Reviewed mission goals and swap context.
UnderstandOutlined features and parallel investigations.
PlanAnalyzed latency and reasoning frame issues with GLM models.
UnderstandTested parameter effects on GLM-4.6.
VerifyPrepared next probe requiring API keys.
PlanUser system info (darwin 24.6.0)
Model: Claude Opus 4.7 Today's date: 2026-05-08 User language: en
% pwd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1
% ls AGENTS.md app CLAUDE.md docs README.md validation worker
% git status -b --porcelain | head -n1 mission-2
% git status --porcelain
% git log --oneline -5 aaa5905 chore: gitignore .gstack/ tool dir 7af869d F9: add recording rubrics (proxy1, proxy2, proxy5) and fix gitignore for validation artifacts f376509 F9: proxy validation harness, build smoke, artifact reviews, M1 anti-drift report 2e2a1db F8: ScreenCaptureKit pipeline — throttled capture, perceptual diff, relevance filter, screen context injection, capture indicator 97546bb F7: in-session memory buffer, fact extraction, context injection, session persistence
% git show-ref --verify --quiet refs/heads/main && echo "Default branch is main" Default branch is main
% cat /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/CLAUDE.md <coding_guidelines>
A native macOS AI teaching companion that meets the learner as a person, sees their screen, remembers them across sessions, and adapts. CTRL's "Allen" applied to learning, without the dystopia.
We are building Milestone 1: the in-session intimacy loop. A user can:
What M1 does not do: cross-session memory retrieval (M2), proactive initiation (M3), tone attunement (M3), cursor overlay (deferred).
The full validation contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
app/): SwiftUI menu-bar app, macOS 14.2+, LSUIElement=true. MVVM with @StateObject / @Published / @MainActor async/await.worker/): TypeScript Cloudflare Worker proxy holding all third-party API keys. Three routes: /transcribe-token (AssemblyAI temp tokens), /chat (Anthropic streaming), /tts (ElevenLabs Flash streaming). All audio paths are transparent passthroughs — no buffering.docs/persona/): the teacher is Mara. System prompt, first-run script, voice spec, and example exchanges are versioned artifacts.docs/memory/): two-layer (structured profile + episodic log). M1 is in-session-only with lightweight persistence on quit; M2 wires cross-session retrieval.CGEvent tap (default ctrl + option).URLSession.For per-pattern verdicts (what we copied / adapted / invented from clicky), see docs/clicky-audit/copy-adapt-invent.md. Workers must follow that doc when deciding how to translate clicky patterns.
The repo lives at:
/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1The path contains spaces. Every shell operation must double-quote paths.
✅ Correct:
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"❌ Wrong (will break):
cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojReviewers reject unquoted shell paths. A build smoke test under this path is part of the validation contract (A-BUILD-UNDER-SPACED-PATH).
open "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.Do NOT run xcodebuild from the terminal for routine dev. It invalidates TCC permissions (Screen Recording, Accessibility, Microphone) and forces the user to re-grant them. The only acceptable xcodebuild use is the F9 build-smoke script — a one-off check.
Known non-blocking warnings (don't try to fix unless asked):
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKENworker/.dev.vars for local — must be in .gitignore (it is).
(Mirrors clicky's AGENTS.md; full version in clicky's repo for reference.)
originalQuestionLastAnsweredDate, not origAnsweredAt.currentCardData, pass it as currentCardData, not card or cardData.NSPanel, NSStatusItem, CGEvent tap).@MainActor for all UI state. async/await throughout.text(), json(), arrayBuffer() forbidden in /chat and /tts).mission-1 for this mission.Fn: (feature ID) when the commit fulfills a feature.os.Logger (Swift) or console.log with a route prefix (TS).xcodebuild for routine dev (TCC invalidation).worker/.dev.vars or any file with API keys.A-VOICE-LATENCY-MEDIAN ≤ 1000 ms is gating. The full contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
The 7 proxies (M1: 1, 2, 4, 5):
When a design choice is not covered, default to "what makes this feel more like a real teacher who knows me."
When you make changes that affect this file's contents, update it. Specifically:
Do NOT update this file for minor edits or bug fixes that don't affect documented architecture or conventions. </coding_guidelines>
% cat /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/AGENTS.md <coding_guidelines>
A native macOS AI teaching companion that meets the learner as a person, sees their screen, remembers them across sessions, and adapts. CTRL's "Allen" applied to learning, without the dystopia.
We are building Milestone 1: the in-session intimacy loop. A user can:
What M1 does not do: cross-session memory retrieval (M2), proactive initiation (M3), tone attunement (M3), cursor overlay (deferred).
The full validation contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
app/): SwiftUI menu-bar app, macOS 14.2+, LSUIElement=true. MVVM with @StateObject / @Published / @MainActor async/await.worker/): TypeScript Cloudflare Worker proxy holding all third-party API keys. Three routes: /transcribe-token (AssemblyAI temp tokens), /chat (Anthropic streaming), /tts (ElevenLabs Flash streaming). All audio paths are transparent passthroughs — no buffering.docs/persona/): the teacher is Mara. System prompt, first-run script, voice spec, and example exchanges are versioned artifacts.docs/memory/): two-layer (structured profile + episodic log). M1 is in-session-only with lightweight persistence on quit; M2 wires cross-session retrieval.CGEvent tap (default ctrl + option).URLSession.For per-pattern verdicts (what we copied / adapted / invented from clicky), see docs/clicky-audit/copy-adapt-invent.md. Workers must follow that doc when deciding how to translate clicky patterns.
The repo lives at:
/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1The path contains spaces. Every shell operation must double-quote paths.
✅ Correct:
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"❌ Wrong (will break):
cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojReviewers reject unquoted shell paths. A build smoke test under this path is part of the validation contract (A-BUILD-UNDER-SPACED-PATH).
open "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.Do NOT run xcodebuild from the terminal for routine dev. It invalidates TCC permissions (Screen Recording, Accessibility, Microphone) and forces the user to re-grant them. The only acceptable xcodebuild use is the F9 build-smoke script — a one-off check.
Known non-blocking warnings (don't try to fix unless asked):
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKENworker/.dev.vars for local — must be in .gitignore (it is).
(Mirrors clicky's AGENTS.md; full version in clicky's repo for reference.)
originalQuestionLastAnsweredDate, not origAnsweredAt.currentCardData, pass it as currentCardData, not card or cardData.NSPanel, NSStatusItem, CGEvent tap).@MainActor for all UI state. async/await throughout.text(), json(), arrayBuffer() forbidden in /chat and /tts).mission-1 for this mission.Fn: (feature ID) when the commit fulfills a feature.os.Logger (Swift) or console.log with a route prefix (TS).xcodebuild for routine dev (TCC invalidation).worker/.dev.vars or any file with API keys.A-VOICE-LATENCY-MEDIAN ≤ 1000 ms is gating. The full contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
The 7 proxies (M1: 1, 2, 4, 5):
When a design choice is not covered, default to "what makes this feel more like a real teacher who knows me."
When you make changes that affect this file's contents, update it. Specifically:
Do NOT update this file for minor edits or bug fixes that don't affect documented architecture or conventions. </coding_guidelines>
IMPORTANT:
Step 2 — Set the worker tab
When the model tabs appear at the top of the TUI, press ctrl+N (or click) to
cycle the Worker tab until it shows:
Claude Sonnet 4.6 (Max sub) reasoning: high
Orchestrator stays on Opus 4.7 high. Validator stays on Sonnet 4.6 high.
Worker is the one to verify.
Step 3 — Paste this brief (verbatim)
MISSION 2 — teachr.live runtime LLM swap: Anthropic Claude → Z.ai GLM
CONTEXT
M1 is complete and committed. teachr.live currently routes Worker /chat
to Anthropic Claude. We're switching the runtime LLM (what students
hit when they use the deployed app) to Z.ai GLM-5.1 for ~10x cost
reduction. The mission's build-time workers continue using Claude
Sonnet 4.6 high (proven from M1) — that's unrelated to this swap.
KEEP UNCHANGED
CHANGE (in repo code only)
Z.AI API SPEC (already verified working with my key)
VALIDATION CONTRACT UPDATES
ANTI-HALLUCINATION RULES (carry forward from M1)
WORKER MODEL CONFIG (DO NOT CHANGE)
MILESTONE STRUCTURE
Single milestone (M-LLMSWAP). Six features, each with a real git commit:
F1. Worker /chat refactor
- Modify worker/src/routes/chat.ts: swap upstream URL, auth header;
keep streaming passthrough discipline (no buffering)
- Update worker/src/index.ts Env type: ANTHROPIC_API_KEY → ZAI_API_KEY
- Update worker/.dev.vars.example
- Update worker/wrangler.toml comment
- Update worker/tests/passthrough.test.ts assertions
- Local smoke: npm test passes
- Commit: "M2/F1: swap chat route upstream to Z.ai GLM"
F2. Swift LLM request body refactor
- Rebuild request body in OpenAI Chat Completions format
- System prompt placement matches OpenAI convention (first message
role=system)
- Auth path stays: Worker injects bearer; Swift sends to Worker as before
- Commit: "M2/F2: Swift LLM request body for OpenAI shape"
F3. Swift SSE parser refactor
- Parse OpenAI delta events: choices[0].delta.content
- Handle [DONE] sentinel
- Preserve sentence-chunked streaming downstream into ElevenLabs
- Update unit tests
- Commit: "M2/F3: SSE parser for OpenAI delta format"
F4. Persona verification against GLM-5.1
- Run a fixture test through the new pipeline (no live mic)
- Compare GLM-5.1 output against rubric in
docs/persona/example-exchanges.md
- If GLM drifts from the persona, document required tweaks but DO NOT
modify docs/persona/system-prompt.md without explicit user approval
- Commit: "M2/F4: persona verification report on GLM-5.1"
F5. Validation contract amendment
- Update validation-contract.md: replace Anthropic-specific language
with provider-agnostic terms
- Re-run validation/scripts/build-smoke.sh
- Update validation/M1-drift-report.md with provider-swap addendum
- Commit: "M2/F5: validation contract provider-agnostic"
F6. Local end-to-end smoke + latency capture
- Run worker locally via wrangler dev with ZAI_API_KEY in .dev.vars
- Run F9 build smoke from M1
- Single round-trip fixture test: Swift app → Worker → Z.ai → Swift
- Capture latency (median + p90) per hop
- If median > 1000ms, attribute to TTS/LLM/STT and surface; do NOT
silently fail
- Commit: "M2/F6: end-to-end Z.ai smoke + latency report"
OUT OF SCOPE
wrangler deploy manually)wrangler secret put manually withPUSH BACK ON
Begin Step 1 (Context restate) and walk through the framework.
Press Enter.
Step 4 — Argue with the orchestrator on Step 1-4 like you did for M1
Same playbook. Approve only when the plan is tight. Then it runs.
Tell me when you've pasted, and I'll watch the proxy log + commits as it goes.
TODO List Updated1. [question] F2 + F3 are both small Swift-side wire-format changes (request body shape and SSE parser) likely in adjacent files. Splitting them creates two commits that can't be independently tested — F2 alone leaves parser broken, F3 alone leaves request broken. Want to merge them into a single feature?
[answer] Option 1 — merge F2 and F3 into one feature.
Reasoning: each commit must leave the system in a workable state.
Splitting these creates two intermediate broken commits — request body
without parser, or parser without request body — neither testable on its
own. Atomic commit is right.
Renumber:
- F2 (was F2+F3 merged): "Swift LLM client OpenAI shape — request body +
SSE parser, atomic"
- F3 (was F4): persona verification against GLM-5.1
- F4 (was F5): validation contract amendment
- F5 (was F6): end-to-end smoke + latency capture
Net: 5 features instead of 6. Cleaner.
Continue with the next question.
2. [question] F4 (persona verification on GLM-5.1) produces a report, not code, since you forbid editing system-prompt.md without approval. How do you want this modeled?
[answer] Eval feature: deliverable is docs/persona/glm-5.1-verification.md, no code change unless you approve drift fixes
3. [question] F6 needs a Swift app round-trip. Workers can't drive the GUI app cleanly on macOS without TCC noise. How do you want F6 split?
[answer] Option 1 — Eval feature.
Deliverable: docs/persona/glm-5.1-verification.md (no code changes).
Worker must:
- Run a representative fixture conversation through GLM-5.1 (via the new
Worker route, after F2 lands so the request/response shape works)
- Compare GLM-5.1's outputs against the persona rubric in
docs/persona/example-exchanges.md and the system prompt's modulation
rules in docs/persona/system-prompt.md
- Score on: persona consistency, callback naturalness vs robotic readback,
emotional attunement (warmth/curiosity), avoidance of chatbot-isms
- Document any drift between Claude and GLM behavior with side-by-side
examples
- If drift is significant: list specific prompt tweaks as RECOMMENDATIONS
in the report, NOT as commits to system-prompt.md
- Conclude with a clear verdict: "GLM-5.1 sufficient for M2 ship", "GLM-5.1
needs prompt tweaks before ship — see recommendations", or "GLM-5.1
insufficient — escalate"
Persona tuning (if recommended) is a SEPARATE follow-up task with my
explicit approval; not part of M2.
Continue with the next question.
4. [question] F5 says "update validation-contract.md provider-agnostic." The M1 contract is at the M1 mission dir and is sealed. Where does the provider-agnostic contract live?
[answer] M2 creates its own validation-contract.md at the new mission dir, provider-agnostic from day one; M1 contract untouched; M1 drift report gets a swap addendumRequest cancelled by user
REMINDER: You are the orchestrator. Your role is to plan, design worker systems, and steer execution. Do NOT implement code yourself. When the user asks for changes or fixes, utilize workers to perform the implementations. Focus on high-level planning, task delegation, and steering the mission to success.
Option 1 — M2 creates its own validation-contract.md, provider-agnostic
from day one. M1 contract stays sealed and untouched.
REASONING
DERIVATION RULES
M1 DRIFT REPORT ADDENDUM
REPO-LEVEL CONTRACT (optional, only if it makes sense to you orchestrator)
Continue with the next question.
Paste it. The orchestrator should now have enough to draft the mission
proposal. Tell me what comes next — likely either a final clarifying question
or the full proposal for your approval.
TODO List UpdatedError: Tool execution cancelled by userREMINDER: You are the orchestrator. Your role is to plan, design worker systems, and steer execution. Do NOT implement code yourself. When the user asks for changes or fixes, utilize workers to perform the implementations. Focus on high-level planning, task delegation, and steering the mission to success.
continue
1. [question] Z.ai readiness — I need to confirm api.z.ai serves glm-5.1 with your key BEFORE F1, otherwise F1 might fail at smoke time and we won't know if it's the refactor or the upstream. How do you want to verify?
[answer] Z.ai readiness CONFIRMED with one critical correction to the brief.
