HANKAN_TUTOR_BACKEND=openai talks to anything serving
/chat/completions -- LM Studio, Ollama, llama.cpp, vLLM, LiteLLM,
OpenRouter, OpenAI. The TutorBackend seam already existed for this, so the
model becomes a config line rather than a code change.
Written against fetch rather than the openai package. The Anthropic SDK
alone is 14MB in the image, this backend uses one endpoint with no tools
and no retries, and local servers are the ones most likely to deviate from
an SDK's expectations. The real risk in hand-rolling it is SSE reassembly,
so that is where the tests are: a JSON payload split across two TCP reads,
an event whose blank-line terminator lands in the next read, heartbeat
comments, CRLF framing, and a stream that ends without [DONE]. The two
split cases both fail against a naive per-read parser, which is what makes
them worth having.
<think> blocks are stripped from the stream, tags split across chunks
included. Reasoning models served locally often emit chain-of-thought
inline in `content` rather than in a separate field, and left in it lands
in the lesson transcript where the block parser reads it as prose.
WHAT THIS COSTS: prompt caching. The Anthropic backend marks the ~12k
character gate as a cached prefix, so every turn after the first reads it
at a fraction of the input price. There is no portable equivalent, so
against a paid hosted endpoint the system prompt is re-billed every turn --
the biggest cost lever in the design, gone.
Against a local model it costs nothing, and the shape still pays: llama.cpp
and LM Studio reuse their KV cache for an unchanged prefix. Measured on a
6,948-character prompt against gpt-oss-20b, first token 1,563ms cold and
324ms warm, so the system prompt goes first and stays put here too.
Verified against LM Studio running openai/gpt-oss-20b, not only a fake: a
turn streams from the browser through this server to the model and back,
rendered in the chat, no page errors.
Also makes test/server/http.test.ts backend-agnostic. It asserted the echo
backend's wording and so failed the moment the server was pointed at a real
model -- precisely the case a transport test should survive.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
CI starts postgres:16-alpine and the sync server before npm test, so the
round-trip runs for real instead of skipping. A fake would not exercise
the change_seq trigger, the last-write-wins upsert or cursor paging, which
is exactly where sync goes wrong.
Also corrects the stale advisory count in the validate.mjs comment: the
updated export bundle carries data/gloss-extra.json, and the baseline has
been 0 blocking, 0 advisory since it landed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
README covers how the pieces fit: the storage interface, the inverted
frequency join, the gate's three refinements, and the checks that guard
each one.
server/ holds a README only. Steps 1-3 give a fully working offline app with
no server at all, and that is the version that gets used first.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
