Daniel Avila

Audit a codebase and bind every claim it makes about itself to a mechanism that fails when the claim stops being true, so it is legible to people and AI agents. Applies Context Architecture's nine principles: make structure say what the system does, place AGENTS.md at boundaries, codify conventions, and bind every claim the repo makes about itself to a mechanism (compiler, linter, automated tests, review) that fails when the claim stops being true. Works greenfield (a repo born legible) and brownfield (a repo restructured in steps). Use when an agent reimplements code that already exists, invents structure, follows stale or deleted docs, propagates a deprecated pattern, or resolves ambiguity at random, or when asked to make a repository "agent-ready", "AI-legible", or to add or fix AGENTS.md / CLAUDE.md files. Related terms: harness engineering, context files, instruction bloat, cognitive debt, agent readiness.

Updated 14 days ago

Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.

Updated 17 days ago

Read bank account balances and transactions from EU/UK banks via the open-banking.io PSD2 API. Use when the user wants to check balances, list recent transactions, categorise spending, or reconcile payments across European bank accounts — without eIDAS certificates or an AISP licence.

Updated last month

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Updated last month

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Updated last month

AI agent payment infrastructure — ACTP escrow, x402 instant payments, USDC settlement on Base L2. Interactive onboarding: asks your preferences, generates customized agent code, and verifies setup. Covers provider/requester patterns, adapter routing, 8-state machine, pricing, disputes, and identity.

Updated last month