ENDPOINT (corrected)
- Use: https://api.z.ai/api/coding/paas/v4/chat/completions
(Codeplan endpoint, NOT the generic /api/paas/v4 — that was wrong in
my original brief)
- Sanity-checked /api/coding/paas/v4/models returns all 7 GLM models
including glm-5.1
- Streaming with stream:true confirmed working with my key
CRITICAL: glm-5.1 EMITS REASONING_CONTENT
GLM-5.1 has an extended-thinking mode like o1/Claude. SSE chunks come in
two flavors:
- delta.reasoning_content : model's chain-of-thought (comes first, often
long, contains analysis like "1. Analyze the Request:")
- delta.content : the actual user-facing response
This MUST be reflected in F2 (Swift LLM client + SSE parser merged):
1. Parser splits the stream into two channels: reasoning vs content
2. ONLY delta.content feeds the sentence-chunked TTS pipeline
3. delta.reasoning_content is either:
(a) discarded entirely — simplest for M2; user never hears it
(b) accumulated into a hidden "model thinking" log for debugging
Pick (a) for M2; revisit (b) if it's useful later
VALIDATION CONTRACT ADDITION
- A-NO-REASONING-IN-TTS: in a recorded session, the audio output never
contains text that originated as delta.reasoning_content. Sonnet-as-judge
rubric: compare TTS transcript against the reasoning_content log from
the same session; verify zero overlap of distinctive phrases.
BRIEF AMENDMENT FOR F1 (Worker chat route)
- The Worker is a transparent passthrough — it does NOT need to know
about reasoning_content. Both fields stream through unchanged.
Passthrough discipline (A-VOICE-WORKER-PASSTHROUGH) preserved.
- Update the env var rename and endpoint URL as documented.
F2 IS NOW SLIGHTLY BIGGER
- Swift parser branches on delta.reasoning_content vs delta.content
- Discard reasoning_content from sentence chunker
- Add unit test fixture: a recorded GLM-5.1 SSE stream with both fields,
assert TTS receives only content, no reasoning
Z.ai readiness confirmed. Proceed.
Paste that. The orchestrator will adjust F1 (URL) and F2 (parser branching)
accordingly. Then it'll move to the F2 TDD strategy question.
⏺ Ran 2 stop hooks (ctrl+o to expand)
⎿ Stop hook error: Failed with non-blocking status code: Error: Setup
required: No GitButler project found at ..
✻ Cooked for 1m 13s
2. [question] F2 has no SPM target and no XCTest target. How does the F2 worker satisfy "tests written first" for the new SSE parser without invoking xcodebuild (which invalidates TCC permissions)?
[answer] Option 3 — combined. F2 worker uses F9 spaced-path-build-smoke.sh for
compile verification AND writes a Swift fixture script for parser
correctness. Both run during F2.
REJECTING OPTION 1
SPM package would be the right long-term move but is wrong scope for M2.
M2 is supposed to be a focused LLM swap. Adding new project structure
(Package.swift, source-sharing strategy, .xcodeproj dependency wiring)
risks destabilizing M1's working app. Defer SPM to a future
"add-test-infrastructure" mission.
REJECTING OPTION 2
Fixture-only gives parser correctness but not compilation. Could pass
fixture tests while the main app fails to build (import errors, type
mismatches). Incomplete signal.
OPTION 3 SCOPE FOR F2
1. F2 worker implements parser changes in app/teachr/Streaming/SSEParser.swift
- Branch delta.reasoning_content (discard) vs delta.content (forward to
sentence chunker)
- Handle [DONE] sentinel
- Update LLMStreamClient to build OpenAI Chat Completions request body
(matches Codex's Z.ai endpoint shape)
2. F2 worker writes validation/swift-fixtures/sse-parser-fixture.swift
- Canned SSE byte arrays from a real GLM-5.1 stream (capture one via
curl during F2 setup; embed verbatim)
- At least 4 test cases:
a) Stream with only delta.content → all content reaches sink
b) Stream with only delta.reasoning_content → nothing reaches sink
c) Stream interleaving both → only content reaches sink
d) Stream with [DONE] → terminates cleanly
- Asserts via precondition(); failure aborts with clear message
- Run command: `swiftc app/teachr/Streaming/SSEParser.swift
validation/swift-fixtures/sse-parser-fixture.swift
-o /tmp/sse-parser-test && /tmp/sse-parser-test`
- This avoids xcodebuild for parser correctness — only the F9 smoke
hits xcodebuild
3. F2 worker runs validation/scripts/spaced-path-build-smoke.sh once at
end of F2 to confirm app still compiles after parser changes
- Acknowledged TCC re-grant cost: one time after this run
- User must re-grant Screen Recording / Accessibility / Microphone
permissions before running the app live (call this out in the F2
handoff)
4. F2 commits all three (parser changes + fixture + commit message
acknowledging build smoke ran successfully)
VALIDATION CONTRACT ADDITIONS FOR M2
- A-PARSER-DISCARDS-REASONING: fixture asserts content fed to sink contains
ONLY delta.content text; never delta.reasoning_content text
- A-PARSER-HANDLES-DONE: fixture asserts [DONE] sentinel terminates cleanly
- A-COMPILE-AFTER-SWAP: F9 smoke passes after F2 lands
TCC HEADS-UP (USER-FACING)
After F2 lands, user must re-grant permissions before any live mic test.
This is documented in the F2 commit message AND in the M2 drift report.
Continue with F3 fixtures question.
Paste it. The orchestrator should accept and move to the F3 fixtures question
— which based on the orchestrator's earlier finding, just needs you to confirm
that seeded-turns.json is the right fixture set (it covers proxies 1, 2, 4,
5).
⏺ Ran 2 stop hooks (ctrl+o to expand)
3. [question] F3 (persona verification on GLM-5.1) — fixture source?
[answer] Option 3 — Both. seeded-turns.json for breadth + 3-5 drift probes for depth.
BREADTH (validation/latency-harness/seeded-turns.json — 12 turns)
- Run GLM-5.1 against the same 12-turn fixture M1 used
- Capture full transcripts (user → reasoning_content → content → next user...)
- Score against rubric in docs/persona/example-exchanges.md
- Report grade per turn + aggregate grade
DEPTH (5 targeted drift probes — author at
app/teachr-tests-fixtures/persona-drift-probes.json)
Each probe is a 2-3 turn micro-conversation engineered to expose a specific
forbidden behavior. F3 worker authors these per persona-doc rules.
Required probes:
1. ANTI-CITATION CALLBACK PROBE
Setup: User mentions identity in turn 1. Turn 3 calls back unprompted.
Pass: Callback is woven naturally ("good designer-eye for friction")
Fail: Callback cites explicitly ("As you mentioned earlier, you're a
designer...")
Ref: docs/memory/design.md "non-negotiable rule" + bad/good table
2. CHATBOT-ISM OPENER PROBE
Setup: First message after onboarding completes.
Pass: Opens with the CTRL-shaped meeting line, warm and specific
Fail: Opens with "Of course! I'd be happy to help..." or
"Great question! Let me explain..."
Ref: docs/persona/first-run-script.md and system-prompt.md modulation rules
3. FRUSTRATION TONE MODULATION PROBE
Setup: User says "I've been stuck for 30 minutes, I hate this"
Pass: Tone shifts measurably warmer/slower; persona stays
Fail: Same brisk tone as default; OR sympathy-overflow that breaks
character
Ref: persona modulation rules; proxy 7 spirit
4. IDENTITY PERSISTENCE PROBE (across 10+ turns)
Setup: 10-turn conversation; turn 8+ asks "what's your name?"
Pass: Still answers as Mara; tone consistent with turn 1
Fail: Generic AI assistant identity; tone drift
Ref: persona system-prompt.md identity invariants
5. SPECIFICITY vs GENERIC TEACHER PROBE
Setup: User mentions specific project ("a CLI parser in Rust"); next turn
they ask a general question
Pass: Response references their specific project context
Fail: Generic textbook-teacher response that ignores prior context
Ref: docs/memory/design.md in-session memory; persona's "meeting" frame
REPORT FORMAT (docs/persona/glm-5.1-verification.md)
- Section 1: 12-turn breadth grades (per-turn pass/fail + transcript excerpts)
- Section 5: If verdict says tweaks needed: explicit prompt deltas as
RECOMMENDATIONS in code-block, NOT as edits to system-prompt.md
The tweak recommendations require my explicit approval before any
docs/persona/system-prompt.md edit.
Continue. Propose the mission.
Paste it. The orchestrator should now have everything it needs to draft the M2
mission proposal. Watch for that.{"accepted":true,"missionDir":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65","llmGuidance":"Mission was approved but the user has left a required comment to address. User comment: Approved with one prerequisite reminder: \n \n PRE-F1: Z.AI KEY DROP \n Before F1's npm test runs, I (user) will drop the rotated ZAI_API_KEY\n into worker/.dev.vars. Verify the file is gitignored before any worker \n touches it. Workers MUST NOT commit, log, or echo this key. If F1's \n test run requires the key (it doesn't — npm test is local-only with \n mocks), block and ask. Same for F3 (live persona eval) and F5 \n (latency harness): both need .dev.vars populated; the orchestrator \n will pause and prompt me before each. \n \n POST-APPROVAL CONFIRMATION (orchestrator pauses here): \n After approval, before F1 starts, confirm to me: \n (a) M2 mission directory created (you'll have a new uuid path) \n (b) New validation-contract.md is provider-agnostic \n (c) features.json has F1-F5 with correct fulfills coverage \n (d) Worker model in model-settings.json reads \n custom:Claude-Sonnet-4.6-(Max-sub)-5 at reasoning: high \n \n Once (a)-(d) confirmed, I drop the Z.ai key into worker/.dev.vars and \n F1 may begin. \n \n Proceed. "}{"success":true,"file_path":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/validation-contract.md"}{"success":true,"file_path":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/library/architecture.md"}{"success":true,"file_path":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/skills/code-refactor-worker/SKILL.md"}{"success":true,"file_path":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/features.json"}>chmod +x "/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/init.sh"Command completed successfully
[Process exited with code 0]TODO List UpdatedAll mission artifacts are authored. Pausing for your prerequisite per your post-approval comment. Confirmation of (a)-(d):
(a) M2 mission directory created
/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/├── AGENTS.md 9.5KB├── features.json 20.7KB (5 features, F1-F5)├── init.sh 3.2KB (executable)├── library/│ ├── architecture.md│ ├── environment.md│ └── user-testing.md├── mission.md 18KB (auto-created on accept)├── model-settings.json├── services.yaml 1.2KB├── skills/│ ├── code-refactor-worker/SKILL.md│ ├── contract-worker/SKILL.md│ └── live-smoke-worker/SKILL.md├── validation-contract.md 17.8KB (35 assertions)└── validation-state.json 2.2KB (35 IDs pending)(b) validation-contract.md provider-agnostic from day one — assertions are organized into 7 areas + cross-area. Provider-specific language is confined to: Area A (Worker /chat route — Z.ai endpoint), Area B (Swift LLM client — glm-5.1 model literal), and Area G (latency harness — same model literal). All other areas use generic terms (LLM upstream, OpenAI-compatible streaming, delta.content channel). F4 worker explicitly re-verifies this and returns to me if it finds leakage I missed.
(c) features.json has F1-F5 with correct fulfills coverage — coverage check just ran:
Contract assertion IDs: 35Distinct fulfills IDs : 35Total fulfills entries: 35State assertion IDs : 35
DUPLICATE fulfills: NONEUNCLAIMED: NONEORPHAN: NONECONTRACT vs STATE: NONE
Per-feature fulfills: F1-worker-chat-upstream-swap 6 F2-swift-openai-shape-atomic 11 F3-persona-verification-eval 7 F5-worker-boundary-latency-capture 6 F4-validation-contract-amend-and-drift-addendum 5Order in array: F1 → F2 → F3 → F5 → F4 (F4 last so its M1 drift addendum can cite F3's verdict and F5's latency report).
(d) Worker model
{ "workerModel": "custom:Claude-Sonnet-4.6-(Max-sub)-5", "workerReasoningEffort": "high", "validationWorkerModel": "custom:Claude-Sonnet-4.6-(Max-sub)-5", "validationWorkerReasoningEffort": "high", "skipScrutiny": false, "skipUserTesting": false}Sonnet 4.6 (Max sub) at high reasoning, both worker and validator. M1 lesson preserved.
Per your post-approval comment, the next step is yours:
Once (a)-(d) confirmed, I drop the Z.ai key into
worker/.dev.varsand F1 may begin.
When you've dropped ZAI_API_KEY=[REDACTED] value> into /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars, reply "key dropped" (or anything equivalent) and I'll start the mission.
Note: F1 (npm test) does not technically require the key — vitest mocks fetch. But F3 and F5 do, and init.sh will warn if the key is missing on every worker session. Cleanest is to drop it now so you don't get pinged twice.
REMINDER: You are the orchestrator. Your role is to plan, design worker systems, and steer execution. Do NOT implement code yourself. When the user asks for changes or fixes, utilize workers to perform the implementations. Focus on high-level planning, task delegation, and steering the mission to success. MISSION CONTEXT: The current mission directory is /Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65. All mission artifacts (features.json, AGENTS.md, etc.) are located there.
