AINA data engine room - VDS local closure report - 2026-06-11

AINA Personalization Engine Closure Handoff

A founder-readable and agent-ready map of what was built, what exists on disk, how it matches the original mission, and what remains open.

Ali Mehdi Mukadam - co-authored with Codex - repo /srv/aina/aina-data-engine-room - branch ali/personalization-engine-mission-2026-06-09

The Single Idea

The repo now contains a local, self-contained AINA data engine that turns the original personalization-engine documents into a working source-backed system: public source snapshots, Hugging Face ingestion, O*NET-backed title/occupation data, role/workflow packets, deterministic planner/runtime paths, evaluator and feedback receipts, GDPval practice gates, local API/UI fixtures, and ICP title service-tier routing. It is not production-live and it is not externally deployed. It is a strong VDS-local internal beta engine with review holds, with the remaining open work centered on clearing the last ambiguous ICP title backlog and then hardening the runtime for real users.

47,284Title rows serviceable after adjudication.

7,201Title rows still ambiguous and not safely routed.

0Production, external write, real-user-data, telemetry, or deployment unlocks.

Founder Summary

AINA's original idea was not "recommend a course." The shared documents described a personalized AI capability engine that understands a person's role, workflows, readiness, goals, constraints, and proof of work, then chooses the next best learning path and evaluates whether the person can actually perform better with AI.

Area	Current status	Plain-English meaning
Source foundation	Built and validated locally	The engine has a local data backbone instead of relying on loose CSVs or chat prompts.
Hugging Face data	Downloaded, processed, and mapped	Anthropic Economic Index and OpenAI GDPval are real bounded inputs on the VDS.
O*NET/public source backbone	O*NET 30.3 loaded; BLS wage/employment gap disclosed	Occupations/tasks are grounded in public taxonomy; BLS facts are not faked when cache/access is absent.
Role/workflow packets	45,564 packet JSON files generated	The engine can generate role/workflow intelligence packets at scale.
Runtime loop	Local API, curriculum, sandbox, submit/evaluation fixtures pass	A synthetic learner loop works locally.
ICP title coverage	47,284 title rows serviceable after adjudication	The engine can serve many title rows now, mostly through fallback-safe routing.
Production release	Blocked by design	No public runtime, external writes, real-user data, production telemetry, or deployment promotion has been unlocked.

Match Against The Original Plan

The original source set was mirrored into this repo under docs/source_foundations/ainpe-files-shared/. The mission synthesis lives at docs/planning/aina-personalization-engine-mission-2026-06-09.md.

Milestone	Original target	Current status	Evidence
M0	Mission and source lock	Complete	Mission plan Markdown/HTML exists.
M1	Canonical source warehouse	Mostly complete, BLS fact gap disclosed	`artifacts/warehouse/`, source authority audit.
M2	Work Intelligence Graph	Complete for local beta surface	Packets and packet-quality gate.
M3	`CurriculumInputPacket` and planner	Complete locally	Schemas, planner, contract tests.
M4	Practice and evaluator loop	Complete locally	Evaluator, event replay, feedback receipts.
M5	GDPval sandbox and HF proof	Complete locally with holds	Sandbox, GDPval receipts, HF runtime map.
M6	Beta API and product surface	Complete as local fixture	API runtime and beta UI shell tests.
M7	ICP title coverage and scale path	Partially complete and paused	Title coverage/adjudication receipts; 7,201 rows remain ambiguous.

What The Engine Can Do Today

The engine can build a local warehouse, load O*NET, process selected Hugging Face data, create role/workflow packets, attach source references, generate local runtime decisions, produce GDPval-style sandbox payloads, evaluate synthetic submissions, replay learner events, classify title rows, and emit validation receipts plus Markdown/HTML reports.

Not yet	Why
Serve real public learners	Real-user data, external writes, public runtime, and production telemetry are intentionally blocked.
Claim production quality for every title	7,201 title rows remain ambiguous and many fallback routes are useful but not deep title-specific proof.
Use GDPval raw reference/deliverable folders broadly	The engine uses selected GDPval task rows and source refs; bulk file downloads still require license/cache review.
Treat BLS wage/employment facts as complete	O*NET is loaded; BLS OEWS wage/employment facts are recorded as a cache/access gap with 0 rows.

