11 · Life Plan
Tasks
What I am actively working on, with status, published so the list itself becomes accountability. Smaller than goals, more granular than the Master Plan's artifact backlog. Edits flow through the vault — the public read-only view is here.
Lifetime
- AI Usage ConstitutiondoneAI
A written rulebook for using AI without destroying learning.
Done means:
- rules for code, math, research, writing, cybersecurity, and philosophy exist
- bad AI use is defined
- verification rules exist
- document is saved and referenced in the master system
Priority: Very high
- Python AI Experiment TemplateopenAI
A reusable repo structure for AI experiments.
Includes:
- notebooks
- scripts
- data folder
- results folder
- README
- environment file
- experiment log template
Done means:
- template repo exists
- one sample experiment is included
- setup instructions work
Priority: High
- Classical ML Basics RepoopenAI
A repository containing basic ML experiments.
Projects:
- linear regression
- logistic regression
- classification metrics
- train/test split
- confusion matrix
- model comparison
Done means:
- at least 5 notebooks/scripts exist
- metrics are explained
- README explains what was learned
Priority: High
- Deep Learning LabopenAI
PyTorch and/or TensorFlow/Keras notebooks covering tensors, training loops, image classification, text classification, and model saving.
Done means:
- basic neural network trained
- results documented
- overfitting discussed
- model saved and loaded
- README explains workflow
Priority: High after ML basics
- LLM API PlaygroundopenAI
A repo for experimenting with model APIs, prompts, structured outputs, latency, cost, model comparison, and failure cases.
Done means:
- API calls work
- structured output example exists
- cost/latency notes exist
- prompt examples are versioned
- failure cases are documented Priority: Very high
- Source-Grounded RAG DocumentopenAI
Assistant A document assistant that answers only from uploaded or indexed sources and provides citations.
Done means:
- document ingestion works
- embeddings/indexing work
- retrieval works
- answer generation works
- citations/source references appear
- retrieval failures are documented
- README explains limitations
Priority: Very high
- Ollama Local Model LabopenAI
A lab for running local models, testing local inference, embeddings, structured outputs, and local RAG.
Done means:
- Ollama setup notes exist
- at least two local models tested
- latency/memory notes exist
- local embedding example exists
- local RAG demo exists
- cloud vs local comparison exists
Priority: Medium-high
- Agent Workflow LabopenAI
A repo for tool-calling workflows, agents, human approval, logs, and failure analysis.
Done means:
- at least one workflow exists
- at least one tool-calling agent exists
- logs/traces are recorded
- human approval point exists
- failure cases documented
Priority: Medium-high
- AI Evaluation LabopenAI
A repo for prompt tests, RAG evals, model comparisons, human grading, LLM-as-judge experiments, and failure taxonomies.
Done means:
- eval dataset exists
- at least one model comparison exists
- RAG evaluation exists
- failure taxonomy exists
- before/after improvement report exists
Priority: High
- AI Product Case StudyopenAI
A full case study of one AI system.
It should cover:
- problem
- users
- model choice
- data
- prompts
- retrieval
- evals
- failures
- risks
- limitations
- improvements
Done means:
- published on website or GitHub
- includes architecture diagram
- includes examples and failure cases
- does not overclaim
Priority: High once one AI project exists