# RocksDB Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/rocksdb
> Markdown URL: https://aitinkerers.org/technologies/rocksdb.md
> Technology record last updated: 2026-09-18T15:13:59Z
> Generated: 2026-09-22T17:53:23Z

RocksDB is a high-performance, embeddable key-value store: optimized for fast storage and server workloads.

RocksDB is an open-source, persistent key-value store, forked from Google's LevelDB in 2012 and engineered by Meta (formerly Facebook). It leverages a Log-Structured Merge-tree (LSM-tree) architecture, specifically optimized to exploit multi-core CPUs and fast storage like SSDs for I/O-bound workloads. This C++ library is designed to be embedded directly into applications (not a standalone server), eliminating network latency. Key features include support for transactions, Column Families, and merge operators. Its performance profile makes it the storage engine of choice for production systems like MySQL's MyRocks, CockroachDB, and Apache Flink's state store.

- Official technology site: https://rocksdb.org/
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building Bob's Cabins Across Three Agent Tools Without Losing My Mind](https://nashville.aitinkerers.org/talks/rsvp_iAJZ16pa6m0)

Revari: A Memory Dataplane for Heterogenous Agent Environments Every multi-agent workflow hits the same wall: switch tools and start over. Context vanishes between Claude, Codex, Cursor, or whatever your stack is. You end up re-explaining architecture decisions, copy-pasting summaries, manually bridging what one agent learned to the next. It's a tax on every tool switch, and it compounds. Revari eliminates that tax. It's a sovereign memory dataplane that sits below your agents, not inside any one of them. Persistent, hybrid retrieval (BM25 + vector + graph fused via RRF) running entirely on local hardware. Any agent reads and writes to the same shared memory. Swap agents mid-workflow and nothing is lost. Decisions, context, and intent carry forward automatically. That's the developer problem. Here's where the architecture goes further. Revari is built for DDIL (denied, degraded, intermittent, limited) environments and connectivity. Agents on edge devices persist memories locally via and reconcile when connectivity returns, over any network. The same memory layer that keeps your coding agents in sync also keeps autonomous systems coherent across factory floors, field hospitals, and forward-deployed environments. The open protocol underneath is post-quantum encrypted by default, with a novel gradient memory system that adapts to available bandwidth. Full context over Ethernet, compressed facts over WiFi, tags over LoRa. Starts with your dev workflow. Scales to infrastructure for agents that operate in the real world. Seeking development partners and seed funding.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_iAJZ16pa6m0

### [EmergentDB: How Evolutionary Algorithms Made My Vector Database 82x Faster](https://singapore.aitinkerers.org/talks/rsvp_hHLd9-vXn8A)

Vector databases power semantic search, RAG, and recommendation systems—but tuning them is a nightmare. Should HNSW use M=8 or M=32? What's the right ef_construction value? Most teams guess and pray. EmergentDB solves this through evolutionary computation. Instead of manual tuning, it uses the MAP-Elites algorithm to evolve the optimal index configuration for your specific workload. The system maintains a "quality-diversity grid" that explores the trade-off space between recall, latency, and memory—automatically selecting between HNSW, Flat, and IVF indices with evolved hyperparameters.

- Event context: AI Tinkerers - The Age of AI & Infrastructure (Singapore) — 2026-02-11 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_hHLd9-vXn8A

### [Graph Memory in Agent Environments — a Live Walkthrough](https://seattle.aitinkerers.org/talks/rsvp_GJN7vv4ZQ1A)

Agents don’t come alive in isolation—they need environments with durable memory, hybrid search, solid orchestration, and clean human-in-the-loop affordances. In this live demo, we’ll run an agent inside Wings Studio, and show exactly how graph memory powers the environment. Zero fluff, just a working system, the internals, and lessons you can reuse.

- Event context: AI Tinkerers Seattle September Meetup — September 30, 2025 — 2025-10-01 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_GJN7vv4ZQ1A

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