment.

Manual symlink

mkdir -p ~/.cursor/skills
ln -sf ~/.cybersec-skills/Anthropic-Cybersecurity-Skills/skills/* ~/.cursor/skills/

Project rules (optional)

Add to .cursor/rules/ or project instructions:

For security investigations, prefer skills from Anthropic Cybersecurity Skills.
Scan skill frontmatter by tags (dfir, threat-hunting, cloud-security) before loading full SKILL.md.
Always complete the Verification section before closing an investigation step.

Verify in Cursor

Open Agent mode and prompt:

I have a Windows memory dump. Which cybersecurity skills apply? Load the best match and show Prerequisites.

Part 8 — GitHub Copilot and Codex CLI

Copilot + Codex CLI — install skills and invoke by name

Both support agentskills.io when configured with a skills path.

Copilot (VS Code / JetBrains)

Clone or npx skills add the repo
Point Copilot’s agent skills setting at skills/
In agent chat: reference skill name in kebab-case (e.g. hunting-for-lateral-movement-with-sysmon)

OpenAI Codex CLI

npx skills add mukul975/Anthropic-Cybersecurity-Skills
codex  # or your configured entrypoint

Codex reads frontmatter for routing; load full skills for multi-step IR workflows.

Part 9 — Gemini CLI and other platforms

Gemini CLI — npx install and skill invocation

Compatible without custom forks:

Gemini CLI: install skills via npx skills add, then invoke by skill name in prompts.

LangChain / CrewAI: mount skills/<name>/SKILL.md as tool description or system prompt segment; use frontmatter tags for retrieval routing.

MCP agents: expose skill search as an MCP resource listing frontmatter; fetch full SKILL.md on match.

Part 10 — Hermes Agent integration

Hermes — copy skills into ~/.hermes/skills/

Hermes uses ~/.hermes/skills/ (same agentskills.io layout).

git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/cybersec-skills
cp -r /tmp/cybersec-skills/skills/* ~/.hermes/skills/
hermes skills list | head

For SOC automation, combine with Hermes cron/Curator so frequently used skills stay prioritized. See Awesome Hermes Agent tutorial.

Example Hermes prompt:

Run a hypothesis-driven hunt for Kerberoasting using the threat hunting skills. Map hits to ATT&CK T1558.003.

Part 11 — Skill anatomy deep dive

Every skill follows a consistent directory structure:

skills/performing-memory-forensics-with-volatility3/
├── SKILL.md              ← Definition (YAML + Markdown)
├── references/
│   ├── standards.md      ← Framework mappings
│   └── workflows.md      ← Deep technical reference
├── scripts/
│   └── process.py        ← Helper scripts
└── assets/
    └── template.md       ← Report templates

YAML frontmatter (real example)

---
name: performing-memory-forensics-with-volatility3
description: >-
  Analyze memory dumps to extract running processes, network connections,
  injected code, and malware artifacts using the Volatility3 framework.
domain: cybersecurity
subdomain: digital-forensics
tags: [forensics, memory-analysis, volatility3, incident-response, dfir]
atlas_techniques: [AML.T0047]
d3fend_techniques: [D3-MA, D3-PSMD]
nist_ai_rmf: [MEASURE-2.6]
nist_csf: [DE.CM-01, RS.AN-03]
version: "1.2"
author: mukul975
license: Apache-2.0
---

Part 12 — How agents discover and execute skills

User prompt: “Analyze this memory dump for signs of credential theft.”

Agent internal process:

Scan 754 frontmatter (~30 tokens each)
→ Match tags: forensics, credential-access, memory-analysis
→ 12 candidate skills
Load top 3:

performing-memory-forensics-with-volatility3
hunting-for-credential-dumping-lsass
analyzing-windows-event-logs-for-credential-access

Execute Workflow — Volatility3 plugins, LSASS access patterns, event log correlation
Verification — confirm IOCs, map to ATT&CK T1003 (Credential Dumping)

Without skills, the agent guesses commands and skips steps. With skills, it follows the same playbook a senior DFIR analyst would use.

Tips for better agent behavior

Ask the agent to name the skill before executing
Require Verification section output in every response
For red team skills, state authorized scope in the prompt
Use subset installs (10–20 skills) if the agent overloads context

Part 13 — Walkthrough: credential theft in a memory dump

Scenario: IR ticket — suspected Mimikatz on a Windows server. You have a .raw memory image.

Step 1 — Activate the right skills

Prompt:

Authorized DFIR on image server01.raw. Find skills for memory forensics and credential dumping. List prerequisites.

Expected skills: memory forensics + LSASS hunting + Windows event logs.

Step 2 — Prerequisites check

Agent should verify from SKILL.md:

Volatility3 installed (vol -h)
Symbol tables / Windows profile for OS build
Sufficient disk space for plugin output
Chain of custody documented

Step 3 — Workflow execution

Typical workflow order (from skills):

windows.info / windows.pslist — baseline processes
windows.malfind / windows.vadwalk — injection indicators
LSASS-focused plugins and handle analysis
Correlate with Security Event ID 4656/4663 if disk logs are available

Step 4 — Verification

Named process accessing lsass.exe with suspicious privileges
In-memory strings or injection matching known dump tools
Timeline aligns with alert timestamp
ATT&CK: T1003.001 OS Credential Dumping: LSASS Memory

Step 5 — Report

Use skill assets/template.md if present; include framework mappings from references/standards.md.

Part 14 — Walkthrough: hypothesis-driven threat hunting

Scenario: Hunt for Kerberoasting in Enterprise SIEM.

Hypothesis

Service accounts may be targeted via Kerberoasting (T1558.003) in the last 30 days.

Skill selection

Tags: threat-hunting, kerberos, sigma, splunk or sentinel.

Agent loads hunting skill → Workflow:

Deploy/validate Sigma rule for Kerberoasting
Query rare RC4/HMAC service ticket requests
Enrich service accounts — SPN exposure, password age
Escalate confirmed anomalies to IR queue

Verification

Non-noise hits with service account + weak crypto ticket
ATT&CK technique documented
Hunt notebook updated for repeatability

Part 15 — Walkthrough: multi-cloud breach scoping

Scenario: Credentials leaked; unknown activity in AWS, Azure, and GCP.

Skills to combine

Agent workflow:

Contain — disable keys, force password reset (Incident Response skills)
Discover — each provider’s log skill in parallel
Collect — unified timeline (Digital Forensics)
Map — ATT&CK cloud techniques (T1078, T1530, etc.)
Report — NIST CSF RS.AN / RS.MI categories

Part 16 — All 26 security domains

Part 17 — MITRE ATT&CK v19.1 coverage

754/754 skills mapped. Validated with official mitreattack-python — no revoked or deprecated IDs.

v19.1 change: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112).

Part 18 — Compliance and risk frameworks in practice

NIST CSF 2.0

Map skill outputs to Govern, Identify, Protect, Detect, Respond, Recover for audit trails. Example: memory forensics → Detect (DE.CM), Respond (RS.AN).

MITRE ATLAS

Use when the incident involves ML models — poisoning, evasion, model theft. Frontmatter field: atlas_techniques.

MITRE D3FEND

Pair offensive findings with defensive countermeasures — e.g. D3-NTA for network traffic analysis skills.

NIST AI RMF

For AI governance — document which agent skills were used, human-in-the-loop checkpoints, and measurement (MEASURE-* subcategories).

See Framework mappings for crosswalk tables and reporting templates.

Part 19 — Casky Playground and GARS-2026

Casky.ai Playground

Hands-on exercises without local install:

→ Launch Playground on Casky.ai

Live cybersecurity skill exercises
Real-time agent execution
Interactive ATT&CK-mapped workflows

GARS-2026 Survey

Global Agentic AI Readiness Survey (SRH Berlin) — measures readiness for MCP, tool calling, and governance.

~10 minutes, anonymous
Results published open access (CC-BY 4.0)
Link in upstream README

Part 20 — Contributing your own skill

Fork Anthropic-Cybersecurity-Skills
Copy the skill template from CONTRIBUTING.md
Add skills/your-skill-name/SKILL.md with full frontmatter + four body sections
Add references/standards.md with ATT&CK + framework IDs
PR title: Add skill: your-skill-name
Review within ~48 hours for technical accuracy and agentskills.io compliance

Improve existing skills: framework mappings, fixed commands, new scripts/templates.

Report issues: inaccurate procedures or broken scripts → GitHub Issues.

Project follows Contributor Covenant.

Part 21 — Security, ethics, and authorized use

These skills describe ** offensive and defensive techniques**. Use only:

On systems you own or have written authorization to test
Within bug bounty/pentest/red team scope
With human oversight for destructive or exfiltration steps

AI agents can execute commands quickly — mis-scoped prompts cause real damage. Always:

State authorization in the prompt
Use read-only modes where available
Keep humans in the loop for containment and legal notification

Upstream Security Policy: responsible disclosure, 48-hour acknowledgment.

Part 22 — Troubleshooting

Run ./verify-install.sh after every pull.

Part 23 — Citation and license

@software{anthropic_cybersecurity_skills,
  author       = {Jangra, Mahipal},
  title        = {Anthropic Cybersecurity Skills},
  year         = {2026},
  url          = {https://github.com/mukul975/Anthropic-Cybersecurity-Skills},
  license      = {Apache-2.0},
  note         = {754 structured cybersecurity skills for AI agents,
                  mapped to MITRE ATT\&CK, NIST CSF 2.0, MITRE ATLAS,
                  MITRE D3FEND, and NIST AI RMF}
}

License

Apache License 2.0 — use, modify, and distribute in personal and commercial projects.

Conclusion

The cybersecurity skills gap is not going to close with generic chatbots alone. Analysts do not win investigations because an LLM can search the web — they win because they know which playbook to run, in what order, and how to verify the result before closing the ticket.

Anthropic Cybersecurity Skills (community-built, Apache 2.0) gives AI agents that same structure: 754 skills across 26 domains, each mapped to MITRE ATT&CK, NIST CSF, ATLAS, D3FEND, and NIST AI RMF. The agentskills.io format makes it practical — scan lightweight frontmatter first, load full workflows only when the incident demands it.

You do not need a custom fork or a new agent runtime. One install command works across Cursor, Claude Code, Copilot, Codex CLI, Gemini CLI, and Hermes. Point your agent at the library, name the skill in your prompt, and require the Verification step before it reports done.

Start here:

npx skills add mukul975/Anthropic-Cybersecurity-Skills

Then walk through the tutorial: inspect a real SKILL.md, run the credential-dump walkthrough, and pick skills by domain or ATT&CK tactic. Use them only on authorized systems — these are practitioner playbooks, not toys.

If this helps your SOC or red-team workflow, star the upstream repo and consider contributing a skill in an underrepresented domain like Deception Technology or Compliance & Governance. The library grows on community PRs — and the agents using it get sharper with every one.

Thank you so much for reading

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Anthropic Cybersecurity Skills — Full Tutorial was originally published in OSINT Team on Medium, where people are continuing the conversation by highlighting and responding to this story.

Build your first chatbot in Voiceflow

Adnan's Random bytes — Thu, 11 Jun 2026 20:59:20 GMT

This post is part of the Voiceflow Series Chatbots have become a common part of modern businesses. Companies use them to answer customer questions, collect leads, assist with online purchases, schedule appointments, and much more. Their conversational nature makes it easier for users to get the information they need quickly, often reducing response times and improving the overall customer experience. As a result, chatbots have become an effective way for businesses to engage with visitors while automating repetitive interactions. There are many chatbot platforms and frameworks available today, but I personally recommend Voiceflow because of its simplicity and, to be honest, it just works. To keep things simple, we’ll build a Pizza Ordering chatbot that collects a few details from the user and displays an order summary. It won’t connect to a backend ordering system or make any API calls. The goal of this tutorial is simply to give you a feel for how chatbot development works in Voiceflow. Let’s get started! What is Voiceflow Voiceflow is a platform for building chatbots and AI assistants without writing much code. It provides a visual canvas where you can design conversations, test them, and deploy them to different channels. What We’ll Build For this tutorial, we are going to build a simple Pizza Ordering chatbot. The bot will ask users to choose a pizza flavor and size, confirm the order, and then display an order number and order details. Building Your First Chatbot It is obvious, but Voiceflow still asks you to sign up (it’s my referral link. I hope you don’t mind). Once you are registered and verified via email, the onboarding process will start. You are asked about the reason for using it. I select Personal for the sake of this series. Next, select a channel for your chatbot. For this tutorial, you can choose Web Chat. Next, Voiceflow will ask you to select a use case. I chose Other because we’ll be starting from scratch. It will ask about the number of requests, click on top of it, and eventually, it’ll be redirected to this screen: Now click the Workflows option, then click the Create workflow button. Do it as I mention below and create the workflow. What you see now is the Canvas or Design Editor, if you prefer. Click the down arrow next to the Agentic heading to see basic sections like Message, Cards, etc. Let’s set the greeting message for our bot. Click and drag the Message option on the canvas and set any message you want, for me it is: Welcome to Little Kids Pizza. Connect the two and hit the Run button. Next, let’s ask the user to select one of the available pizzas. Variants feature uses AI to generate 3 different varieties of the same message, giving the bot a humanish feeling. Next, we’ll use a Carousel component to display the available pizzas and allow users to make a selection: When you run it, it looks like the following: Next, I am going to introduce Variables. Variables, as the name suggests, hold some value. We will be using two variables:selected_pizza and selected_size. Click on the Variables option in the left pane and add these variables. The goal is to assign values to these variables. To do that, we’d be using the Set step that will help to assign values: Let’s run it. Select your desired pizza. To see the current variable values, select the Variables section in the debug window and search selected_pizza variable, you should see something like below: Next, select the size. I am going to add 3 buttons for it: And when you run, it shows buttons like this: Like before, we are going to assign values to the variable, selected_size Next, we are going to grab contact details; for that, we will be using the Listen step. Last, we will be generating a random order number; for that, I introduce the Code step. You write code in JavaScript const randomNumber = Math.floor(1000 + Math.random() * 9000) {order_number} = `ON-${randomNumber}` When you run the workflow till now, it produces the following: In the end, we are going to add a screen that tells you about the order confirmation and end the session. Conclusion This was an intro of the world of Vocieflow. As you can see, it is easy to create a chatbot in it. It was a very basic having no flavor of AI in it. The purpose was to introduce you to the environment of the app. In future posts, we will be discussing more features of it. Sign up with my link and upgrade to the paid package, and in case you find an issue, lemme know.

The post Build your first chatbot in Voiceflow first appeared on Adnan's Random bytes.

Jamie Tanna | Software Engineer — Thu, 11 Jun 2026 19:17:00 GMT

Listened to The Last Handsome Cockroach Standing - S21, Ep.9 by PodBean Development

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It's the penultimate episode of series 21, but a first for episode titles and speaking Welsh. In this episode Jack and Jenny discuss the evolution of Joanna's storytelling, why Armando Iannuci is an inspiration in getting it wrong, a possible answer to Joel's egg timer conundrum, Kumail Nanjiani's famous planking, and why people who've shat themselves can learn a lot from Joanna Page. Plus Jack's still not convinced you can dance on custard and a listener writes in to point out a TMPP first.Send all your Series 21 thoughts along to fans@taskmaster.tvDownload the Taskmaster App here:https://taskmaster.tv/appIf you're in the UK you can watch all of Taskmaster on All 4 www.channel4.com/programmes/taskmasterAnywhere else, it's the Taskmaster YouTubeyoutube.com/taskmasterVisit the Taskmaster Store for all your TM goodies!taskmasterstore.com

Honeypot.net — Thu, 11 Jun 2026 18:51:32 GMT

I was talking to some AI industry leaders last night. Their words helped crystalize my own thoughts on Anthropic’s “guardrails” around using their models.

A lot of what I do is legal only because of context. I carry lock picks in my bag. I use them to get into my own stuff (and to satisfy my own curiosity) and never use them to gain access I shouldn’t have. Is it bad to use lock picks? That’s impossible to say without knowing why they’re used.

I ask AI to find security flaws in my software to identify those problems (and to satisfy my own curiosity) and never use it to gain access I shouldn’t have. Is it bad to use Fable — or any number of “hacking” tools like Burp Suite or Metasploit or John The Ripper? That’s impossible to say without knowing why they’re used.

And damned if I want to come to Anthropic, hat in hand, and beg them to please, sir, may I use this tool I’m paying you for, and I’ll let you watch over my shoulder the whole time to see if I’m doing it alright? If they think they might not want my money sometimes, they shouldn’t take it at all.

I didn’t have to explain to the lock picks guy what I’m doing with them.

Sol Cesto: Rescuing the Sun - Review

Boiling Steam — Thu, 11 Jun 2026 18:49:04 GMT

There are quite a few gems out there when it comes to roguelite games, and from now on we need to add Sol Cesto to the list. The concept is dead simple: the sun has disappeared, and the world has plunged into darkness. The sunlight has been captured by an underground den of monsters, and your role, as a hero raising to the challenge, is to make it to the bottom of that deadly pit to rescue the sun to save humanity. Regular daily quest, right?

Finding Vivian Maier

Ben Oliver — Thu, 11 Jun 2026 18:39:23 GMT

"The real question is, why did she do this kind of work?"

In 2007 John Maloof, a local historian in Chicago, found a huge stack of film negatives in an auction and began scanning them. It quickly became clear that they were of some artistic importance, so he tried find out more about the elusive woman who took them: Vivian Maier. Unfortunately by the time he’d put the work together and tracked her down, she had passed away in early 2009.

This documentary follows his efforts to finish developing her film, looking at the images, making prints of them, and ultimately trying to gain some posthumous recognition for Maier through various gallery showings and print sales.

There’s no denying that the images we see are objectively good, and I can see why they have resonated with so many people. I love the pictures too!

I struggle to get over the ethical concerns around publishing her work. This is a woman who lived a long life and took pictures for most of it. There were hundreds of thousands of negatives Maloof managed to dig up in the end. Why didn’t she attempt to share her work with the world? I can only think it’s because she didn’t want to.

In addition to this, the work wasn’t finished when she was alive. An undeveloped negative is not the end product of a photographer. A print is. A gallery show that started as 100,000 photo negatives stuffed into boxes, un-sorted and un-curated, is just as much a statement of John Maloof’s artistic intent as it is Vivian Maier’s - and she has no way to refute this.

