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
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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.
This video explains Hermes Agent's updated computer-use capability, which runs in the background instead of hijacking your screen. Using a "CUA-Driver" that posts input events directly to target apps, the agent clicks and types…
Kevin Stratvert walks through using the Granola MCP connector to pipe meeting transcripts into Claude, ChatGPT, and Claude Cowork. Once connected, the AI pulls meeting context automatically — without being asked — to generate…
Tech With Tim explains why running an AI coding agent around the clock takes more than your own machine: because the agent uses your local environment to run terminal commands and edit files, your computer has to stay awake and…
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.
Nate B Jones argues the leap from prompting to agents is a mental shift, not a tooling one. A prompt is one request; a loop is one recurring job with memory; a loop of loops is when those recurring jobs notice each other, hand…
Alex Finn walks through what he calls Hermes Agent's largest update yet — eight changes spanning native iMessage chat (via the free Photon service), background sub-agents that now spin up automatically with a live sub-agent…
In this conversation, AI advisor Allie K. Miller and former NYU data-science professor Dr. Andrea Jones-Roy discuss how Jones-Roy went from "just using ChatGPT" to running a 34-agent "digital workforce" in Claude Code — wired to…
Nate B Jones argues the real constraint with frontier models like Fable 5 isn't capability — it's our ability to imagine a big enough ask. He makes the case for "task imagination": handing a model whole, ambiguous jobs (not…
SkillOpt from Microsoft Research treats a markdown skill document as the trainable "weights" — improving it with a rollout → reflect → gate loop while the model itself never changes. This walkthrough installs SkillOpt, serves a…
Nate Herk takes Sakana's viral Fugu Ultra — a single API that orchestrates frontier models (Opus, GPT, Gemini) like a multi-agent router — and runs it head-to-head against Claude Opus 4.8 across 38 tasks. The result: 36 ties,…
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.
News digests update weekly. Comparison tables, pricing, and security notes are refreshed at least monthly. 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": "2026-04-05",
"agents": [
{
"name": "OpenClaw",
"open_source": true,
"self_hosted": true,
"model_flexibility": "any",
"best_for": "DIY home agents"
}
]
}