Rivet (YC W23) – Scale and Orchestrate Agents on S3
Hey HN, this is Nathan and Nicholas from Rivet (https://rivet.dev) – a fast, high-density, and scalable orchestrator for agentic workloads using the actor model. Rather than running a heavyweight sandbox per agent, each agent gets a lightweight actor backed by S3.Actors provide a long-lived process for long agent turns, in-memory state for context, a WebSocket server for multiplayer, durable SQLite storage backed by S3, and hibernation when idle.It's a similar model to Cloudflare Durable Objects, but it’s open source and self-hostable.Here’s a video walkthrough: https://www.youtube.com/watch?v=3Ev6I7DOV9cWhen we went through YC in 2023, we were building infrastructure for multiplayer games like Diep and Krunker. This involved heavily optimizing high-throughput matchmaking, real-time player messages, and blitz scaling. Through that process, we spent a lot of time on workloads where Postgres couldn't keep up with our throughput. We started out by hacking together Lua scripts on top of Redis, but this became too cumbersome to maintain and upgrade. This led us to extend our game server orchestrator to support what we call Rivet Actors today: many lightweight workers with in-memory state and durable storage. By using actors, we were able to serve tens of millions of monthly active users.As agents grew in popularity, it became clear that running agents in sandboxes is inefficient. Sandboxes are expensive, mostly idle, and slow to start. Developers started using actors because they are far more memory efficient (72 KB baseline for actors vs 512 MB for a minimal sandbox) and fast to start (4.9 ms p99 for actors vs 1.7 s p99 for sandboxes).For example, Amp is using Rivet to orchestrate multiplayer agents. They originally tried to build on Cloudflare Durable Objects, but couldn’t launch because of the platform’s limitations and unresponsive support. They migrated to Rivet Actors in a weekend and deploy on-prem. Rivet’s flexible architecture
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Rivet is an open-source, scalable orchestrator for lightweight agent workloads that improves speed and cost efficiency over traditional sandbox methods.
The pain
The gap
Build angle
Strengths
- Clear technical advantage in resource efficiency and speed
- Open-source and self-hostable, appealing to developers needing control
- Proven traction with real users and YC backing
- Addresses a real and growing pain in AI agent infrastructure
Risks
- Niche market with potentially slow adoption outside specialized users
- Competition from cloud providers and proprietary solutions
- Requires continuous technical innovation to maintain edge
- Potential complexity in onboarding and developer education
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
- 80
- Willingness to pay
- 80
- Buildability
- 83
The idea addresses a clear and costly inefficiency in running agent workloads with sandboxes, offers a distinct technical improvement, has paying customers, and is technically feasible to build and maintain.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Rivet solves a painful scalability and cost problem for developers running agentic workloads, with a clear value proposition in speed and resource efficiency, but faces challenges in market education and competition.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The product fits a niche with strong technical differentiation and early traction, though the market size and adoption speed may be limited to specialized users initially.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The solution is innovative and technically sound, with a strong founding team and YC backing, but requires ongoing investment to capture and grow the market effectively.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Rivet aligns well with YC’s focus on scalable infrastructure startups, demonstrates product-market fit through existing users, and shows potential for growth in AI agent orchestration.
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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