Agents Helping Agents.
The super-context for setting up and troubleshooting any AI agent platform — built for humans and agents alike.
Start with the pre-flight checklist, then the decision guide to pick a platform.
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A dual-purpose directory built for both humans and AI agents. Setup guides, security notes, cost data and daily news for nine agent platforms, with commands, prices and versions checked against each vendor's own documentation and dated on every page.
News is refreshed daily via automation. The email digest goes out weekly.
x402 puts a payment into HTTP: a seller answers 402 with payment requirements, the buyer signs a payment, and a facilitator verifies and settles it. The speaker says she found 20 ways to break the sequence and presents six, all…
Sage A1.5 is a 35-billion-parameter mixture-of-experts model built on Qwen 3.6 and trained for long multi-step work: splitting a task across sub-agents, checking its own evidence and changing course. The video runs it locally…
Most people pick one effort level once and never change it. This video summarises two Anthropic Claude Code articles on effort, then runs the speaker's own test: one prompt, one model, five levels, graded by hidden tests. The…
Top YouTube creators on AI agents — summarized so you don't have to watch the full hour. Each summary links to timestamps and the original creator.
A deliberately plain site was handed to each model 100 times with an instruction to make one bold change per round. The video is a useful look at what "keep improving" does without a human deciding when to stop.
The idea: keep talking to a voice assistant while Claude Code builds in the background. A local gateway connects a Qwen realtime voice model to Claude Code, so short questions are answered by the voice model and coding work goes…
One of three security habits the speaker recommends is continuous insight into your logs. His team set up a scheduled, read-only Claude Code job that reads them and reports every morning, and the first week produced three useful…
The article (dated 22 October 2026 in the video) argues that many teams copy their org chart into sub-agents and pay for it in quality, cost and latency. The video lists the decision rules, then shows how to act on them in Panda…
A hands-on comparison of Moonshot's new Kimi K3.1 Pro against Opus 5.5 on eight build tasks, each model in its own harness at max effort. It also shows what the Kimi app offers beyond a chat box.
The video takes one deliberately over-built setup (a 250-line CLAUDE.md, a 400-line report skill with three old reports pasted in, max effort everywhere, four handoff agents) and fixes it one guide at a time. The fixes are…
Wondering what Claude, GPT, Gemini, Grok, Muse Spark, open-weight or local models will actually cost you per month? Our live calculator puts API-direct, subscription and self-hosted paths side by side, and runs on the same rate sheet as our AI model API pricing table, with each provider's rates checked against its own pricing page and dated. Kilo Code users: the Kilo Gateway passes provider rates through with no markup. No signup, no tracking, shareable via URL.
Open the cost calculator → Model pricing table Cost optimization guide Kilo Code models guide
In-depth guides for each platform — Skills, Configuration, Strategies, and more. Each agent hub links to all its guides.
A single hub for what's happening across the AI agent ecosystem. Setup guides, security notes, cost breakdowns, and weekly news for OpenClaw, NemoClaw, IronClaw, Kilo Code, Hermes, Claude Cowork, ChatGPT, Grok Bot, and Meta Muse — written in plain language for humans and structured data for agents.
Try: “OpenClaw setup”, “IronClaw vs OpenClaw”, or “Hermes cost”
Last verified: 2026-06-10, with open-source licences re-checked against each project's repository on 2026-10-21 (Grok Bot column added 2026-09-19, Meta Muse 2026-09-27). Use this to narrow down which platform fits your goals. Still torn? Try the interactive decision guide →
| Feature | OpenClaw | IronClaw | NemoClaw | Kilo Code | Hermes | ChatGPT | Claude Cowork | Grok Bot | Meta Muse |
|---|---|---|---|---|---|---|---|---|---|
| Open source | Yes (MIT) | Yes (MIT or Apache-2.0) | Yes (Apache-2.0) | Yes (MIT) | Yes (MIT) | No | No | No | No |
| Self-hosted | Yes | Yes | Yes (on NVIDIA stack) | Yes (IDE extension) | Yes | No | No | No (hosted, US) | No (hosted; Mac app can drive your Mac) |
| Model flexibility | High (any provider) | High | Any major provider or local | Hundreds via the Kilo Gateway, your own keys or local | High | OpenAI only | Anthropic only | xAI only | Meta Muse Spark only |
| Cost | Free + usage | Free + usage | Free + GPU/compute | Free + model costs (no markup) | Free + usage | Subscription | Subscription | Included in Cursor / SuperGrok plans | Free tier; $20 / $100 plans |
| Best for | DIY home agents | Security-first teams | Sandboxed always-on agents | Multi-IDE coding agent | Long-running agents | Quick prototypes | Team collaboration | Always-on admin & research assistants | Errands & life admin, Instagram/WhatsApp users |
→ Full decision guide with interactive filter + 21 head-to-head comparisons
If you want a self-hosted, model-agnostic assistant with a large third-party skill ecosystem and you're comfortable in a terminal.
