EVOKE
OS-like memory management for the LLM KV cache.
14 projects, grouped by kind. Each entry links to a detail page with the full write-up, stack, and source.
Public repositories, MIT or Apache licensed.
OS-like memory management for the LLM KV cache.
Reactive LLM inference. When you edit an LLM's context, redraft recomputes only what the edit actually changed on both sides of the call: the prompt (reused KV prefix) and the answer (salvaged via self-speculative replay), implemented as a streaming mode inside llama.cpp.
Incremental computation engine for Rust. Tracks dependencies between computations automatically and only reruns what's actually affected.
A universal memory layer for AI coding agents. Ingests conversation history from Claude Code, Cursor, and other tools into a single SQLite-backed store with vector and full-text search, then serves it via MCP so any AI agent can recall what you've discussed, decided, and built across projects and machines.
Content-addressed cache for AI agent loops. Returns exact bytes from prior executions instead of re-running tools or re-calling LLMs.
Algorithmic context-window selection for LLM coding tools. Treats context as a constrained optimization problem, not retrieval.
Put your wardrobe in rows. Snap. Organize. Wear.
LoRA-as-memory experiment: per-user durable memory lives in a rank-16 LoRA adapter on a Qwen3 student rather than in the prompt window.
A standard for AI-generated code to carry machine-checkable certificates of correctness, plus a Python reference implementation and a finetuned model that generates them.
Automated testing for LLM skills. Launches Claude Code or Cursor as subprocesses, runs scenarios in isolated workspaces, and reports what passed and what didn't.
Closed-source products and client work.
The hosted version of wardrowbe. Virtual try-on, iOS and Android apps, cloud sync, fashion subscription.
Generative-AI image SaaS. 2,000+ paying customers, 7,000+ images transformed. AI headshots, glamour shots, conceptual art.
Smaller builds and experiments.
Older work from 2018–2020 is in the archive. Interactive explainers live at learn.anyesh.me ↗.