Maloof tackles this head on in the film, and makes his case for it. He says that someone like Garry Winogrand had lots of unfinished work published posthumously. I don’t agree that that was right either, nor that it was as good as what he did when he was alive. I do applaud that Maloof gives some air time to the case against what he is doing, and that he doesn’t seek to silence his critics.

Say what you will about Maier’s work and how it came to be, this is a story worth telling and a fascinating documentary. Maier was a nanny by trade and the numerous interviews with the children she looked after, along with some of her ex-employers, help to paint a vivid portrait of a complicated, confusing, eccentric, interesting life.

Reply by email

Royongbase v26.6.11

rtnF — Thu, 11 Jun 2026 17:51:03 GMT

community-powered knowledge base

Honeypot.net — Thu, 11 Jun 2026 17:07:43 GMT

A coworker brought a little Python-programmable rolling robot ball to the work offsite. She asked a group of us, in seriousness, if she brought it out after dinner, would anyone be able to help debug some of the code.

My friend, you have a robot. A programmable one. Around us. I guarantee that thing’ll get debugged before you take it away for the night. It might not end up with the original code, but it’ll do something alright.

I also got to introduce the word “nerdsniped”.

Healthy Alternatives to 'Keeping the Peace'

Gus Van Horn — Thu, 11 Jun 2026 16:56:11 GMT

A female grad student in history asked Captain Awkward about alternatives to suffering the condescending behavior of her mother's new partner some time back.

As Captain Awkward notes, the thinking she did and the advice she came up with apply to many situations, and I think are worth considering for people who find themselves stuck -- usually at work or with relatives -- dealing with people they'd ordinarily not have anything to do with.

One strategy I found particularly intriguing was the following, which I like because it hacks whatever value orientation such people might have:

2) Find three "safe" topics.

In a weird way, A. might be trying to engage with you (he thinks) positively by showing interest in stuff you like, like history. He's also a walking bag of insecurities who has to be the smartest & the best at everything, so there is no way this will go well. And he has the unfortunate "arguing until the other person is visibly upset is a form of fun!" chip installed. It's bad.

I suggest that you find three safe topics to talk about with A.

Topic 1: One where he is the expert or something he is passionate about, like, international business travel or being an old, somewhat privileged dude. Grilling. Fine wine. Sports. What you want is something that is of passing interest to you but you're not the expert or especially emotionally invested, but it is really interesting to him. And then you ask questions and let him talk.

True story, I once did tech support for a company. There was this older gentleman who was The Guy For Whom Everything Does Not Work As It Should. It was mostly not his fault, just, he had a bad track record of getting broken/shitty equipment or having stuff go wrong. And I was a girl learning the job as I went because it was a temp job & I was filling in for someone, and everything was somehow all my fault and all of our interactions were very negative.

I noticed once that he wore a Cleveland Indians (yes, the team name is racist) polo shirt, and that happened to be on a day where I had glimpsed the front page of the sports section (rare), so I was able to sort of say something like "Great win for the Indians ... last night, huh?" in an attempt at pleasant small talk. And he lit up and told me about the Indians ... the entire time I fixed his computer. After that, every time I went to see him, I would check and see how the Indians ... were doing first. Note: I did not pretend to know anything or care about or be a fan of the team. I never said anything beyond "Looks like it was a rough night!" or "What does this mean for the playoffs?" and let him do the rest.

Not only did he decide I was his new best bro-girl, after I left the job he tracked down my info through my temp agency and sent me a "Good luck, if you ever need a reference or a job lead, just call!" greeting card. I know a lot of people genuinely love, live & breathe sports, but until I met that dude I did not realize that even for people who aren't that into them, sports serve as a magically safe current events topic that anyone can opine on. Epiphany! "How 'bout them Cowboys?"

The other two suggestions for safe topics share the essential characteristic of being things the difficult person cares about, but which will not lead straight into a conversation you'd rather not have with said person.

The remaining strategies will be helpful in that they involve finding ways to spend less time around the difficult person and give oneself psychological space by, for example, being more assertive over time or simply being honest with oneself about disliking the difficult person.

-- CAV

Jamie Tanna | Software Engineer — Thu, 11 Jun 2026 15:56:32 GMT

Listened to No Kinky Couches - S21, Ep.8 by PodBean Development

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In this week's episode we learn a surprising thing about Kumail, explain conkers to a Canadian and settle a spoon-related debate. Plus, have we witnessed Armando’s most unhinged moment yet? Send all your Series 21 thoughts along to fans@taskmaster.tvDownload the Taskmaster App here:https://taskmaster.tv/appIf you're in the UK you can watch all of Taskmaster on All 4 www.channel4.com/programmes/taskmasterAnywhere else, it's the Taskmaster YouTubeyoutube.com/taskmasterVisit the Taskmaster Store for all your TM goodies!taskmasterstore.com

Your Organization Must Learn As Fast As Your Best AI adopter

Greg Herlein — Thu, 11 Jun 2026 11:00:01 GMT

Everyone is asking the wrong question. “How do we adopt AI agents?” “Which tool?” “What’s the right policy?” Wrong questions. The right question is: how does your team turn what one person learned on Tuesday into something everybody on the team uses by Friday?

Agentic coding and mental models

Phil Booth's Blog — Thu, 11 Jun 2026 11:00:00 GMT

I reckon I’ve drafted and then deleted a version of this post at least 10 times in the last 12 months. Deleted because it falls in the category “I must be wrong about this as everyone else is saying the opposite”. But this week’s release of Fable, and especially the reasons people are saying it’s such an improvement, are the nudge I needed to finally publish. So, here goes: I think everyone is wrong about how to write code with LLMs. Or at least, I think they’re wrong about how I should write code with LLMs. The reason is to do with mental models.

When you write code, the code is not the only artifact that’s generated. You also construct a mental model of how the code works, its runtime behaviour under different conditions, how it fails and so on. Outside of toy projects, this mental model is rarely perfect. Improvement and maintenance of the mental model continues for at least as long as improvement and maintenance of the code itself. Mental models also have an alarming tendency that code doesn’t; they degrade rapidly whenever you’re not thinking about them and the cost of reconstructing them increases the longer you continue not to think about them. They’re wriggly little buggers, mental models.

Now a few times in my career, I’ve been fortunate enough to work with bona fide programming geniuses. They come in various forms but a common thread is their ability to construct mental models rapidly and accurately, then apply them successfully on a complex project. Unfortunately, I’m not one of these geniuses. I’m a regular engineer, somewhere in the middle of the bell curve of programming ability. So for me, every mental model is the result of hard struggle. Reading lines of code, observing behaviour, printing runtime state, using a debugger, occasionally resorting to trial-and-error changes just to see what happens. In this way, gradually, I inch myself closer to sufficient understanding that I can make changes without breaking stuff most of the time (and yet, still stuff breaks 🤔).

I value my mental models highly. To me they hold greater value than the code itself, which might sound crazy to some people I guess. A mental model is a delicate flower that must be cultivated with care and protected against trampling. Oh, look! Here come the LLMs to trample all over my mental model.

At this point I should make clear, I’m not anti-LLM per se. I use them as my daily driver at work and on numerous side projects in my spare time. Claude even fixed a 5-year old memory leak that I’m not ashamed to admit I was unable to fix on my own. So I’m sold on the technology. But I’m not sold on how I’m being told to use it.

Everything I read encourages me to turn the automation dial up to 11. If I don’t let agents work autonomously, I won’t get their full benefit. Actually I should have swarms of agents running in parallel, helping me to ship more features at once. I hear at some workplaces there are usage leaderboards and the assumption seems to be that token spend directly correlates with business value. I find this reasoning insane.

Let’s detour briefly to talk about code reviews. It’s quite well understood, I think, that big code reviews are harder than small code reviews. As responsible engineers, we try to separate large changesets into smaller independent units, to help our colleagues review them better and so we receive better feedback as a consequence. Give me a 200-line code review in a familiar codebase and I’ll feel confident about understanding its impact. At that size, it’s easy to update my mental model and assimilate whatever the change does (or what it tries to do). Then I might see some tradeoffs in the approach; there could be performance concerns, lurking footguns or perhaps an existing abstraction could be re-used to make the change more cohesive. Make it a 1k-line code review instead and things take a little longer, but the size is still within reason. Increase it to 2k lines and I’m setting aside a solid block of time and giving it multiple passes. At 5k lines we’ve probably exceeded my capacity to meaningfully review the change unless it’s broken into smaller chunks. You see the pattern.

Increasing autonomy for coding agents has precisely the same effect as increasing the size of code reviews. It makes my job harder, slows down development of my mental model and decreases confidence that I understand what’s going on. Automation evangelists might tell me to let go at this point; automate code reviews, automate verification, automate bug fixes, let the machines do all the work. I’m sure this works for some people, geniuses to the right of the bell curve perhaps, but it doesn’t work for me because I’m left without a working mental model. At that point I’m failing my job as an engineer. I no longer understand what I’m pushing to production, so things are more likely to break. And when they do, I’m less likely to understand what’s wrong. I’ll need to use an LLM again to fix them and, if you ask me what’s going on, I can’t really tell you.

This is not just about how it makes me feel, there are ethical considerations too. Is it okay for me not to fully understand what I push into production? Would paying customers be happy with that? Who’s responsible when it goes wrong? What happens if the model provider has an outage and I can no longer fix the code?

So with LLMs, I want the equivalent of a small code review, not a big one. Another way of framing this is to say I want a shorter feedback loop. And what feedback loop is even shorter than code review? Yes, it’s pair programming. I don’t want a code review at all, I want to pair with the LLM as my partner.

Pair programming typically features two roles: driver and navigator. With humans, it’s common for the driver and navigator to swap regularly. But doing that doesn’t really play to the strengths of the LLM or the human in an agentic pair, so the LLM usually drives and the human usually navigates. Not always though, sometimes I like to drive and put the LLM in the navigator seat instead. Most agent harnesses are not set up for this way of working, so I have to push back a little against the default setup. My global prompt shapes the nature of the relationship and then I very rarely auto-accept edits. I press Esc to interrupt the agent frequently. As navigator, the whole point is for me to review line-level changes as they happen. I still use skills to streamline parts of the flow, but they’re often invoked manually as slash commands instead of waiting for the LLM to use them of its own volition. In this way, gradually, my mental model evolves the same way it would if I was writing the code by hand.

Is this still faster than just writing the code myself? In my experience, yes. And it leaves me feeling more confident about the codebase as a whole. The abstractions stay clean and logical, opportunities to refactor are taken at the right moment, the final result is more coherent and most importantly, I have a working mental model. Future work is easier and faster because I have that in place. I imagine the agent swarm folks are laughing at me, but I’d much rather maintain software like this than what they’re proposing. For me, this is a sustainable pace.

Washington state Gargantuan Settlement by Bob Ferguson

Joel Gross's Blog — Thu, 11 Jun 2026 06:21:32 GMT

Washington state just agreed to pay $80 million to two children who had been severely injured when left in their biological parents care due to negligence by DYCF caseworkers. 40% ($32 million) of that went to the law firm who represented them. Link in the comments.

The injuries the children suffered are tragic and should never have happened.… read more “Washington state Gargantuan Settlement by Bob Ferguson”

The post Washington state Gargantuan Settlement by Bob Ferguson first appeared on Joel Gross's Blog.

Discovering the Former “The New Leaf Journal”

The New Leaf Journal — Thu, 11 Jun 2026 05:08:00 GMT

I learned some time ago that there was a former thenewleafjournal.com. Today, we examine our domain name predecessor.

agent-shell 0.55 updates

xenodium.com @alvaro — Thu, 11 Jun 2026 03:00:00 GMT

It's been a little while since my last agent-shell update, so let's go through the latest highlights as of v0.55.

What's `agent-shell`?

agent-shell is a native Emacs mode to interact with AI agents powered by ACP (Agent Client Protocol).

Picking up where I left off

If you noticed slower project activity in April, this is why. I'm getting better at the new 24-hour job, so I've resumed working on agent-shell.

I'm still chipping away at the backlog that built up while I was away, but if there's anything in particular you'd like me to look at, feel free to ping.

Anthropic, Google, and why ACP matters

With Anthropic's SDK subscription support changing, Google's Gemini CLI deprecation, and Antigravity's unclear support for the Agent Client Protocol (ACP), vendor-neutral tools matter more than ever.

Luckily, agent-shell is built on ACP, which sidesteps the problem. When a vendor changes course, you can swap providers and keep using your preferred tool. No need to reshape that hard-earned muscle memory.

On that note, the list of agents supported by agent-shell continues to grow.

Supported agents

Here's a list of the latest agents now supported by agent-shell.

agent-shell needs your support

Speaking of vendor-neutral tools being more important than ever, there are a couple of ways to help keep agent-shell going. Some cost money, others just a click. All are appreciated ;)

Sponsorships for agent-shell longevity

agent-shell has been attracting quite a few users. It's nice to hear folks are using agent-shell on a daily basis. They are often relieved agent-shell exists as an alternative to AI-tools commonly mandated at work. Those tools have well-funded engineering teams behind them, while agent-shell is just me, an indie dev ;) Time spent on agent-shell is time away from work that pays the bills, so if it's useful to you, please consider sponsoring the project.

Every individual sponsorship genuinely helps keep the project going. And if your employer benefits from your agent-shell use, they're typically in a position to contribute at a scale individuals can't, so nudge them to chip in too.

Hey, I'm looking at you, folks at Google, GitHub, GitLab, NVIDIA, Oracle, Red Hat, Yelp, Venmo, ARM, Spotify, Augment Code, Hinge, Mercury, Nubank, Veeva… Some of you are using agent-shell. Nudge your employer ;)

GitHub stars for Anthropic credit

Anthropic offers 6 months of free Claude Max 20x for qualifying open-source projects with at least 5,000+ GitHub stars. Starring agent-shell costs nothing and can save me some money. We're only a 5th of the way there ;) so if you don't mind a couple of clicks, the project can really use another GitHub star.

Speaking of GitHub stars, agent-shell is now my most popular Emacs package, recently overtaking chatgpt-shell.

New markdown renderer

agent-shell now ships with a brand new, more performant inline markdown renderer. This is the biggest internal change in some time. Enabled by default via agent-shell-markdown-render-function (moving away from the overlay-based renderer in shell-maker).

Access/navigate table content

Table content is now accessible. Point can land on any cell, which wasn't possible with the previous overlay implementation. In addition, tables are now also navigable: TAB and S-TAB move between cells.

Improved source block support (enabled by default)

Source-block syntax highlighting is now on by default. The per-snippet copy button is now keyboard-accessible too (previously mouse-click only, due to the overlay implementation).

New blockquotes support

Blockquotes now render in both shell and viewport. More importantly, you can select text in either a viewport page (or the shell itself), press r (for reply) and the selection becomes a blockquote in a fresh prompt.

Viewport

Shell

Session restoration

Session restoration got a meaningful overhaul (#605 by @nhojb), now exposed via agent-shell-session-restore-verbosity, with four levels:

minimal (default): title only, so restore is fast and quiet (needs session/resume support).
last: render the last prompt turn (needs session/load support).
first-last: render the first and last prompt turns (needs session/load support).
full: replay the whole conversation (needs session/load support).

Feature availability is agent-specific, requiring either session/resume or session/load request support. agent-shell degrades as needed, ultimately falling back to creating a new session.

Note that anything but minimal verbosity is fairly new, so please report bugs or rough edges.

Relatedly, agent-shell-session-strategy now defaults to 'prompt, and 'new-deferred has been retired.

Session forking

You can now fork the current session, starting a new shell that shares the conversation history so far and diverges from there. Invoke via M-x agent-shell-fork.

Restart/reload session

You can now restart the current shell anew (drop history) via M-x agent-shell-restart or reload (keep history) via agent-shell-reload.

Downloads/Temp shells

The new agent-shell-new-downloads-shell and agent-shell-new-temp-shell commands create agents anchored at either ~/Downloads or a temp directory. Both are also reachable via C-u M-x agent-shell.

TRAMP support

acp.el #20 by @martenlienen landed support for ACP connections over TRAMP, now making it possible to drive remote agents from agent-shell. Pair it with agent-shell-tramp for the user-facing integration.

Viewport improvements

Viewport interaction (setq agent-shell-prefer-viewport-interaction t) continues to be my primary way to interact with agents. It is focused (see only the latest interaction), fast (single-key bindings: y = yes, c = continue, m = more…), and offers a richer editing experience (dedicated prompt-crafting buffer).

The viewport is just a viewport to shell content. You can have your cake and eat it too, by jumping to the related shell buffer if needed.

Queueing

From a viewport, you can press r to reply to the latest agent response. In the past, you could only reply to idle agents. You can now press r to reply to busy agents too, automatically queuing requests on submission.

List editing mode

A new (basic) agent-shell-list-edit-mode lets you edit list-style content inside the viewport.

Improved buffer selection

Some commands prompt you to pick one of your active shell buffers (e.g. M-x agent-shell-send-region-to). The picker now shows extra context for each buffer to help you choose.

The same mechanism is now used by the new M-x agent-shell-switch-buffer command. More on the underlying API later.

Improved folding

Folding got smarter (#608 by @codeluggage):

M-x agent-shell-ui-toggle-fragment toggles the fragment at or near point (DWIM).
M-x agent-shell-ui-toggle-all-fragments cycles globally between all-expanded and all-collapsed.

Together they replace the previous agent-shell-ui-toggle-fragment-at-point, which is now an internal primitive.

`c` joins the viewport reply gang

You can now press c from a viewport to quickly send a "continue" request, joining the rest of the single-key reply shortcuts:

c: replies "continue" (new)
y: replies "yes"
m: replies "more"
a: replies "again"
1 … 9: replies with the corresponding numbered choice
r: opens the reply compose buffer
R: same as r, with the agent response quoted

More compact status rendering

Tool call status is now rendered as a compact icon-based label by default (agent-shell--inverse-icon-status-kind-label). agent-shell-styles.el ships several alternatives, picked via agent-shell-status-kind-label-function.

