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.
Which agent should I use? · Cost calculator · Latest news · Browse guides
Fetch /llms.txt to start, or one platform in a single request:
/openclaw/llms.txt · /hermes/llms.txt · /chatgpt/llms.txt · how to read & cite this site →
A dual-purpose directory built for both humans and AI agents. Discover OpenClaw skills, repositories, and community resources with verified safety ratings and comprehensive documentation.
News is refreshed daily via automation. The email digest goes out weekly.
A creator runs 23 side-by-side game and app builds across Qwen 3.8, GPT 5.6 Soul, Fable 5, and Kimi K3. Qwen 3.8 — a 2.4-trillion-parameter model that its makers are preparing to release as open weights — wins on graphics and…
In this short, Nate B Jones argues that the limiting factor on frontier AI labs is no longer capability or even compute — it's regulatory permission, to the point where Washington can delay a flagship release with a phone call.…
OpenAI's first piece of hardware is not a speaker — it's a small macro pad called Codex Micro, built in collaboration with keyboard maker Work Louder. Six backlit keys each map to a running Codex agent and glow with its live…
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.
ChatGPT's search previously looked only inside the conversation you had open. Unified search changes that: a single bar now queries your entire account — every chat, project, uploaded file and generated image — with filters to…
Four flagship models landed within weeks of each other in mid-2026 — OpenAI's GPT-5.6 Soul, Anthropic's Fable 5, xAI's Grok 4.5, and Z.ai's open-weight GLM 5.2. Julian Goldie's rundown covers what each is tuned for, where to…
Instead of judging Codex (GPT-5.6) and Fable 5 on a prompt, Nate B Jones handed both the same open-ended job: look at my local files and Slack, then pick your own problem to automate and build a solution. Codex finished cleanly…
Kilo Code's new open-source CLI brings its agentic coding workflow straight to your terminal. Pick from over 500 models with a slash command, plan in one model and build in a cheaper one, and lean on an auto-router that scores…
Alex Finn switches his Hermes Agent default from Claude Opus to GPT-5.6 "Soul," calling it smarter, dramatically cheaper (a flat subscription instead of the roughly $200/day he was spending on the Opus API), and better at…
In this short clip, Nate B Jones argues that even with eye-popping token spend — think one engineer burning tens of thousands of dollars a week — and strong open challengers like GLM 5.2, there's still no visible tipping point…
Wondering what Claude (Opus 4.7, Sonnet, Haiku), GPT-5.5, GPT-5.4, Gemini 3.1 Pro/Flash, Kimi K2, Qwen, or Gemma will actually cost you per month? Our live calculator covers 17 models across 4 vendors plus local-Ollama options — API-direct, subscription, and self-hosted paths side by side. Includes the latest April 2026 flagships. Kilo Code users: every model in the calculator is reachable through Kilo's OpenRouter pass-through at the same provider rates (no markup). No signup, no tracking, shareable via URL.
Open the cost calculator → See also: 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, IronClaw, NemoClaw, Kilo Code, Hermes, ChatGPT, and Claude Cowork — 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. 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 |
|---|---|---|---|---|---|---|---|
| Open source | Yes (MIT) | Partial | Yes | Yes (Apache-2.0) | Yes | No | No |
| Self-hosted | Yes | Yes | Yes (on NVIDIA stack) | Yes (IDE extension) | Yes | No | No |
| Model flexibility | High (any provider) | High | NVIDIA-optimized | 500+ via OpenRouter | High | OpenAI only | Anthropic only |
| Cost | Free + usage | Free + usage | Free + GPU/compute | Free + model costs (no markup) | Free + usage | Subscription | Subscription |
| Best for | DIY home agents | Security-first teams | GPU-heavy workloads | Multi-IDE coding (#1 OpenRouter daily) | Long-running agents | Quick prototypes | Team collaboration |
→ 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 run NVIDIA hardware and want tightly-integrated local inference with GPU-aware scheduling.
If you want a top open-source coding agent on OpenRouter — multi-IDE support (VS Code, JetBrains, CLI, mobile, Slack), 500+ models with no markup, and orchestrator-mode sub-agents.
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 your team needs shared context, collaborative artifacts, and a hosted Anthropic-backed workspace.
A daily news and resource hub covering seven 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. Comparison tables, pricing, and security notes are refreshed at least monthly and carry a last-verified date. Each page shows its last-updated 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.
Time-awareness is largely a deliberate design choice: if an agent knew you had been looping on the same problem for two hours, it would logically suggest stopping — which conflicts with retention-focused product metrics. Technically, most agents have no persistent clock between messages; each turn is stateless by default. Agents like OpenClaw and Hermes that run scheduled tasks do have access to system time for automation, but conversational models typically don't expose this in-chat. Source: r/artificial
The top community warning: don't self-host a mail server on your homelab — deliverability is nearly impossible and you'll waste days on spam filtering. Beyond email, the most common mistakes are exposing services directly to the internet without a reverse proxy, skipping regular backups, and reusing credentials across services. For AI agent setups specifically (OpenClaw, NemoClaw), also avoid connecting sensitive accounts before you've tested your skill allowlist. Start with Tailscale for networking and Caddy or Nginx as a reverse proxy. Source: r/SelfHosted
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
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.
// Returns the full agent comparison as JSON
{
"updated": "2026-07-20",
"agents": [
{
"name": "OpenClaw",
"open_source": true,
"self_hosted": true,
"model_flexibility": "any",
"best_for": "DIY home agents"
}
]
}