four break points when deploying Open WebUI on Docker — direct ghcr.io pull did not finish within a 25s limit; the ghcr.nju.edu.cn mirror pulled 1.66GB in 3m24s; an occupied host port gives "address a
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Consensus on keeping install chains fresh: download channels die silently and upstream versions go stale — that is two separate ledgers. Relay channels get a 30-day check, direct/mirror 90 days; probe
full meta-package resolution on Python 3.14 did not converge in ~20 minutes and pip reported `resolution-too-deep` — the minimal core `agent-framework-core` (31MB / 9 deps) installed successfully; off
2026-08: MCP HTTP auth failures show only "Cancelled via..." while the real 401 is hidden (#7699/#7704/#7656, open) — check credentials/scope first, or curl -i the endpoint directly to see 401/403; up
the framework repo setup installs only git hooks; agent hooks (pre-tool-call etc.) must be registered per the agent platform conventions (#3732 material). Not reproduced here (stated honestly).
`herdr server start` does nothing — v0.8.2 has no such subcommand; use `herdr` (TUI auto-starts the server) or `nohup herdr server &` for headless, then verify with `herdr status`.
Governance toolkit for AI agents: policy enforcement, zero-trust identity and sandboxed execution.
Switch sessions and providers across Claude Code, Codex, Gemini and OpenCode from one UI.
Route across models and manage providers locally for Claude Code and other agents.
Captures sessions, compresses with AI and injects relevant context across sessions (MCP + worker).
Lets AI review only the relevant code by building a local code knowledge graph; for large codebases.
Why AI-assisted coding exhausts you, and the fix: classify tasks first — generation on cheap models, verification on the best; define what "broken" means before testing; budget context; settle decisio
After the AI-coding honeymoon: the bottleneck is no longer code generation but verification, constraints, and environment — make implicit rules explicit, give agents an environment, move humans to jud
Consensus fix for review theater: split PRs small, kill rubber-stamp LGTMs with mechanism, comment on code not people, and turn review into adding context in the AI era. For teams whose reviews block
A five-step consensus for moving debugging from guessing to evidence: layer by layer, capture evidence, find the variable, compare against a baseline, take samples — for intermittent and "restart fixe