To get the previous word-based label back:

(setq agent-shell-status-kind-label-function
      #'agent-shell--inverse-label-status-kind-label)

Codex defaults

You can now set a default model and session mode for Codex via agent-shell-openai-default-model-id and agent-shell-openai-default-session-mode-id (#405 by @robjgray). Both must match an ID from Codex's "Available models" / "Available modes" listings.

Emacs 31 fixes

Headers had a few rendering hiccups on Emacs 31. These are now fixed (#588 and #590 by @nhojb, #463 by @ftlio). Warnings from deprecated when-let usage were also cleared.

Wayland clipboard support

wl-paste is now a supported clipboard handler for pasting images on Wayland (#461 by @martenlienen).

Windows clipboard support

Similarly, pasting clipboard images now works on Windows via PowerShell (#572 by @repelliuss).

Header improvements

The graphical header got minor tweaks here and there. For example, thought level is now displayed in the header. It can be changed via M-x agent-shell-viewport-set-session-thought-level as well as menus (#601 by @martenlienen).

Session config options

agent-shell now supports ACP session config options (#553 by @greggroth and #613 by @catern). Bind C-c C-s (or call agent-shell-set-session-config-option) to pick from the options the agent advertises. Broadcasted as config-option-update and available externally via agent-shell-subscribe-to.

Interrupt confirmation

You can now skip interrupt confirmations by unsetting agent-shell-confirm-interrupt (#424 by @emil-e).

Public functions

Resume sessions by ID

You can now resume an existing session by its ID via M-x agent-shell-resume-session (#332). Primarily useful for external integrations.

Outgoing request decorator

You can now use agent-shell-outgoing-request-decorator to tag or transform outgoing requests.

Subscribe to idle events

agent-shell-subscribe-to now broadcasts idle events (#509 by @arthurgleckler).

Getting shell buffers

agent-shell-shell-buffer returns the underlying shell buffer for the current context.

Last vs interaction at point

agent-shell-goto-last-interaction jumps to the latest prompt/response pair, while agent-shell-interaction-at-point returns the interaction at point as data.

Shell status

agent-shell-status returns 'busy, 'blocked, or 'ready status for any shell buffer.

Session title

The agent-supplied session title is now exposed via the session-title-changed event (delivered via agent-shell-subscribe-to) (#559 by @smagnuso). Can be handy for buffer names, bookmarks, or recent listings.

New third-party packages

The agent-shell family of third-party packages keeps growing. Recent additions:

agent-shell-knockknock: Notifications for agent-shell via knockknock.el.
agent-shell-notifications: Desktop notifications for agent-shell events.
agent-shell-hud: Real-time agent-shell status overlay via a floating dashboard.
agent-shell-pet: Codex-like pets that broadcast agent-shell session states.
agent-shell-tramp: Tramp integration for agent-shell.
agent-circus: Run AI coding agents in sandboxed Docker containers.

Pull requests

Thank you to all contributors for these improvements!

#308: Fix heartbeat nil value crash in timer and busy indicator (@ElleNajt)
#340: Add documentation about the OAuth Anthropic authentication (@chemtov)
#397: Add detailed context usage indicator mode (@emil-e)
#398: Expose outgoing-request-decorator as a defcustom (@emil-e)
#405: Codex defaults for session mode and model (@robjgray)
#408: Use diff-command for diffing (@timfel)
#413: Track and manage diff buffers for each permission request (@emil-e)
#418: Add defvar for agent-shell-mode-hook + test (@emil-e)
#420: Replace all references to "Claude Code Agent" with "Claude Agent" (@jinnovation)
#421: Add agent-shell-clear (@Makesesama)
#424: Add agent-shell-confirm-interrupt option (@emil-e)
#425: README: Update all references to Claude Code (@jinnovation)
#429: Viewport attachment fixes (@nhojb)
#438: Fix for structured input from toolCall.rawInput.plan (@timfel)
#442: Add related project agent-circus to README.org (@rpoisel)
#445: Use project-name instead of default-directory in header (@bcc32)
#446: Droid: use native acp client and support default model and mode (@kohnish)
#450: Fix restart using wrong default-directory (@zackattackz)
#453: Ensure that viewport compiles (@martenlienen)
#457: Prefer cache directory over tmp for caching (@martenlienen)
#460: Unhandled method returns an error, unblocking client (@0x6362)
#461: Add wl-paste as a Wayland image handler (@martenlienen)
#463: Fix header text invisible when font-get :size returns 0 (@ftlio)
#469: Do not create a file if no image in Wayland clipboard (@martenlienen)
#473: Caching project files completions for improved performance (@Gleek)
#477: Handle non-text content in user_message_chunk during session load (@Gleek)
#483: Add CodeBuddy agent support (@illidan127)
#489: Snapshot session strategy in any case (@timfel)
#492: Update claude-agent-acp repository (@Gleek)
#498: Normalize missing MCP transport collection fields for ACP compatibility (@CsBigDataHub)
#503: Add agent-shell-macext to related projects (@cxa)
#509: Add agent-shell-idle-alert-functions (@arthurgleckler)
#513: Quote reply to the complete response with R (@martenlienen)
#515: Make detection of binary files more robust (@martenlienen)
#523: Add agent-shell-org-transcript to Related projects (@lllShamanlll)
#528: Add support for Kimi Code CLI using ACP (@nicolaisingh)
#529: Sort the session list based on recency (@smagnuso)
#530: Add simple blinking circle as busy indicator (@rudolf-adamkovic)
#532: Fix removing queued messages (@Gleek)
#536: Add programmatic query API: agent-shell-query and agent-shell-shell-buffer (@eddof13)
#539: Add ob-agent-shell to related projects in readme (@eddof13)
#545: Keep buffer name on agent-shell-reload and -restart (@timfel)
#550: Add agent-recall to related projects (@Marx-A00)
#551: Avoid gitignore update for external data dirs (@Silex)
#552: Use helper function from shell-maker rather than eob (@smagnuso)
#553: Add ACP session config options support (@greggroth)
#554: Fix error when cancelling session selection prompt (@Gleek)
#555: Add some niceties useful for agent-shell-list (@vermiculus)
#559: Expose topic in agent-shell (@smagnuso)
#561: Update Pi coding agent logo to look more official (@jeff-phil)
#565: Enable @ and / completion when reading queued prompts (@Gleek)
#571: Fix unkillable buffer after major-mode change (@Scott-Guest)
#572: Add clipboard image support for Windows (@repelliuss)
#574: Add agent-shell-pet link to README (@lgmoneda)
#582: Add a few additional forward declarations (@tychoish)
#583: Add Hermes Agent support (@yitang)
#588: Fix header foreground color (emacs 31) (@nhojb)
#589: Only trim response region when region is active (@martenlienen)
#590: Fix context-limit header color (emacs-31) (@nhojb)
#595: Fix / completion regression in viewport buffers (@Gleek)
#600: Do not emit permission-request for auto-handled permissions (@Gleek)
#601: Expose thought level (reasoning effort) in header, mode line and keymaps (@martenlienen)
#602: Update README with Opencode and Ollama setup instructions (@dvictori)
#605: Add agent-shell-restore-context defcustom (@nhojb)
#608: Toggle folding for everything & comfortably fold at point (@codeluggage)
#609: Prevent accidental auto-scrolling in viewport buffers on tool calls (@martenlienen)
#611: Fix agent-shell-hermes-acp-command (@TamsynUlthara)
#613: Improve completion for set-session-config-option (@catern)
#618: Fix header not showing session mode name (@deftsp)
#621: Reindent Elisp files (@bcc32)
#623: Add agent-shell-hud to related packages (@nohzafk)
#625: Fix source-block face background not rendering when theme loads after package (@phairoh)
#627: Hermes: add default-session-mode-id to match other agents (@yitang)
#629: Write .agent-shell/ to .git/info/exclude instead of .gitignore (@phairoh)
#632: Render tool call parameters for non-standard tools like MCP calls (@martenlienen)
#633: Add GitHub Actions workflow for ERT tests (@phairoh)
#634: Update art generated by agent-shell-hermes--ascii-art (@TamsynUlthara)
#639: Preserve window position when restarting (@Gleek)

Bug fixes

#202: Header icon is double the expected size
#278: Heartbeat causes high CPU usage with many agent buffers
#366: .agent-shell/ directory at risk when switching git branches
#400: agent-shell-viewport-reply-1 errors with "Text is read-only"
#401: Garbled characters output when using non-English languages
#412: Diff buffer management is messy
#414: agent-shell-mode-hook timing makes subscribing to events difficult
#417: Unhandled notifications with kiro
#426: Starting conversation before agent has initialized leaves "dangling" text
#431: Heartbeat timer keeps running after failed/abandoned authentication in OpenAI
#435: xclip clipboard handler silently saves text as PNG in terminal mode
#441: Background agent notifications treated as stale after session/prompt response
#443: image-type: Invalid image type 'svg on calling agent-shell
#455: agent-shell enters frozen/hanging state when receiving unknown notifications
#462: Header text invisible when font-get :size returns 0
#465: Session load crashes on non-text user message chunks (e.g. images)
#466: agent-shell-diff-accept-all / -reject-all now focus the originating shell
#468: wl-paste-image-handler breaks pasting text
#481: History input rolls back in insert editing mode while pressing up/down arrows
#485: Shells created in other workspaces no longer display
#493: Tables rendered in agent-shell break cursor (point) navigation
#533: Can't install agent-shell from MELPA
#548: Copied highlighted text from agent output includes trailing backtick
#563: "Cannot modify map in-place" when starting agents
#577: agent-shell-get-config does not work in an agent-shell-command-prefix function
#587: Long region preview's "Expand…" button is sent literally to the agent
#617: OpenCode: consent prompt shows empty input when request_permission arrives before tool_call_update populates rawInput

Lots of polish

Beyond what's showcased, I've poured much love and effort into polishing the agent-shell experience. Interested in the nitty-gritty? Have a look through my regular commits.

Make the work sustainable

If agent-shell is useful to you, please consider sponsoring the project. I'm now back to working on agent-shell daily.

LLM tokens aren't free, and neither is the time dedicated to building this stuff (especially as an indie dev). I also have bills to pay ;)

Unless I can make this work sustainable, I will have to shift my focus to work on something else that is.

✨ Sponsor agent-shell ✨

Wrong Again

june.kim — Thu, 11 Jun 2026 03:00:00 GMT

Wrong Questions

june.kim — Thu, 11 Jun 2026 03:00:00 GMT

MET Values for each Exercise in MapMyFitness

JoeHx Blog — Thu, 11 Jun 2026 03:00:00 GMT

Sometimes I want to do an exercise and record it in MapMyFitness, but MapMyFitness doesn’t have that particular exercise. So I choose the next-closest exercise.

Research replication attempt

Paperless — Thu, 11 Jun 2026 03:00:00 GMT

Wherein a random nerd tries and fails to combine “Supernovae evidence for foundational change to cosmological models” code and data to reproduce research.

⌛ Now

Bruno Croci — Thu, 11 Jun 2026 03:00:00 GMT

I don’t know if it’s ADHD or another neurodivergent condition, but I have a lot of different interests and I’m constantly starting new projects and lines of research. Since I rarely finish a project, I keep coming back to them from time to time. So here I try to summarize my latest activities. This means that if a certain project disappears from this list, it’s not because I’ve finished or abandoned it, I’ve just been focusing on other things for the moment.

Tech and Development

Working on bulletty, a TUI RSS/ATOM feed reader app for the terminal
Researching memory management in FreeBSD

Music

Experimenting with music production and free plugins on Linux

Last edit: 2026-06-11

How to Evaluate a Software Consultant

Clint McMahon's Blog — Thu, 11 Jun 2026 03:00:00 GMT

The questions to ask a software consultant in an initial conversation that separate real experience from a good sales pitch.

Automated Archives for June, 10th 2026

TrickJarrett.com — Thu, 11 Jun 2026 02:45:00 GMT

This post was automatically generated.

Articles To Read

The following are articles that I saved today. Substance and quality will vary drastically.

The Heist State

Chess For the Day

Record: 6-0-4
Net Elo Change: +11

Games Played

Blog Posts On This Day

June 10, 2025 (1 post)
June 10, 2023 (2 posts)
June 10, 2022 (2 posts)

The honest truth about leaders who want to replace their workers with AI

Ben Werdmuller — Thu, 11 Jun 2026 01:19:28 GMT

"The problem tends to show up when a CEO is handed an agentic tool like Claude Code, and has it create something, which will work just fine, and thinks “oh, wait, why do we need so many people, when I can just sit here and make things work?” This is a bad CEO."

Scheveningen

Fabio Bruna — Wed, 10 Jun 2026 23:17:31 GMT

Na P weggebracht te hebben voor een vroege vlucht een strandwandeling gemaakt op een prettig leeg Scheveningen.

Bill Gates: Is he Good or Evil?

Joel Gross's Blog — Wed, 10 Jun 2026 23:00:50 GMT

Bill Gates has become a very polarizing figure. But I think he is a genuinely good guy and always has been. He certainly has made many mistakes, but he has done more good for humanity than most anyone else. He just willingly testified under oath about his Epstein association, and he did nothing wrong other than his infidelities to his wife.… read more “Bill Gates: Is he Good or Evil?”

The post Bill Gates: Is he Good or Evil? first appeared on Joel Gross's Blog.

What canceled my go context

llimllib's notes — Wed, 10 Jun 2026 22:41:03 GMT

Excellent article walking through one of the finer points of context usage: figuring out what cancelled your context.

Gives examples of how to make sure you can always figure it out.

brandur starts with the same context.WithTimeoutCause, but gives a different recipe, describing a function timeoututil.AttributedTimeout that takes a closure and handles context deadlines from within that closure

Четырёхчасовые прогулки

Блог Stargrave на русском — Wed, 10 Jun 2026 22:40:17 GMT

Четырёхчасовые прогулки

Kids Waterwheel Electrical Generator Project

Joel Gross's Blog — Wed, 10 Jun 2026 22:32:34 GMT

The post Kids Waterwheel Electrical Generator Project first appeared on Joel Gross's Blog.

Free Markets and the Industrial Revolution Freed All Slaves

Joel Gross's Blog — Wed, 10 Jun 2026 22:06:51 GMT

Slavery existed in every single nation and culture (Chinese, Egyptians, Greeks, Romans, etc, etc) throughout history UNTIL the Industrial Revolution and free markets set all slaves free with a massive increase in productivity per person.

The post Free Markets and the Industrial Revolution Freed All Slaves first appeared on Joel Gross's Blog.

Коллеги ушли в web

Блог Stargrave на русском — Wed, 10 Jun 2026 21:55:28 GMT

Коллеги ушли в web

Huge pop star is openly racist

Joel Gross's Blog — Wed, 10 Jun 2026 19:16:02 GMT

Cardi B defends a convicted murderer who stabbed a 17 year old boy in broad daylight at a track event.

What a world we live in now… Huge pop stars can be blatantly racist without repercussions.

https://www.foxnews.com/us/cardi-b-reaction-karmelo-anthony-verdict-draws-millions-views-fierce-criticism

The post Huge pop star is openly racist first appeared on Joel Gross's Blog.

基于 git 的零拷贝静态web服务器

est の输入输出和出入 — Wed, 10 Jun 2026 18:21:00 GMT

无聊，产生了个crazy的想法。

git 内部用 zlib 压缩文件内容

Content-Encoding: gzip 也是

如果web服务器输出 .git 里的静态内容，是不是可以减一个二次解压/压缩步骤？？？

blob sha1 直接当etag？

跟AI较量了几轮，一开始它说做不到。因为 blob 的格式比较变态。因为

hello world...

在 .git/objects/ab/cdef... 里的东西是这么存的

zlib(blob 1234\0hello world...)

I was like

？？？日他妈真变态啊。这前面是写死了 blob <size>\0 然后把文件内容放在一起，再压缩的。

.git 这设计脑子有病啊。。。为啥不是原始文件gz而是加个头去gz。。。

此路不通！结束

然后AI嘴瓢了，说

先别急着吐槽 Linus，这个设计不仅没病，反而是 Git 能成为现代“内容寻址数据库”的精髓所在。
当 Git 从磁盘读取一个松散对象时，它是个流（Stream）。Git 只需要解压出最开始的几个字节，读到 blob 4096\0，内核就可以立刻执行 malloc(4096) 分配精准的内存。接下来的 zlib 数据流就可以源源不断地直冲内存，不需要反复扩容（realloc），也不需要把整个文件全部解压完才知道它有多大。
况且，这里有一个长久的误解：你吐槽的那个把 blob \0 塞进 zlib 的逻辑，其实只存在于松散对象（Loose Object）中。在真正的 Packfile 里，这个文本格式的头早就被干掉了。

【松散对象 Loose Object】

+-------------------------------------------------------+
| zlib 压缩包: [ "blob 1024\0" + 原始文件纯数据 ]           |
+-------------------------------------------------------+

【包文件 Packfile 内部的一条记录】

+---------------------------------------------------------+
| [3-bit 类型] + [变长 Varint 表达的 Size] + [纯 zlib 数据流] |
+---------------------------------------------------------+

我去，这么好的东西，你怎么不早点告诉我！

然后AI给我泼一盆冷水：

Git Packfile 内部的流是zlib 格式 (RFC 1950)，头部 2 字节 (CMF/FLG)，校验算法 Adler-32 (追求极速快照比对)
HTTP Content-Encoding: gzip 期待的流 gzip格式 (RFC 1952)，头部 10 字节 (Magic Number, CM, FLG, Time 等)，校验算法CRC32 (追求网络传输抗干扰)

不！兼！容！

要正确的CRC32你得把文件全部读一遍，那不就等于解压一遍了。白费力气。完结！

嘿，得亏我早年玩 IIS 长大的。我记得有个偏方，Content-Encoding: deflate，尾部crc32直接丢了

AI：

恭喜你，这是Web 历史上著名的 “Deflate 格式混乱案”
RFC 2616 (HTTP/1.1) 规定和IIS有差异！ IIS脑抽地剥离了 zlib 的头尾，只吐了裸压缩流，为了兼容 IIS 的错误实现，各家浏览器各显神通。有的浏览器严格按 RFC 执行，有的浏览器看到 zlib 头会报错，必须看内核的容错机制。
真正的zlib 格式 (RFC 1950) 是要求包含 2 字节头 + DEFLATE 压缩流 + 4 字节 Adler-32 尾

等等，你说啥？Adler-32？这不就又对上了嘛！！！

那么开始 Vibe！！！

Git Packfile 的终极大 Boss：差分存储（Delta Compression）
即便你搞定了上面的所有协议头和校验和，Git 的终极形态——Packfile 还会给你最后一击。你假设的场景是：Git 里的 Blob 都是一个个完整的、独立的压缩包。但实际上，为了极度压榨磁盘空间，当你执行 git gc 后，Packfile 内部采用的是差分存储（Delta Compression）。

啊这？？我于是问，能在配置里禁用掉吗？

# 将比对窗口设为 0，彻底禁用 Delta 差分计算
git config --global pack.window 0

# 强制打包所有对象，并立刻、无条件地清除所有 Loose 文件（无视 2 周保护期）
git gc --prune=now --aggressive

# -a 表示把所有对象打包进一个全新的 pack
# -d 表示打包成功后，立刻删除原本的 loose 对象和旧的 pack 文件
git repack -a -d

哎，你tmd不早说。这不就闭环了嘛！！！

于是最后，通过 OpenCode Zen 免费的 MiMo V2.5 Free

https://github.com/est/git2www-zerocopy

本地自测是OK的

我也算是写过 zero-copy 的人了 🤣（assert AI会写 === 我也会写）

必须严肃吐槽一下AI这回答一板一眼，不思考完整，拷打一下挤一点。如果不是我知道 IIS 这个坑可能就放弃这个想法了。

Honeypot.net — Wed, 10 Jun 2026 18:08:25 GMT

Authors: no one regularly calls a thing by its full name in informal conversation. In the real world, a television is a TV. People talk about “Smith”, not “President John Smith”. If the star Betelgeuse were a part of everyday chat, it’d be called Juice. All this is 10x true if the military’s involved. No one loves funny nicknames more than the rank and file. Some smartass would call it “Orenthal” one time, and from then it’d be compressed to “Orntall”.