Current Quantitative State

Metric	Current value
Full validation status	`pass`
`ai_suitability` rows	74,225
`linkedin_jobs` rows	129,165
Generated packet JSON files	45,564
Semantic review rows checked	45,564
Hugging Face downloaded bytes	186,743,670
GDPval task rows processed	220
O*NET occupation rows	1,016
BLS wage/employment fact rows	0

ICP title bucket	Count	Meaning
Serviceable after adjudication	47,284	Exact or fallback-safe local service.
Excluded or not ICP	19,740	Deliberately held out of service.
Still ambiguous	7,201	Needs deterministic or GPT/Codex adjudication.
Remaining prompt candidates	6,872	Rows ready for the next model-review lane.
Structured model decisions applied	4,786	Accepted model decisions already merged.

What Happened In The Latest Title Run

Phase	Result
Base deterministic coverage	Classified all 74,225 rows and made 45,564 serviceable before adjudication.
Deterministic residual sweep	Reduced the ambiguous queue from 8,514 to 7,491.
Normal GPT salvage	Merged batches 038, 040, 041, and 042, adding 304 consensus decisions and reducing the ambiguous queue to 7,201.

Batch 039 is partial: reviewer a exists, reviewer b timed out, so it was not merged. Historical Claude/Haiku/Sonnet and Codex Spark artifacts remain on disk as evidence history, but the latest user direction was normal GPT/Codex only.

Safety And Production Boundary

Boundary	Current state
Public runtime	Blocked
External writes	Blocked
Real-user data	Blocked
Production telemetry	Blocked
Deployment promotion	Blocked
Local internal synthetic beta	Ready with review holds

The production approval receipt is production_blocked_approval_required. That is good: the repo is not pretending to be safer or more production-ready than it is.

Local Disk And Git Inventory

Item	Value
Repo path	`/srv/aina/aina-data-engine-room`
Branch	`ali/personalization-engine-mission-2026-06-09`
Latest local commit before report	`a2e9b29 Checkpoint ICP title inventory after GPT salvage`
Remote	No remote configured
Tracked files	473 before this closure handoff; 477 after committing this handoff plus the inventory CSV/JSON
Untracked non-ignored files	0

Path	Approx size	Meaning
`/srv/aina/aina-data-engine-room`	13G	Entire repo folder including venv and generated artifacts.
`.venv/`	5.2G	Python environment.
`artifacts/`	7.2G	Generated data, packets, receipts, reports, reviewer outputs.
`artifacts/packets/`	892M	45,564 generated packet JSON files.
`artifacts/semantic_review/`	210M	Full deterministic semantic review outputs.
`artifacts/raw/huggingface/`	179M	Selected Hugging Face files.
`src/`	5.1M	59 Python modules.
`tests/`	1.7M	45 pytest files.

The full file inventory is written to docs/handoff/2026-06-11-local-file-inventory.csv, with a machine-readable summary at docs/handoff/2026-06-11-local-file-inventory-summary.json. It inventories every non-.git file under the repo, including .venv, caches, generated packets, generated artifacts, source files, docs, tests, scripts, reports, and the inventory files themselves.

Classification	Count	Meaning
`generated_runtime_data`	45,607	Generated packets, DuckDB/parquet/fixture/runtime artifacts.
`environment_cache`	30,887	`.venv` dependency/environment files.
`generated_review_output`	417	Model review outputs, logs, prepared outputs, repair outputs, merge receipts.
`source_material`	184	Imported/shared source foundation docs and curriculum references.
`generated_evidence`	157	Validation receipts and generated report companions.
`tool_cache`	152	Codegraph, pytest, ruff, Python bytecode caches.
`hand_authored_code`	59	Python source modules under `src/`.
`hand_authored_test`	46	Test/support files under `tests/`.
`downloaded_source_cache`	42	Hugging Face and public source cache files.
`agent_deliverable`	18	Planning, reports, runbooks, and handoffs authored/rendered by agents.
`generated_review_input`	7	Reviewer prompts and review input/merge artifacts.
`hand_authored_config`	4	Repo readme/config files.
`hand_authored_script`	2	Operational scripts.
`lockfile`	1	Dependency lockfile.

The inventory CSV intentionally excludes .git internals because staging and committing mutate them. The summary JSON records .git separately as 2,298 files and 15,092,838 bytes at generation time.

Repository Map

Area	Purpose
`README.md`, `pyproject.toml`, `uv.lock`	Repo overview, package metadata, dependencies, CLI entrypoint.
`scripts/rebuild_all.sh`	Main rebuild helper.
`scripts/run_icp_spark_backlog.py`	Bounded Codex/GPT title-adjudication wave runner. The model is controlled by `--model`.
`src/aina_data_engine/`	Engine modules: contracts, ingestion, HF maps, title coverage, packets, runtime, quality, GDPval gates, telemetry, CLI.
`tests/`	45 pytest files covering source ingest, runtime, title coverage, GDPval gates, deployment boundaries.
`docs/source_foundations/`	Original planning and curriculum documents.
`docs/planning/`	Mission, milestones, and execution board.
`docs/reports/` and `docs/handoff/`	Founder reports and handoffs.
`artifacts/validation/`	Machine-readable receipts for every validation gate.
`artifacts/reports/`	Markdown/HTML report companions for validation gates.