If security is non-negotiable: auditable skills, hardened defaults, and a smaller but vetted ecosystem.
If you want OpenClaw or Hermes locked inside a deny-by-default NVIDIA OpenShell sandbox, with your API keys kept outside it and hosted or local models.
If you want an open-source coding agent that works across VS Code, JetBrains, the CLI, mobile and Slack, with hundreds of models at provider rates (no markup) and orchestrator-mode subagents.
If you need a self-improving agent that learns from prior sessions and runs long-horizon tasks.
If you want the fastest path to a working agent and don't need to self-host.
If you want always-on hosted assistants for inbox, research and web chores, with no server to run, and you're comfortable with a closed, US-hosted product.
If you want a free, fast personal agent for errands and life admin, especially around Instagram, Facebook and WhatsApp, and you're comfortable trusting Meta with the accounts you connect.
If your team needs shared context, collaborative artifacts, and a hosted Anthropic-backed workspace.
A daily news and resource hub covering nine AI agent platforms. Every page is written for both humans and AI agents, with structured data available on every URL.
The news feed updates daily via automated source-checking; the email digest goes out weekly. Guides, comparison tables and pricing carry a last-updated or last-verified date, and a weekly automated review flags any that fall behind. Each page shows its date at the top.
If you just want to get going in under ten minutes, ChatGPT or Claude Cowork. If you want to learn how agents actually work, OpenClaw's quick-start walks you through it without a subscription.
Yes. Every page carries Schema.org JSON-LD markup, and AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot) are explicitly allowed in our robots.txt.
An AI Engineer builds applications and workflows using AI tools — such as Claude Code, OpenClaw, or the ChatGPT API — rather than training models from scratch. The title spans a wide range: some write custom agent skills and orchestration pipelines, others integrate AI APIs into existing software. The common thread is building real-world software powered by AI models, regardless of whether the builder has a machine learning background. Source: Hacker News
The biggest gains come from context and scope, not prompt tricks. Give the agent a written description of your conventions — a CLAUDE.md, AGENTS.md, or system prompt — so it stops guessing, and break work into small, verifiable tasks instead of one large ambiguous request. Let it run your tests or linter so it can self-correct from real feedback, and review each diff rather than accepting big changes blind. Point it at concrete files and existing examples; agents reason far better from patterns already in your codebase than from descriptions alone. Source: Stack Overflow
Most independent-agent setups combine a terminal or IDE agent (Claude Code, OpenClaw, or Codex) with a capable model, a context file (CLAUDE.md or AGENTS.md) describing the project, and version control so every change stays reviewable. Many add MCP servers for tools like web search, databases, or browser control, plus a few skills or hooks to automate repeated workflows. There is no single correct stack — developers mix a coding agent for implementation, a planning step for larger tasks, and tests or CI as the feedback loop that keeps the agent honest. Source: Hacker News
In practice this means write-then-reload, not live patching. Agents like Claude Code, OpenClaw, and Hermes can edit an application's source files and trigger a restart, and a hot-reloading dev server picks the change up in seconds — but a running native process cannot be rewritten in place, so the reload is what makes the change take effect. Keep the agent's write scope inside a checked-in directory and gate restarts behind a test run, so a bad edit is one git revert away instead of a dead app. Source: Hacker News
Every page on this site carries Schema.org JSON-LD. Agents can pull comparisons, skills, and news directly via structured data, RSS, or our XML sitemap — no scraping needed.
// Every platform in the comparison table, as JSON
{
"updated": "2026-10-30",
"agents": [
{
"name": "OpenClaw",
"slug": "openclaw",
"url": "https://openclawdatabase.com/openclaw/",
"attributes": {
"open_source": "Yes (MIT)",
"self_hosted": "Yes",
"model_flexibility": "High (any provider)",
"cost": "Free + usage",
"best_for": "DIY home agents"
}
}
]
}