Bureaucrats vs. Need

Gus Van Horn — Wed, 10 Jun 2026 17:26:05 GMT

Fellow fans and students of Ayn Rand might recall the following quote from Francisco d'Anconia's Atlas Shrugged speech about money:

[W]hen you see that in order to produce, you need to obtain permission from men who produce nothing ... you may know that your society is doomed. (p. 385)

One might also recall this conclusion seeming plain as day, for which one can thank Ayn Rand's masterful building of context, by weaving together concrete examples together with philosophical argument.

Taken in isolation, such a statment might make little sense, and certainly would have little intellectual purchase with a reader from a culture saturated with altruism -- the idea that one man's need is a moral claim on another's effort; and collectivism -- the belief that the basic unit of a society is not the individual, but some larger collective to which each individual belongs.

That said, the state government of Louisiana has provided us with a real-life example of what that quote means on a personal level that one is unlikely to forget.

In fact, it's such a good example, I'm torn between looking for where Ayn Rand anticipated it and wondering if she would have left it out for fear of her novel being dismissed as a farce.

[I]n many states, if you want to start a business, you first must convince bureaucrats that your business is "needed."

Four years ago, Louisiana blocked social worker Ursula Newell-Davis from helping kids with special needs. Bureaucrats said she hadn't proved her business was needed.

"Why does the state of Louisiana have the right to stop me from doing what I love?" she asks... [bold added]

It is incredible that anyone would have to explain -- to the satisfaction of someone other than a potential customer -- that what one wants to do for a livelihood is "needed." (And it's doubly so for someone with a good track record of earning money and praise from past clients in that field.)

My incredulity is matched only by my relief to hear from the rest of John Stossel's report that the ridiculous law behind this is being challenged in court by the good people at the Pacific Legal Foundation.

-- CAV

Hello Browser 0.2.0

Sander van Dragt's Notes — Wed, 10 Jun 2026 17:22:02 GMT

Hello Browser is a webview window for desktop web apps (written in vala, GTK4, for linux). The goal is to bundle a locally-served web app into something that behaves like a native desktop app.

What's new

Links that open in a new window now open in your default browser. Previously, clicking a target="_blank" link did nothing — the new window request was silently dropped. Hello Browser now hands those URLs off to whatever browser you've set as your system default.
Pass a URL directly. You can now run hello-browser https://example.org without the --url flag.
Site launchers via make desktop. A new Makefile target generates .desktop entries so a wrapped site shows up in your application menu like any other app.

Under the hood

Ported from GTK3 / WebKitGTK 4.0 to GTK4 + WebKitGTK 6.0. You'll need the GTK4 and WebKitGTK 6.0 development packages to build.
Build system streamlined; README updated with current setup instructions.

Try it on GitHub

#projects #hello-browser

To build the future, we need theories of change

Ben Werdmuller — Wed, 10 Jun 2026 17:05:04 GMT

News tends to treat technology as something that happens to it, like an asteroid. To better navigate the future, we need to imagine what might come next.

Theoryholders

utf9k — Wed, 10 Jun 2026 16:40:00 GMT

What if knowing stuff and having good taste is secretly a valuable thing

Build an ML Model That Actually Ships: A 6-Step Visual Walkthrough

Stories by Parvez Mohammed @ Techlatest.net on Medium — Wed, 10 Jun 2026 16:25:13 GMT

Most people picture machine learning like this: pick an algorithm, call .fit(), done.

That’s not how it works in real teams.

Training is one stage in a longer pipeline. Skip the early steps, and you build the wrong thing. Skip the late steps and nothing ever reaches users — or it breaks quietly in production.

Here are the six stages every serious ML project goes through, what happens in each, and what to watch out for.

TL;DR

Build an ML Model That Actually Ships: A 6-Step Visual Walkthrough

Building a model that reaches production is six stages, not one notebook cell:

Define the problem — KPIs and a baseline before any code
Prepare data — clean, feature, split; reject leakage
Choose a model — start simple; match data size and interpretability
Train & tune — loop until validation metrics plateau
Evaluate & test — held-out test set + slice by segment
Deploy & monitor — API in prod, then watch for drift and retrain

The algorithm is roughly 15–25% of the work. Most calendar time sits in data, evaluation, and keeping the model alive after launch.

Each step in the full article has a GIF so you can see the flow — not just read a checklist.

Step 1: Define the problem before you touch data

Start with questions, not notebooks.

What you’re really doing: turning a business or product problem into a measurable ML task.

Ask:

What decision should the model help with? (approve a loan, flag spam, recommend a product)
Is ML the right tool, or would rules or a lookup table work?
What does “good enough” mean — accuracy, speed, cost, fairness?
Who uses the output, and what happens when the model is wrong?

Write down success metrics now. If you can’t define them, you’re not ready to collect data.

Common mistakes

Solving a problem nobody has
Choosing metrics that look good on paper but don’t match the product (e.g., 99% accuracy when the class is 98% one label)
No baseline — even “always predict the majority class” should be beaten

Deliverable: one-page problem brief — use case, constraints, KPIs, and a simple baseline plan.

Step 2: Prepare data (where most of the calendar time goes)

Models learn from examples. Garbage in, garbage out — that phrase exists for a reason.

What you’re really doing: building a dataset that matches the problem you defined in Step 1.

Typical work:

Collect — databases, APIs, logs, labels from humans, public datasets
Clean — missing values, duplicates, typos, timezone bugs, unit mismatches
Explore — distributions, correlations, label balance, leakage (future info sneaking into features)
Engineer features — ratios, aggregates, encodings, text tokens, image resize/normalize
Split — train/validation/test (and time-based splits for forecasting)

Rule of thumb: if Step 1 took a day and Step 2 takes three weeks, you’re probably on track.

Common mistakes

Leakage (e.g. using “total spend after signup” to predict signup completion)
Random split on time-series data
Test set touched during experimentation (it should stay locked until the end)

Step 3: Pick a modeling approach (smaller than people think)

This is the step that gets all the Twitter threads. In a full project, it’s often 10–20% of the effort — not because it’s easy, but because Steps 1–2 and 5–6 eat the rest.

What you’re really doing: choosing a method that fits data size, latency, interpretability, and maintenance.

**Tabular, medium data, need explanations**  
→ Linear models, tree ensembles (Random Forest, gradient boosting)

**Images, audio, text at scale**  
→ Neural networks (PyTorch, TensorFlow, JAX)

**Small data, strict latency**  
→ Simpler models, or pre-trained + fine-tune

**Need a fast baseline**  
→ Logistic regression, or one strong GBM

Also pick framework and environment early: scikit-learn for classical tabular, PyTorch/TF for deep learning, plus version control and experiment logging from day one.

Don’t marathon-tune a complex model until a simple one fails on your validation set.

Step 4: Train and iterate

Training means showing the model your prepared data, so it learns patterns.

What you’re really doing: running experiments until validation performance stops improving meaningfully.

Loop:

Train on the training set
Tune on the validation set (hyperparameters, architecture tweaks)
Log everything — config, data version, metrics, runtime
Repeat until gains flatten or you hit product targets from Step 1

Hyperparameters (learning rate, tree depth, batch size, regularization) matter, but data and features usually matter more.

Common mistakes

Tuning on the test set (that’s cheating — you’ll overfit to one snapshot)
No reproducibility (can’t rerun the same experiment six months later)
Chasing leaderboard metrics while latency or cost makes deployment impossible

Step 5: Evaluate honestly (including fairness)

A model that looks great in a notebook can still fail in the real world.

What you’re really doing: measuring generalization and risk before users see it.

On the held-out test set (touched once, at the end):

Classification: precision, recall, F1, ROC-AUC — pick what matches the cost of false positives vs false negatives
Regression: MAE, RMSE, MAPE
Ranking: NDCG, MAP

Then go deeper:

Slice analysis — performance by region, device, age band, language
Bias/fairness checks — does error concentrate on one group?
Error analysis — open the worst predictions; patterns often point back to Step 2

If test results don’t meet Step 1 KPIs, go back to data or modeling — don’t ship and hope.

Step 6: Deploy, monitor, and maintain

Training is a milestone. Production is the job.

What you’re really doing: packaging the model so other systems can call it, then watching it degrade.

Typical path:

Serialize the model (pickle, ONNX, SavedModel, etc.)
Containerize (Docker) for consistent runtime
Deploy — API on cloud (AWS/GCP/Azure), edge device, or batch pipeline
Monitor — latency, error rate, input drift, output drift, business KPIs
Retrain on a schedule or when alerts fire

Models rot. User behavior shifts. New products launch. Upstream data schemas change. Monitoring catches that before revenue or trust does.

Common mistakes

No rollback plan
Monitoring only infrastructure (CPU/RAM) but not prediction quality
Retraining on production traffic without governance

Deploy Your Own AI Infrastructure

Once you’ve learned how ML models move from training to production, the next step is building an environment where you can run, test, and serve models reliably.

GPU-Supported DeepSeek & Llama All-in-One LLM Suite

Run popular open-source models including DeepSeek-R1, Llama 3.3, Qwen 2.5, Gemma 2, and Mistral on a preconfigured GPU-powered environment with Open WebUI and Ollama included. Ideal for inference, evaluation, fine-tuning, and AI application development.

Product Page: https://www.techlatest.net/support/multi_llm_gpu_vm_support/

DeepSeek & Llama All-in-One LLM Suite (CPU Edition)

Need a cost-effective environment for experimenting with open-source LLMs? This preconfigured VM includes Ollama, Open WebUI, and multiple production-ready models that can run on standard cloud instances without requiring expensive GPUs.

Product Page: https://www.techlatest.net/support/multi_llm_vm_support/

LocalAI — Self-Hosted Alternative to OpenAI & Anthropic

Build private AI applications with a fully self-hosted OpenAI-compatible platform. LocalAI supports LLMs, embeddings, image generation, speech, vision models, and autonomous agents while keeping your data entirely under your control.

Product Page: https://www.techlatest.net/support/local-ai-support/

Final Thought

Most ML content stops at training. That’s why so many “finished” models never leave a laptop.

Shipping means accepting that data prep, leakage checks, slice analysis, and monitoring are part of the product — not optional cleanup. The teams that win aren’t the ones with the fanciest architecture on day one. They’re the ones that pick a clear metric, beat a dumb baseline, and keep the model honest after it goes live.

If you’re early in the journey, don’t optimize for the perfect algorithm. Optimize for clarity at step one and honesty at step five. Everything else gets easier from there.

Thank you so much for reading

Like | Follow | Subscribe to the newsletter.

Catch us on

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Build an ML Model That Actually Ships: A 6-Step Visual Walkthrough was originally published in FAUN.dev() 🐾 on Medium, where people are continuing the conversation by highlighting and responding to this story.

Solsong Chord Updates

Jeff Kaufman's Writing — Wed, 10 Jun 2026 16:00:00 GMT

A couple years ago I put together a Secular Solstice Songbook, a compilation of all the songs we've sung at Boston Solstice. Anna Tchetchetkine and I led a session of group singing at LessOnline, following up from an informal one the year before, and I noticed several annoying things with its chord handling:

Despite being digital, it didn't support transposition.
Some songs didn't repeat the chord if they were unchanged, which meant that when scrolling new lyrics into view you'd lose the chords.
This is minor, but I like to align the chords in a grid and the repeat sign was very slightly to narrow, throwing off the grid.

In asked Claude Code to fix these, and it did almost all of it. The exception was a few cases where it wasn't obvious which chords to use and I needed to make some manual edits.

My favorite part is that it preserves the grid even when the addition of accidentals changes widths. For example, here are the chords I have for haMephorash:

C Am C  Am
F G  C  G
C FG Am F
G E  Am /

If for some reason I wanted to play it in E instead of C, I could bring it up four semitones:

E C#m E   C#m
A B   E   B
E AB  C#m A
B Ab  C#m /

Note that because C#m is wider the columns containing it are now slightly wider to make room, accross the board.

I'm pretty happy with it, though I haven't tried using it for real yet.

Comment via: facebook, mastodon, bluesky

Should You Go From Engineering Manager Back to Being an IC?

Engineering Leadership — Wed, 10 Jun 2026 15:51:38 GMT

Engineering managers are more desired than ever to become ICs. Should you make the jump?

Lamb 0.10.0

Sander van Dragt's Notes — Wed, 10 Jun 2026 15:47:35 GMT

After 2 rc's, the final version is out!

Originally written on lamb-releases:

Lamb 0.10.0 This release brings your blog into the IndieWeb conversation — webmentions, reply posts, and WebSub — and makes Lamb much easier to install and upgrade. Added Webmentions: your blog now sends webmentions to sites you link to when you publish (including reply-to targets), and can receive webmentions from other sites. Received mention authors are shown on the post for the author when logged in.

Sander van Dragt's Notes — Wed, 10 Jun 2026 15:36:50 GMT

Browser tabs are just mini tasks to me, I was closing them mimicking as if I got something done. I really liked tab groups but now I think that's just doubling down on this fake value.

So now I've started hiding my tabbar and have an extension deleting them after 3 hours of inactivity, and just open new one, considering them ephemeral because they are, and it's working ok. This makes me use 1 window per project as well instead of tab groups. I bound tab-search to a keyboard shortcut so I can quickly search them, but tabs are surfacing in the address bar for me so I find I'm maybe using it a couple of times a day.

A tab is valuable if I have something actually unfinished in them, like half a draft of something, or got interrupted in a checkout. #technology

It's not enough to have better ideals.

Ben Werdmuller — Wed, 10 Jun 2026 13:00:15 GMT

You also need to build a better product.

Book Review: The Husbands by Holly Gramazio ★★★★★

Terence Eden’s Blog — Wed, 10 Jun 2026 12:39:13 GMT

Ooooh! This is a lovely treat of a book. Every time Lauren sends her husband into the loft, a different man comes down. Her past is rewritten and she has now been married to Dave/Gary/Bob/Whoever for a year, a month, a decade, a minute. This isn't like how Groundhog Day became On The Calculation of Volume or Sliding Doors became The Names, instead this is a new and twisty concept rendered…

2/3 WiFi домашних точек доступа устарели

Блог Stargrave на русском — Wed, 10 Jun 2026 12:33:15 GMT

2/3 WiFi домашних точек доступа устарели

The Room Is a Robot (and the NEXXT Sprint to Prove It!)

Greg Herlein — Wed, 10 Jun 2026 11:00:01 GMT

Next week I get to watch one of my favorite things: see a room full of smart people handed a hard problem and watch them figure it out in real time. I’m headed to InfoComm to mentor, judge, and sit on a panel for an idea sprint about AI at the edge. And I could not be more excited.

Tokenmining: low token plans maximize your output

Kevin Coleman — Wed, 10 Jun 2026 08:49:55 GMT

I realized recently that limiting my token use (opting for a low token plan), saves me money and encourages me to stay focused on delivering software, not just writing it.

Weekly Update 507

Troy Hunt — Wed, 10 Jun 2026 08:31:56 GMT

1,000 breaches is one hell of a milestone. It's not just the process of getting data, verifying it, loading it, sending notifications etc, it's all the other stuff that goes into keeping the whole thing afloat. Legal docs. Trademarks. Accounting. Agreements. The most mind-numbingly

Honeypot.net — Wed, 10 Jun 2026 07:42:05 GMT

I’m finally getting around to watching the WWDC keynote. Fun fact: if a presenter lowers their hands below waist level, or goes more than 3 seconds before waving them about, they’re penalized stock options.

That’s the only explanation I have for it. Every single presenter, every time. If you had a drinking game for whenever someone swings their hands wide and brings them back together, you’d be dead by the first section.

Social Security vs. Everything Else

jodavaho.io — Wed, 10 Jun 2026 03:00:00 GMT

Prior to the “invention” of 401ks we didn’t have much option, but we do now. The point here is to play with just how bad SSC is vs a 401k (or bonds!).

SSC is bad because it takes $1 from everyone, gives it to the gov, who then distributes those dollars to people who currently expect them .. because they had dollars taken from them already. Investments are good because people take their own $1 and let it accrue interest for decades, then they have it.

