Basically, this approach means the base agent is insanely capable, and can do pretty much everything you ask it. I use it for almost everything now. I've literally spent less time building this than I used to spend debugging OpenClaw.
LLMS
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Decoupling AI Models from Agentic Orchestration Layers
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Exactly. Skills as self-contained units is the key design choice. It means you can swap the model underneath without rewriting your capabilities. The harness becomes the durable layer, the model becomes interchangeable.
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Technical Discussion on Model Weighting and User Distribution
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Are you updating the weights for your experiences and focuses vs the normal distribution of all the users a model is being served to
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The Future of Continual Learning and Local AI
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Continual Learning has already been solved, it just requires the model weights to be running locally on your own hardware so big labs are avoiding the topic altogether Local / Opensource AI will win Inevitable
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Hermes Agent Orchestration Workflow Cheat Sheet
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The /goal Cheat Sheet for Hermes Agent Codex Builds, Claude Code Reviews and Hermes Agent Orchestrates.
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How Prompt Styles Affect AI Output
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Better prompts = better AI outputs • Structured → precision
• Analytical → accuracy
• Conversational → creativity
• Planning → execution The model doesn’t change. Your prompting style does. Via Giuliano Liguori (
@ingliguori
) #AI #Prompts #GenAI -
Optimizing AI Agent Costs through Intelligent Orchestration
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THE BIGGEST ROADBLOCK TO SCALING AI AGENTS IS NOT INTELLIGENCE, IT IS TOKEN COST If your agent uses a premium reasoning model to answer a basic greeting, you are burning money. @OpenSquilla is an open-source orchestrator that fixes this, delivering a documented 60-80% in
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Moving beyond LLMs toward world models in AI research
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Truly an all-star cast, on one of the most important questions in AI. Thrilled to see some many people finally willing to confront the hard questions of how we can move beyond LLMs, and into what world models are really about.
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Comparing AI Agents for Automated GitHub Scraping and Dashboard Creation
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Hermes vs OpenClaw on a local Qwen 35B is MINDBLOWING! 🤯@atomicbot_ai asked both to scrape GitHub s⭐, find growth spikes, and build a live dashboard.
— Charly Wargnier (@DataChaz) 16 mai 2026
> @OpenClaw nailed it in 12m by fixing broken HTML.
> Hermes took 33m, pivoting to DuckDuckGo when rate-limited.
Epic run ↓ https://t.co/paSuC88fPPHermes vs OpenClaw on a local Qwen 35B is MINDBLOWING! @atomicbot_ai asked both to scrape GitHub s, find growth spikes, and build a live dashboard. > @OpenClaw nailed it in 12m by fixing broken HTML.
> Hermes took 33m, pivoting to DuckDuckGo when rate-limited. Epic run ↓