William Valentin
23 lines
4.0 KiB
Markdown
23 lines
4.0 KiB
Markdown
# Daily Hermes + AI Research Brief — 2026-05-19
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## Important updates
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- **Hermes Agent v0.14.0 / v2026.5.16 is the main Hermes item to track.** GitHub release search shows a May 16 release with **808 commits, 633 merged PRs, and 1,393 files changed** since v0.13.0; snippets highlight PyPI install support, ~19s faster cold start, much faster Browser CDP calls, and new messaging work. This matters because Will’s default Atlas gateway is source-installed and gateway uptime/update safety is more important than chasing the release immediately. Source: [GitHub releases](https://github.com/NousResearch/hermes-agent/releases) / [release search result](https://github.com/NousResearch/hermes-agent/releases/tag/v2026.5.16).
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- **Hermes v0.13.0 / v2026.5.7 shipped the “Tenacity” durable-work direction.** NewReleases summarizes it as Kanban becoming a durable multi-agent board with heartbeat, reclaim, zombie detection, and auto-block behavior. This aligns directly with Will’s specialist-profile/worker setup; it is worth treating Kanban as the safe path for long agent tasks instead of ad-hoc background spawns. Source: [NewReleases v2026.5.7](https://newreleases.io/project/github/NousResearch/hermes-agent/release/v2026.5.7).
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- **Hermes docs continue to emphasize self-improving skills, persistent memory, profiles, messaging gateway, and provider-agnostic routing.** That is not a “new today” item, but it confirms Atlas’s current architecture choices: keep `default` as the production Telegram gateway, use specialist profiles for isolation, and save durable research outputs into Obsidian rather than memory. Source: [Hermes Agent docs](https://hermes-agent.nousresearch.com/docs/).
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- **MCP scaling pattern worth adopting: expose many tools as code APIs inside execution environments, not as hundreds of direct LLM tools.** Anthropic’s engineering writeup says code execution with MCP can make agents more efficient by letting the model write code that calls MCP APIs, reducing tool-schema/token pressure. For Will, this suggests future Atlas/n8n/local-swarm integrations should prefer compact wrapper APIs and executable client libraries when tool count grows. Source: [Anthropic Engineering — Code execution with MCP](https://www.anthropic.com/engineering/code-execution-with-mcp).
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- **Open-source infra maintainers are now explicitly dealing with AI-generated PR load.** MLSys has an invited talk, “Rethinking Open Source Contribution in the Age of AI Agents,” framed around vLLM and the surge of AI-generated pull requests. This matters for Will’s CoreWeave/k8s/LLM-infra work: review gates, narrow tests, provenance, and anti-slop contribution policies are now part of production LLM ops, not just repo hygiene. Source: [MLSys 2026 schedule](https://mlsys.org/virtual/2026/day/5/18) / [invited talk](https://mlsys.org/virtual/2026/invited-talk/10000).
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## Actionable ideas for us
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- **[quick]** Check local Hermes safely before updating: `hermes --version`, `git status --short --branch`, and `git rev-list --left-right --count main...origin/main`; do **not** run `hermes update` automatically if the tree is dirty.
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- **[quick]** Review whether Atlas’s Browser/CDP and messaging paths benefit from v0.14.0, but route any upgrade through the safe update workflow because the gateway is production.
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- **[experiment]** Prototype one “code API over tool flood” integration for local swarm services: a small Python client that wraps n8n, llama.cpp, Ollama embeddings, and Obsidian REST behind a few stable calls.
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- **[watch]** Track Hermes issues/releases around v0.14.0 for Windows/PyPI/lazy dependency fallout and any gateway regressions before adopting it on `default`.
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## Worth ignoring
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- Generic “agent landscape 2026” listicles unless they include concrete implementation details, benchmarks, or repo links.
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- Funding/market stories about AI agents with no deployable tooling, protocol, model, or infra takeaway.
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- Consumer-only agent announcements unless they expose useful MCP/tooling/local-first patterns.
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