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Mixpeek is multimodal AI infrastructure — we turn unstructured content (video, images, audio, documents) into searchable, programmable assets through a unified API. Think of us as the data infrastructure layer that sits between your raw media and your AI applications. If you've ever tried to build a pipeline that extracts features from video, makes them retrievable, and serves them at scale — you know this is a 12-18 month infrastructure project. We compress that to an afternoon.

We're currently building MVS (Mixpeek Vector Store) — a distributed vector database built on Ray + S3, designed to handle 100B+ vectors at a fraction of the cost of existing solutions. Architecture details: shard-level WAL, LIRE-based adaptive search, replica sets, and agent-native query primitives. If you've ever wanted to rethink how vector search works from the storage layer up, this is that project.

Some things we're shipping right now:

- IP safety for media & sports — our copyright detection platform (https://copyright.mixpeek.com) helps brands and leagues detect unauthorized use of visual IP at scale. We're working with partners in the media/sports ecosystem including Backblaze for storage-native integration.

- Healthcare pipelines — multimodal extraction for clinical trial recruitment and SNF/MDS coding workflows, working with enterprise partners in the space.

- Ad verification — we contribute to the IAB Tech Lab ARTF working group and power contextual intelligence for ad safety.

Our core primitives: feature extractors, retrievers, taxonomies, clusters. Decompose with extractors, recompose with retrievers. Docs: https://docs.mixpeek.com

Stack: Python, Ray, S3, FastAPI, React/TypeScript. We also maintain amux [https://github.com/mixpeek/amux], an open-source tmux multiplexer for running parallel Claude Code agent sessions — if you're into agentic dev workflows, check it out.

I'm Ethan (founder/CEO, previously led search at MongoDB). Small team, high ownership, real problems. We're preparing for NAB Show next week and scaling enterprise pipeline work across healthcare, adtech, and media.

Reach out: ethan [at] mixpeek [dot] com — mention HN.

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The core loop: amux parses ANSI-stripped tmux output to detect state — working, stuck, needs input, context running low. When context drops below 20%, it sends /compact. When the session crashes from a redacted_thinking error, it restarts and replays the last message. When a YOLO session hits a safety prompt, it auto-answers. The watchdog has kept sessions running unattended for 12+ hours.

What made it actually useful was the web dashboard. I can monitor all my agents from my phone (it’s a PWA), send them messages, check the kanban board, and peek at any terminal — all without SSH-ing into my machine.

Last night I had 8 agents working across 3 repos: one writing API endpoints, one on tests, one doing a migration, others on smaller tasks. I checked in twice from my phone, answered two prompts, and woke up to PRs ready for review.

Agents started coordinating on their own The coordination part surprised me the most. Agents share a REST API so they can peek at each other’s output, claim tasks from a shared board (SQLite with atomic CAS), and send messages between sessions. I didn’t plan for agent-to-agent orchestration initially — I just exposed the API in their global memory and they started using it naturally.

It’s a single Python file (~23k lines, inline HTML/CSS/JS), no build step, no external services. Python 3 + tmux. Auto-generates TLS certs so phone access works over Tailscale. The server watches its own mtime and restarts on save, so you can edit it while it’s running.

What it doesn’t do: it doesn’t replace Claude Code or modify it in any way. It’s purely a wrapper — ANSI parsing, no hooks, no patches. It works with whatever Claude Code version you have.

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The core problem: most vector search assumes your query is a sentence or a single image. But we kept getting customers who wanted to pass entire video files as queries — a media company searching their archive with a raw broadcast clip, a legal team querying with a full contract PDF, an IP safety pipeline scanning videos frame-by-frame against a brand index.

The key insight was that the decomposition pipeline we already use for ingestion (split → embed → store) is the same operation needed at query time — just routing output to search instead of write. Same extractor, same chunking, same embedding model. This guarantees query and index vectors are always in the same space.

The execution is: detect large input → decompose via extractor → batch embed in parallel → N concurrent ANN searches → fuse results (RRF/max/avg). From the caller's perspective, the API shape doesn't change at all.

One decision I'd be curious to get feedback on: we explicitly dropped an "auto" mode that would pick chunking strategy based on file type. The right decomposition depends on what you're searching for, not just the file itself. Felt like the wrong abstraction to hide. Curious if others have found ways to make auto-config work well here.

Happy to answer questions about the fusion strategies, the credit model, or the architecture.

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