Selected results
By the numbers
Live products
What I've shipped
Three production systems, each on its own live domain.
MemDB · memdb.ai
Self-hosted long-term memory database for AI agents. Pure Go core, zero Python in the hot path, one docker-compose. 72.5% on LoCoMo LLM-Judge, beating Mem0 by 5.62pp with open-source primitives only. My Postgres + pgvector graph backend was also merged into MemOS (PR #966), so it self-hosts without a separate graph store. Apache 2.0, v0.23.0 in production, with a Discord community of early adopters. GoPostgreSQLApache AGEpgvectorQdrantRedis
go-code · krolik.tools
Code intelligence MCP server for AI coding agents. 35 tools across 16 languages: semantic search, call-graph and dataflow analysis, automated PR review. Apache AGE knowledge graph + pgvector + ColBERT reranker. Bridges Prometheus alerts and Jaeger traces directly to file:function hypotheses. Gotree-sitterApache AGEpgvectorColBERTPrometheusJaegerMCP
OxPulse Chat · oxpulse.chat
Censorship-resistant encrypted chat and real-time video, usable down to 1 KB/s. Verified a 5,000-user-per-room broadcast at 99.6% delivery (load test) on a 4-core box. Multi-domain partner-edge architecture (oxpulse.chat + 4 partner brands). Rust backend, SolidJS widget, production CSP. RustAxumSolidJSPostgreSQLWebRTC
Engineering & infrastructure
What makes it run
Libraries, engines, and MCP servers, built by the same hands. Most are open source and link to their repo.
go-kit
The shared Go toolkit the whole fleet is built on: a tiered L1/L2 cache (S3-FIFO + Redis), circuit breaker, rate limiter, hedged requests, in-process pub/sub, plus LLM, rerank, and sparse-embedding clients for hybrid retrieval. Two dozen independent packages, most stdlib-only, one module. GoRedisPrometheus
GitHub →go-mcpserver
The bootstrap framework behind all nine of my MCP servers: one Run() call instead of ~80 lines of boilerplate, on the official MCP Go SDK. GoMCP
GitHub →go-workflow
A standalone DAG workflow engine for multi-turn LLM and agent tool-loops: 15 step types, native MCP integration, distributed execution over a Postgres SKIP LOCKED queue, crash recovery, and approval flows. Apache 2.0. GoPostgreSQLMCP
GitHub →oxpulse-sfu-kit
Reusable WebRTC SFU library on str0m: simulcast, dual BWE (Kalman + googcc), pacer, AV1-DD, active speaker. Published v0.11.0 on crates.io. Ruststr0mTokioWebRTC
crates.io →ox-whisper
Self-hosted, OpenAI-compatible speech-to-text in Rust: a single ARM64 CPU with no GPU, 8 languages, real-time WebSocket streaming, and word-level timestamps. Built on sherpa-onnx + Moonshine v2. RustONNXWebSocket
GitHub →go-twitter
A Twitter/X scraping library that gets through the anti-bot wall: TLS fingerprinting, x-client-transaction-id, a multi-account pool with health tracking, TOTP 2FA, and CAPTCHA solving. Built on go-stealth. GoGraphQLgo-stealth
GitHub →go-search
A self-hosted Perplexity: an MCP server that fuses web scrapers, API integrations, and LLM summarization into cited answers across web, GitHub, Hacker News, YouTube, and Hugging Face. GoMCPLLM
dozor
AI-first server monitoring and deploy, exposed over MCP: it walks any Linux fleet (Docker, systemd, SSL, remote hosts) and returns LLM-optimized output instead of dashboards, with non-blocking webhook deploys and a Telegram alert bus. GoMCPDockersystemd
GitHub →Why now
Choosing an IC seat, on purpose
For 14 years I ran my own products and carried production myself: on call, no team to escalate to, every outage mine to fix. That taught me what holds up under real load and what only looks good in a demo.
I'm choosing an individual-contributor role deliberately: I want to go deep on hard infrastructure problems alongside a strong team, not manage one. I ship, I operate, and I take the pager. And I work inside code I don't own, not only my own: my graph backend was merged into MemOS (PR #966). Contributing upstream is the part of the job I want more of.
Open to
What I'm looking for
Staff+ engineering roles at AI infrastructure companies
Anthropic, Cursor, Sourcegraph, Cognition, and similar: code intelligence, agent memory, distributed systems, platform, internal tooling. After 14 years running production end to end, I want to take on the hard parts with a strong team behind me.
Founding-engineer roles at early-stage AI startups
AI-infrastructure and developer tools, where deep ownership and 0-to-1 building matter. Same work, earlier stage.
Let's talk
Staff and Senior engineering roles at AI-infrastructure and dev-tools companies: code intelligence, agent memory, distributed systems, platform. Open to founding-engineer roles at early-stage startups too. SF Bay Area, onsite or hybrid.
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