Self-hosted company OS, Claude Code and Codex agents in departments
Hi HN, I am Dimitris. This is a company OS that I built and use to run my business and anyone can install it and self host it for free. Think of it as Claude Code, Cowork and the cloud sessions in one self hosted application.It is a Multi-tenant application by design where many people can collaborate on the company agents with 4 different modes of collaboration, and it runs with your Anthropic or OpenAi subscription or even with local models.Every agent can run on Claude Code or Codex CLI running as a persistent process on your server in a kernel sandbox (bubblewrap) with network isolation always on (pasta), with its own workspace, memory, schedules and tools. The same agents can also be configured to run identical on any remote computer through one outbound WebSocket (no inbound ports, no VPN needed).The agent already have lots of built in features, as an example they can answer and place phone calls through Twilio or your own Asterisk, they can edit videos and excel, word, ppt files and preview them directly inside the chat with collabora and many more.Trying it is one install script and a docker compose, no signup. The license is Fair Source, all the code is public and self hosting is free up to 5 users. 1.6.0 went out today.AI Disclaimer: large parts of OtoDock are written using OtoDock itself, running Claude Code.I would love people to read the code, take a look in the sandbox model, and tell me what would stop you from running this on your own hardware.GitHub: https://github.com/OtoDock/oto-dock Website: https://otodock.io
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Self-hosted workflow automation platform that runs AI agents across departments with built-in collaboration, sandboxing, and integration capabilities.
The pain
The gap
Build angle
Strengths
- Technical depth: kernel sandboxing, network isolation, and persistent agent processes show real engineering
- Self-hosting removes major friction point for enterprise adoption
- Fair Source licensing with free tier up to 5 users lowers barrier to trial
- Multi-tenant design and 4 collaboration modes address real team coordination problems
- Founder actively using it in production (not theoretical)
- Supports multiple LLM backends (Anthropic, OpenAI, local models) prevents lock-in
- Built-in integrations (Twilio, video, office files) show practical thinking beyond basic automation
Risks
- Positioning unclear: is this for SMBs, enterprises, or developers? Messaging conflates multiple use cases
- Adoption friction: requires Docker, server infrastructure, and technical setup. Not plug-and-play for non-technical users
- Market timing: n8n, Make, and others are rapidly adding AI capabilities. Unclear if self-hosting alone justifies switching
- Monetization model untested: Fair Source with 5-user free tier may struggle to convert to paid. Enterprise sales cycles are long
- Competition from established players: if Anthropic or OpenAI release self-hosted versions, this becomes obsolete
- Documentation and community: GitHub shows active development but unclear if docs support non-technical users
- Vendor risk: depends on Anthropic/OpenAI APIs for most use cases. Local model support is niche
- Feature creep risk: too many integrations (Twilio, video, Excel) without clear focus. Depth vs. breadth problem
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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