“But wait, Jodavaho, doesn’t that make the rich richer?” Yes! So it’s fairly easy to scrape a % from those high earners and subsidize the accounts of the lower earners (versus sending the dollars to current retirees). This means that $1 from a high earner becomes $3 or so for a lower earner! I’d take that deal. It sure beats the heck out of the current <$1 given for every $1 taken.

Of course, the point is that we can’t just “flip” from SSC to bonds overnight, so I propose the following: A 1% drawdown / year in SSC mandatory “investment”, and a 1%/y increase in everyone’s new 401ks/IRAs/Bond accounts. The result zeros out the losses over time and ensures a smooth transition.

Surely this is better than yet another bernie campaign about increasing taxes!

Try it for yourself.

Enter your salary. The charts show what your mandatory 12.4% Social Security contribution (you pay 6.2%, your employer pays 6.2% of money that would otherwise be yours) would become over your career if you could invest it instead and what each option pays you in retirement. Everything is in today’s dollars, so returns are real (after inflation).

TL;DR: dollars received by your planning age, per $1 contributed (today's dollars, computed from the settings below; every option annuitized to pay $0 to heirs, like Social Security):

Working years: what your contributions become

Retirement: guaranteed annual income each option pays until your planning age

Retirement: balance left if each account pays you the same check Social Security would

Implied real return of Social Security by salary, under your settings

A way out: phase SS contributions down 1pt/yr from 2027 into bonds + 60/40, honor benefits already earned

Methodology, assumptions, fine print

The model. Everything is computed in constant 2026 dollars. Your salary grows only by the "real raises" rate; investment returns are real returns. Each working year, the chosen percentage of your salary (up to the 2026 taxable maximum of $184,500) is contributed. Social Security's benefit is computed by the actual statutory formula: average your highest 35 years of covered earnings (AIME), then apply the 2026 bend points (90% of the first $1,286/month, 32% up to $7,749, 15% above that) and adjust for your claiming age (about -30% at 62, +24% at 70, relative to the full retirement age of 67). Because Social Security indexes past wages, bend points, and the taxable maximum to wage growth, holding them all fixed in real terms is a reasonable simplification.

Payouts. The strategies differ only during the working years. At retirement, every balance converts into the same dumb-careful instrument: a guaranteed level real payout priced at the bond rate, exhausting exactly at your planning age. In other words, you buy a TIPS ladder with the whole nest egg. No strategy holds risky assets in retirement. The drawdown chart instead keeps the balance in bonds and withdraws the same check Social Security would pay, to show what's left over (or when the money runs out). Social Security's "value at retirement" is the present value of its benefit stream at the bond rate.

TL;DR table. Lifetime payout divided by lifetime contributions, undiscounted, in today's dollars. Every strategy converts to the guaranteed bond payout at retirement and exhausts exactly at your planning age, so every column plays by Social Security's rules: nothing left to heirs. Time value of money is ignored here; the implied return column and chart below are the discounted view. The investment columns barely vary with salary (only through the taxable cap). Social Security's column varies because the formula is progressive.

Implied real return. The last chart solves for the real rate of return at which your contribution stream exactly pays for your benefit stream (an internal rate of return), swept across salaries under your current settings. The dot marks your inputs. The bend points make the implied return fall as salary rises. For the investment strategies, the lines are the lifetime rate implied by accumulating at the strategy's return and paying out at the bond rate, so they sit below the working years return.

Benefits are not proportional to pay-in. The dashed line on the first chart is the present value, at each age, of the benefit you've earned so far. It is concave, not linear: the formula replaces your first dollars of average earnings at 90% and your last at 15%, and only your top 35 years count. With a flat real salary, every year you work past 35 costs the full tax and earns nothing extra.

What this ignores, in Social Security's favor or against:

Longevity insurance. Social Security pays until you die, not until your planning age. If you live to 100, the comparison shifts in its favor. Raise the planning age to see by how much.
Disability and survivor insurance. About 1.8 points of the 12.4% fund disability insurance, and survivors of deceased workers get benefits. Use the 10.6% option to compare retirement-for-retirement, or price separate term life + disability policies yourself.
Spousal benefits. A non-working spouse adds up to 50% on top of your benefit. Single-earner couples do better under Social Security than this model shows.
Market risk. The portfolios assume their average return arrives smoothly, every year, during the working years. It won't. The payout is guaranteed once converted at retirement, but the nest egg you arrive with is in reality a wide distribution around the smooth curve shown.
Taxes. Contributions are compared pre-tax (like a traditional 401k); benefit taxation and RMDs are ignored on both sides.
Political risk. The trust fund selector models the two ends of current law. "Scheduled" assumes Congress closes the funding gap without touching benefits. "Payable" applies what payroll taxes alone can cover, year by calendar year: 100% before the projected 2032 depletion, 77% at depletion, declining roughly linearly to about 67% by 2100 (per the 2025 and 2026 trustees reports). Your career is assumed to start in 2026, so the share applied to each retirement check depends on the calendar years you would actually be retired. The table and bars show the average check, and the implied return calculation uses the full declining stream. An actual fix will land somewhere between the two, and could also arrive as higher taxes or a higher cap instead of benefit cuts.

The transfer switch. An optional progressive layer for the account world: workers earning above $45,000 (roughly the median covered wage) divert the chosen number of points of their covered wages into the accounts of workers earning below it, as a flat yearly credit. The calibration is hard coded in the source: about $11T of covered payroll, 185 million covered workers, and about 85% of payroll earned above the cutoff, so each point transferred funds a credit of roughly $1,000 per year per low wage worker. Your cost never changes; the full contribution is still taken, it just lands in someone else's account. Unlike the bend points, the transfer happens up front where you can see it, and it compounds in the recipient's own account. Watch the strategy curves kink at $45k on the implied return chart. It does not apply to the phase-out chart below.

The phase-out chart. Starting in 2027, the Social Security contribution falls by one point per year (zero by 2039). Each freed point goes half to guaranteed bonds, half to the 60/40 portfolio, into an account you own. Benefits already earned are honored in full, paid from general revenue in proportion to what you actually contributed, so nobody who paid in gets stiffed. Retirement income becomes your accrued benefit plus your accounts, annuitized at the bond rate like everything else on this page. The comparison lines are doing nothing (payable benefits under current law) and raising taxes to pay scheduled benefits in full. Two modeling choices worth knowing: accrued benefits are pro-rated by contributions, which shortchanges the plan a little (the 90% band means a computation on a frozen earnings record would credit mid-career workers somewhat more), and paying accruals from general revenue is an assumption about policy, not current law. The cost is real: payroll revenue diverted to the accounts reaches roughly $1.4T/yr by 2039, and winding down legacy benefits runs on the order of twice the $25T gap that already exists. In exchange, every cohort retires on more, and everyone after the transition gets the full multiples in the TL;DR table.

Sources: SSA 2026 COLA fact sheet (taxable maximum, tax rates), SSA benefit formula bend points, SSA benefit computation, Trustees report summary.

Compress and Unfold

june.kim — Wed, 10 Jun 2026 03:00:00 GMT

But Our Devenv Is In Another Repo

trv.rs — Wed, 10 Jun 2026 03:00:00 GMT

For the past year of software consulting I've been using nix to define the development environments. I had been using flakes, but recently I've been using devenv.sh. Devenv is a system on top of nix that makes creating common dev environment recipes a lot easier. Setting up a language, some dependencies, services… even setting up postgres and ensuring some databases are created is a breeze.

There are several other similar nix-based dev-ex systems, but (so far that I can see) devenv is the only one that insists on using nix to configure it. So many others try to give you nix without making you write nix… which is a nice idea but in my limited experience is a handicap.

When you just devenv init and check in the resulting files, devenv works great. What I've been working through is "what if you don't want to impose devenv on your whole team?" As a contractor I don't really want to foist my development preferences on everyone else.

I can .git/info/exclude the devenv files. However last few projects I've been working are complex. Four or more repositories, env variables shared between two or three but not all, separate languages, constant motion… not tracking the dev environment in git loses a large part of why to use a tool like devenv in the first place.

Here's what I'm currently doing: I'm working a contract with a team that isn't using nix or devenv. Because I want to track the configuration in git and not just .git/info/exclude the important devenv files, I've been:

Make a git repo to store the configurations in a sibling folder. I'll call this the Configuration Repo.
1. devenv init in a sibling folder and tracking with git.
2. Defining all the repos/projects for my contract as profiles…

&lbrace
  pkgs,
  lib,
  config,
  ...
&rbrace:
&lbrace

  packages = [
    pkgs.acli
  ];

  env = &lbrace
    CLAUDE_CONFIG_DIR = "../.claude";
    AWS_CONFIG_FILE = "../.aws/config";
    AWS_SHARED_CREDENTIALS_FILE = "../.aws/credentials";
    ENABLE_LSP_TOOL = 1;
  &rbrace;

  profiles = &lbrace
    frontend.module = &lbrace
      languages = &lbrace
        javascript = &lbrace
          enable = true;
        &rbrace;
      &rbrace;

      hosts = &lbrace
        "local.fake-company-name.com" = "127.0.0.1";
      &rbrace;
    &rbrace;

    backend.module = &lbrace
      languages = &lbrace
        javascript = &lbrace
          enable = true;
          package = pkgs.nodejs_24;
          npm.enable = true;
          lsp.enable = true;
        &rbrace;
      &rbrace;

      packages = [
        pkgs.vscode-js-debug
      ];

      certificates = [ "local.fake-company-name.com" ];

      aws-vault = &lbrace
        enable = true;
        profile = "fake-company-name";
      &rbrace;
    &rbrace;
&rbrace

In the "real" Project Repo folder:
1. devenv init and git excluding all files.
2. Deleting everything out of devenv.nix so that it's a "bare" file.
3. Then I setup the inputs and imports in devenv.yaml:

# yaml-language-server: $schema=https://devenv.sh/devenv.schema.json
inputs:
  nixpkgs:
    url: github:cachix/devenv-nixpkgs/rolling
  fake-company-name:
    url: path:../fake-company-name-devenv/
    flake: false
profile: frontend
allowUnfree: true
imports:
  - fake-company-name

I used to trigger this using direnv with an .envrc, I'm currently playing with using devenv's shell hook and devenv allow. This makes it clearer where the ENV variables should live… now they all are forced into a devenv.nix. If they need to be secret I can still use my .envrc or I can use devenv's new secrets system.

This works… but I've noticed a couple problems: I think that devenv treats the .devenv cache in the Project Repo as a source of truth, so when I update the Configuration Repo I often don't see changes until I rm -rf .devenv in the Configuration Repo.

I'm currently chatting in the discord to see if there's a better way to achieve what I want. So far… it's the best way… but it leaves something to be desired. While it stands up reliably and just works, it's resistant to change because of that local Project Repo cache. If I find something better I'll post it.

I should write more

Sunil Shenoy — Wed, 10 Jun 2026 02:46:36 GMT

and so should you. And of all the advice I would pass on to my niece if she were to ask for advice, writing down your thoughts either in private or in public would be quite high up on the list of things I would tell her.

I am getting back into reading more long form content these days and it’s been great to come across so many new people through their blogs. My RSS reader has a lot more content for me to go through on weekends.

What should you write about? This thought has always stopped me from writing here. Pick a topic and write more about it. Sleep, running, exercise. Or don’t, and write about your life. What did you get up to today? Maybe you don’t have anything to say today. Have a picture to share about something you observed today? It’s your place on the internet.

Go on, start a blog. Write more. Tell me about it. I can’t wait to read more.

Automated Archives for June, 9th 2026

TrickJarrett.com — Wed, 10 Jun 2026 02:45:00 GMT

This post was automatically generated.

Chess For the Day

Record: 2-0-1
Net Elo Change: +5

Games Played

Blog Posts On This Day

June 9, 2023 (9 posts)
June 9, 2022 (5 posts)
June 9, 2018 (1 post)

Why You Are Not Getting Full Value From Your Analytics

Win Vector LLC — Wed, 10 Jun 2026 01:17:31 GMT

We have a short pitch called “Why You Are Not Getting Full Value From Your Analytics” that we would like to share (and hope some of you will re-share in your network). My group, Win Vector LLC, has some neat methods to extract more value out of business analytics called […]

Hallo Den Haag :)

Fabio Bruna — Wed, 10 Jun 2026 00:02:28 GMT

Blogging with LLMs as a non-native speaker

Vincent Bernat — Tue, 09 Jun 2026 23:15:13 GMT

AI slop is invading the web. A recent story about disallowing LLM-generated submissions on Lobsters triggered a lot of debate. My personal worst offenders are LinkedIn articles with AI-generated images and uninspired articles filled with emojis from people trying to masquerade as experts on a subject they don’t care enough to write themselves. While I am unhappy about this situation, I rely on LLMs for grammar, copyediting, and translation. I don’t see this as a contradiction.

I am a native French speaker, but I blog in both English and French. When I started writing this blog in 2011, I was composing in French and translating to English, but I found it was better to work in the reverse order to avoid unnatural and non-idiomatic constructions. One of my goals is to write “good” English but I never felt it was my strong point.¹ For example, verb tenses are often an issue, even if I mostly stick with the present tense. I learn the rules and forget them right away. I also don’t feel like hiring an editor for something I see as a hobby.

As an example, I have kept the history of the successive iterations when writing “Scaling Akvorado BMP RIB with sharding”:

the first draft, authored with the help of a thesaurus,²
the edited copy revised by the copyediting skill,
the translation to French generated with the translation skill, and
the human proofread of the French translation, with minor edits to the English version.

I know that LLMs may alter the author’s voice when editing, but the corrections in the second step are minor. The prompt asks to “apply light stylistic edits,” with some guidance around avoiding passive voice, long sentences, bland verbs, and filler words. It also defines the target audience: technical with a B2 level in English.

In the following excerpt, I used “long time” instead of “long-standing.” The former is missing a hyphen and applies to people—a long-time friend, while the later relates to a situation—a long-standing agreement. I had a hard time understanding the reason of the second change: the LLM prefers a defining relative clause to provide the definition of “RIB sharding.”

As the Internet routing table contains more than 1 million routes, Akvorado needs to scale to tens of millions of routes. This has been a ~~long time~~ long-standing challenge, but I expect this issue is now fixed by using RIB sharding, a method ~~to split~~ that splits the routing database into several parts to enable concurrent updates.

In the next modification, the LLM puts “device” instead of “equipment.” This is correct as “equipment” is an uncountable noun. I know that, but I still fall into this trap.

When Akvorado does not find a route from a specific device, it falls back to a route sent by another ~~equipment~~ device.

I ask the LLM to use “descriptive verbs” and it complies by replacing a multi-word predicate with a lexically rich verb:

The benchmarks demonstrate it ~~has better performance than~~ outperforms other ~~packages, both~~ packages for lookups, insertions, and memory usage.

It also fixes grammar errors. In the next excerpt, a “list of routes” is a singular expression. Moreover, “stored” is a state and I should not use “into” as it expresses a change.

The list of routes for each prefix ~~are~~ is not stored directly ~~into~~ in the prefix tree.

As a last example, consider the following snippet. The “require” verb accepts a noun or an object followed by a to-infinitive. I can’t use it with just a to-infinitive.

An alternative would be to have one prefix tree for each peer but it would require ~~to configure~~ configuring all routers to export their routes.

As someone who didn’t grow up speaking English, I struggle with these grammar rules despite reading a lot of English material.³ French is more complex to get started but more systematic. English is full of irregularities.

On each page, I disclose in the footer whether an AI modified the content. There are three levels:

🧠: no AI or almost no AI (e.g., grammar corrections)
✨: enhanced (e.g., copyediting)
🤖: generated (e.g., translated from another language, even if human-edited)

Hover or tap the icon to reveal the AI’s name and its role in the document.

Example of AI usage disclosure: Claude Sonnet 4.5 edited this article.

The graph below shows which tool altered each post, year by year. Recently, I applied the grammar skill to past articles. Since 2018, French articles have been translated with the help of DeepL first, then of an LLM. Since 2024, English articles are copyedited.

🖼 Graph showing the AI usage over the years. Each level get its own color.

AI usage over the years. Hover or tap a band for the details.

If you are strongly against any usage of LLMs specifically for writing, I hope you accept my more nuanced position on the usage of these tools as a trade-off to provide clearer and more engaging articles. Years of literature on improving English told us it is important to choose the right word to keep the reader engaged.

[…] Good writing consists of mastering the fundamentals (vocabulary, grammar, the elements of style) and then filling the third level of your toolbox with the right instruments.

― Stephen King, On Writing

Note

Unlike other recent articles, I did not use an LLM to edit this post: an unnamed person kindly accepted to proofread it. I translated it to French without using an LLM either.

I recently read cover to cover “Writing for Developers” and I found it stimulating. Michael Lynch is currently writing “Refactoring English” on the same topic and I have subscribed to the early access. ↩
I am quite happy with the writing tools provided by Kagi. Both the translate tool and the dictionary are a valuable help to find different wordings. I also lean on Kagi’s research assistant when researching a topic. ↩
When I was ten, I played Monkey Island 2 in English without having taken any classes. I used a dictionary to translate word by word and I found the irregular verbs confusing—and not in the dictionary. ↩

Detecting AI Generated Images

Quantable Analytics — Tue, 09 Jun 2026 21:37:33 GMT

We’ve just launched a new browser extension called “SlopGuard” which does AI image generation detection locally inside your browser! You can get it from the Chrome Web Store here. Note: before you click the “Add to Chrome” button, you should know that this is a very large extension (400M), so it will take a few […]

The post Detecting AI Generated Images appeared first on Quantable Analytics.

I threw my last “5-star” book against the wall. So I built BookTribes.

Mark Greville — Tue, 09 Jun 2026 21:33:22 GMT

I spend half of my life reading; the rest I waste. Articles, websites, books, the back of a cereal pack - you get the gist. Books give me more than anything else, when they are ‘good’. Unfortunately, I can’t judge a good book until I’m an hour in

Регистрация только через отечественный email

Блог Stargrave на русском — Tue, 09 Jun 2026 20:49:30 GMT

Регистрация только через отечественный email

Surprise! Pay $1000

Allen Pike — Tue, 09 Jun 2026 20:32:57 GMT

My turn writing for the Forestwalk blog:

Now typically, when you try a SaaS product for free without a credit card, and you hit the limit, you get cut off. Also known as “disruption to your service”. Instead, we were invoiced $1000, which was immediately overdue.