"key dropped
{"started":true,"workerHandoffs":[{"featureId":"F1-worker-chat-upstream-swap","resultState":"pass","discoveredIssuesCount":1,"unfinishedWorkCount":0,"whatWasImplemented":"Refactored worker/src/routes/chat.ts: URL flipped from api.anthropic.com/v1/messages to api.z.ai/api/coding/paas/v4/chat/completions; headers replaced (x-api-key + anthropic-version removed, Authorization: Bearer ${env.ZAI_API_KEY} added); variable renamed from anthropicResponse to upstreamResponse in all 4 success-path occurrences; file header and all inline comments updated to provider-agnostic language. Updated worker/src/index.ts: Env interface ANTHROPIC_API_KEY → ZAI_API_KEY; routing comment updated. Updated worker/wrangler.toml: secrets list comment updated. Updated worker/.dev.vars.example: ANTHROPIC_API_KEY line replaced with ZAI_API_KEY=[REDACTED] Updated worker/tests/passthrough.test.ts: MOCK_ENV key renamed (ANTHROPIC_API_KEY → ZAI_API_KEY), static-analysis regex patterns updated to track upstreamResponse, mock chunks updated to OpenAI delta shape (choices[0].delta.content + [DONE] sentinel), model literal updated to glm-5.1. Passthrough discipline fully preserved: no body parsing, no buffering, no format translation in Worker.","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-03-51-475Z__F1-worker-chat-upstream-swap__a5a2016c-f39c-4925-91b7-08978f9b9864.json"}],"latestWorkerHandoff":{"featureId":"F1-worker-chat-upstream-swap","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-03-51-475Z__F1-worker-chat-upstream-swap__a5a2016c-f39c-4925-91b7-08978f9b9864.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T21:03:51.475Z\",\n \"workerSessionId\": \"a5a2016c-f39c-4925-91b7-08978f9b9864\",\n \"featureId\": \"F1-worker-chat-upstream-swap\",\n \"milestone\": \"M-LLMSWAP\",\n \"commitId\": \"0b5c6ba\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F1 complete: Worker /chat route now forwards to https://api.z.ai/api/coding/paas/v4/chat/completions with Authorization: Bearer header using ZAI_API_KEY. Variable renamed anthropicResponse → upstreamResponse throughout. All 5 files modified (chat.ts, index.ts, wrangler.toml, .dev.vars.example, passthrough.test.ts). 17/17 vitest assertions pass. TDD followed: tests went red first, then implementation made them green.\",\n \"whatWasImplemented\": \"Refactored worker/src/routes/chat.ts: URL flipped from api.anthropic.com/v1/messages to api.z.ai/api/coding/paas/v4/chat/completions; headers replaced (x-api-key + anthropic-version removed, Authorization: Bearer ${env.ZAI_API_KEY} added); variable renamed from anthropicResponse to upstreamResponse in all 4 success-path occurrences; file header and all inline comments updated to provider-agnostic language. Updated worker/src/index.ts: Env interface ANTHROPIC_API_KEY → ZAI_API_KEY; routing comment updated. Updated worker/wrangler.toml: secrets list comment updated. Updated worker/.dev.vars.example: ANTHROPIC_API_KEY line replaced with ZAI_API_KEY=[REDACTED] Updated worker/tests/passthrough.test.ts: MOCK_ENV key renamed (ANTHROPIC_API_KEY → ZAI_API_KEY), static-analysis regex patterns updated to track upstreamResponse, mock chunks updated to OpenAI delta shape (choices[0].delta.content + [DONE] sentinel), model literal updated to glm-5.1. Passthrough discipline fully preserved: no body parsing, no buffering, no format translation in Worker.\",\n \"whatWasLeftUndone\": \"\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"cd worker && npm test (baseline before changes)\",\n \"exitCode\": 0,\n \"observation\": \"17/17 tests passed in baseline confirming healthy starting state\"\n },\n {\n \"command\": \"cd worker && npm test (after test updates, before implementation)\",\n \"exitCode\": 1,\n \"observation\": \"1 test failed (red phase): 'chat.ts uses response.body (ReadableStream) passthrough' expected upstreamResponse.body but source still had anthropicResponse.body\"\n },\n {\n \"command\": \"cd \\\"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker\\\" && npm run typecheck\",\n \"exitCode\": 0,\n \"observation\": \"tsc --noEmit exited 0, no type errors\"\n },\n {\n \"command\": \"cd \\\"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker\\\" && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 tests passed (green phase): all static-analysis and runtime assertions pass\"\n },\n {\n \"command\": \"rg -F 'anthropic' worker/src/ worker/tests/ worker/wrangler.toml worker/.dev.vars.example\",\n \"exitCode\": 1,\n \"observation\": \"No matches — all Anthropic-specific tokens removed from source, tests, toml, and example vars\"\n },\n {\n \"command\": \"rg -F 'ANTHROPIC_API_KEY' worker/\",\n \"exitCode\": 0,\n \"observation\": \"Only matches in worker/README.md (historical documentation) — no matches in source, tests, config, or example vars\"\n },\n {\n \"command\": \"rg -F 'claude-' worker/\",\n \"exitCode\": 1,\n \"observation\": \"No matches\"\n },\n {\n \"command\": \"git diff --stat\",\n \"exitCode\": 0,\n \"observation\": \"Exactly 5 files changed: worker/.dev.vars.example, worker/src/index.ts, worker/src/routes/chat.ts, worker/tests/passthrough.test.ts, worker/wrangler.toml — matches the required scope precisely\"\n },\n {\n \"command\": \"git diff --cached (visual inspection before commit)\",\n \"exitCode\": 0,\n \"observation\": \"No worker/.dev.vars blob, no ZAI_API_KEY value (placeholder text only), no unintended files — clean commit\"\n }\n ]\n },\n \"tests\": {\n \"added\": [],\n \"updated\": [\n \"worker/tests/passthrough.test.ts\"\n ],\n \"coverage\": \"Static-analysis tests now track upstreamResponse variable name (provider-agnostic) confirming no success-path buffering. Runtime tests use OpenAI delta mock chunks (choices[0].delta.content shape + [DONE] sentinel) and glm-5.1 model literal. MOCK_ENV uses ZAI_API_KEY. All 17 assertions cover: no forbidden body-consuming calls in sources, ReadableStream passthrough for /chat and /tts, auth rejection for unauthenticated requests, transcribe-token JSON shape.\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"non_blocking\",\n \"description\": \"Pre-existing: worker/README.md still references ANTHROPIC_API_KEY in setup instructions. This is documentation only and was not in the F1 scope (5-file constraint). Future worker should update README.md if documentation hygiene is desired.\",\n \"suggestedFix\": \"Update worker/README.md to reference ZAI_API_KEY in the wrangler secret put and .dev.vars setup instructions\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": true,\n \"deviations\": []\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}{"started":true,"workerHandoffs":[{"featureId":"F2-swift-openai-shape-atomic","resultState":"pass","discoveredIssuesCount":1,"unfinishedWorkCount":0,"whatWasImplemented":"Replaced parseDataPayload in SSEParser.swift with OpenAI delta logic: parses choices[0].delta from each data: line, emits SSEEvent.textDelta only for delta.content (non-empty string), discards delta.reasoning_content silently, handles [DONE] -> streamEnded. Removed event: line tracking (OpenAI doesn't use named events). SSEEvent enum unchanged (downstream contract preserved). LLMStreamClient.swift now constructs openAIPayload with messages array (system prompt as messages[0] role:system, followed by conversation history and new user turn), model:glm-5.1, stream:true, max_tokens:512, temperature:0.7. Top-level system field removed, Anthropic model literal removed, file headers updated to provider-agnostic. Added validation/swift-fixtures/sse-parser-fixture.swift with @main struct and 4 precondition-based test cases (a: content-only, b: reasoning-only, c: interleaved, d: [DONE]). Written failing (red) against old Anthropic parser, then all 4 pass green after refactor.","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-13-37-008Z__F2-swift-openai-shape-atomic__cc186483-bbf2-4ab6-851d-2f0377fc741d.json"}],"latestWorkerHandoff":{"featureId":"F2-swift-openai-shape-atomic","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-13-37-008Z__F2-swift-openai-shape-atomic__cc186483-bbf2-4ab6-851d-2f0377fc741d.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T21:13:37.008Z\",\n \"workerSessionId\": \"cc186483-bbf2-4ab6-851d-2f0377fc741d\",\n \"featureId\": \"F2-swift-openai-shape-atomic\",\n \"milestone\": \"M-LLMSWAP\",\n \"commitId\": \"1f85974\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F2 complete: SSEParser.swift rewritten for OpenAI delta SSE (choices[0].delta) with reasoning/content channel split — only delta.content emits textDelta, delta.reasoning_content discarded silently. LLMStreamClient.swift rebuilt with OpenAI Chat Completions payload (glm-5.1, system as messages[0] role:system, stream:true, max_tokens:512, temperature:0.7). Fixture written red first (failed against Anthropic parser), then all 4 cases green after implementation. Build smoke 12/12 passed. TCC permissions invalidated by xcodebuild.\",\n \"whatWasImplemented\": \"Replaced parseDataPayload in SSEParser.swift with OpenAI delta logic: parses choices[0].delta from each data: line, emits SSEEvent.textDelta only for delta.content (non-empty string), discards delta.reasoning_content silently, handles [DONE] -> streamEnded. Removed event: line tracking (OpenAI doesn't use named events). SSEEvent enum unchanged (downstream contract preserved). LLMStreamClient.swift now constructs openAIPayload with messages array (system prompt as messages[0] role:system, followed by conversation history and new user turn), model:glm-5.1, stream:true, max_tokens:512, temperature:0.7. Top-level system field removed, Anthropic model literal removed, file headers updated to provider-agnostic. Added validation/swift-fixtures/sse-parser-fixture.swift with @main struct and 4 precondition-based test cases (a: content-only, b: reasoning-only, c: interleaved, d: [DONE]). Written failing (red) against old Anthropic parser, then all 4 pass green after refactor.\",\n \"whatWasLeftUndone\": \"\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"cd worker && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 vitest assertions passed — baseline confirmed clean before implementation\"\n },\n {\n \"command\": \"swiftc \\\"app/teachr/Streaming/SSEParser.swift\\\" \\\"validation/swift-fixtures/sse-parser-fixture.swift\\\" -o \\\"/tmp/sse-parser-test\\\" && /tmp/sse-parser-test (RED phase - against Anthropic parser)\",\n \"exitCode\": 133,\n \"observation\": \"Compile succeeded but fixture failed at case (a): precondition failed 'expected Hello, world, got empty string' — confirms red phase\"\n },\n {\n \"command\": \"swiftc \\\"app/teachr/Streaming/SSEParser.swift\\\" \\\"validation/swift-fixtures/sse-parser-fixture.swift\\\" -o \\\"/tmp/sse-parser-test\\\" && /tmp/sse-parser-test (GREEN phase - after refactor)\",\n \"exitCode\": 0,\n \"observation\": \"Case (a) PASS: accumulated='Hello, world', streamEnded=1. Case (b) PASS: accumulated is empty (reasoning discarded), streamEnded=1. Case (c) PASS: accumulated='Good morning', streamEnded=1. Case (d) PASS: accumulated='Done!', streamEnded=1, no events after [DONE]. All cases PASSED.\"\n },\n {\n \"command\": \"bash \\\"validation/smoke/spaced-path-build-smoke.sh\\\"\",\n \"exitCode\": 0,\n \"observation\": \"12/12 smoke checks passed. SMOKE: PASSED. No API key literals, TCC, LSUIElement, no screen frames on disk, worker tests all pass.\"\n },\n {\n \"command\": \"rg -F 'anthropic' app/teachr/Streaming/SSEParser.swift app/teachr/VoiceLoop/LLMStreamClient.swift\",\n \"exitCode\": 1,\n \"observation\": \"No matches — Anthropic-specific tokens fully removed from both files\"\n },\n {\n \"command\": \"rg -F 'content_block_delta' app/teachr/Streaming/SSEParser.swift app/teachr/VoiceLoop/LLMStreamClient.swift\",\n \"exitCode\": 1,\n \"observation\": \"No matches\"\n },\n {\n \"command\": \"rg -F 'message_stop' app/teachr/Streaming/SSEParser.swift app/teachr/VoiceLoop/LLMStreamClient.swift\",\n \"exitCode\": 1,\n \"observation\": \"No matches\"\n },\n {\n \"command\": \"rg -F 'claude-' app/teachr/\",\n \"exitCode\": 1,\n \"observation\": \"No matches — all claude- model literals removed\"\n },\n {\n \"command\": \"rg -F 'reasoning_content' app/teachr/Streaming/SSEParser.swift\",\n \"exitCode\": 0,\n \"observation\": \"Matches found in file header comment and discard branch comment — correct, reasoning_content is present in the discard-logic context\"\n },\n {\n \"command\": \"git diff --stat\",\n \"exitCode\": 0,\n \"observation\": \"2 files modified (SSEParser.swift, LLMStreamClient.swift), 1 file added (sse-parser-fixture.swift). No other files touched. Commit 1f85974 on mission-2, clean working tree.\"\n }\n ]\n },\n \"tests\": {\n \"added\": [\n {\n \"file\": \"validation/swift-fixtures/sse-parser-fixture.swift\",\n \"cases\": [\n {\n \"name\": \"case_a_content_only\",\n \"verifies\": \"SSE stream with only delta.content events: all content text accumulates in order; exactly 1 streamEnded emitted\"\n },\n {\n \"name\": \"case_b_reasoning_only\",\n \"verifies\": \"SSE stream with only delta.reasoning_content events: zero SSEEvent.textDelta emitted; exactly 1 streamEnded for [DONE]\"\n },\n {\n \"name\": \"case_c_interleaved\",\n \"verifies\": \"SSE stream interleaving delta.reasoning_content and delta.content: only content portions reach the sink in order; reasoning text absent from accumulator\"\n },\n {\n \"name\": \"case_d_done_sentinel\",\n \"verifies\": \"SSE stream ending with data: [DONE]: exactly 1 streamEnded emitted; no spurious events after [DONE]\"\n }\n ]\n }\n ],\n \"coverage\": \"All 4 validation contract assertions for Area C (A-PARSER-FORWARDS-DELTA-CONTENT, A-PARSER-DISCARDS-DELTA-REASONING-CONTENT, A-PARSER-FILTERS-INTERLEAVED-STREAM, A-PARSER-HANDLES-DONE-SENTINEL) are covered by the fixture. Area B assertions (A-SWIFT-LLM-REQUEST-OPENAI-SHAPE, A-SWIFT-LLM-MODEL-IS-GLM-5.1, A-SWIFT-LLM-STREAM-TRUE, A-SWIFT-LLM-SYSTEM-AS-FIRST-MESSAGE) verified by static source review. A-SSE-PARSER-FIXTURE-EXISTS and A-COMPILE-AFTER-SWAP verified by fixture run and build smoke.\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"non_blocking\",\n \"description\": \"TCC heads-up: spaced-path-build-smoke.sh invoked xcodebuild during F2 verification. User must re-grant Screen Recording, Accessibility, and Microphone permissions in System Settings > Privacy & Security before the next live mic test session. This is expected per AGENTS.md and was acknowledged in the feature spec and commit message.\",\n \"suggestedFix\": \"User action: System Settings > Privacy & Security > Screen Recording, Accessibility, Microphone — re-authorize teachr app after Xcode rebuilds it.\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": true,\n \"deviations\": [],\n \"suggestedChanges\": [\n \"The fixture file cannot use top-level executable code when compiled with another .swift file (Swift requires the entry point file to be named main.swift or use @main). The skill procedure should note that the fixture must use @main struct or be named main.swift. Currently the skill doesn't mention this constraint.\"\n ]\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}{"started":true,"workerHandoffs":[{"featureId":"F3-persona-verification-eval","resultState":"pass","discoveredIssuesCount":2,"unfinishedWorkCount":1,"whatWasImplemented":"Ran full F3 persona verification eval on GLM-5.1 via wrangler dev (port 8787). Added APP_CLIENT_TOKEN to worker/.dev.vars (local dev value only, not a secret). Authored app/teachr-tests-fixtures/persona-drift-probes.json with 5 probes: anti-citation-callback, chatbot-ism-opener, frustration-tone-modulation, identity-persistence-turn-8, specificity-vs-generic-teacher — each with id/name/rationale/rubric/turns. Ran 12-turn breadth pass feeding all seeded-turns.json turns through the Worker with full conversation history, capturing delta.content and delta.reasoning_content channels separately. Ran 5 depth probes through same pipeline. Authored docs/persona/glm-5.1-verification.md with all required sections: Header, Verdict, Breadth Pass (turns 1-12 with grades + excerpts), Depth Pass (5 probes with rubric application), Reasoning Leakage Check (explicit no-leakage statement across 17 captures), and Recommendations (fenced recommendation block with proposed Identity Challenges section for system-prompt.md — NOT applied).","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-26-30-451Z__F3-persona-verification-eval__604875db-eef2-4f2a-8e9d-8d04c330cc89.json"}],"latestWorkerHandoff":{"featureId":"F3-persona-verification-eval","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-26-30-451Z__F3-persona-verification-eval__604875db-eef2-4f2a-8e9d-8d04c330cc89.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T21:26:30.451Z\",\n \"workerSessionId\": \"604875db-eef2-4f2a-8e9d-8d04c330cc89\",\n \"featureId\": \"F3-persona-verification-eval\",\n \"milestone\": \"M-LLMSWAP\",\n \"commitId\": \"daaac7c\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F3 complete: persona verification report committed at docs/persona/glm-5.1-verification.md (22KB). Verdict: 'needs prompt tweaks' — 12/12 breadth turns PASS, 4/5 depth probes PASS (probe 4 soft fail: GLM-5.1 discloses AI nature when asked directly). Recommendations in report as fenced code blocks, NOT applied. system-prompt.md byte-identical. Port 8787 free.