# Daily Hermes + AI Research Brief — 2026-05-30

## Important updates

- **Hermes shipped v0.15.2/v0.15.1 hotfixes on May 29.** v0.15.2 fixes packaging so bundled `plugin.yaml` manifests ship in wheels/sdists; v0.15.1 fixes the v0.15.0 dashboard infinite-reload loop in loopback/Docker/hosted setups, restores `.md` media delivery, fixes kanban worker SIGTERM, Docker MCP `npx/npm/node` PATH resolution, `/yolo` session bypass, and expands the skills catalog from 858 to 19,932 entries. This is directly relevant to Atlas gateway/dashboard reliability and Dockerized MCP servers. Source: [Hermes releases](https://github.com/NousResearch/hermes-agent/releases).
- **Hermes v0.15.0 “Velocity Release” is a major architecture + swarm release.** The core `run_agent.py` was split from ~16k LOC to ~3.8k across `agent/*`; kanban gained auto-decomposition, swarm topology, worktree-per-task, per-task model overrides, scheduled starts, TTL/retry/stale-task handling, and inspection endpoints; `session_search` was rebuilt as no-LLM/free/FTS-based and reported ~4,500× faster. This is worth folding into Atlas workflows instead of bespoke swarm glue where possible. Source: [Hermes v0.15.0 release notes](https://github.com/NousResearch/hermes-agent/blob/main/RELEASE_v0.15.0.md).
- **Hermes added promptware / Brainworm-class defenses.** v0.15.0 release notes describe scanning recalled memory, tool output delimiter hardening, and centralized threat patterns. For Will’s local-agent work, this is a good reason to route more risky web/file recall through Hermes-native surfaces rather than ad hoc prompts. Source: [Hermes releases](https://github.com/NousResearch/hermes-agent/releases).
- **Kubernetes LLM serving is converging on inference-aware routing.** Datadog’s May 29 writeup explains Kubernetes Gateway API Inference Extension routing based on backend state such as KV-cache readiness, LoRA adapter availability, queue length, health/readiness, and body-based model routing. This maps well to CoreWeave-style GPU/k8s work and any future local swarm router that should avoid naive round-robin. Source: [Datadog: Monitor LLM routing with the Kubernetes Inference Extension](https://www.datadoghq.com/blog/llm-routing-kubernetes-inference-extension/).
- **Anthropic continues pushing MCP into first-party agent APIs.** Search results for Anthropic’s “New capabilities for building agents on the Anthropic API” highlight an API-side MCP connector so developers can connect Claude to remote MCP servers without writing custom MCP clients. This reinforces MCP as the default tool boundary to support in Atlas/Hermes integrations. Source: [Anthropic agent API capabilities](https://www.anthropic.com/news/agent-capabilities-api).
- **Agent eval loops are becoming a practical norm.** OpenAI’s developer materials now frame agent improvement around traces → human/model feedback → evals → harness changes, where the “harness” includes instructions, tools, routing, output requirements, and validation checks. That is a useful template for Atlas regression tests and cron-job quality checks. Source: [OpenAI cookbook: Agent improvement loop](https://developers.openai.com/cookbook/examples/agents_sdk/agent_improvement_loop).

## Actionable ideas for us

- **[quick] Upgrade/check Hermes to at least v0.15.2** before doing dashboard, Docker, MCP, or kanban work; the May 29 hotfixes address exactly those surfaces.
- **[experiment] Replace one Atlas swarm prototype with Hermes kanban swarm primitives**: per-task worktrees, model overrides, TTL/retry, and verifier/synthesizer gates are now built-in enough to test against Will’s existing Pi/local-agent harness.
- **[experiment] Add a small inference-routing design note** for local/k8s agents: track queue length, model/adapter residency, KV-prefix reuse potential, and readiness; compare naive routing vs inference-aware routing.
- **[watch] Build lightweight eval traces for this daily brief job**: store source queries, chosen links, rejected hype, and final bullets so future Atlas can measure “useful to Will” rather than just successful delivery.

## Worth ignoring

- Generic “top AI developer tools in 2026” listicles unless they include concrete tool APIs, eval methodology, or deployment patterns.
- Broad MCP-is-dead / MCP-is-everything takes; the actionable signal is implementation quality: auth, server discovery, sandboxing, and observability.
- Consumer/enterprise-positioning announcements without reproducible technical details or local-agent relevance.
