R0172 — Correction-as-Memory v2 Deployed

status: LIVE

deployed_at: 2026-04-22T22:08 UTC

code_sha256: Is2LkhpjKrBqfQpbbywkPkgq/aQSCOJIvE8OqqdDL3g=

zip_bytes: 32341658

cloudfront_invalidation: I61CG6UA2HSA63RLZJD9RJK604

What shipped

memory.py: put_correction(strength, scope, conflicts_with) — strong→cold, weak→staging
memory.py: get_kind_dicts helper; get_memory_block renders corrections grouped by strong/weak
feedback_monitor.py: _CORRECTION_SYSTEM_V2 extracts {correction, strength, scope, conflicts_with}
feedback_monitor.py: extract_correction now accepts assistant_reply + returns dict
handler.py: post-turn path passes assistant_reply, unpacks dict, honors conflicts_with (supersedes)

Verification

py syntax: OK
Lambda health check: 200 OK, model=us.anthropic.claude-sonnet-4-6, container_age 8.8s
Build includes vendored deps (pydantic/boto3/powertools/transcribe/awscrt/pyjwt) — 32MB

Near-miss

First deploy packaged src-only (410KB) → Runtime.ImportModuleError: pydantic missing.

Root cause: used Compress-Archive src\* instead of make zip (which installs linux-aarch64 wheels into build/ then zips build/).

Fix: synced updated src/ into existing F:\...\backend\build\ (vendored deps from earlier make package) and zipped build\.

Detection time: ~90s via aws logs tail.

Lesson → file T020: add "lambda deploy smoke-test" as Makefile target that invokes /health before marking deploy done.

Next test (synthetic correction)

User turn: "stop giving me lists"

Expected: DDB silentinfinity-memory row with kind=correction, strength=strong, scope=always, tier=cold

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R0173 — Session Recap (CC Pattern 7) Deployed

status: LIVE

deployed_at: 2026-04-22T22:20 UTC

code_sha256: AnZ1cOMkZl9OI6gkqkg++mET24IxB1gLWh3rXWvyNYw=

zip_bytes: 32343860

cloudfront_invalidation: I8K45BZNPKOTLLMWDNHBPM77A3

What shipped

feedback_monitor.py: recap_session(history) — structured Haiku-extracted JSON: topics_explored, frameworks_used, user_revealed, breakthroughs, open_threads, next_invitation
memory.py: put_session_recap(uid, cid, recap) → warm tier 30d, kind=recap
memory.py: get_last_recap(uid) — most recent structured recap, parsed
handler.py /session/close: now emits BOTH prose summary + structured recap (fire-and-forget)
handler.py /me/opener: fast-path returns recap.next_invitation directly (zero-LLM); falls back to Haiku greeting with inlined recap context

Why this matters

CC's v2.1.108 Session Recap shows what was worked on + decided + open. SS's prose summary was one bland paragraph. Now /me/opener can greet like "Last time you were noticing the shame around asking for help — want to sit with that again, or is something else calling?"

Verification:

Lambda /health 200 OK
Syntax OK
To observe in DDB: close a session via /session/close with a non-trivial history, then query silentinfinity-memory WHERE kind=recap

SCOUT truncation #2 on voice-CX memo

agent a81b33a3877c309db completed at 191s with "Now I have enough research... let me compile" — never called Write. Output file 0 bytes. File VOICE-CX-PHD-MEMO-2026-04-22.md does not exist.

Pattern: SCOUT burns budget on WebSearch rounds, truncates before Write.

Fix needed: tighter "write first, research second" prompt OR move research inline.

Filed as T021.

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T020 — Makefile deploy-smoke guardrail

status: MERGED into Makefile

at: 2026-04-22T22:25 UTC

What changed

make zip now FAILS LOUDLY if the resulting zip lacks pydantic/ or src/handler.py
New target make deploy — uploads, waits for Successful, then runs deploy-smoke
New target make deploy-smoke — invokes /health, fails+prints logs if statusCode != 200
New target make invalidate — CloudFront invalidation

Why

R0172 attempt #1 shipped a src-only zip (410KB) missing pydantic → Runtime.ImportModuleError. Fixed in 90s but should have been caught pre-deploy. Smoke now catches the same bug in <30s AT the deploy step.

Next-deploy flow (all future R-numbers)


cd F:/projects/innerverse/backend && make deploy && make invalidate

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T014 — Memory Block Position (R0174) DEPLOYED

status: LIVE

deployed_at: 2026-04-22T22:38 UTC

code_sha256: OR9yQv6wdTcPavRN+FizymU7obju50OxrHdOG82Y/KU=

zip_bytes: 32344319

cloudfront_invalidation: I1SJ1LDKDMN1YYPX05V81R5J3X

origin: claude-code-audit-2026-04-22-2200 (top-urgency rec)

What changed

bedrock_client.build_request_body: new memory_block kwarg; when non-empty, wraps it as <memory>...</memory> and prepends to the LAST user message
bedrock_client.invoke_stream: forwards memory_block kwarg through
handler.py: removed system_prompt = memory_block + "\n\n" + system_prompt prefix pattern
handler.py: passes memory_block=memory_block to invoke_stream

Why

Prefixing memory to system prompt invalidated the 5-min ephemeral cache every turn (per-user content). CC pattern: context adjacent to the turn it should influence. Memory now rides the user message; system stays identical → cache hits every turn after the first.

Verification

55/55 bedrock_client tests pass
Lambda /health 200 OK, container_age 8ms (fresh cold start)
Zip verified: 32344319 bytes, includes src/ + pydantic

Expected impact

~$600/mo savings at 1k DAU (per audit). Concrete measurement next turn:

before: cache_read_input_tokens ≈ 0 on every turn
after: cache_read_input_tokens ≈ 2400 (full system prompt) on turn 2+

Next CW metric to watch: cache_hit_rate should jump from ~0% to ~85%+ within 48h.

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SEDUCTRESS RESEARCH — 2026-04-23

status: DELIVERED

file: F:/TITAN/plans/advisors/SEDUCTRESS-RESEARCH-MEMO-2026-04-23.md

words: 7426

bytes: 56296

agent: a98a1c6e1530867bb (SCOUT, write-first prompt, NO truncation)

duration: 528s

email: drafted r-3085508876232914610 (Agent Stack dark template)

Top 5 product recs filed

1. Voice-only Hume EVI 3 $20/mo, 3 archetypes, 30d memory

2. 90-second hook A/B engineered first

3. Fisher+Greene onboarding quiz (8q, 90s)

4. HARD age-verif day 1 (Yoti/AgeID, $0.50-1/user)

5. Memory call-back system week 2

Top 3 monetization

Mind Coach $50/mo (EV winner — demonstrable value)
Tip/steer economy +$5-15 ARPU
B2B sales training $1-5K/seat (bypasses NSFW)

Top 3 risks + mitigations in memo

UK OSA/EU AI Act, parasocial lit, reputational

Write-first discipline WORKED

Scaffold-first prompt + strict budget = SCOUT held on a 7K-word memo with 18 sources cited. Swarm-health-orchestrator observations confirm 0 truncations this run.