Genuinely curious how common this practice is. Just because I was surprised by it, doesn’t mean it’s unheard of.

WWDC 2026: weird but better than I feared

anderegg.ca — Tue, 09 Jun 2026 19:54:50 GMT

This year’s WWDC keynote was an odd one. It was relatively short at around 76 minutes, for one thing. It also eschewed the usual OS-by-OS structure, but I think that’s for the best. The biggest change was that there was really only one major thing Apple wanted to talk about: “Apple Intelligence”.

Rethinking 'Transformation': From projects to long-term capability

Scott Logic — Tue, 09 Jun 2026 19:18:00 GMT

‘Digital transformation’ is used so widely that it risks meaning everything and nothing. In this blog, I explore why how we define transformation shapes how it’s funded and delivered, why treating it as separate from business as usual creates costly distortions, and how a project‑led, cost‑cutting mindset often undermines the very change organisations are trying to achieve.

Omega Race on the C64 — A Tale of Two Cartridges

mass:werk – Now Go Bang! — Tue, 09 Jun 2026 18:50:00 GMT

A quite literal look into a curious duplication.

Siri AI is a Malware Vector

Lou Franco: code, apps, and writings — Tue, 09 Jun 2026 18:09:48 GMT

I hope you are able to use the latest Apple OSs with Siri AI completely turned off. I believe that, as described, it will be a fertile ground for malware reminiscent of Windows 20 years ago. I would love if anyone has information about the details of Siri AI that refute this. 1. Stopping prompt […]

Sabbatical #17: Melbourne

Darek Kay — Tue, 09 Jun 2026 17:44:01 GMT

A travel journal from my visit to Melbourne, Australia.

OpenClaw or Hermes? Choosing the Right AI Agent Stack in 2026

Stories by Parvez Mohammed @ Techlatest.net on Medium — Tue, 09 Jun 2026 17:31:35 GMT

The AI model race is slowing down. The agent runtime race is just getting started.

In 2025, everyone compared Claude, GPT, Gemini, and Qwen. In 2026, the conversation has shifted. The real question is no longer which model you use, but which system orchestrates that model.

For self-hosted agents, two projects stand out: OpenClaw and Hermes Agent.

Both can connect to Telegram, Discord, Slack, WhatsApp, local tools, and cloud models. Both support skills. Both can automate tasks and execute workflows.

Yet after spending time with both systems, I came away with a simple conclusion:

OpenClaw is a better control plane. Hermes is a better self-improving runtime.

The choice depends entirely on what you expect your agent to become.

Repos: NousResearch/hermes-agent · openclaw/openclaw

Part 1 — What problem do they solve?

At first glance, OpenClaw and Hermes look similar.

You connect a model.
You give it tools.
You chat with it through Telegram, Discord, WhatsApp, or the terminal.

But their philosophies diverge quickly.

OpenClaw treats agents as members of a larger system.

Hermes treats agents as individuals that learn and improve over time.

That difference influences everything else.

| Category           | OpenClaw                                                | Hermes                                               |
| ------------------ | ------------------------------------------------------- | ---------------------------------------------------- |
| **Core Idea**      | Agent control plane                                     | Self-improving runtime                               |
| **Primary Focus**  | Channels, routing, and orchestration                    | Learning, memory, and automation                     |
| **Ideal User**     | Operators, builders, and teams managing multiple agents | Researchers, automation enthusiasts, and power users |
| **Long-Term Goal** | Manage and coordinate many agents                       | Continuously improve a single agent over time        |

Both projects answer: “How do I talk to an AI agent from Telegram/WhatsApp/Discord and have it use tools on my machine?”

They diverge on what happens after the first week:

| | OpenClaw | Hermes |
|---|----------|--------|
| **Product feel** | Polished personal assistant — gateway, channels, dashboard | Research-grade agent platform — tools, memory, evolution |
| **Skills** | You install or write `SKILL.md`; ClawHub registry | Agent can **author** skills; Curator maintains quality |
| **Stack** | Node.js, TypeScript, npm global | Python CLI, bash installer |
| **Sweet spot** | "Message my assistant anywhere" | "My assistant gets better at my workflows over time" |

Neither is a hosted SaaS. You run the gateway on your laptop, homelab, or VPS.

Part 2 — Architecture side by side

OpenClaw

Gateway = single control plane (default http://127.0.0.1:18789/)
Workspace = ~/.openclaw/workspace with AGENTS.md, SOUL.md, TOOLS.md
Skills = ~/.openclaw/workspace/skills/<name>/SKILL.md
Daemon = launchd/systemd user service after openclaw onboard --install-daemon

Docs: Architecture · Gateway

Hermes

CLI + TUI = hermes, hermes --tui
Gateway = hermes gateway for messaging platforms
Skills = procedural memory in ~/.hermes/skills/
Curator (v0.12+) = periodic grading/pruning of learned skills

Docs: Hermes user guide

Shared pattern

Both normalize inbound chat JSON → agent message → tool/skill execution → outbound reply. Both use Markdown skills as the extension point for custom workflows.

Architecture Verdict

Choose OpenClaw when:

You need multiple agents
You need channel separation
You need orchestration

Choose Hermes when:

You want a single powerful assistant
You care about automation
You value simplicity

Winner: OpenClaw

Deploy on OpenClaw VM

Want to skip infrastructure setup?

Available with both CPU and GPU configurations for development, testing, and production workloads.

Skills: Static Catalog vs Living Knowledge

This is where Hermes becomes interesting.

OpenClaw uses a traditional skill ecosystem.

You install skills.
You update skills.
You manage skills.

The model stays mostly separate from the skill lifecycle.

Hermes takes a different approach.

Repeated workflows can become reusable skills.

Instead of treating skills as software packages, Hermes treats them as procedural memory.

Over time, the agent begins to recognize recurring patterns and formalize them.

This fundamentally changes the relationship between user and system.

With OpenClaw, you manage skills.

With Hermes, you train skills.

Skills Verdict

If you want predictability:

OpenClaw

If you want adaptation:

Hermes

Winner: Hermes

Memory: Rich Context vs Focused Context

Memory is often marketed as a feature.

In reality, memory is usually a tradeoff.

OpenClaw maintains richer context across workflows and channels.

That can be incredibly useful.

It can also create noise.

As systems grow, context retrieval becomes harder to manage.

Hermes intentionally keeps memory lean.

Instead of aggressively pulling context into every task, it retrieves information progressively.

The result is a system that often feels more focused.

OpenClaw remembers more.

Hermes remembers more selectively.

Memory Verdict

For long-running agent ecosystems:

OpenClaw

For daily workflows and repeated tasks:

Hermes

Winner: Hermes

User Experience and Control

This was one of the most surprising differences.

OpenClaw generally feels mature and stable.

Once configured, it stays out of the way.

Hermes feels more transparent.

Tool execution is easier to inspect.
Context usage is easier to understand.
Interrupting workflows feels more natural.

If you enjoy seeing what your agent is doing, Hermes provides a clearer window into the system.

If you simply want the system to work, OpenClaw’s maturity is reassuring.

UX Verdict

Transparency: Hermes

Stability: OpenClaw

Overall Winner: Hermes

Part 3 — Prerequisites

| Requirement | OpenClaw | Hermes |
|-------------|----------|--------|
| OS | macOS, Linux, Windows (WSL2) | macOS, Linux, WSL |
| Runtime | Node **22.19+** or **24** | Python (installer handles deps) |
| API key or local model | Yes | Yes |
| Disk | ~500MB+ for Node + workspace | ~1GB+ depending on browser tools |

Check versions:

node -v          # v22.19+ or v24 for OpenClaw
which hermes     # after Hermes install
which openclaw   # after OpenClaw install

Part 4 — Install OpenClaw

npm install -g openclaw@latest
openclaw onboard --install-daemon

The onboarding wizard configures:

Gateway bind address and auth
LLM provider (or Ollama for local models)
At least one channel (Telegram is the fastest smoke test)
Workspace path and bundled skills

Verify:

openclaw doctor
openclaw status
# Dashboard (if gateway running):
# http://127.0.0.1:18789/

Local model (optional): follow the OpenClaw + Gemma + RAG tutorial to point OpenClaw at gemma4:e2b via Ollama.

OpenClaw skills smoke test

openclaw skills list
openclaw skills install <skill-from-clawhub>   # example — see clawhub.ai

Skills load from (highest priority first):

<workspace>/skills/
Project /.agents/skills
~/.agents/skills
~/.openclaw/skills
Bundled skills

See Skills docs.

Part 5 — Install Hermes

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
source ~/.zshrc   # or ~/.bashrc
hermes setup --portal

hermes setup --portal is the fastest path to a working cloud model + tool gateway. For local-only, use hermes model and configure Ollama per Hermes docs.

Verify:

hermes doctor
hermes --tui

First TUI prompts to try:

“List tools you have access to”
“List skills in ~/.hermes/skills”
“What is the Curator and when does it run?”

Full Hermes depth: Awesome Hermes Agent tutorial.

Hermes gateway smoke test

hermes gateway

Configure channel tokens via hermes setup or config files. Run hermes doctor after any gateway change. Keep DM pairing/allowlists enabled until you trust exposure.

Part 6 — Feature comparison (hands-on)

Use the same three prompts on both systems and compare behavior.

| Test prompt | What to observe |
|-------------|-----------------|
| *"What skills do you have?"* | OpenClaw lists workspace/ClawHub skills; Hermes lists `~/.hermes/skills` + may mention learned skills |
| *"Run a shell command: uname -a"* | Tool permission / sandbox behavior |
| *"Remember that my project codename is NEPTUNE"* | Memory persistence on next session |

Record results in a simple table:

| Test | OpenClaw | Hermes |
|------|----------|--------|
| Skill list | | |
| Shell tool | | |
| Memory | | |

Full static matrix: feature matrix.

Part 7 — Skills: same format, different lifecycle

OpenClaw skill anatomy

~/.openclaw/workspace/skills/my-skill/
├── SKILL.md          # YAML frontmatter + instructions
└── scripts/          # optional Python/shell helpers

Install from ClawHub:

openclaw skills install <skill-id>
openclaw skills verify <skill-id>    # trust envelope when available

Operator maintains skills — update via openclaw skills update or ClawHub sync.

Hermes skill anatomy

~/.hermes/skills/my-skill/
└── SKILL.md

Invoke explicitly: /skill my-skill or let the agent auto-select.

Learning loop: after repeated workflows, Hermes can draft new SKILL.md files from session traces. Curator (v0.12+) reviews and prunes them on a ~7-day cycle so quality does not drift.

Porting a skill between stacks

Copy the skill directory to the other runtime’s skills path.
Adjust tool names in SKILL.md (OpenClaw vs Hermes tool schemas differ).
Update any script paths (~/.openclaw ↔ ~/.hermes).
Restart gateway / start a new session.

Example: our agentic-rag skill targets OpenClaw — a Hermes port would call the same LitServe RAG API with Hermes shell tool syntax.

Part 8 — Channels & gateway

| Concern | OpenClaw | Hermes |
|---------|----------|--------|
| Start daemon | Installed by onboard | `hermes gateway` (or systemd per your setup) |
| Multi-channel | One gateway, many channels | One gateway, 18+ platforms |
| Config | `openclaw.json` + wizard | Hermes config under `~/.hermes/` |
| Chat commands | `/status`, `/new`, `/restart`, … | Hermes TUI + channel-specific |

Recommendation: enable one channel (Telegram) on both for comparison, then expand. Running both gateways on the same bot token will conflict — use separate bots or run one at a time.

Part 9 — Models: cloud vs local

OpenClaw + Ollama (this repo’s pattern)

ollama pull gemma4:e2b
# Configure in openclaw.json — see openclaw-gemma-rag/config/
openclaw gateway restart

Hermes + local model

Configure via hermes model or provider section in Hermes docs. Cloud APIs remain the path of least resistance for tool-heavy tasks on modest hardware.

| Workload | Suggestion |
|----------|------------|
| Phone assistant, mostly chat | Cloud model on either stack |
| Private docs, RAG, homelab | OpenClaw + [Gemma RAG guide](https://ayush7614.github.io/agentic-ai-ecosystem/guides/openclaw-gemma-rag/) |
| Heavy browser automation | Hermes with sandbox backend (Modal/Daytona) or skip browser on small VPS |

Part 10 — Memory & self-improvement

| | OpenClaw | Hermes |
|---|----------|--------|
| **Session history** | Session tools (`sessions_history`, etc.) | Built-in session + TUI history |
| **Long-term memory** | Workspace files + operator-managed | Memory layer + ecosystem plugins (honcho, hindsight, plur) |
| **Automatic skill growth** | No | **Yes** — core differentiator |
| **Quality control** | Manual review, `openclaw skills verify` | **Curator** automated

Choose Hermes when you want the agent to accumulate procedural memory. Choose OpenClaw when you want predictable, curator-controlled skill sets from ClawHub.

Part 11 — Migrate OpenClaw → Hermes

Hermes ships a native migration path:

hermes claw migrate

This imports OpenClaw workspace layout, channel configuration, and compatible skills where possible.

After migration:

hermes doctor
hermes claw migrate --help   # inspect flags
# Compare cron + channel config manually
hermes gateway

Community fallback for older Hermes versions: openclaw-to-hermes.

Side-by-side cutover (recommended for production personal assistants):

Migrate with hermes claw migrate
Run Hermes gateway on a new Telegram bot
Keep OpenClaw on the old bot until Hermes passes your test checklist
Switch DNS/webhooks if applicable
Decommission OpenClaw daemon when satisfied

Part 12 — Security comparison

| Risk | OpenClaw mitigation | Hermes mitigation |
|------|---------------------|-------------------|
| Malicious skill | `openclaw skills verify`, review scripts | Review `SKILL.md` + scripts before enabling |
| Shell/RCE | Docker sandbox (docs strongly recommend) | Remote sandboxes, minimal VPS install (`--skip-browser`) |
| Open gateway | Local bind, auth tokens | `hermes doctor`, pairing/allowlists |
| Prompt injection via chat | Model choice, tool allowlists | Same — use strongest model available |

Rule for both: skills are code. Treat ClawHub and awesome-hermes-agent entries as untrusted until reviewed.

Part 13 — Run both side by side (this repo)

From the repo root:

cd guides/hermes-vs-openclaw
chmod +x verify-comparison.sh
./verify-comparison.sh

Optional full stack:

| Terminal | Command |
|----------|---------|
| A | Start RAG API per [qwen-agentic-rag](https://ayush7614.github.io/agentic-ai-ecosystem/guides/qwen-agentic-rag/) |
| B | `openclaw gateway` (messaging assistant) |
| C | `hermes --tui` (compare tool/skill behavior) |

OpenClaw consumes RAG via the agentic-rag skill. Hermes can call the same HTTP API via a custom skill or MCP wrapper.

Part 14 — Decision guide

| Profile | Pick |
|---------|------|
| Indie hacker, Telegram/WhatsApp only, loves npm | **OpenClaw** |
| ML researcher, multi-agent, Nous ecosystem | **Hermes** |
| Existing OpenClaw user, curious about learning loop | **Hermes** via `hermes claw migrate` |
| Need reproducible skill catalog, not auto-writes | **OpenClaw** + ClawHub |
| Building on this repo's RAG guides | **OpenClaw** primary; Hermes optional second runtime |

You can also run OpenClaw for channels and Hermes for batch/cron evolution against the same RAG API — they are not mutually exclusive at the API layer.

Part 15 — Troubleshooting

| Symptom | OpenClaw fix | Hermes fix |
|---------|--------------|------------|
| CLI not found | `npm i -g openclaw@latest`; check `node -v` | `source ~/.zshrc`; re-run installer |
| Doctor fails | Re-run `openclaw onboard` | `hermes setup --portal` |
| Gateway won't start | `openclaw gateway restart`; check port 18789 | `hermes doctor`; check channel tokens |
| Skills missing | `openclaw skills list`; workspace path | `ls ~/.hermes/skills`; new session |
| Node too old | nvm install 22; [`use-node22.sh`](https://github.com/Ayush7614/agentic-ai-ecosystem/blob/main/guides/openclaw-gemma-rag/use-node22.sh) | N/A |
| Migration incomplete | — | `hermes claw migrate`; compare cron/channels; try [openclaw-to-hermes](https://github.com/0xNyk/openclaw-to-hermes) |
| Both fight for Telegram | Use two bot tokens | Use two bot tokens |

Summary

| Dimension | Winner (typical) |
|-----------|------------------|
| Channel polish + dashboard | OpenClaw |
| Self-improving skills | Hermes |
| npm / TypeScript ecosystem | OpenClaw |
| Multi-agent + research tooling | Hermes |
| Local Gemma + RAG (this repo) | OpenClaw |
| OpenClaw → Hermes migration | Hermes (`hermes claw migrate`) |

Next steps:

Deep dive OpenClaw: openclaw-gemma-rag tutorial
Deep dive Hermes: awesome-hermes-agent tutorial
Feature reference: feature matrix

Real-World Recommendations

Choose OpenClaw if you need:

Telegram and WhatsApp assistants
Multi-agent orchestration
Team-based agent systems
Mature skill marketplaces
Channel-centric workflows

Choose Hermes if you need:

Research automation
Self-improving workflows
Personal knowledge systems
Daily reports and recurring tasks
VPS-friendly automation

Ecosystem and Community

OpenClaw currently has the stronger ecosystem.

ClawHub gives users access to a growing catalog of reusable skills.

Documentation is mature.

Community content is abundant.

Hermes is newer and more experimental.

The ecosystem is smaller, but the pace of innovation is significantly faster.

OpenClaw wins on maturity.

Hermes wins on direction.

Ecosystem Verdict

Winner Today: OpenClaw

Most Interesting Future: Hermes

Final Verdict

The most common mistake is treating OpenClaw and Hermes as direct competitors.

They solve adjacent problems.

OpenClaw is an operating system for agents.

Hermes is an operating system for learning.

If your challenge is coordinating agents across channels, OpenClaw remains the strongest choice.

If your challenge is building an assistant that improves through repetition, Hermes is the more compelling platform.

For most developers building chat-based assistants today, I would recommend OpenClaw.