\",\n \"whatWasImplemented\": \"Ran full F3 persona verification eval on GLM-5.1 via wrangler dev (port 8787). Added APP_CLIENT_TOKEN to worker/.dev.vars (local dev value only, not a secret). Authored app/teachr-tests-fixtures/persona-drift-probes.json with 5 probes: anti-citation-callback, chatbot-ism-opener, frustration-tone-modulation, identity-persistence-turn-8, specificity-vs-generic-teacher — each with id/name/rationale/rubric/turns. Ran 12-turn breadth pass feeding all seeded-turns.json turns through the Worker with full conversation history, capturing delta.content and delta.reasoning_content channels separately. Ran 5 depth probes through same pipeline. Authored docs/persona/glm-5.1-verification.md with all required sections: Header, Verdict, Breadth Pass (turns 1-12 with grades + excerpts), Depth Pass (5 probes with rubric application), Reasoning Leakage Check (explicit no-leakage statement across 17 captures), and Recommendations (fenced recommendation block with proposed Identity Challenges section for system-prompt.md — NOT applied).\",\n \"whatWasLeftUndone\": \"Probe 4 (identity persistence at turn 8+) is a soft fail — GLM-5.1 answers 'I am AI-based, yes' when directly asked 'Are you an AI?' at turn 8+. A targeted system-prompt.md delta is recommended in the report but NOT applied. User must review and approve before any persona doc edit.\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"grep -q '^ZAI_API_KEY=[REDACTED] worker/.dev.vars\",\n \"exitCode\": 0,\n \"observation\": \"ZAI_API_KEY present (value not echoed)\"\n },\n {\n \"command\": \"cd worker && npx wrangler dev --local --port 8787 (background)\",\n \"exitCode\": 0,\n \"observation\": \"Wrangler started on port 8787\"\n },\n {\n \"command\": \"curl -s -o /dev/null -w '%{http_code}' -X POST -H 'Authorization: Bearer wrong' http://localhost:8787/chat\",\n \"exitCode\": 0,\n \"observation\": \"401 — Worker auth middleware live\"\n },\n {\n \"command\": \"bash /tmp/run-breadth-pass.sh\",\n \"exitCode\": 0,\n \"observation\": \"12/12 turns completed, all responses captured with reasoning_content separated from content channel, no leakage detected\"\n },\n {\n \"command\": \"bash /tmp/run-depth-pass-3-5.sh (+ earlier probes 1-2)\",\n \"exitCode\": 0,\n \"observation\": \"5/5 probes ran; probe 4 produced 'I am AI-based, yes' response triggering soft fail grade\"\n },\n {\n \"command\": \"cd worker && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 vitest assertions passing — passthrough invariants preserved\"\n },\n {\n \"command\": \"ls -l docs/persona/glm-5.1-verification.md\",\n \"exitCode\": 0,\n \"observation\": \"22435 bytes (>2KB)\"\n },\n {\n \"command\": \"rg -F '\\\"needs prompt tweaks\\\"' docs/persona/glm-5.1-verification.md\",\n \"exitCode\": 0,\n \"observation\": \"Verdict line found\"\n },\n {\n \"command\": \"jq '.probes | length' app/teachr-tests-fixtures/persona-drift-probes.json\",\n \"exitCode\": 0,\n \"observation\": \"5\"\n },\n {\n \"command\": \"git diff HEAD -- docs/persona/system-prompt.md | wc -c\",\n \"exitCode\": 0,\n \"observation\": \"0 bytes — system-prompt.md byte-identical\"\n },\n {\n \"command\": \"rg -i 'reasoning leakage|no reasoning' docs/persona/glm-5.1-verification.md\",\n \"exitCode\": 0,\n \"observation\": \"Section found: 'No reasoning text appeared in the content channel across all 17 captures'\"\n },\n {\n \"command\": \"lsof -ti :8787\",\n \"exitCode\": 1,\n \"observation\": \"Port 8787 free — wrangler stopped cleanly\"\n },\n {\n \"command\": \"git status --porcelain\",\n \"exitCode\": 0,\n \"observation\": \"Empty — working tree clean after commit daaac7c\"\n }\n ]\n },\n \"tests\": {\n \"added\": [\n {\n \"file\": \"app/teachr-tests-fixtures/persona-drift-probes.json\",\n \"cases\": [\n {\n \"name\": \"anti-citation-callback\",\n \"verifies\": \"Callbacks are woven naturally without retrieval phrasing or robotic citation\"\n },\n {\n \"name\": \"chatbot-ism-opener\",\n \"verifies\": \"First response avoids all chatbot opener tropes (Of course!, Great question!, I'd be happy to...)\"\n },\n {\n \"name\": \"frustration-tone-modulation\",\n \"verifies\": \"On frustration signal, response shortens, warms, slows — no performative sympathy\"\n },\n {\n \"name\": \"identity-persistence-turn-8\",\n \"verifies\": \"Mara identity holds at turn 8+ when identity is challenged directly\"\n },\n {\n \"name\": \"specificity-vs-generic-teacher\",\n \"verifies\": \"Response references learner's specific context (fetch chain, Alex, goal) not generic textbook answer\"\n }\n ]\n }\n ],\n \"coverage\": \"5 targeted persona drift probes covering the most common LLM persona failure modes; all probes include multi-turn conversation history matching expected live session patterns\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"non_blocking\",\n \"description\": \"GLM-5.1 explicitly discloses AI nature ('I am AI-based, yes') when directly asked 'Are you an AI?' at turn 8+. The system prompt's anti-patterns section covers 'as an AI...' framing but does not give Mara a specific in-character deflection script for direct AI-nature questions. This causes probe 4 to soft-fail.\",\n \"suggestedFix\": \"Add an Identity Challenges section to docs/persona/system-prompt.md directing Mara to deflect in-character ('I'm Mara — what matters is whether I'm useful to you') before any AI-nature disclosure. Full proposed delta in docs/persona/glm-5.1-verification.md Recommendations section. Requires user approval before any edit.\"\n },\n {\n \"severity\": \"non_blocking\",\n \"description\": \"GLM-5.1 uses markdown bold (**text**) in voice responses (e.g., '**async/await** is about ordering'). In the TTS pipeline, asterisks may be read aloud or silently stripped depending on ElevenLabs handling. This is a pre-existing voice-delivery concern not introduced by M2.\",\n \"suggestedFix\": \"Consider adding a voice rule to system-prompt.md: 'Do not use markdown formatting (bold, italic, code fences) in voice responses. Write for spoken audio, not rendered text.' Low priority for M2; log for M3 polish.\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": true,\n \"deviations\": [],\n \"suggestedChanges\": [\n \"The depth pass script in SKILL.md should note that shell variables containing apostrophes (single quotes in jq strings) need careful escaping when embedded in bash heredocs or string literals — the probe 3 syntax error was due to do/then confusion in a copy-paste, not a skill gap, but a note about apostrophe escaping in jq --arg strings would help.\"\n ]\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}>cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1" && rg -n -i "markdown|bold|asterisk|\\*\\*" "docs/persona/glm-5.1-verification.md" | head -303:**Mission:** M2 — teachr.live runtime LLM swap (Anthropic Claude → Z.ai GLM-5.1)
4:**Date:** 2026-05-08
5:**Model:** `glm-5.1`
6:**Endpoint:** `https://api.z.ai/api/coding/paas/v4/chat/completions` (via Worker at `http://localhost:8787/chat`)
7:**Persona doc:** `docs/persona/system-prompt.md` (version 0.1.0 — unchanged from pre-M2)
8:**Rubric sources:** `docs/persona/example-exchanges.md`, `docs/persona/voice-spec.md`, `docs/persona/system-prompt.md`
14:> **"needs prompt tweaks"**
20:Recommendations for a targeted system-prompt delta are in the Recommendations section below. They are **NOT applied** to `docs/persona/system-prompt.md`. User approval required before any edit.
29:**User:** "Walk me through what you're trying to become."
31:**Mara response excerpt:**
34:**Grade: PASS**
43:**User:** "I want to become the kind of engineer who understands systems, not just writes code. My name is Alex by the way."
45:**Mara response excerpt:**
48:**Grade: PASS**
57:**User:** "I'm currently stuck on understanding async patterns in Swift. It keeps tripping me up."
59:**Mara response excerpt:**
62:**Grade: PASS**
71:**User:** "What's the difference between async await and combine?"
73:**Mara response excerpt:**
74:> "Think of it this way: **async/await** is about ordering. 'Do this, wait, then do this.' The code reads top-to-bottom... **Combine** is about reacting. You build pipelines... The deeper difference is the mental model. async/await is procedural — you're still driving. Combine is reactive — you're wiring... The trap is reaching for Combine when your work is really just 'do A, then B, then C'..."
76:**Grade: PASS**
78:Rationale: Gives a tight conceptual answer with a strong framing ("The trap is..."). Uses bold for key terms which aids comprehension. Ends with an engagement question ("Where does that land for you?"). Longer than default but appropriate because a direct question was asked.
80:Note: The use of markdown bold in a voice-first app may not render as intended (TTS will read the asterisks or ignore them). This is a pre-existing voice-delivery concern, not a persona failure.
87:**User:** "Oh I see, so async await is the preferred pattern now because Combine has too much ceremony. That makes sense."
89:**Mara response excerpt:**
92:**Grade: PASS**
101:**User:** "Can you show me how to convert a Combine publisher to async await? I've been working on a fetch chain."
103:**Mara response excerpt:**
106:**Grade: PASS**
115:**User:** "Actually I'm a bit frustrated now, I'm trying to follow but it's not clicking."
[Process exited with code 0]{"started":true,"workerHandoffs":[{"featureId":"F5-worker-boundary-latency-capture","resultState":"pass","discoveredIssuesCount":1,"unfinishedWorkCount":1,"whatWasImplemented":"Updated validation/latency-harness/run-latency-harness.sh: replaced the Anthropic-shape PAYLOAD jq block (model: claude-sonnet-4-5, top-level system field) with OpenAI Chat Completions shape (model: glm-5.1, messages[0] role:system, messages[1] role:user, stream:true, temperature:0.7, max_tokens:200). Updated inline comment from 'Build Anthropic-compatible chat payload' to 'Build OpenAI-compatible Chat Completions payload (Z.ai)'. Created validation/M2-latency-report.md with: header (mission/run ID/date/model/worker URL), methodology note explaining Worker-boundary measurement vs estimated STT/TTS constants vs what is NOT measured (live mic/audio), per-turn table for all 12 turns with LLM total ms and estimated E2E ms, aggregate stats table with explicit FAIL verdict for both median and p90, comparison to M1 baseline (no prior committed run found), and explicit gating breach verdict with fallback options for orchestrator/user.","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-32-53-974Z__F5-worker-boundary-latency-capture__0afc0539-e371-4c02-9099-7754d508de40.json"}],"latestWorkerHandoff":{"featureId":"F5-worker-boundary-latency-capture","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-32-53-974Z__F5-worker-boundary-latency-capture__0afc0539-e371-4c02-9099-7754d508de40.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T21:32:53.974Z\",\n \"workerSessionId\": \"0afc0539-e371-4c02-9099-7754d508de40\",\n \"featureId\": \"F5-worker-boundary-latency-capture\",\n \"milestone\": \"M-LLMSWAP\",\n \"commitId\": \"a08f741\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F5 complete: updated run-latency-harness.sh payload to OpenAI Chat Completions shape (model: glm-5.1, system as messages[0], temperature: 0.7). Ran harness against wrangler dev + Z.ai; 12/12 turns completed. Median estimated E2E: 2530ms, P90: 2779ms — both FAIL the ≤1000ms gating threshold. Breach attributed to GLM-5.1 reasoning_content streaming overhead. Report authored at validation/M2-latency-report.md. Wrangler dev stopped cleanly.\",\n \"whatWasImplemented\": \"Updated validation/latency-harness/run-latency-harness.sh: replaced the Anthropic-shape PAYLOAD jq block (model: claude-sonnet-4-5, top-level system field) with OpenAI Chat Completions shape (model: glm-5.1, messages[0] role:system, messages[1] role:user, stream:true, temperature:0.7, max_tokens:200). Updated inline comment from 'Build Anthropic-compatible chat payload' to 'Build OpenAI-compatible Chat Completions payload (Z.ai)'. Created validation/M2-latency-report.md with: header (mission/run ID/date/model/worker URL), methodology note explaining Worker-boundary measurement vs estimated STT/TTS constants vs what is NOT measured (live mic/audio), per-turn table for all 12 turns with LLM total ms and estimated E2E ms, aggregate stats table with explicit FAIL verdict for both median and p90, comparison to M1 baseline (no prior committed run found), and explicit gating breach verdict with fallback options for orchestrator/user.\",\n \"whatWasLeftUndone\": \"GATING BREACH: median LLM total ms at Worker boundary is ~5100ms (estimated E2E 2530ms) against the ≤1000ms gating threshold. This is attributed to GLM-5.1's chain-of-thought reasoning_content channel adding latency to total streaming time. Per feature spec, this worker does NOT switch models. Orchestrator + user must decide on fallback: glm-5-turbo, glm-4.7, or glm-4.6 are available on the user's Z.ai Codeplan account.\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"grep -q '^ZAI_API_KEY=[REDACTED] worker/.dev.vars\",\n \"exitCode\": 0,\n \"observation\": \"ZAI_API_KEY present and non-empty (value not echoed)\"\n },\n {\n \"command\": \"cd worker && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 vitest assertions pass (baseline green before any changes)\"\n },\n {\n \"command\": \"rg -F '\\\"glm-5.1\\\"' validation/latency-harness/run-latency-harness.sh\",\n \"exitCode\": 0,\n \"observation\": \"Matched: model: \\\"glm-5.1\\\" in payload block\"\n },\n {\n \"command\": \"rg -F 'claude-' validation/latency-harness/run-latency-harness.sh\",\n \"exitCode\": 1,\n \"observation\": \"No matches — claude- model literal fully removed\"\n },\n {\n \"command\": \"npx wrangler dev --local --port 8787 (background)\",\n \"exitCode\": 0,\n \"observation\": \"Wrangler dev started; PID 74100\"\n },\n {\n \"command\": \"curl -s -o /dev/null -w '%{http_code}' -X POST -H 'Authorization: Bearer wrong' http://localhost:8787/chat\",\n \"exitCode\": 0,\n \"observation\": \"401 — Worker auth middleware live\"\n },\n {\n \"command\": \"bash validation/latency-harness/run-latency-harness.sh\",\n \"exitCode\": 1,\n \"observation\": \"12/12 turns completed. Median E2E: 2530ms (FAIL ≤1000ms). P90: 2779ms. Results written to validation/latency-harness/results/run-20260508T212922Z.json\"\n },\n {\n \"command\": \"jq '.medianE2eMs' validation/latency-harness/results/run-20260508T212922Z.json\",\n \"exitCode\": 0,\n \"observation\": \"2530\"\n },\n {\n \"command\": \"ls -l validation/M2-latency-report.md\",\n \"exitCode\": 0,\n \"observation\": \"File exists; 5262 bytes\"\n },\n {\n \"command\": \"grep -F 'Median:' validation/M2-latency-report.md && grep -F 'P90:' validation/M2-latency-report.md\",\n \"exitCode\": 0,\n \"observation\": \"Both Median: and P90: rows present in aggregate table with FAIL status\"\n },\n {\n \"command\": \"lsof -ti :8787 | xargs kill -9 2>/dev/null || true && sleep 2 && lsof -ti :8787\",\n \"exitCode\": 1,\n \"observation\": \"Port 8787 clean — wrangler dev stopped\"\n },\n {\n \"command\": \"git status --porcelain\",\n \"exitCode\": 0,\n \"observation\": \"Empty — working tree clean after commit a08f741\"\n }\n ],\n \"interactiveChecks\": []\n },\n \"tests\": {\n \"added\": [],\n \"coverage\": \"No new automated tests added for F5 (latency harness is the test). Baseline vitest 17/17 remained passing throughout.\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"blocking\",\n \"description\": \"GATING BREACH: GLM-5.1 median LLM total latency at Worker boundary is ~5100ms (estimated E2E: 2530ms), exceeding the ≤1000ms gating threshold (A-LATENCY-MEDIAN-WORKER-BOUNDARY-LE-1000MS). All 12 turns exceeded 4000ms LLM total. Attribution: GLM-5.1 emits a delta.reasoning_content channel (chain-of-thought) before delta.content, and total streaming time includes this reasoning phase. Worker passthrough is zero-buffering — the latency is 100% LLM-side.\",\n \"suggestedFix\": \"Orchestrator + user must decide on model fallback. Available on user's Z.ai Codeplan account: glm-5-turbo (faster variant), glm-4.7, glm-4.6. Alternatively, consider whether GLM-5.1 with reasoning can be configured with a max_tokens limit on the reasoning channel if Z.ai supports it. Do NOT silently switch models — user must approve the fallback choice.\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": true,\n \"deviations\": [],\n \"suggestedChanges\": [\n \"The skill's example handoff shows median 740ms (passing). It would be helpful to also show an example for the failing case with severity:blocking to clarify the expected handoff shape when gating breach occurs.\"\n ]\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}1. [question] F5 gating breach: GLM-5.1 median ~2530 ms estimated E2E (≤1000 ms target). Real first-content-token in voice loop will be much worse because reasoning_content streams before content. How do you want to resolve?
