These are the artifacts. Posts argue. These prove.
Memori — flagship
Persistent memory for AI coding agents. Rust core, SQLite, FTS5, vector search. One file. No API dependency.
Problem. Every agent session starts from zero. Context you already paid for is gone the next time you open the repo.
Thesis. Memory is not a notes database. It is three jobs: capture useful state, preserve it cheaply, retrieve the right item at the right moment. If previously learned information cannot be recovered when needed, memory did not happen.
Architecture. Hybrid search with RRF, access decay scoring, cosine deduplication. Brute-force over an index until the collection size forces otherwise.
Implementation. Python bindings, a CLI designed for agents, Claude Code integration, a published package and crate.
Results. About 190 tests, benchmarks, and a retrieval loop that actually survives across sessions.
Lessons. Designing the CLI for agents made it better for humans. Hard constraints belong in code, not in the prompt.
Code. github.com/archit15singh/memori · py-memori on PyPI · memori-ai-core on crates.io · docs.rs
Research.
Luffy
A comment-triggered PR review control plane. The agent can shell into the workspace, reproduce failures, post a structured review, store a redacted trace, and grow hub memory so the next run on that repo is not starting from zero.
Problem. Most PR bots read a diff, emit soft prose, and vanish. No memory of the last review. No package you can open later to see which tools ran. No honest score when the code is wrong.
Thesis. Agents need state, observability, and a control plane. Not a better prompt.
Architecture. GitHub comment → Actions → Hermes Agent through OpenRouter → tools in the workspace → structured Markdown review → redacted trace → hub memory.
Implementation. You type @luffy review this pr. Model choice is explicit. Memory is per-repo and accumulates.
Results. Live Odoo review on a real bug: REQUEST CHANGES, score 42/100, ten API calls, about four minutes, roughly $0.59. The trace package is in the repo under docs/showcase/.
Lessons. If you cannot audit the loop, you do not have a review agent. You have a comment generator.
Code. github.com/archit15singh/luffy-pr-review-agent
Research. Building Luffy
Vital Few
An agent skill that compresses a field. Name an object, compete, commit, stop. Two files, no runtime.
Problem. Give an agent a terse pointer and it tours the field. Stars get ranked. The tail expands. You still do not know what you would work tomorrow.
Thesis. Ordering is the product. The list is not. Stop is a four-check (frame names the ontology, k is from the front, item-1 witness is yes, no further search), not a feeling.
Architecture. name → instance → siblings → rank → ontology → frame → explode → cca → hypothesis-rank → top-k → extract loop. Each move derives from one property of a trustworthy cut: Zwicky/Wohlin completeness, Juran’s cut, cross-consistency, Chamberlin/Platt/Pugh disagreement, Simon’s stop. Recursion is bounded.
Implementation. SKILL.md is the runbook. LOOP.md is the spec. Copy both into the agent’s skills directory.
Results. One worked pass in-repo: anthropic autonomous vulnerability patcher → closed-loop CRS, witness as proof object as item 1.
Lessons. Implementing the top-k inside the ranking loop is a fail. Running the candidates is the next loop.
Code. github.com/archit15singh/vital-few
Research. Vital Few: compress a field, then stop