For researchers, automation enthusiasts, and anyone interested in procedural memory, I would recommend Hermes.

Both are excellent.

The better question is not which one is best.

The better question is what kind of agent you want to build.

Thank you so much for reading

Like | Follow | Subscribe to the newsletter.

Catch us on

LinkedIn: https://www.linkedin.com/in/techlatest-net/

OpenClaw or Hermes? Choosing the Right AI Agent Stack in 2026 was originally published in Towards Dev on Medium, where people are continuing the conversation by highlighting and responding to this story.

Honeypot.net — Tue, 09 Jun 2026 17:20:03 GMT

I bought a DM42n calculator a while ago and it’s been a fun desk toy that I admittedly have little practical need for. I’ve since updated it to use the C47 system which is the most delightfully nerdy, ludicrously powerful calculator mankind’s ever schemed up. My kingdom for a printed manual, but still!

Reading of Anthropic's Contextual Retrieval

🦉 olshansky 🦁 — Tue, 09 Jun 2026 16:58:49 GMT

Introduction - Why talk about RAG in 2026?

As we keep surfing the AI wave, one of the recurring topics of discussion is cost.

If you’re a frontier lab like OpenAI or Google, you scale up by building data centers and buying put options on energy.

If you’re a major company like Uber or Snowflake, you start by building “tokenmaxxing dashboards”, until you inevitably need to place guardrails on how much your employees spend; Uber is capping its engineers to $1,500 per month.

Has Trump Created a Monster?

Gus Van Horn — Tue, 09 Jun 2026 16:52:07 GMT

From Politico comes a story about one of the kook factions that Trump successfully pandered to to win the Presidency a second time.

The so-called Make America Healthy Again "movement" (MAHA) -- a collection of environmentalist-adjacent Luddites who oppose such boons to human flourishing as vaccination, modern fertilizers, and pesticides -- has become impatient because they feel Trump hasn't handed them enough favors. Some of them are now threatening to withdraw their support for him in the mid-terms.

This comes, predictably, with Republican officials protesting that they've done more for MAHA than the Democrats ever did:

"We thought we were getting a different administration that wanted to stand up to special interests, stand up to the pharmaceutical industry, stand up to the food companies, stand up to Big Ag, Big Chem. And that is not what we got. What we have right now is business as usual," said Vani Hari, a leader in the MAHA movement known as Food Babe who rose to prominence through her fight against chemicals in food.

"We're going to vote for whatever candidate puts the health of our children first," Hari said. "I don't have loyalty to anybody. And a lot of people feel that way within the MAHA movement."

This reminds me of a couple of things, the first being how conservatives -- seemingly until yesterday -- once correctly ridiculed such people as the lunatics they are.

Second, it reminds me of how effective voter blocs work in a two-party system: By being up for grabs, thereby making both parties responsive to their agenda.

Whether accurate or not, the perception that Trump needed MAHA to win in 2024, which he reinforced by handing Bobby Kennedy a cabinet post, is now part of the conventional wisdom. Based on the Republicans' past, sorry history of appeasing the Democrats on everything else, it's a safe bet that this will continue indefinitely along this new frontier. And the taste of political power Trump handed MAHA will have emboldened them and made them not content with being taken for granted by either party.

In an age where neither party qualifies as a champion of science -- with Democrat misuse of science to pretend their political aims are legitimate making Republican rejection of science seem less bad than it is -- Trump may well have made this army of crackpots into a political force that can press for horrible government science policy for decades to come.

-- CAV

High Dynamic Range DIY Air Testing

Jeff Kaufman's Writing — Tue, 09 Jun 2026 16:00:00 GMT

DIY testing of air cleaning is practical, and thoughtful experimental design can substitute for high-quality sensors including for evaluating air purifier setups that give >100,000x particle reductions.

I've done a lot of DIY testing over the years ( 1, 2, 3, 4, 5, 6). The goal is generally to understand how well something removes particles from the air. A professional particle counter (example) costs thousands of dollars, and they're amazing devices, but what you're paying for is convenience, reliability, calibration, and dynamic range. If we're willing to give up on convenience and buy multiple devices for reliability, we can cheaply address calibration and dynamic range with experimental design.

The cheapest ready-to-go option for DIY work today is probably the Temtop P600 which I see as $70. While I haven't tried it, it's a stripped-down version of the Temptop M2000 which I bought several years ago to use for my DIY experiments. If you want to make something cheaper, you can get a PMS5003, which I see as $21, and connect it to a cheap SoC (~$10) or to an Android phone (adapters in the $15 range). At scale I think you could get this down below $15: a PMS5003 or clone at high volume would be ~$7, the phone adapter would be under $1 at this scale, software <$1, then a box, assembly, and some QC.

The Temtop and PMS5003 are somewhat calibrated, but fortunately we don't need to know absolute particle counts. We just need some number that is, within a reasonable range, linearly proportional to particle counts. As long as the meter is stable over time we can look at ratios. For example, if you're trying to see how quickly something can clear smoke from a room you don't need to generate a target amount of smoke or know exactly how much smoke you've generated: you can just measure the half life. This gives you relative efficacy directly or CADR if you have a sealed room of known volume.

Dynamic range is harder, but still doesn't require professional sensors. Let's say you want to measure the efficacy of a DIY cleanroom setup. (Note: if you're excited about this Coefficient Giving might be willing to fund you). You have some kind of outer room where you'll fill the air with particles, and some kind of inner area where you want to ensure you're keeping particle counts down. The sensors I've talked about above can measure particle concentrations over a ~500-1,000x range, but if you're trying to assess whether you've successfully achieved a larger reduction a simple experiment won't have the range. A level of particles you can measure outside will give "below range" inside, and a level you can measure inside will give "above range" outside. What can you do?

The simplest option is just to wait longer. This is really not bad! Particle counters are really very good at only reporting a particle when there is one, which means you can get 10x the sensitivity by running for 10x as long. Still, if we have 1000x range and want to measure a 100,000x reduction those are some long waits. We can speed it up (or extend our range further) by bringing air concentration into the range of our sensors.

The next simplest option would be to have one sensor inside and one outside, along with an air purifier outside. Calibrate the sensors to each other ahead of time and then start off the experiment with a very high concentration (above range outside, within range inside). Let your air purifier bring levels down outside. After passing through a middle region (above range outside, below range inside) you get within range outside (but below range inside). Here's an example of what an idealized version of this experiment might look like:

There's no time during which we have both the internal and external measurement, but we can extrapolate our curves and estimate that when the inside sensor is reading 10 the outside sensor would read 1,000,000.

Instead of relying on the air purifier to remove a consistent 10% of particles from the outside each minute, however, we can add a third sensor. A MERV-16 fan removes at least 95% of the particles, so we can make a box with a fan and a MERV-16 filter and measure counts inside that box. The box should not be sealed; positive pressure from the fan is enough to ensure we're only measuring the post-MERV concentration:

Unfortunately this is still not enough to connect our Inside and Outside curves, but we can add a fourth and final link in the chain with a HEPA filter to remove at least 99.97% of particles:

Now we have substantial temporal overlap between each pair of sensors and can plot their ratios:

The parts of this plot we care about are the horizontal sections: that's where the values reported by each sensor are moving proportionally. Sloped (and ratio=1) sections aren't meaningful, since they're cases where a sensor is out of range.

We can then read off a 20x reduction for the MERV-16, a 167x reduction from the MERV to the HEPA, and a 30x reduction from HEPA to inside the cleanroom. These stack to give the expected 100,000x reduction end to end.

Of course real data would be much messier, but the basic idea should be solid.

Additional logistical notes:

The particle levels we're talking about here are really high, and you don't want to be breathing them. Ideally you can set it up so you run the whole experiment from outside a sealed room, monitoring levels remotely. If you do need to go inside, use a well-fitting P100 (and keep in mind that they don't work with beards).
I've used smoke, but smoke is sticky and poorly behaved. Better to use aerosolized salt. You can get it in the air with an ultrasonic humidifier and salt water, and as long as the relative humidity is below 45% the droplets will dry out to pure salt crystals. If you're doing this in a humid place you could use a dehumidifier.
Even levels much lower than this will set off your smoke alarm, and levels this high might break it. I'd remove it, or at least turn it off and seal it well with plastic.
Apparently the salt gets everywhere and is mildly corrosive (like living by the beach for a long time). Take everything out of the room, and either encase the room in disposable plastic sheeting (thin painter's sheeting is very cheap) or wipe down all surfaces with a wet cloth after.

Comment via: facebook, lesswrong, mastodon, bluesky

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools

Metadata — Tue, 09 Jun 2026 13:56:43 GMT

The transformative power of LLMs in coding has been irrefutable, and it feels like we are living through a magical computing renaissance. On the socials, we hear impressive numbers of lines of code generated, features delivered, and bugs fixed. But, the macroeconomic indicators seem to be still lagging. Heck, if you talk with an engineering manager, you find that their product shipping dates haven't miraculously compressed by a factor of five, either.

This paper just landed 10 days ago. It is from MIT and Wharton by Mert Demirer, Leon Musolff, and Liyuan Yang. Their study attempts to provide a structured economic model for evaluating actual productivity obtained from AI coding tools. By pairing confidential Microsoft telemetry with the public footprints of over 100,000 GitHub developers (tracking everything from open-source utilities to web app repositories), the authors show significant systemic friction downstream of AI code generation.

Of course, I do my usual skeptical critic of the paper. In this case, this is especially heightened because these are economists peeking into the messy non-linear world of software engineering and trying to impose a "production hierarchy" abstraction onto it. But if we reconsider their analysis from a different perspective, it becomes possible to translate their complex production functions into Amdahl's Law terms, and then we can start doing our own evaluations and draw our own conclusions as I discuss below.

The Monotonic Decay

The core of the paper rests on this monotonic decay argument. The sheer task-level velocity gains we see from AI coding tools start to bleed out as they move up the production hierarchy. The authors break down AI tool adoption into three distinct generational tiers:

Autocomplete (intelligent text prediction),
Synchronous (Sync) Agents (interactive, real-time code modifiers like Claude Code or Cursor),
Asynchronous (Async) Agents (autonomous async agents).

When we look at the task-level velocity of these tools, we see impressive numbers. The paper's abstract claims that autocomplete, interactive sync agents, and autonomous async agents increase overall commit activity by cumulative totals of 40%, 140%, and 180% respectively. But as that work climbs toward an official production milestone, the improvements decay significantly.

For Autocomplete, the +228.2% explosion in raw lines of code bleeds out layer-by-layer until becomes a meager +10.2% increase in actual shipped software releases. For Sync agents, a gigantic +741.3% surge in code syntax reduces down to a modest +20.3% final weekly releases.

My immediate reaction to this vertical hierarchy (Lines -> Files -> Commits -> PRs -> Repositories -> Releases) is skepticism. Treating code production like a neat production line feels superficial. Software engineering is not a linear conveyor belt, as coding is highly nonlinear, and a single commit routinely alters fifty files. However, giving the authors the benefit of doubt and reading onwards, I find that there is still value to this naive abstraction, as it points to human gatekeeping and coordination overhead at higher levels of the CI/CD pipeline. AI can write lines of code instantly, not being bogged down by the code syntax at the lower layers. But as that work climbs toward an official production milestone, the structural constraints of the system and human bottlenecks take over, and the massive improvements at the task level decay down to nearly nothing.

Let's dive deeper on the mathematical modeling behind this. By taking only the performance of Autocomplete into account (because it operates exclusively at the code-writing level), the authors chose parameters that minimized the differences between their model's predictions and actual developer behavior. Through this exercise, they extracted a local Upstream Output Elasticity ($\theta$) of approximately 0.75. In this layered production model, $\theta = 0.75$ acts as a vertical pass-through metric. It means that at any given stage (say, turning raw commits into clean pull requests), 75% of that layer's success leans entirely on the upstream technical assets flowing into it from the layer below, while a remaining fraction represents the local human effort added at that layer. Because they model software development as a vertically sequential aggregation process, that human intervention operates like a compounding efficiency tax. A massive initial code productivity surge at the bottom layer gets relentlessly multiplied by 0.75 over and over again as it attempts to climb the hierarchy, mathematically forcing the steep vertical attenuation we see in the empirical data.

Now, the sync and async agents aren't just typing lines inside an editor. They operate at a level where they directly manage files, commits, and pull requests. This expanded layer coverage allows agentic workflows to drop their productivity contributions closer to the finish line. By short-circuiting the early stages of the vertical decay chain, agents handle the work more efficiently, doubling the final impact on shipped software compared to autocomplete as seen in Figure 1.

Translating the Economics formulas to Amdahl’s Law

To model what happens inside each of these individual layers, the paper transitions to a nested Constant Elasticity of Substitution (CES) production function. Here, they extract an Elasticity of Substitution ($\sigma$) between AI-generated code and human review effort of a rigid 0.25. In economic lingo, an elasticity of substitution well below $1.0$ means the inputs are "strong complements". That means they are tied/dependent together like a car chassis and tires. It doesn't matter if an automated factory line can manufacture tires 10,000% faster; if you don't speed up the production of the chassis, you don't get more cars.

This of course looks a whole lot like Amdahl’s Law, which dictates that the overall speedup of a system is strictly limited by its sequential un-parallelizable bottlenecks:

$S_{\text{total}} = \frac{1}{(1-P) + \frac{P}{S_{\text{task}}}}$

where $S_{\text{task}}$ is the speedup achieved at the automated task level, and $P$ is the "Global Parallelizable Fraction" of the entire system workload.

When the elasticity of substitution ($\sigma$) between machine output and human validation drops to 0.25, the economic model behaves almost exactly like a Leontief production function. (A Leontief production function describes a strict, zero-flexibility production process where inputs must be combined in exact, unalterable proportions, meaning an excess of one ingredient cannot substitute for a shortage of another.) This dictates that human code review is a non-negotiable strictly sequential bottleneck ($1 - P$). If $\sigma$ were infinite, you could completely substitute human verification with raw AI text volume effectively parallelizing the entire layer. But because $\sigma = 0.25$, throwing an infinite mountain of automated code ($S_{\text{task}} \to \infty$) at the problem does nothing to diminish the fixed, sequential time investment required for a human to review it.

So I decided to translate the paper's findings to Amdahl's Law, in terms I can understand better. When we apply the paper's empirical findings through the Amdahl's Law equation (isolating the global parallel fraction $P$ for commits against final releases), we find the following:

Autocomplete: $S_{\text{task}} = 1.359\times$, $S_{\text{total}} = 1.102\times \implies \mathbf{P \approx 35.0\%}$
Sync Agents: $S_{\text{task}} = 2.091\times$, $S_{\text{total}} = 1.202\times \implies \mathbf{P \approx 32.2\%}$
Async Agents: $S_{\text{task}} = 2.800\times$, $S_{\text{total}} = 1.300\times \implies \mathbf{P \approx 35.9\%}$

Huh! Even going from a simple inline autocomplete tool to fully autonomous agents that clone repositories and run test suites out-of-band, the global parallelizable fraction ($P$) refuses to budge and hovers around 35% across all three generations of AI!

Then how come Sync and Async agents manage to squeeze out 2-3x more final software releases than Autocomplete? While Autocomplete notches a modest 10.2% release expansion, Sync agents push it to 20.3%, and the cumulative stack of Async tools lifts the final output baseline up by 30%. If the global parallelizable envelope ($P$) is locked at 35% from the formulas above, how is the system actually accelerating? This is because according to Amdahl's Law, a system has two entirely separate levers for optimization. You can either increase $P$, or you can aggressively push harder and increase $S_{\text{task}}$. Autocomplete achieved a commit-level speedup ($S_{\text{task}}$) of just $1.359\times$ relative to releases. But Sync agents drive that localized task speedup to $2.091\times$, and Async agents push $S_{\text{task}}$ to $2.800\times$. But this hits a wall of diminishing returns quickly. When the parallelizable footprint ($P$) is pinned to 35%, scaling the localized task speedup ($S_{\text{task}}$) toward infinity yields a hard asymptotic ceiling. Mathematically, the absolute maximum total speedup this configuration can ever achieve is $1 / (1 - 0.35)$, which works out to a hard cap of a 53% overall increase ($1.53\times$) in shipped software. So, no matter how fast an autonomous bot can process a commit, the remaining 65% human sequential bottleneck ($1 - P$) acts as a hard stop.

Is P=0.35 sensible? I think that 35% parallel fraction passes the smell test. If you ask any developer what percentage of their week is spent actually writing code, they'll give you a number right in this ballpark, around 20% to 40%. The remaining 65% of the developers' time is consumed by finding/defining the task, planning, and paying the inevitable human communication tax of meetings and team alignment.

This is why that flatlining 35% profile across all three tool generations makes sense to me. Generative AI can supercharge the coding sandbox by churning out code at high speed (maxing out $S_{\text{task}}$), but it can't parallelize the systemic reasoning, organizational consensus, and deep problem/model comprehension that dominates the rest of the job. Until we find a way to automate those parts as well, shipping software will remain an inherently human-throttled process.

The Traveling Salesman Problem

rtnF — Tue, 09 Jun 2026 13:36:16 GMT

OSRM v26.6.1

Git for Agent Memory: Why You Should Treat Hermes Skills Like Code

Stories by Parvez Mohammed @ Techlatest.net on Medium — Tue, 09 Jun 2026 13:33:54 GMT

Go from zero to a productive Hermes Agent setup with community skills, optional GUI, messaging gateway, and a map of the full ecosystem.

Based on awesome-hermes-agent (last reviewed 2026–05–06, Hermes v0.12.0 “The Curator release”).

What you’ll build

Hermes Agent CLI on your machine
LLM provider + Tool Gateway configured
Starter skills from the ecosystem
Verification scripts for your team
Full coverage of Skills & Plugins, Tools & Utilities, Integrations & Bridges, and Multi-Agent & Swarms

Deploy on OpenClaw VM

Want to skip infrastructure setup?

Available with both CPU and GPU configurations for development, testing, and production workloads.

Architecture

Part 1 — Install Hermes Agent

macOS / Linux / WSL2 / Termux

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
source ~/.zshrc   # or source ~/.bashrc

Headless VPS (skip browser deps):

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash -s -- --skip-browser

Windows (PowerShell)

iex (irm https://hermes-agent.nousresearch.com/install.ps1)

Or use the Hermes Desktop installer on macOS/Windows.

