docs(log): compact changelog to entry law (406 -> 284 lines)

Changelog entries were essay-length incident writeups duplicating the
concept pages they created. New entry law recorded at top of log.md:
one dated entry = what changed + durable lesson + concept-page link;
full narratives recoverable via git log --follow. Every date, headline,
incident, and durable lesson preserved; narrative prose that already
lives on concept pages (caddy handle-ordering, gluetun/limiter field
notes, quadlet gotchas, faillock law, credential-store trap) replaced
by pointers. Historical pi-era entries tagged '(pi era)'.

Also: self-hosted-web-agents.md '44.5% peer-reviewed CUA' figure could
not be re-attributed to any published benchmark via web search today
— dropped with an explicit note; the valid core claim (87.4% = managed
Cloud, not the self-hostable library) kept.

Refs #29

omp-docs/agentic-web-research/self-hosted-web-agents.md

@@ -29,10 +29,17 @@ Key ideas:
 - Ten watchdogs handle CAPTCHAs, crashes, popups, downloads, and security.
 - Real browser profiles carry saved logins.
 
-**On the "87.4 percent" number:** that result refers to Browser Use's managed Cloud agent (the Hybrid type that blends structured browser tools with screenshots). Peer-reviewed results for vision-only (CUA) models are much lower, about 44.5 percent on the same task type. Treat any single accuracy number as one benchmark, not a universal score.
+**On the "87.4 percent" number:** that result refers to Browser Use's managed
+Cloud agent (the Hybrid type that blends structured browser tools with
+screenshots), not the open-source library you self-host. Academic
+computer-use results on comparable web tasks run far lower, and an earlier
+"44.5 percent peer-reviewed CUA" figure carried in this page could not be
+re-attributed to a specific published benchmark (checked 2026-10-19) and was
+dropped. Treat any single accuracy number as one benchmark, not a universal
+score.
 
 ```python
-# Local model via llama.cpp (OpenAI-compatible endpoint)
+# Local model via any OpenAI-compatible endpoint
 from browser_use import Agent
 from browser_use.llm.openai import ChatOpenAI