Twitter Archive Learning Pipeline

This repo owns the tracked code, schemas, prompts, and eval contracts for Timmy's private Twitter archive learning loop.

Privacy Boundary

Raw archive files stay outside git.
Derived runtime artifacts live under ~/.timmy/twitter-archive/.
twitter-archive/ is ignored by timmy-home so private notes and training artifacts do not get pushed by accident.

Tracked here:

deterministic extraction and consolidation scripts
output schemas
eval gate contract
prompt/orchestration code in timmy-config

Not tracked here:

raw tweets
extracted tweet text
batch notes
private profile artifacts
local-only DPO pairs
local eval outputs

Runtime Layout

The runtime workspace is:

~/.timmy/twitter-archive/
  extracted/
  notes/
  knowledge/
  insights/
  training/
  checkpoint.json
  metrics/progress.json
  source_config.json
  pipeline_config.json

Source Config

Optional local file:

{
  "source_path": "~/Downloads/twitter-.../data"
}

Environment override:

TIMMY_TWITTER_ARCHIVE_SOURCE=~/Downloads/twitter-.../data

Knowledge Candidate Schema

Each batch candidate file contains:

id
category
claim
evidence_tweet_ids
evidence_quotes
confidence
status
first_seen_at
last_confirmed_at
contradicts

The consolidator computes durable vs provisional vs retracted from these fields.

Candidate Eval Contract

Local eval JSON files under ~/.timmy/twitter-archive/training/evals/ must use:

{
  "candidate_id": "timmy-archive-v0.1",
  "baseline_composite": 0.71,
  "candidate_composite": 0.76,
  "refusal_over_fabrication_regression": false,
  "source_distinction_regression": false,
  "evidence_citation_rate": 0.98,
  "rollback_model": "timmy-archive-v0.0"
}

Promotion gate:

candidate composite improves by at least 5%
no refusal regression
no source distinction regression
evidence citation rate stays at or above 95%

Training Command Contract

Optional local file pipeline_config.json can define:

{
  "train_command": "bash -lc 'echo train me'",
  "promote_command": "bash -lc 'echo promote me'"
}

If these commands are absent, the pipeline still prepares artifacts and run manifests, but training/promotion stays in a ready state instead of executing.

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