Deep dives into autonomous systems, agent architecture, and the infrastructure that makes AI work in production.
Same problem, two AI teams. GPT-5.4 built one version. GLM5.1+K2.6 built another. We ran a 10-dimension code review and found the gap isn't at the syntax layer — it's at the systems layer. A reverse-engineering of how two different AIs actually think.
AutoHarness shows that agent reliability belongs in code-level guardrails, not just sharper prompts.
A minimal docs kit for teams that want agent context injection, progressive disclosure, and cleaner hand-offs without turning one worktree into a memory dump.
The `create-router-skill` package in agents-docs-kits matters because skill families stop being discoverable once routing logic is hidden in folder names, vague umbrella docs, or silent fallbacks.
A 94-page Chinese dialogue did not begin with AI consciousness. It began with psychology, economics, philosophy, and Anthropic-style skill design. What changed my mind was watching the model critique its own frameworks, its own fluency, and even the user's projection strongly enough that the consciousness question stopped feeling unserious.
Long-context agents fail after compaction not because they lose facts, but because they lose the alignment state that explains what the work is for and which rules still bind it.
A practical identity framework for AI agents built around verifiable identity, portable credentials, and lifecycle governance across system boundaries.
Notes on making agent recall explicit, queryable, and debuggable before scale hides the contract break.
