Every AI coding assistant today generates code from scratch — for every developer, every request, every time. EVOHLV builds a living library of evolved code. Requests become retrievals. Cost bends down. Quality bends up.
Every developer using an AI coding tool who needs a login page triggers a full LLM inference cycle. The AI has no memory of the last million login pages it built. It starts from zero every time.
This is not a cost optimization problem. It is a structural design problem. The entire industry has built tools that are incapable of learning from their own output at scale.
A 10-turn coding conversation uses 15,000+ tokens as context compounds with every revision. One trivial change — fixing a typo in a README — consumed 21,000 tokens in a documented real-world test. Developers predicted AI would make them 24% faster; a peer-reviewed randomized trial found they were 19% slower.
Natural language request is normalized, embedded as a vector, and matched against the category taxonomy.
Cosine similarity against category centroids produces a confidence score. Above threshold: retrieve. Below: generate.
The most evolved version for this category is returned instantly. Zero LLM calls. Near-zero cost.
Seeded from the closest champion. Generated code is immediately categorized, scored, and stored as a library candidate.
Every accept, edit, and reject is a signal. User iterations become promotion candidates. The library evolves continuously.
At launch the rate is 0% — every request generates. As the library matures, the rate climbs. At 80%+, EVOHLV has become infrastructure. Generation is the exception.
This is the inversion of every current AI coding tool. More usage → better output → lower cost. The curve bends permanently in the right direction.
Every module is a self-contained unit with a defined input/output contract. Swap the LLM provider, add a categorization engine, or replace the champion algorithm — the rest of the system doesn't notice.
Normalize → embed → match → route. Fully deterministic. No LLM until generation is confirmed necessary.
pgvector-backed artifact store. Per-category, per-track champions. Immutable artifacts with full lineage.
Every accept, edit, reject, and export is logged. The primary fitness signal that drives promotion decisions.
5–20 concurrent categorization approaches per artifact. Consensus mechanism selects final placement.
Multiple hierarchy algorithms running in parallel. A/B tested against retrieval quality and user acceptance.
Claude, GPT-4o, Gemini, local models — one interface. Route by cost, capability, or availability.
Back-run against curated GitHub repositories to seed the library before first user exposure.
Automated promotion with fitness + usage gates. Champion challenger A/B before full promotion.
Real-time retrieval rate, cost per request, category health, engine agreement rates.
Before CDNs, every user requested assets from the origin server. The millionth user paid the same cost as the first. CDNs didn't improve the web server — they built a global cache in front of it. The millionth asset request became free.
AI coding tools today are the pre-CDN web. Every developer, every request, full inference cost every time. EVOHLV is the cache layer — the first developer to build and evolve a login page bears the generation cost. Every developer after that retrieves at near-zero cost.
"Every AI tool makes the developer smarter. EVOHLV makes the system smarter — and unlike individual developer skills, systems don't have upper limits."
The model compounds. More usage → deeper library → higher retrieval rate → lower marginal cost → more competitive pricing → more usage. This is the loop that makes EVOHLV defensible at scale.
Request pipeline, library storage, single-engine categorization, confidence routing, champion retrieval. The full request → retrieve/generate → store → signal cycle functional. Deployable and demonstrable.
Scrape and process curated GitHub repositories across target categories. Run the full categorization and evaluation pipeline on 50,000+ artifacts. Validate categorization quality and retrieval precision before any user exposure. Build the library so retrieval works from day one.
5–20 concurrent categorization engines with consensus mechanism. 2–5 champion determination algorithms running in parallel. LLM provider interface allowing model swaps without code changes. A/B framework to validate which approaches produce the best retrieval quality. Engine retirement logic for low-agreement, high-cost approaches.
Automated promotion engine with fitness + usage gates. Champion challenger A/B before full promotion. Signal decay weighting. Rollback mechanism on champion degradation. The retrieval rate metric becomes the system's north star, tracked in real time.
Public-facing product with API access for IDE integrations. Shared library across users (with privacy controls). Taxonomy expansion to 2,000+ categories. Observability dashboard public-facing to demonstrate the retrieval rate climb. Enterprise pricing tier with private library namespaces.
Early access is limited. Founding users shape the taxonomy, influence the engine design, and get permanent preferential pricing.