Verify from this guide

cd guides/awesome-hermes-agent
chmod +x verify-install.sh
./verify-install.sh

Expected: hermes on PATH, hermes doctor clean or with fixable warnings.

Config lives under ~/.hermes/ (Windows: %LOCALAPPDATA%\hermes).

Part 2 — Choose a provider

Easiest: Nous Portal (recommended for first run)

One OAuth flow — models + Tool Gateway (search, images, TTS, browser):

hermes setup --portal

Interactive picker

hermes model

Bring your own keys

Copy reference keys:

cp .env.example .env
# Edit .env — then configure via:
hermes config set

Ollama (local) — set OpenAI-compatible base URL in hermes model or config docs.

Docs: Configuration · Nous Portal

Part 3 — First conversation

hermes --tui      # modern TUI (recommended)
# or
hermes            # classic CLI

Try:

“What tools do you have enabled?”
“Create a skill for how I like commit messages formatted.”
hermes --continue — resume last session

Quick reference:

| Command | Purpose |
|---------|---------|
| `hermes` | Chat |
| `hermes doctor` | Diagnose |
| `hermes update` | Upgrade |
| `hermes tools` | Enable/disable tools per platform |
| `hermes gateway` | Start messaging bridge |

Part 4 — Skills & Plugins

Hermes creates skills from experience and maintains them via the Curator (v0.12+). Plugins extend core tools (search, memory, shell compression). Together they are procedural + operational memory.

4.1 — Install skills layer

chmod +x install-ecosystem.sh install-starter-pack.sh
./install-ecosystem.sh skills
# or lightweight starter only:
./install-starter-pack.sh

| Skill | Tag | Install path | Why |
|-------|-----|--------------|-----|
| [wondelai/skills](https://github.com/wondelai/skills) | production | `~/.hermes/skills/wondelai-skills` | 380+ cross-platform skills |
| [litprog-skill](https://github.com/tlehman/litprog-skill) | beta | `~/.hermes/skills/litprog-skill` | Literate programming |
| [youtube-skills](https://github.com/therohitdas/youtube-skills) | production | `~/.hermes/skills/youtube-skills` | VPS-safe YouTube transcripts |
| [drawio-skill](https://github.com/Agents365-ai/drawio-skill) | production | `~/.hermes/skills/drawio-skill` | NL → architecture diagrams |
| [Anthropic-Cybersecurity-Skills](https://github.com/mukul975/Anthropic-Cybersecurity-Skills) | production | optional clone | 753+ MITRE security skills (large) |
| [open-design](https://github.com/nexu-io/open-design) | production | per repo README | 31 design skills, 129 design systems |
| [hermes-skill-factory](https://github.com/Romanescu11/hermes-skill-factory) | beta | skill folder | Auto-generate skills from workflows |
| [hermes-incident-commander](https://github.com/Lethe044/hermes-incident-commander) | beta | skill folder | Autonomous SRE / self-healing |

4.2 — Install plugins layer

./install-ecosystem.sh plugins

Plugins clone to ~/.hermes/plugins/. Enable in Hermes config (see Plugins docs).

| Plugin | Tag | What it does |
|--------|-----|--------------|
| [hermes-web-search-plus](https://github.com/robbyczgw-cla/hermes-web-search-plus) | beta | Route search across Serper, Tavily, Exa |
| [rtk-hermes](https://github.com/ogallotti/rtk-hermes) | beta | Compress shell output 60–90% before LLM |
| [mnemo-hermes](https://github.com/hernanqwz/mnemo-hermes) | beta | pgvector semantic memory on Ollama |
| [Mnemosyne](https://github.com/AxDSan/Mnemosyne) | beta | Local hybrid search + knowledge graph |
| [hermes-curator-evolver](https://github.com/pingchesu/hermes-curator-evolver) | beta | Evidence-driven Curator companion |
| [plur](https://github.com/plur-ai/plur) | beta | Portable shared memory (YAML engrams) |
| [hermes-payguard](https://github.com/nativ3ai/hermes-payguard) | experimental | USDC / x402 payments with limits |
| [agent-analytics-hermes-plugin](https://github.com/Agent-Analytics/agent-analytics-hermes-plugin) | beta | Signals analytics dashboard tab |

4.3 — Curator + skill evolution

Built-in Curator (v0.12+) grades, consolidates, and prunes skills every 7 days. Pair with:

| Tool | Tag | Role |
|------|-----|------|
| Built-in Curator | production | Automatic skill library maintenance |
| [SkillClaw](https://github.com/AMAP-ML/SkillClaw) | production | Evolve/dedupe skills from session data |
| [hermes-dojo](https://github.com/Yonkoo11/hermes-dojo) | beta | Find weak skills, auto-iterate |
| [hermes-agent-self-evolution](https://github.com/NousResearch/hermes-agent-self-evolution) | official | DSPy/GEPA prompt evolution |

Verify skills load:

ls ~/.hermes/skills/
hermes --tui
# Ask: "What skills are available? Try /skill-name if configured."

Part 5 — Tools & Utilities

GUIs, linters, browsers, and operator utilities that sit beside the CLI — not replacements.

./install-ecosystem.sh tools

Clones to ~/.hermes/ecosystem-tools/. Follow each repo's README for npm install, pip install, or Docker.

5.1 — GUI dashboards

| Tool | Tag | Best for | Install notes |
|------|-----|----------|---------------|
| [hermes-workspace](https://github.com/outsourc-e/hermes-workspace) | production | Chat + terminal + skills manager | Nous Hackathon winner; Hermes-native |
| [mission-control](https://github.com/builderz-labs/mission-control) | production | Fleet, tasks, cost tracking | SQLite self-hosted dashboard |
| [hermes-web-ui](https://github.com/EKKOLearnAI/hermes-web-ui) | production | Token/cost analytics, cron, 8 channels | Vue 3 + BFF |
| [hermes-ui](https://github.com/pyrate-llama/hermes-ui) | beta | Single-file glassmorphic UI | Python proxy on :3333 |
| [hermes-desktop](https://github.com/dodo-reach/hermes-desktop) | beta | Native macOS workspace | Direct SSH to host |

Example — hermes-workspace:

cd ~/.hermes/ecosystem-tools/hermes-workspace
# Follow README: typically pnpm install && pnpm dev
# Point at your local Hermes gateway / CLI socket

5.2 — Operator & quality utilities

| Tool | Tag | Role |
|------|-----|------|
| [SkillClaw](https://github.com/AMAP-ML/SkillClaw) | production | `skillclaw doctor hermes` — skill health |
| [lintlang](https://github.com/roli-lpci/lintlang) | beta | Lint prompts/configs (HERM v1.1 score) |
| [agenttrace](https://github.com/luoyuctl/agenttrace) | beta | Post-run session audit TUI |
| [Clarvia](https://github.com/clarvia-project/clarvia) | production | Score MCP servers for agent-readiness |
| [flowstate-qmd](https://github.com/amanning3390/flowstate-qmd) | beta | Anticipatory memory / pre-fetch RAG |

5.3 — Browser & headless tooling

| Tool | Tag | When to use |
|------|-----|-------------|
| [camofox-browser](https://github.com/jo-inc/camofox-browser) | production | VPS blocked by Cloudflare — stealth headless API |
| [vessel-browser](https://github.com/unmodeled-tyler/vessel-browser) | experimental | Full AI-native Linux browser |
| Built-in Playwright | production | Default; skip with `--skip-browser` on install |

5.4 — Deployment utilities

| Tool | Tag | Notes |
|------|-----|-------|
| [hermes-agent-docker](https://github.com/xmbshwll/hermes-agent-docker) | beta | Minimal sandbox image |
| [nix-hermes-agent](https://github.com/0xrsydn/nix-hermes-agent) | beta | Reproducible NixOS module |
| [evey-setup](https://github.com/42-evey/evey-setup) | beta | One-command stack + 29 plugins |
| [openclaw-to-hermes](https://github.com/0xNyk/openclaw-to-hermes) | beta | Migration helper |

Part 6 — Integrations & Bridges

Connect Hermes to memory backends, MCP servers, productivity suites, and other agents.

./install-ecosystem.sh integrations

6.1 — MCP integration pattern

Add server block to Hermes MCP config (see MCP docs)
Restart session; verify with hermes tools or ask Hermes to list MCP tools
Score servers with Clarvia before trusting production workflows

| MCP / integration | Tag | Surface |
|-------------------|-----|---------|
| [MeiGen-AI-Design-MCP](https://github.com/jau123/MeiGen-AI-Design-MCP) | production | Image/video gen (9 models) |
| [mistral-mcp](https://github.com/Swih/mistral-mcp) | beta | OCR, audio, Codestral FIM, agents |
| [Not Human Search](https://github.com/unitedideas/not-human-search) | production | Discover 8,600+ MCP servers |
| [Global Chat](https://github.com/pumanitro/Global-Chat) | production | Cross-protocol agent discovery |
| [hermes-blockchain-oracle](https://github.com/gizdusum/hermes-blockchain-oracle) | experimental | Solana on-chain data |
| [hermes-council](https://github.com/Ridwannurudeen/hermes-council) | experimental | Adversarial multi-perspective debate |

Example MCP config snippet (adjust paths after clone):

# Reference only — merge into your Hermes MCP settings
mcp_servers:
  meigen-design:
    command: node
    args: ["~/.hermes/ecosystem-tools/MeiGen-AI-Design-MCP/dist/index.js"]

6.2 — Memory bridges

| Integration | Tag | Pattern |
|-------------|-----|---------|
| [hindsight](https://github.com/vectorize-io/hindsight) | production | retain / recall / reflect over long history |
| [honcho-self-hosted](https://github.com/elkimek/honcho-self-hosted) | beta | Self-hosted Honcho user modeling |
| [yantrikdb-hermes-plugin](https://github.com/yantrikos/yantrikdb-hermes-plugin) | beta | Rust backend with explainable recall |
| [plur](https://github.com/plur-ai/plur) | beta | Portable YAML engram memory |

Memory hygiene: keep USER.md / MEMORY.md concise; let Curator prune stale skills.

6.3 — Productivity & device bridges

| Integration | Tag | Connects |
|-------------|-----|----------|
| [microsoft-workspace-skill](https://github.com/Andrew-Girgis/microsoft-workspace-skill) | beta | Outlook / M365 via Graph API |
| [hermes-nextcloud](https://github.com/adnw-vinc/hermes-nextcloud) | beta | WebDAV, Notes, CalDAV, CardDAV |
| [hermes-android](https://github.com/raulvidis/hermes-android) | beta | Android device control |
| [agent-android](https://github.com/AIVaneLabs/agent-android) | beta | LAN Android over WiFi |
| [hermes-spotify-skill](https://github.com/Alexeyisme/hermes-spotify-skill) | beta | Headless Linux / Raspberry Pi Spotify |
| [clawsocial-hermes-plugin](https://github.com/mrpeter2025/clawsocial-hermes-plugin) | beta | Social discovery network |

6.4 — Cross-agent bridges

| Bridge | Tag | Handoff |
|--------|-----|---------|
| [evey-bridge-plugin](https://github.com/42-evey/evey-bridge-plugin) | beta | Claude Code ↔ Hermes context share |
| [hermes-agent-acp-skill](https://github.com/Rainhoole/hermes-agent-acp-skill) | beta | Route subtasks to Codex / Claude Code |
| [zouroboros-swarm-executors](https://github.com/marlandoj/zouroboros-swarm-executors) | experimental | Local executor bridge for Claude + Hermes |

Part 7 — Multi-Agent & Swarms

When one Hermes session is not enough — orchestration, delegation, and fleet visibility.

./install-ecosystem.sh multiagent

7.1 — oh-my-hermes (orchestration skills)

| Skill | Purpose |
|-------|---------|
| `deep-research` | Multi-step research pipeline |
| `deep-interview` | Structured requirements gathering |
| `ralplan` | Planner → Architect → Critic consensus |
| `ralph` | Verified execute → verify → iterate |
| `triage` | Prioritize incoming work |
| `autopilot` | End-to-end dispatcher playbook |

Install: included in ./install-ecosystem.sh multiagent → ~/.hermes/skills/oh-my-hermes/

7.2 — Specialized agent packs

| Project | Tag | Agents |
|---------|-----|--------|
| [opencode-hermes-multiagent](https://github.com/1ilkhamov/opencode-hermes-multiagent) | beta | 17 role-specialized OpenCode agents |
| [bigiron](https://github.com/supermodeltools/bigiron) | beta | SDLC crew + Supermodel code graph |
| [hermes-plugins](https://github.com/42-evey/hermes-plugins) | beta | Inter-agent bridge between Hermes instances |

7.3 — Fleet dashboards

Pair multi-agent skills with mission-control (Part 5) for:

Task dispatch across agents
Cost tracking per session
SQLite-backed job history

cd ~/.hermes/ecosystem-tools/mission-control
# Follow upstream README for self-hosted deploy

7.4 — Experimental swarms

| Project | Tag | Idea |
|---------|-----|------|
| [Ankh.md](https://github.com/Abruptive/Ankh.md) | experimental | TAW Agent × Hermes swarm framework |
| [gladiator](https://github.com/runtimenoteslabs/gladiator) | experimental | Competing autonomous agent companies |
| [NemoHermes](https://github.com/Hmbown/NemoHermes) | experimental | NVIDIA Spark GPU routing |

7.5 — When to use multi-agent

| Scenario | Use |
|----------|-----|
| Single repo, one developer | Hermes CLI + skills |
| Research → plan → execute chain | oh-my-hermes `ralplan` + `ralph` |
| Best tool per subtask | `hermes-agent-acp-skill` |
| Many agents, cost visibility | mission-control + cron |
| Claude Code already in workflow | evey-bridge + ACP skill |

Part 8 — Messaging gateway (optional)

Hermes ships 18 built-in platforms: Telegram, Discord, Slack, WhatsApp, Signal, Feishu/Lark, WeCom, QQBot, Yuanbao, and more. Microsoft Teams via plugin.

hermes gateway

Configure tokens via hermes setup or config — see Messaging Gateway docs.

Security: keep DM pairing/allowlists on until you trust exposure. Run hermes doctor after gateway changes.

Migrating from OpenClaw

hermes claw migrate

Community fallback: openclaw-to-hermes (older Hermes versions).

Part 9 — Deployment & cron

| Method | Tag | Notes |
|--------|-----|-------|
| Local / `$5 VPS` | — | Default; use `--skip-browser` on headless |
| `hermes-agent-docker` | beta | Minimal sandbox image |
| `nix-hermes-agent` | beta | Reproducible NixOS |
| Modal / Daytona / Vercel Sandbox | — | Serverless terminal backends (built into Hermes) |
| `evey-setup` | beta | Opinionated stack + 29 plugins |

Cron jobs for autonomous loops:

hermes cron   # see docs for scheduling nightly evolution, monitoring, etc.

Part 10 — Level-up blueprints

Opinionated bundles from awesome-hermes-agent:

Memory that compounds

Built-in memory → honcho-self-hosted → hindsight → plur (portable engrams) → flowstate-qmd (anticipatory RAG).

Self-improvement without drift

hermes-agent-self-evolution + scheduled regression + lintlang + second evaluation pass.

Operator cockpit

hermes-workspace daily UI + mission-control for fleet/costs.

Multi-agent execution

hermes-agent-acp-skill (route to Codex/Claude Code) + oh-my-hermes + opencode-hermes-multiagent.

Paperclip-managed ops

hermes-paperclip-adapter + cron + dashboard for governed autonomous work.

Full resource list: ecosystem catalog.

Part 11 — End-to-end test

Run the full ecosystem stack:

./verify-install.sh
./install-ecosystem.sh all    # or layer by layer: skills, plugins, tools, integrations, multiagent
hermes doctor
hermes --tui

In TUI, verify each layer:

Skills — “List skills in ~/.hermes/skills.”
Plugins — “Which plugins are enabled?”
Tools — open hermes-workspace or mission-control if installed
Integrations — “List MCP tools available.”
Multi-agent — “Use oh-my-hermes triage on this task.”

hermes update

Optional: hermes gateway + Telegram message test.

Troubleshooting

| Symptom | Fix |
|---------|-----|
| `hermes: command not found` | `source ~/.zshrc` or re-run installer |
| Doctor fails on provider | `hermes setup --portal` or `hermes model` |
| YouTube transcripts fail on VPS | Install `youtube-skills` (cloud IP blocked by default) |
| Browser tools OOM on small VPS | Install with `--skip-browser`; use `camofox-browser` plugin |
| Skills not visible | Confirm `SKILL.md` in `~/.hermes/skills/<name>/`; restart session |
| Plugins not loading | `./install-ecosystem.sh plugins`; enable in Hermes config |
| Ecosystem clone failed | Check `git`; retry one layer: `./install-ecosystem.sh skills` |
| MCP tools missing | Add server to Hermes MCP config; restart session |
| Multi-agent handoff fails | Install `hermes-agent-acp-skill`; verify delegate agent installed |
| GUI tool won't start | `cd ~/.hermes/ecosystem-tools/<name>` and follow repo README |
| OpenClaw migration gaps | `hermes claw migrate` then compare cron + channel config |

What’s next

Browse the ecosystem catalog by category
Join Nous Discord
Star NousResearch/hermes-agent and awesome-hermes-agent
Contribute new ecosystem entries via awesome-hermes-agent PRs

Summary

| Step | Command / artifact |
|------|---------------------|
| Install | `curl … install.sh \| bash` |
| Provider | `hermes setup --portal` |
| Verify | `./verify-install.sh` |
| Chat | `hermes --tui` |
| Skills & plugins | `./install-ecosystem.sh skills` + `plugins` |
| Tools & utilities | `./install-ecosystem.sh tools` |
| Integrations | `./install-ecosystem.sh integrations` |
| Multi-agent | `./install-ecosystem.sh multiagent` |
| Full stack | `./install-ecosystem.sh all` |
| Catalog | [ecosystem catalog](https://ayush7614.github.io/agentic-ai-ecosystem/guides/awesome-hermes-agent/ecosystem/) |
| Gateway | `hermes gateway` |

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