Hugging Face And Public Source Map

Source	Current local use
`Anthropic/EconomicIndex`	Selected files downloaded and processed into Economic Index role/signal maps.
`openai/gdpval`	Train parquet downloaded and processed into 220 GDPval tasks and sandbox/replacement lanes.
O*NET 30.3	1,016 occupations and 18,796 tasks cached into warehouse parquet files.
BLS OEWS	Official-cache import path exists; current VDS has 0 wage/employment rows because source/cache access is absent.
LinkedIn/job-market rows	Used as market signals, not canonical truth.

Validation State

cd /srv/aina/aina-data-engine-room
uv run pytest -q
uv run ruff check .
uv run aina-data-engine --root /srv/aina/aina-data-engine-room validate

Check	Result
Pytest	`172 passed`
Ruff	`All checks passed`
Engine validation	`status: pass`

How To Resume

cd /srv/aina/aina-data-engine-room
git status --short --branch
jq '{status, tables, icp_title_adjudication_summary, production_deployment_approval_summary}' artifacts/validation/full_validation.json

Continue clearing the title backlog with normal GPT/Codex only:

uv run python scripts/run_icp_spark_backlog.py \
  --root /srv/aina/aina-data-engine-room \
  --start-index 043 \
  --wave-size 3 \
  --batch-size 100 \
  --max-waves 1 \
  --max-parallel-reviewers 3 \
  --review-timeout 1200 \
  --repair-timeout 600 \
  --usage-limit-retries 1 \
  --usage-limit-sleep 120 \
  --model gpt-5.4-mini

Next-Agent Prompt

Codex - Continue ICP title coverage - preserve local-only safety boundary

You are continuing the AINA Personalization Engine title-coverage milestone on the VDS at /srv/aina/aina-data-engine-room.

Read first:
- docs/handoff/2026-06-11-personalization-engine-closure-handoff.md
- docs/planning/aina-personalization-engine-mission-2026-06-09.md
- artifacts/validation/full_validation.json
- artifacts/validation/icp_title_adjudication_v1.json
- scripts/run_icp_spark_backlog.py

Current state:
- Branch: ali/personalization-engine-mission-2026-06-09
- Latest checkpoint before this handoff: a2e9b29
- Serviceable title rows: 47,284
- Excluded/not ICP rows: 19,740
- Remaining ambiguous rows: 7,201
- Remaining prompt candidates: 6,872
- Production/external/real-user/deployment unlocks: 0

Task:
Continue reducing the ambiguous ICP title backlog using deterministic filters where obvious and normal GPT/Codex adjudication where evidence is weak or ambiguous. Do not use Claude/Haiku/Sonnet/Spark for new review unless Ali explicitly changes direction. Keep all production boundaries closed. Do not push or merge; keep this self-contained on the VDS unless Ali changes the git policy.

Watch-out: do not let the next agent call the goal complete just because validation is green; M7 still has ambiguous title rows.

Recommended Next Steps

Step	Reason
Clear the remaining title backlog in bounded waves.	The next milestone is reducing 7,201 ambiguous rows while preserving evidence and safety.
Add deterministic filters only where obvious and tested.	Do not turn fuzzy occupational judgment into silent rules.
Rerun or discard partial batch 039.	Do not merge one-reviewer output.
Keep GDPval and production approval receipts green.	Title coverage should not accidentally unlock public runtime.
Define production-quality thresholds.	Decide what separates `serve_now` from `serve_with_fallback`.

Status Line

Where to start

This is a strong local internal beta data engine checkpoint, not a production launch. The open blocker is the remaining ambiguous ICP title backlog plus the broader production approval boundary.

Ali Mehdi Mukadam - co-authored with Codex - 2026-06-11

topics:
  - aina-personalization-engine
  - data-engine-room
  - icp-title-coverage
  - hugging-face-datasets
  - gdpval
  - handoff
subtopics:
  - mission-reconciliation
  - repo-inventory
  - vds-local-execution
  - title-adjudication
  - production-boundaries
  - next-agent-resume

aina-personalization-engine data-engine-room icp-title-coverage hugging-face-datasets gdpval handoff