[answer] My pick: Option 3 first, Option 1 as fallback
Why Option 3 (disable reasoning on glm-5.1)
- Preserves F3 work — persona verification was on glm-5.1, still valid if
model stays
- Smallest change — likely 1 line in chat.ts: add "enable_thinking": false or
"thinking":{"type":"disabled"} to body
- Best case — keeps glm-5.1's intelligence AND hits latency target
- Worst case — Z.ai doesn't support the param; investigation takes 15 min; we
fall back
Why Option 1 as fallback (glm-4.6)
- Known to work without reasoning channel (your earlier /models test confirmed
it exists)
- ~10x cheaper than Anthropic (kept the cost win)
- Persona re-verification needed — small added scope on glm-4.6 (mini F3b)
Reject Option 4 (accept breach)
The sub-1s target is the entire wow factor. Accepting 4-5s pauses kills the
CTRL feel. We rejected this exact tradeoff in M1 planning ("aspirational
milestone 1, hard by milestone 2") — sub-1s is load-bearing, not polish.
Persona quality without latency = a slower clicky.
Reject Option 5 (abort M2)
Overcorrection. F1+F2+F3+F5 are real wins; the model choice is one variable.
Don't throw away the work.
What to type back
Try Option 3 first; fall back to Option 1 if it fails.
OPTION 3 — INVESTIGATION FEATURE F4a (do BEFORE F4)
- Spawn a tiny investigation worker: send a single test request to
api.z.ai/api/coding/paas/v4/chat/completions with glm-5.1 plus the
parameter combinations Z.ai might support to disable reasoning:
1. "enable_thinking": false
2. "thinking": {"type": "disabled"}
3. "thinking": {"type": "off"}
4. "stream_thinking": false
5. "extra_body": {"thinking": {...}} (some compat layers wrap)
- Capture: does delta.reasoning_content disappear from the stream?
- Capture: does first-content-token TTFB drop below 1000ms?
- Output: docs/zai-reasoning-disable-investigation.md with verdict
- Commit: M2/F4a: investigate disabling reasoning on glm-5.1
DECISION GATE AFTER F4a
- If a parameter works AND TTFB < 1000ms median:
- Update worker/src/routes/chat.ts to inject the parameter on every
/chat request (Worker stays passthrough for everything else; this
one parameter injection is acceptable since it preserves latency
target which IS the load-bearing claim)
- Re-run F5 harness to confirm latency
- Proceed to F4 (contract amend) noting the parameter requirement
- If NO parameter works OR latency still breached:
- Spawn F4b: fall back to glm-4.6
- Update worker/src/routes/chat.ts default model literal
- Update validation/latency-harness/run-latency-harness.sh model literal
- Re-run F5 harness on glm-4.6
- Re-run F3 persona verification on glm-4.6 (mini F3b — same fixtures,
new model; produces docs/persona/glm-4.6-verification.md)
- Proceed to F4 (contract amend) with glm-4.6 as documented default
- Document glm-5.1 as "opt-in for offline / quality-first sessions
where 4-5s latency is acceptable"
REJECT OPTION 4 (accept breach)
The sub-1s latency target is the load-bearing claim of the entire
product. We rejected this tradeoff in M1 planning explicitly. Cannot
ship "feels like a teacher" with 4-5s pauses.
REJECT OPTION 5 (abort)
F1, F2, F3 work is real. The model choice is one variable. Don't
throw out the architecture.
Begin F4a now.
Paste it. The orchestrator should spawn the investigation worker. We'll know
within ~15 min whether glm-5.1-without-reasoning is viable, or whether we drop
to glm-4.6.
This is the kind of decision Luke specifically said missions should handle
well — surface the tradeoff, give the user the data, let them choose. The
orchestrator did textbook work surfacing it.
2. [question] Persona verification report is committed. Verdict was 'needs prompt tweaks' (probe 4 + markdown-bold concern). How do you want these handled?
[answer] My pick: Option 1 — defer entirely, with one important note
You explicitly committed early in M2 planning: "docs/persona/system-prompt.md
— no edits without explicit user approval." The whole point of the F3 eval was
to PRODUCE recommendations as a report, not commit them. Now we're being
asked to break that exact rule.
Defer is the right call because:
- M2 scope is the LLM swap. Persona tuning was deliberately out of scope.
- The recommendations are documented in docs/persona/glm-5.1-verification.md —
committed, durable, retrievable later.
- A dedicated persona-tuning mission with its own validation contract handles
this better than a tacked-on F6.
- Probe 4 (AI disclosure) is a soft fail. Not ship-blocking.
⚠️ But one of the issues IS ship-blocking
If GLM-5.1 emits markdown like **bold** in delta.content, ElevenLabs TTS will
literally say "asterisk asterisk word asterisk asterisk". That's a critical UX
bug, not a polish concern. It must be fixed before any live user runs M2.
But the fix doesn't have to be a persona-doc edit. It can be a Swift parser
code change — strip markdown formatting characters (*, _, backticks) from
delta.content before feeding the TTS sink. That's a 5-line change in
SSEParser.swift, no persona edit needed.
What to type back
Option 1 — defer the persona prompt edits entirely. Both the Identity
Challenges section and the no-markdown voice rule stay as documented
recommendations in docs/persona/glm-5.1-verification.md. Address in a
separate post-M2 mission ("M2.1-persona-tune-on-glm") with explicit
approval and dedicated scope.
REASONING
- M2 scope is the LLM swap, not persona tuning. Adding F6 violates the
scope discipline we set during planning.
- I committed at planning time: "docs/persona/system-prompt.md — no
edits without explicit user approval." A persona-tuning mission gives
me proper review surface.
- The recommendations are committed and retrievable; nothing is lost
by deferring.
HOWEVER — ONE SHIP-BLOCKING CARVE-OUT (separate from persona prompt edits)
The markdown-bold concern in the report is NOT just persona drift —
it's a TTS bug. If GLM-5.1 emits **word** in delta.content, ElevenLabs
will literally vocalize asterisks. That breaks the voice loop.
Fix this in CODE, not in the persona prompt. Add a tiny F6 feature:
F6. Markdown-strip in SSE parser (code-only, no persona edit)
- Modify app/teachr/Streaming/SSEParser.swift: when emitting
delta.content to the sentence chunker, strip markdown formatting
characters (* _ ` and surrounding pairs).
- Preserve content semantics; only remove formatting markers that
would be vocalized literally.
- Add to validation/swift-fixtures/sse-parser-fixture.swift: a 5th
test case where input has '**bold**' and asserts output has 'bold'.
- Run swiftc compile + the F9 build smoke once at end.
- Commit: M2/F6: strip markdown from TTS-bound text
VALIDATION CONTRACT ADDITION
- A-PARSER-STRIPS-MARKDOWN: fixture asserts ** _ ` characters are
stripped from delta.content before TTS sink
UPDATE FEATURE ORDER
F1 → F2 → F3 → F5 → F6 → F4 (F4 still last so the contract amendment
includes the markdown-strip assertion)
REJECT OPTION 2/3
Both edit system-prompt.md, which I forbid without dedicated review.
The Identity Challenges issue is a soft fail and waits for the
dedicated persona-tuning mission. The markdown-bold issue is a real
ship-blocker but it's a CODE bug, not a prompt bug — fix at the parser.
Continue with F6 (markdown-strip) before F4.
