Respan (YC W24) – Open-source Rust AI gateway built around plugins
Hey HN, this is Andy from Respan (https://www.respan.ai). We are building an open-source Rust AI gateway. It is a decoupled gateway, so everything is a plugin, including different providers, protocols (MCP, voice), modalities (images, videos), and gateway features (fallbacks, PII redaction, smart routing).Instead of packaging tons of providers and features into one gateway, Respan provides a small core and public interfaces that teams can use to assemble the AI infrastructure their application needs. That way, users can build their own features as additional plugins without having to change the gateway's core.Here’s a video demo: https://www.youtube.com/watch?v=pVv2ibXiTXMThere are countless LLM gateways and most of them solve a common problem: calling different providers from a single endpoint. However, adding new features can become prohibitively difficult. So developers can end up building another gateway to solve a specific problem.We've been building our gateway based on LiteLLM since 2024, and now it's providing services for thousands of orgs. Each org needs different API key settings, routes, and limits. To support that, we kept wrapping LiteLLM, adding more features to it, and at one stage, our wrapper code exceeded 100,000 lines.This January, a routine LiteLLM upgrade caused intermittent connection errors, and we had to roll back. We still haven't figured out the cause, but since then, we no longer upgrade LiteLLM directly and instead manually backport upstream fixes. These experiences made us realize that continuing to patch LiteLLM is not a sustainable long-term solution, and we had to redesign the gateway ourselves.While redesigning our gateway, we wanted to make it easier to add features without breaking existing ones. Inspired by Nginx, we think an AI gateway should have a really small core, only responsible for dispatching events.We believe that everything should be a plugin in our gateway, including provid
FL score
out of 100
Verdict
high confidence
Competition
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An open-source, plugin-based Rust AI gateway that simplifies building and extending AI infrastructure for organizations.
The pain
The gap
Build angle
Strengths
- Clear technical pain point validated by real usage and issues with existing solutions
- Modular plugin architecture encourages extensibility and customization
- Rust implementation offers performance and safety benefits
- Early traction with thousands of organizations using the underlying LiteLLM wrapper
Risks
- Competition from established AI gateway projects and cloud providers
- Requires strong community and ecosystem growth to realize full value
- Ongoing maintenance and complexity of plugin compatibility
- Monetization and willingness to pay may be limited if open-source remains dominant
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 85
- Solution gap
- 75
- Willingness to pay
- 70
- Buildability
- 80
The problem of complex, brittle AI gateways is clear and affects many organizations, there is a gap for a modular, plugin-based solution, customers likely pay for reliability and flexibility, and the core can be built by a small focused team.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a painful problem with a clear value proposition but faces competition and requires ongoing maintenance, which may limit rapid scaling and monetization.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The solution fits a real developer pain point with a modular architecture that encourages extensibility, though adoption depends on community engagement and plugin ecosystem growth.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The product targets a niche but growing market of AI infrastructure developers, with a defensible technical approach but uncertain long-term differentiation.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The team has identified a real technical bottleneck in AI gateways and built a working product with early traction, positioning well for YC’s focus on technical founders solving infrastructure problems.
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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