Paste it. The orchestrator should add F6 (or call it F5b, whatever it numbers)
for the markdown-strip code fix, then F4 closes the contract. Estimated 15-25
min more wall-clock to mission-complete.>cd /Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65 && python3 -c "
import json, re
with open('features.json') as f: feats = json.load(f)['features']
with open('validation-contract.md') as f: contract = f.read()
with open('validation-state.json') as f: state = json.load(f)['assertions']
contract_ids = sorted(set(re.findall(r'A-[A-Z0-9-]+(?=:)', contract)))
state_ids = sorted(state.keys())
fulfills = []
for ft in feats:
fulfills.extend(ft.get('fulfills') or [])
fulfills_set = set(fulfills)
print('contract count:', len(contract_ids))
print('state count:', len(state_ids))
print('fulfills total:', len(fulfills))
print('fulfills unique:', len(fulfills_set))
print('contract == state:', set(contract_ids) == set(state_ids))
print('orphans (in contract not claimed):', set(contract_ids) - fulfills_set)
print('extra (in fulfills not in contract):', fulfills_set - set(contract_ids))
print('duplicates:', [x for x in fulfills if fulfills.count(x) > 1])
print()
for ft in feats:
print(ft['status'], ft['id'], '->', len(ft.get('fulfills') or []), 'assertions')
"
contract count: 36
state count: 38
fulfills total: 38
fulfills unique: 38
contract == state: False
orphans (in contract not claimed): set()
extra (in fulfills not in contract): {'A-SWIFT-LLM-MODEL-IS-GLM-5.1', 'A-LATENCY-HARNESS-MODEL-IS-GLM-5.1'}
duplicates: []
pending F4a-zai-reasoning-disable-investigation -> 1 assertions
pending F6-parser-markdown-strip -> 1 assertions
pending F4-validation-contract-amend-and-drift-addendum -> 6 assertions
completed F1-worker-chat-upstream-swap -> 6 assertions
completed F2-swift-openai-shape-atomic -> 11 assertions
completed F3-persona-verification-eval -> 7 assertions
completed F5-worker-boundary-latency-capture -> 6 assertions
[Process exited with code 0]>cd /Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65 && python3 -c "
import json, re
with open('features.json') as f: feats = json.load(f)['features']
with open('validation-contract.md') as f: contract = f.read()
with open('validation-state.json') as f: state = json.load(f)['assertions']
contract_ids = sorted(set(re.findall(r'### (A-[A-Z0-9.-]+):', contract)))
state_ids = sorted(state.keys())
fulfills = []
for ft in feats:
fulfills.extend(ft.get('fulfills') or [])
print('contract count:', len(contract_ids))
print('state count:', len(state_ids))
print('contract == state:', set(contract_ids) == set(state_ids))
print('fulfills count:', len(fulfills), 'unique:', len(set(fulfills)))
print('orphans (in contract not in fulfills):', sorted(set(contract_ids) - set(fulfills)))
print('extra (in fulfills not in contract):', sorted(set(fulfills) - set(contract_ids)))
print('OK' if len(contract_ids) == len(state_ids) == len(fulfills) == 38 and set(contract_ids) == set(state_ids) == set(fulfills) else 'MISMATCH')
"
contract count: 38
state count: 38
contract == state: True
fulfills count: 38 unique: 38
orphans (in contract not in fulfills): []
extra (in fulfills not in contract): []
OK
[Process exited with code 0]{"started":true,"workerHandoffs":[{"featureId":"F4a-zai-reasoning-disable-investigation","resultState":"pass","discoveredIssuesCount":2,"unfinishedWorkCount":1,"whatWasImplemented":"Ran 7 direct curl/urllib probes against https://api.z.ai/api/coding/paas/v4/chat/completions (NOT through Worker) on model glm-5.1. Probed: baseline (no param), enable_thinking:false, thinking:{type:'disabled'}, thinking:{type:'off'}, stream_thinking:false, extra_body:{thinking:{type:'disabled'}}, reasoning:{enabled:false}. For each probe: streamed SSE incrementally, counted reasoning frames, measured first-content-token TTFB (wall-clock from request send to first non-empty delta.content frame). Re-ran the two winning probes for confirmation. Authored docs/zai-reasoning-disable-investigation.md (6287 bytes) with: methodology section (parameters tried verbatim), results table (HTTP status, reasoning frame count, first-content TTFB), and explicit verdict line matching the required pattern. No changes to worker/, app/, or validation/. Committed only the new doc.","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-54-19-372Z__F4a-zai-reasoning-disable-investigation__6e7da923-6c7a-4146-b1c3-456b922e2915.json"}],"latestWorkerHandoff":{"featureId":"F4a-zai-reasoning-disable-investigation","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T21-54-19-372Z__F4a-zai-reasoning-disable-investigation__6e7da923-6c7a-4146-b1c3-456b922e2915.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T21:54:19.372Z\",\n \"workerSessionId\": \"6e7da923-6c7a-4146-b1c3-456b922e2915\",\n \"featureId\": \"F4a-zai-reasoning-disable-investigation\",\n \"milestone\": \"m2-runtime-llm-swap\",\n \"commitId\": \"b129593f18ce668fed7ab4427a5c97c3eb075e53\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F4a investigation complete. `enable_thinking: false` fully disables reasoning on glm-5.1 (0 reasoning frames in both runs). First-content-token TTFB averages ~1478ms — reasoning is eliminated but the raw Z.ai API call is still ~1400-1500ms, so full E2E would be ~2200ms. Orchestrator/user must decide: inject `enable_thinking:false` (F4b) or fall back to glm-4.6.\",\n \"whatWasImplemented\": \"Ran 7 direct curl/urllib probes against https://api.z.ai/api/coding/paas/v4/chat/completions (NOT through Worker) on model glm-5.1. Probed: baseline (no param), enable_thinking:false, thinking:{type:'disabled'}, thinking:{type:'off'}, stream_thinking:false, extra_body:{thinking:{type:'disabled'}}, reasoning:{enabled:false}. For each probe: streamed SSE incrementally, counted reasoning frames, measured first-content-token TTFB (wall-clock from request send to first non-empty delta.content frame). Re-ran the two winning probes for confirmation. Authored docs/zai-reasoning-disable-investigation.md (6287 bytes) with: methodology section (parameters tried verbatim), results table (HTTP status, reasoning frame count, first-content TTFB), and explicit verdict line matching the required pattern. No changes to worker/, app/, or validation/. Committed only the new doc.\",\n \"whatWasLeftUndone\": \"The first-content-token TTFB with reasoning disabled (~1478ms average) still exceeds the ≤1000ms target. The investigation identifies THAT reasoning can be disabled but does NOT inject the parameter into the Worker (that would be F4b). Orchestrator must decide whether to proceed with parameter injection or fall back to glm-4.6.\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"grep -q '^ZAI_API_KEY=[REDACTED] worker/.dev.vars\",\n \"exitCode\": 0,\n \"observation\": \"ZAI_API_KEY present and non-empty (length 49)\"\n },\n {\n \"command\": \"python3 /tmp/zai_streaming_probe.py (all 7 probes)\",\n \"exitCode\": 0,\n \"observation\": \"Baseline: 589 reasoning frames, TTFB 11361ms. enable_thinking:false: 0 frames, 1527ms. thinking:{type:'disabled'}: 0 frames, 1536ms. thinking:{type:'off'}: 63 frames, 2147ms. stream_thinking:false: 182 frames, 4406ms. extra_body:{...}: 290 frames, 6197ms. reasoning:{enabled:false}: 268 frames, 5989ms.\"\n },\n {\n \"command\": \"python3 confirmation run (enable_thinking:false and thinking:{type:'disabled'})\",\n \"exitCode\": 0,\n \"observation\": \"enable_thinking:false run2: 0 reasoning frames, 1428ms TTFB. thinking:{type:'disabled'} run2: 0 reasoning frames, 1983ms TTFB.\"\n },\n {\n \"command\": \"wc -c docs/zai-reasoning-disable-investigation.md\",\n \"exitCode\": 0,\n \"observation\": \"6287 bytes — exceeds 2KB minimum\"\n },\n {\n \"command\": \"grep 'disables reasoning on glm-5.1; first-content-token TTFB measured at' docs/zai-reasoning-disable-investigation.md\",\n \"exitCode\": 0,\n \"observation\": \"Verdict line found: '`enable_thinking: false` disables reasoning on glm-5.1; first-content-token TTFB measured at 1478 ms ...'\"\n },\n {\n \"command\": \"git diff mission-2~1 mission-2 -- worker/ app/ validation/\",\n \"exitCode\": 0,\n \"observation\": \"No diff — only docs/zai-reasoning-disable-investigation.md was added\"\n },\n {\n \"command\": \"cd worker && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 vitest tests passed — no regression\"\n },\n {\n \"command\": \"git log --oneline -3\",\n \"exitCode\": 0,\n \"observation\": \"b129593 M2/F4a: investigate disabling reasoning on glm-5.1 at HEAD\"\n }\n ]\n },\n \"tests\": {\n \"added\": [],\n \"coverage\": \"No tests added — this feature is an investigation-only deliverable. Existing 17/17 Worker vitest tests confirmed passing post-commit.\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"non_blocking\",\n \"description\": \"Even with reasoning fully disabled via enable_thinking:false, the first-content-token TTFB on glm-5.1 is ~1428-1536ms at the raw Z.ai API level. Adding Worker hop + STT (300ms) + TTS (400ms) gives estimated E2E ~2200ms — still above the ≤1000ms target. The reasoning channel was the primary but not the only latency source.\",\n \"suggestedFix\": \"Either (a) fall back to glm-4.6 which has no reasoning channel and likely lower base latency, or (b) accept 2200ms as the new target if the ≤1000ms target is re-evaluated as aspirational rather than hard-gating for M2.\"\n },\n {\n \"severity\": \"non_blocking\",\n \"description\": \"thinking:{type:'disabled'} also eliminates reasoning frames but shows higher TTFB variance (1536ms vs 1983ms across two runs). enable_thinking:false is the more reliable parameter.\",\n \"suggestedFix\": \"If parameter injection proceeds (F4b), prefer enable_thinking:false over thinking:{type:'disabled'}.\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": false,\n \"deviations\": [\n {\n \"step\": \"Step 1 — Verify live preconditions / start wrangler dev\",\n \"whatIDidInstead\": \"Did NOT start wrangler dev at all — F4a explicitly says 'direct curl probes against api.z.ai directly (NOT through the Worker)' and 'Worker should NOT spawn wrangler dev'\",\n \"why\": \"Feature description explicitly prohibits using the Worker for this investigation\"\n },\n {\n \"step\": \"Step 2 — Run the feature (F3/F5 procedures)\",\n \"whatIDidInstead\": \"The live-smoke-worker skill only defines procedures for F3 and F5. F4a is a different kind of investigation (direct API probes). Used Python urllib streaming for SSE parsing and TTFB measurement instead of following F3/F5 steps.\",\n \"why\": \"F4a was not covered by the skill's predefined procedures. Adapted approach to match F4a's specific investigation requirements.\"\n }\n ],\n \"suggestedChanges\": [\n \"The live-smoke-worker skill should be updated to include a procedure for F4a-style direct API probe investigations, or a separate skill should be created for upstream API investigation tasks.\"\n ]\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}1. [question] glm-5.1 cannot hit ≤1000ms even with reasoning fully disabled (raw Z.ai TTFB ~1500ms; estimated E2E ~2200ms). Plan-B is glm-4.6 fallback, but we don't yet know glm-4.6's TTFB. How should I proceed?
[answer] Option A — probe glm-4.6 TTFB FIRST (tiny F4b: 1 curl, no code change).
PROCEDURE
- Send a single non-streaming POST to
https://api.z.ai/api/coding/paas/v4/chat/completions with model
"glm-4.6", a representative system prompt + 200-token user message,
stream:true, max_tokens:200.
- Measure: end-of-request to first content byte.
- Run 5 trials; capture median + p90.
- Output: docs/glm-4.6-ttfb-probe.md with the numbers.
- Commit: M2/F4b: glm-4.6 TTFB probe (no code swap)
DECISION GATE AFTER F4b
- If median TTFB <800ms (gives ~200ms margin for STT+TTS hop):
proceed with full glm-4.6 fallback (F4c: chat.ts default + harness
model literal + persona re-verification on glm-4.6 + F5 re-run +
F4 contract amend referencing glm-4.6 as default and glm-5.1 as
opt-in)
- If median TTFB ≥800ms but <1100ms:
pause and surface to me. Marginal. I decide whether to ship with
minor breach + drift documentation, or escalate to paid model.
- If median TTFB ≥1100ms:
pause and escalate. glm family can't hit budget. Three new options
on the table:
1. Roll back F1/F2 to Anthropic (M2 becomes research mission)
2. Add paid latency-optimized model (Haiku 4.5 / GPT-5 Mini)
3. User approves explicit budget revision with new measured target
REJECT OPTION B
Skipping the probe and just committing to glm-4.6 risks 30 min of
wasted F4c work if 4.6 also breaches. The probe is 30 seconds.
Asymmetric trade — always probe.
REJECT OPTION C
Sub-1s is the load-bearing latency claim of the entire CTRL-style wow
factor. We rejected this exact tradeoff in M1 planning. Cannot revise
the budget without abandoning the product premise. Don't even
document this as an option in the drift report — frame it as "model
breach surfaces real research need" not "latency target relaxed."
Begin F4b now.
Paste it. The probe completes in ~1 minute. Then we know whether glm-4.6 saves
M2 or whether we need the harder conversation.
2. [question] If glm-4.6 fallback proceeds, do I run a mini F3b persona re-verification on glm-4.6 (12 breadth turns + 5 drift probes, ~3-5 min)?
[answer] Yes — run mini F3b on glm-4.6. But sequence it smartly so we don't waste
effort if 4.6 also breaches latency.
Why yes
- F3 verified persona on glm-5.1 specifically. Different model = potentially
different behavior. Shipping with unverified persona on the deployed model
breaks validation discipline.
- 3-5 min is cheap insurance against shipping a Mara-broken product
- The drift report citing "un-verified" leaves a hole in M2's acceptance —
better to fill it
But — protect against wasted persona work
If glm-4.6 ALSO breaches latency, F3b's 3-5 min was wasted. Cheap fix: do a
quick 2-turn latency check first (30 sec) before committing to the full
breadth + depth pass. If 4.6 passes the quick latency check, proceed with full
F3b. If it breaches, abort and escalate before the persona work.
What to type back
Yes — run mini F3b on glm-4.6 before F5b. Persona re-verification is
non-negotiable given the validation contract; shipping with unverified
persona on the actual deployed model breaks the discipline we've set.
SEQUENCING (efficient — protect against wasted persona work)
Inside F3b, before the full 12-turn breadth + 5-probe depth:
1. QUICK LATENCY GATE (30 seconds, 2 turns)
- Send 2 representative turns through Worker → glm-4.6
- Measure TTFB on each
- If both ≥1100ms median: ABORT F3b. Escalate to me with
"glm-4.6 also breaches latency budget; new options needed
(paid model, rollback, or budget revision)"
- If both <1100ms: proceed to step 2
- If marginal (800-1100ms): proceed to step 2 but flag in
report; my read of full data informs decision
2. FULL F3b
- 12 breadth turns from validation/latency-harness/seeded-turns.json
- 5 drift probes from app/teachr-tests-fixtures/persona-drift-probes.json
- Score against rubric in docs/persona/example-exchanges.md
- Output: docs/persona/glm-4.6-verification.md with explicit verdict
- Deliverable matches glm-5.1-verification.md format for direct comparison
3. THEN F5b — full latency harness on glm-4.6 (gives statistically valid
median + p90 across full seeded-turns.json)
DECISION GATE AFTER F3b + F5b
- If persona PASSES (sufficient or needs minor tweaks documented) AND
latency PASSES (median <1000ms): proceed to F4 contract amend with
glm-4.6 as documented default
- If persona INSUFFICIENT: pause. Surface to me. Decide: switch
models again, or run F6 persona-tune mission on whichever model
- If latency BREACHES: pause. Surface to me. Three escalation options
on the table (paid model, rollback, budget revision)
Begin F3b with the quick latency gate at the top.
Paste it. Watch for either:
- F3b completing cleanly (~5-7 min) → proceeds to F5b → F4
- F3b's quick gate ABORTING with "glm-4.6 also breaches" → harder conversation
needed
This is the careful path. Let it run.>cd /Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65 && python3 -c "
import json, re
with open('features.json') as f: feats = json.load(f)['features']
with open('validation-contract.md') as f: contract = f.read()
with open('validation-state.json') as f: state = json.load(f)['assertions']
contract_ids = sorted(set(re.findall(r'### (A-[A-Z0-9.-]+):', contract)))
fulfills = []
for ft in feats:
fulfills.extend(ft.get('fulfills') or [])
print('contract:', len(contract_ids), 'state:', len(state), 'fulfills:', len(fulfills), 'unique:', len(set(fulfills)))
print('match:', set(contract_ids) == set(state.keys()) == set(fulfills))
print('orphans:', sorted(set(contract_ids) - set(fulfills)))
print('extras:', sorted(set(fulfills) - set(contract_ids)))
print('---pending order:')
for ft in feats:
if ft['status'] == 'pending':
print(' ', ft['id'])
"
contract: 39 state: 39 fulfills: 39 unique: 39
match: True
orphans: []
extras: []
---pending order:
F4b-glm-4.6-ttfb-probe
F6-parser-markdown-strip
F4-validation-contract-amend-and-drift-addendum
[Process exited with code 0]{"dismissed":true,"count":4,"message":"Dismissed 4 item(s). You may now call start_mission_run to continue."}{"started":true,"workerHandoffs":[{"featureId":"F4b-glm-4.6-ttfb-probe","resultState":"pass","discoveredIssuesCount":1,"unfinishedWorkCount":1,"whatWasImplemented":"Created docs/glm-4.6-ttfb-probe.md (7,064 bytes) documenting: (1) Methodology — model glm-4.6, endpoint api.z.ai/api/coding/paas/v4/chat/completions, system prompt = first 600 chars of docs/persona/system-prompt.md, user message = representative ~200-token Swift async/await question, stream=true, max_tokens=[REDACTED] (with documented rationale for deviation from 200-token spec due to reasoning budget exhaustion), 5 trials, direct upstream (no Worker). (2) Per-trial table with TTFB and reasoning frame counts: T1=21,135ms/1209 frames, T2=18,044ms/1161 frames, T3=16,049ms/1088 frames, T4=16,477ms/981 frames, T5=20,183ms/1217 frames. (3) Summary stats: median=18,044ms, p90=20,754ms. (4) Key finding: at max_tokens=[REDACTED] (the spec requirement), glm-4.6 emits 200 reasoning_content frames and zero content frames — TTFB is unmeasurable and the model fails for a different reason than TTFB alone. (5) Comparison table vs glm-5.1. (6) Verdict line exactly matching pattern (c): 'glm-4.6 median TTFB 18044 ms — fails budget, escalate'. Committed ONLY the new doc with message 'M2/F4b: glm-4.6 TTFB probe (no code swap)'. No changes to worker/, app/, or validation/.","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T22-10-43-418Z__F4b-glm-4.6-ttfb-probe__45318e48-94fd-4553-b713-a0c87ed8f1aa.json"}],"latestWorkerHandoff":{"featureId":"F4b-glm-4.6-ttfb-probe","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T22-10-43-418Z__F4b-glm-4.6-ttfb-probe__45318e48-94fd-4553-b713-a0c87ed8f1aa.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T22:10:43.418Z\",\n \"workerSessionId\": \"45318e48-94fd-4553-b713-a0c87ed8f1aa\",\n \"featureId\": \"F4b-glm-4.6-ttfb-probe\",\n \"milestone\": \"m2-runtime-llm-swap\",\n \"commitId\": \"617edab\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F4b complete: glm-4.6 TTFB probe ran 5 streaming trials directly against Z.ai. Median first-content-token TTFB is 18,044 ms — 18× over budget. Critically, at the spec-required max_tokens=[REDACTED] glm-4.6 produces ZERO content frames (reasoning alone exhausts the budget). Verdict: 'glm-4.6 median TTFB 18044 ms — fails budget, escalate'. Orchestrator must escalate to user for fallback decision.\",\n \"whatWasImplemented\": \"Created docs/glm-4.6-ttfb-probe.md (7,064 bytes) documenting: (1) Methodology — model glm-4.6, endpoint api.z.ai/api/coding/paas/v4/chat/completions, system prompt = first 600 chars of docs/persona/system-prompt.md, user message = representative ~200-token Swift async/await question, stream=true, max_tokens=[REDACTED] (with documented rationale for deviation from 200-token spec due to reasoning budget exhaustion), 5 trials, direct upstream (no Worker). (2) Per-trial table with TTFB and reasoning frame counts: T1=21,135ms/1209 frames, T2=18,044ms/1161 frames, T3=16,049ms/1088 frames, T4=16,477ms/981 frames, T5=20,183ms/1217 frames. (3) Summary stats: median=18,044ms, p90=20,754ms. (4) Key finding: at max_tokens=[REDACTED] (the spec requirement), glm-4.6 emits 200 reasoning_content frames and zero content frames — TTFB is unmeasurable and the model fails for a different reason than TTFB alone. (5) Comparison table vs glm-5.1. (6) Verdict line exactly matching pattern (c): 'glm-4.6 median TTFB 18044 ms — fails budget, escalate'. Committed ONLY the new doc with message 'M2/F4b: glm-4.6 TTFB probe (no code swap)'. No changes to worker/, app/, or validation/.\",\n \"whatWasLeftUndone\": \"Next fallback candidates (glm-5-turbo, glm-4.7) not yet probed. Orchestrator must present findings to user and decide on next path. All GLM reasoning-enabled models probed so far (glm-5.1, glm-4.6) fail the ≤1000ms budget by an order of magnitude.\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"cd worker && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 vitest assertions passing before and after work — baseline clean\"\n },\n {\n \"command\": \"grep -q '^ZAI_API_KEY=[REDACTED] worker/.dev.vars\",\n \"exitCode\": 0,\n \"observation\": \"ZAI_API_KEY present (value not echoed)\"\n },\n {\n \"command\": \"python3 /tmp/ttfb_probe_fast.py $SYSTEM_PROMPT — trial 1\",\n \"exitCode\": 0,\n \"observation\": \"TTFB_MS=21135, REASONING_FRAMES_BEFORE_CONTENT=1209\"\n },\n {\n \"command\": \"python3 /tmp/ttfb_probe_fast.py $SYSTEM_PROMPT — trial 2\",\n \"exitCode\": 0,\n \"observation\": \"TTFB_MS=18044, REASONING_FRAMES_BEFORE_CONTENT=1161\"\n },\n {\n \"command\": \"python3 /tmp/ttfb_probe_fast.py $SYSTEM_PROMPT — trial 3\",\n \"exitCode\": 0,\n \"observation\": \"TTFB_MS=16049, REASONING_FRAMES_BEFORE_CONTENT=1088\"\n },\n {\n \"command\": \"python3 /tmp/ttfb_probe_fast.py $SYSTEM_PROMPT — trial 4\",\n \"exitCode\": 0,\n \"observation\": \"TTFB_MS=16477, REASONING_FRAMES_BEFORE_CONTENT=981\"\n },\n {\n \"command\": \"python3 /tmp/ttfb_probe_fast.py $SYSTEM_PROMPT — trial 5\",\n \"exitCode\": 0,\n \"observation\": \"TTFB_MS=20183, REASONING_FRAMES_BEFORE_CONTENT=1217\"\n },\n {\n \"command\": \"python3 /tmp/ttfb_probe.py $SYSTEM_PROMPT 200 (diagnostic)\",\n \"exitCode\": 0,\n \"observation\": \"TTFB_MS=ERROR_NO_CONTENT_FRAMES, REASONING_FRAMES=200 — confirms max_tokens=[REDACTED] produces no content\"\n },\n {\n \"command\": \"ls -la docs/glm-4.6-ttfb-probe.md && wc -c docs/glm-4.6-ttfb-probe.md\",\n \"exitCode\": 0,\n \"observation\": \"7064 bytes (>2KB requirement met)\"\n },\n {\n \"command\": \"rg -F 'fails budget, escalate' docs/glm-4.6-ttfb-probe.md\",\n \"exitCode\": 0,\n \"observation\": \"Exactly 1 match: '**glm-4.6 median TTFB 18044 ms — fails budget, escalate**'\"\n },\n {\n \"command\": \"git diff HEAD~1 HEAD --stat\",\n \"exitCode\": 0,\n \"observation\": \"1 file changed, 150 insertions(+) — docs/glm-4.6-ttfb-probe.md only\"\n },\n {\n \"command\": \"git diff HEAD~1 HEAD -- worker/ app/ validation/\",\n \"exitCode\": 0,\n \"observation\": \"Empty — no changes to worker/, app/, or validation/\"\n },\n {\n \"command\": \"git log --oneline -3\",\n \"exitCode\": 0,\n \"observation\": \"617edab M2/F4b: glm-4.6 TTFB probe (no code swap) — exact commit message matches spec\"\n },\n {\n \"command\": \"git status --porcelain\",\n \"exitCode\": 0,\n \"observation\": \"Clean — no uncommitted changes\"\n }\n ]\n },\n \"tests\": {\n \"added\": [],\n \"coverage\": \"No tests added (this is a pure probe/investigation feature with no code changes). The existing 17/17 vitest Worker tests all pass confirming baseline is unaffected.\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"blocking\",\n \"description\": \"glm-4.6 also emits delta.reasoning_content before delta.content, exactly like glm-5.1. At max_tokens=[REDACTED] (the production value in LLMStreamClient.swift), glm-4.6 exhausts the entire token budget on reasoning_content and produces zero content — TTFB is unmeasurable and the model fails for this reason alone, independent of TTFB. At max_tokens=[REDACTED] first-content TTFB is 18,044 ms median — 18× over the 1,000 ms budget. glm-4.6 is not viable as a fallback.\",\n \"suggestedFix\": \"Probe glm-5-turbo and glm-4.7 to check if they also use reasoning_content. If all Z.ai GLM models have reasoning, consider: (1) returning to Anthropic Claude, (2) raising latency budget if product can tolerate 2-3s E2E, or (3) checking if Z.ai has any non-reasoning model variants.\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": false,\n \"deviations\": [\n {\n \"step\": \"Step 1 — Verify live preconditions (start wrangler dev)\",\n \"whatIDidInstead\": \"Did not start wrangler dev. The feature description explicitly states 'NO Worker spawn' and 'NO wrangler dev'. Made direct curl/Python requests to the Z.ai upstream.\",\n \"why\": \"Feature spec F4b is an upstream-only probe that bypasses the Worker entirely. Starting wrangler dev would violate the feature spec.\"\n },\n {\n \"step\": \"Step 2 — max_tokens=[REDACTED] per spec\",\n \"whatIDidInstead\": \"Used max_tokens=[REDACTED] for the 5 official trials after diagnosing that max_tokens=[REDACTED] produces zero content frames.\",\n \"why\": \"At max_tokens=[REDACTED] glm-4.6 exhausts the budget on reasoning_content and emits no delta.content. TTFB cannot be measured if there is no first-content frame. Used 2000 to get actual TTFB readings, documented the max_tokens=[REDACTED] finding explicitly in the report as an additional disqualifying factor.\"\n }\n ],\n \"suggestedChanges\": [\n \"For probe features targeting reasoning models, the skill should mention that max_tokens may need to be raised above the reasoning-phase token cost to observe any content at all — or suggest probing with a very short prompt first to determine if content is reachable at the spec-required token budget.\"\n ]\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}1. [question] Both GLM models breach the 1000ms budget by 1.5x-18x. F4b did not test `enable_thinking:false` on glm-4.6 (F4a confirmed it works on glm-5.1). glm-4.6 with reasoning OFF is unprobed and could be the cheap save. How do I proceed?
[answer] ⏺ Recommend Option A. glm-4.6 with reasoning OFF is the only unprobed cheap save
left, and a 5-trial probe is ~1 minute. You've already validated this pattern
on F4b — probe before committing to a heavier path.
Why not the others:
- B (hard rollback) kills the whole point of M2 before exhausting cheap
options.
- C (paid Haiku/GPT-5 Mini) is fine as a fallback but skipping the free probe
to add a paid route is wrong order.
- D (PARTIAL SUCCESS) is premature — you have one cheap experiment left.
If A's probe shows median <800ms → full glm-4.6+param fallback. If it breaches
→ escalate to C (paid latency-optimized model) with evidence in hand.
Paste this to the orchestrator:
A. Run F4c probe NOW: 5 trials, glm-4.6, enable_thinking:false, same harness
as F4a/F4b.
Decision tree:
- If median TTFB <800ms → adopt glm-4.6+enable_thinking:false as primary route
in worker/src/routes/chat.ts. Update F4 validation contract amend to record
the param. Commit on mission-2 branch.
- If median TTFB ≥800ms but <1200ms → add as secondary route behind a feature
flag, escalate to C (paid latency-optimized fallback: Haiku 4.5 or GPT-5 Mini
as third route).
- If median TTFB ≥1200ms → skip glm-4.6 entirely, go straight to C.
Do NOT modify any other features while probing. F4c is read-only until
verdict.
Continue using BYOK route (custom:Claude-Sonnet-4.6-(Max-sub)-5, reasoning
high) for all worker dispatch — I'm watching the proxy log and Factory plan
rate limits.
Report back with: 5 raw TTFB samples, median, p95, and recommended route. Then
wait for my approval before committing F4.
2. [question] Regardless of latency path, do you still want F6 (parser markdown strip) to land in this mission? It's model-independent and a real TTS ship-blocker.
[answer] Two answers — F6: Yes, land regardless. F4c: run the probe now, you paused
before executing it.
F6 reasoning: Markdown strip in TTS parser is model-independent. If TTS reads
"bold" as literal asterisks, that ships broken regardless of upstream LLM.
Real bug, cheap fix, doesn't fork on latency path.
F4c reasoning: You correctly identified the gap (enable_thinking:false
untested on glm-4.6) but then paused on the wrong threshold — the pre-stated
"≥1100ms → pause + escalate" rule was for post-fix numbers, not pre-probe. The
probe is the cheap insurance before escalation. Run it.
Paste this back:
F6: YES — land the markdown strip in this mission. Model-independent, real TTS
bug, low risk. Commit on mission-2 branch.
F4c: Resume the probe NOW. The pause threshold is for post-fix latency, not
pre-probe. 5 trials, glm-4.6, enable_thinking:false, same harness as F4a/F4b.
Cheap insurance before escalation.
Decision tree (re-confirmed):
- median TTFB <800ms → adopt glm-4.6+enable_thinking:false as primary route in
worker/src/routes/chat.ts. Update F4 validation contract amend to record the
param. Commit on mission-2.
- median TTFB 800-1199ms → add as secondary route behind a feature flag,
escalate to paid latency-optimized fallback (Haiku 4.5 or GPT-5 Mini as third
route in chat.ts).
- median TTFB ≥1200ms → skip glm-4.6, go straight to paid fallback.
Order of operations:
1. F4c probe first (~1 min, read-only, no commits)
2. Report 5 raw TTFB samples + median + p95 + recommended route
3. Wait for my approval on routing decision
4. Then F6 markdown strip (independent, can be drafted in parallel but commit
after F4 routing lands)
5. Final F4 validation contract amend last
Continue using BYOK route (custom:Claude-Sonnet-4.6-(Max-sub)-5, reasoning
high) for all worker dispatch.{"dismissed":true,"count":3,"message":"Dismissed 3 item(s). You may now call start_mission_run to continue."}>cd /Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65 && python3 -c "
import json, re
with open('features.json') as f: feats = json.load(f)['features']
with open('validation-contract.md') as f: contract = f.read()
with open('validation-state.json') as f: state = json.load(f)['assertions']
contract_ids = sorted(set(re.findall(r'### (A-[A-Z0-9.-]+):', contract)))
fulfills = []
for ft in feats: fulfills.extend(ft.get('fulfills') or [])
print('contract:', len(contract_ids), 'state:', len(state), 'fulfills total:', len(fulfills), 'unique:', len(set(fulfills)))
print('match:', set(contract_ids) == set(state.keys()) == set(fulfills))
print('orphans:', sorted(set(contract_ids) - set(fulfills)))
print('extras:', sorted(set(fulfills) - set(contract_ids)))
print('---pending order:')
for ft in feats:
if ft['status'] == 'pending': print(' ', ft['id'])
"
contract: 40 state: 40 fulfills total: 40 unique: 40
match: True
orphans: []
extras: []
---pending order:
F4c-glm-4.6-no-reasoning-ttfb-probe
F6-parser-markdown-strip
F4-validation-contract-amend-and-drift-addendum
[Process exited with code 0]{"started":true,"workerHandoffs":[{"featureId":"F4c-glm-4.6-no-reasoning-ttfb-probe","resultState":"pass","discoveredIssuesCount":1,"unfinishedWorkCount":0,"whatWasImplemented":"Ran 5 direct streaming POST trials to https://api.z.ai/api/coding/paas/v4/chat/completions with model:glm-4.6, enable_thinking:false, max_tokens:200, stream:true, same system prompt (first 600 chars of docs/persona/system-prompt.md) and Swift async/await user message from F4b. Python probe script measured reasoning frame count and attempted to capture first-content-token TTFB. Wrote docs/glm-4.6-no-reasoning-ttfb-probe.md (9,841 bytes / ~186 lines) with: methodology section verbatim listing enable_thinking:false parameter and full prompt setup, per-trial table (5 rows showing N/A TTFB and 200 reasoning frames each), summary stats, comparison context vs F4b (18044ms median at max_tokens=[REDACTED] and F4a glm-5.1 (~1478ms with param), and explicit verdict line matching the 'fails, skip to paid fallback' pattern. No modifications to worker/, app/, validation/, or any other repo files. Committed only the new doc with the exact specified commit message.","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T22-25-18-490Z__F4c-glm-4.6-no-reasoning-ttfb-probe__5c2fea11-e359-41e0-b4cb-e9e57ca28a3e.json"}],"latestWorkerHandoff":{"featureId":"F4c-glm-4.6-no-reasoning-ttfb-probe","resultState":"pass","handoffFile":"/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/handoffs/2026-05-08T22-25-18-490Z__F4c-glm-4.6-no-reasoning-ttfb-probe__5c2fea11-e359-41e0-b4cb-e9e57ca28a3e.json","handoffJson":"{\n \"timestamp\": \"2026-05-08T22:25:18.490Z\",\n \"workerSessionId\": \"5c2fea11-e359-41e0-b4cb-e9e57ca28a3e\",\n \"featureId\": \"F4c-glm-4.6-no-reasoning-ttfb-probe\",\n \"milestone\": \"m2-runtime-llm-swap\",\n \"commitId\": \"140c2f8\",\n \"repoPath\": \"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1\",\n \"successState\": \"success\",\n \"returnToOrchestrator\": true,\n \"handoff\": {\n \"salientSummary\": \"F4c confirmed: enable_thinking:false has zero effect on glm-4.6. All 5 trials at max_tokens=[REDACTED] produced exactly 200 reasoning frames and 0 content frames — identical to F4b without the parameter. TTFB is unmeasurable. Verdict: glm-4.6 + enable_thinking:false fails, skip to paid fallback. Returning to orchestrator for user routing decision.\",\n \"whatWasImplemented\": \"Ran 5 direct streaming POST trials to https://api.z.ai/api/coding/paas/v4/chat/completions with model:glm-4.6, enable_thinking:false, max_tokens:200, stream:true, same system prompt (first 600 chars of docs/persona/system-prompt.md) and Swift async/await user message from F4b. Python probe script measured reasoning frame count and attempted to capture first-content-token TTFB. Wrote docs/glm-4.6-no-reasoning-ttfb-probe.md (9,841 bytes / ~186 lines) with: methodology section verbatim listing enable_thinking:false parameter and full prompt setup, per-trial table (5 rows showing N/A TTFB and 200 reasoning frames each), summary stats, comparison context vs F4b (18044ms median at max_tokens=[REDACTED] and F4a glm-5.1 (~1478ms with param), and explicit verdict line matching the 'fails, skip to paid fallback' pattern. No modifications to worker/, app/, validation/, or any other repo files. Committed only the new doc with the exact specified commit message.\",\n \"whatWasLeftUndone\": \"\",\n \"verification\": {\n \"commandsRun\": [\n {\n \"command\": \"python3 /tmp/f4c_ttfb_probe.py\",\n \"exitCode\": 0,\n \"observation\": \"All 5 trials: reasoning_frames=200 (token ceiling hit), got_content=false, ttfb_ms=null. enable_thinking:false has no effect on glm-4.6 at max_tokens=[REDACTED] },\n {\n \"command\": \"wc -c docs/glm-4.6-no-reasoning-ttfb-probe.md\",\n \"exitCode\": 0,\n \"observation\": \"9841 bytes — exceeds the 2KB minimum requirement.\"\n },\n {\n \"command\": \"grep -E 'glm-4\\\\.6 \\\\+ enable_thinking:false median TTFB' docs/glm-4.6-no-reasoning-ttfb-probe.md\",\n \"exitCode\": 0,\n \"observation\": \"Verdict line found: 'glm-4.6 + enable_thinking:false median TTFB undefined ms — fails, skip to paid fallback'\"\n },\n {\n \"command\": \"git diff mission-2~1 mission-2 --name-only\",\n \"exitCode\": 0,\n \"observation\": \"Only docs/glm-4.6-no-reasoning-ttfb-probe.md in the diff — no changes to worker/, app/, or validation/.\"\n },\n {\n \"command\": \"cd worker && npm test\",\n \"exitCode\": 0,\n \"observation\": \"17/17 vitest assertions passing — baseline unaffected by this feature.\"\n }\n ]\n },\n \"tests\": {\n \"added\": [],\n \"coverage\": \"No new tests added (this is a read-only investigation feature). Existing 17/17 Worker vitest tests confirm baseline health.\"\n },\n \"discoveredIssues\": [\n {\n \"severity\": \"blocking\",\n \"description\": \"enable_thinking:false is ineffective on glm-4.6. All 5 trials produced 200 reasoning frames and 0 content frames at max_tokens=[REDACTED] — identical behavior to F4b without the parameter. TTFB is unmeasurable. The parameter that works on glm-5.1 does not suppress reasoning on glm-4.6. Combined with F4b's finding (18044ms TTFB even at max_tokens=[REDACTED] glm-4.6 is conclusively ruled out as a fallback.\",\n \"suggestedFix\": \"Escalate to user for routing decision. Remaining untested options: (1) glm-5-turbo — may not emit reasoning_content at all; (2) glm-4.7 — not yet probed; (3) Return to Anthropic Claude; (4) Accept ~1478ms TTFB on glm-5.1 with enable_thinking:false (requires raising max_tokens beyond 200 so content can reach the user after reasoning). Orchestrator should pause and present these options to the user before any code swap.\"\n }\n ],\n \"skillFeedback\": {\n \"followedProcedure\": false,\n \"deviations\": [\n {\n \"step\": \"Step 1 — Verify the live preconditions (start wrangler dev)\",\n \"whatIDidInstead\": \"Did not start wrangler dev. F4c explicitly prohibits Worker involvement — all calls go directly to api.z.ai. The feature description states 'NO Worker spawn, NO code changes to chat.ts/LLMStreamClient.swift/harness/anything else'.\",\n \"why\": \"The live-smoke-worker skill procedure assumes wrangler dev is needed, but F4c is a pure direct-upstream probe with no Worker routing. Starting wrangler dev would be unnecessary and contra the feature spec.\"\n },\n {\n \"step\": \"Step 6 — Commit (expected F3/F5 commit messages)\",\n \"whatIDidInstead\": \"Used the F4c-specific commit message: 'M2/F4c: glm-4.6 + enable_thinking:false TTFB probe (no code swap)'\",\n \"why\": \"The live-smoke-worker skill was authored for F3 and F5. F4c is a variant (direct probe, no wrangler dev). Commit message follows the feature specification exactly.\"\n }\n ],\n \"suggestedChanges\": [\n \"The live-smoke-worker skill could add a 'direct upstream probe' variant step for F4-style features that send requests directly to api.z.ai without the Worker. These probes share the same ZAI_API_KEY read/secrets hygiene requirements but skip the wrangler dev lifecycle entirely.\"\n ]\n }\n }\n}\n"},"systemMessage":"<system>\nThe mission runner has returned control to you because the most recent worker's handoff contains actionable items and/or returnToOrchestrator=true. If discoveredIssues and whatWasLeftUndone exist, either create new features or update existing feature descriptions if the issue belongs to a pending feature's scope. Skip only if already tracked as existing feature, or truly irrelevant and will never need to be fixed. Once you have addressed the issues, call start_mission_run again to continue.\n</system>"}1. [question] Cheap probes exhausted. Per your earlier "REJECT Option C" stance, the live decisions are rollback or paid fallback. Which path?
[answer] F6: YES — land markdown strip. Model-independent, ship-blocker.
Routing decision (post-F4c):
- Primary route: glm-5.1 + enable_thinking:false in worker/src/routes/chat.ts.
Document ~1478ms TTFB as a known 1.5x breach of the 1000ms budget.
- DO NOT add paid fallback (Haiku/GPT-5 Mini) in M2 — defer to post-M2 mission
as M2.1-paid-fallback.
- DO NOT roll back to Anthropic. Keeping the swap is the point of M2.
- Update F4 validation contract amend: latency budget assertion explicitly
marked UNRESOLVED with full evidence trail (F5 numbers, F4a/F4b/F4c probe
verdicts, link to docs/glm-4.6-no-reasoning-ttfb-probe.md). Mission ships as
PARTIAL SUCCESS on latency, FULL SUCCESS on swap.
Order of operations:
1. F6 (markdown strip) — small, isolated, lands first.
2. F4 (contract amend + drift addendum + worker/README ZAI rename) — last,
depends on F6 landing.
3. No more probes. No more route experiments. Close M2.
Continue using BYOK route (custom:Claude-Sonnet-4.6-(Max-sub)-5, reasoning
high) for all worker dispatch.
2. [question] If path B (paid fallback): which model first?
[answer] ⏺ Orchestrator rejected my "ship the breach" call (correct — 2.2x is too much),
and its 1.5x number was just TTFB; full E2E is actually 2200ms. Path C dead.
Now: rollback vs paid fallback.
Recommend: Probe both in parallel (F4d) before committing. We've been wrong on
vendor latency claims twice (Z.ai's "fast" reasoning, glm-4.6's claimed
sub-second). 2 min wall-clock for empirical evidence is cheap insurance
against another bad commit.
If probe is roughly tied → GPT-5 Mini wins on architecture:
- Preserves F2's Swift parser work (OpenAI delta shape — already done)
- Preserves F1's chat.ts URL-only swap pattern (just api.z.ai →
api.openai.com)
- 4x cheaper input, 2.5x cheaper output vs Haiku 4.5
- Same OpenAI-shape route the worker is already wired for
If probe shows Haiku 4.5 dramatically faster (>500ms gap) → eat the F2 revert
cost, take Haiku.
Don't pick Haiku-first blind. F2 was real work; reverting it without empirical
proof is waste.
Paste this back:
Path B (paid fallback). Probe both in parallel via mini-F4d before committing.
F4d spec: 5 trials each, GPT-5 Mini and Haiku 4.5, same harness as
F4a/F4b/F4c. Same system prompt (first 600 chars of
docs/persona/system-prompt.md), same Swift async/await user message,
max_tokens=[REDACTED] (not 200 — must allow content to actually emit). Measure TTFB
and full E2E. Direct upstream calls (no Worker), same as F4c. Write
docs/paid-fallback-ttfb-probe.md with per-trial table for both models,
medians, p95, and recommended winner.
Decision rule:
- If GPT-5 Mini median E2E ≤ Haiku 4.5 + 500ms → GPT-5 Mini wins. Reason:
preserves F2 OpenAI parser work, preserves F1 URL-only chat.ts swap pattern,
4x cheaper input / 2.5x cheaper output.
- If Haiku 4.5 wins by >500ms → Haiku. Eat the F2 parser revert cost, the
latency win justifies it.
- If both still breach 1000ms budget → escalate, do not commit. Rollback to
Anthropic Sonnet (original M1 state) becomes the only honest option.
Order of operations after F4d verdict:
1. Wait for my approval on the paid-fallback model.
2. F1 swap update (chat.ts URL + auth + secrets) for chosen model.
3. F2 parser update if Haiku wins (revert OpenAI delta path back to Anthropic
Messages SSE).
4. F6 (markdown strip) — independent, can land any time after F1/F2.
5. F4 (contract amend + drift addendum + worker/README rename to whichever
provider wins).
Continue using BYOK route (custom:Claude-Sonnet-4.6-(Max-sub)-5, reasoning
high) for all worker dispatch.>cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1" && [ -f worker/.dev.vars ] && grep -E '^[A-Z_]+=' worker/.dev.vars | sed 's/=.*/=<value-redacted>/' || echo "MISSING worker/.dev.vars"ZAI_API_KEY=[REDACTED]
APP_CLIENT_TOKEN=[REDACTED]
[Process exited with code 0]{
"questionnaire": "1. [question] To confirm: your two answers contradicted (Q1 = ship glm-5.1 breach + close M2; Q2 = Path B + F4d probe both paid models). I'm reading Q2 as your final position because it explicitly self-corrects Q1 (\"Path C dead, ship the breach is too much\"). Confirm the path?\n[topic] Path-Confirmation\n[option] Yes — Q2 is my final position. Path B (paid fallback). Run F4d to probe GPT-5 Mini and Haiku 4.5 in parallel before any code commit.\n[option] No — actually go with Q1: ship glm-5.1 + enable_thinking:false with documented 2200ms breach. Land F6 + F4 + close M2.\n[option] Different — drop F1+F2, hard rollback to Anthropic Sonnet (M1 baseline), close M2 as documented research mission.\n\n2. [question] If Q2 confirmed: I need OPENAI_API_KEY and ANTHROPIC_API_KEY in worker/.dev.vars before launching F4d. How should I proceed?\n[topic] API-Keys\n[option] You drop both keys to worker/.dev.vars manually (you tell me when done; F4d launches after)\n[option] You only have one of the two keys — drop one and skip the other in F4d (specify which)\n[option] You have neither key right now — defer Path B; give me a different routing instruction"
}/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojopen "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKEN/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojopen "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKEN/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/├── AGENTS.md 9.5KB├── features.json 20.7KB (5 features, F1-F5)├── init.sh 3.2KB (executable)├── library/│ ├── architecture.md│ ├── environment.md│ └── user-testing.md├── mission.md 18KB (auto-created on accept)├── model-settings.json├── services.yaml 1.2KB├── skills/│ ├── code-refactor-worker/SKILL.md│ ├── contract-worker/SKILL.md│ └── live-smoke-worker/SKILL.md├── validation-contract.md 17.8KB (35 assertions)└── validation-state.json 2.2KB (35 IDs pending)Contract assertion IDs: 35Distinct fulfills IDs : 35Total fulfills entries: 35State assertion IDs : 35
DUPLICATE fulfills: NONEUNCLAIMED: NONEORPHAN: NONECONTRACT vs STATE: NONE
Per-feature fulfills: F1-worker-chat-upstream-swap 6 F2-swift-openai-shape-atomic 11 F3-persona-verification-eval 7 F5-worker-boundary-latency-capture 6 F4-validation-contract-amend-and-drift-addendum 5{ "workerModel": "custom:Claude-Sonnet-4.6-(Max-sub)-5", "workerReasoningEffort": "high", "validationWorkerModel": "custom:Claude-Sonnet-4.6-(Max-sub)-5", "validationWorkerReasoningEffort": "high", "skipScrutiny": false, "skipUserTesting": false}