Stoa Markets (YC S26) – A Marketplace for GPUs and AI Servers
Hi HN, we’re Eren, Berat and Kaan. We’re building Stoa (https://www.stoaexchange.com), a marketplace for new and used GPUs and AI servers.GPUs are the collateral in the data center buildout. Today, financing terms mostly depend on the offtaker, meaning the company that has committed to use the compute.If that company is a hyperscaler, the financing can look investment grade. If it’s a smaller cloud or startup, terms get expensive fast, even with the same hardware as collateral.The lender’s problem is pretty reasonable. If the borrower defaults and we need to sell these servers, what can we actually get for them? There isn’t a good answer today.We started brokering GPU deals to understand why. It was much more manual than we expected. The hardware is still traded through phone calls, forwarded spreadsheets and long email threads.One week, a seller quoted us $200k for a server node and another quoted $240k for what looked like the same thing. Neither was necessarily wrong. They had different information and could only see their own corner of the market.Before we could compare the quotes, we had to sort out the configuration, condition, warranty, location and delivery terms. It’s the same information Kelley Blue Book attaches to a used-car price through the year, trim, mileage and condition. “An H100 server” isn’t enough information to know what something is worth, just as “a used BMW” isn’t.This is also just a bad way to buy or sell hardware. A buyer looking for the best price shouldn’t have to contact several brokers and dealers separately, repeat the same request and then untangle a pile of different quotes. Sellers shouldn’t have to search for demand one buyer at a time. A market of this size deserves better liquidity.Stoa puts the request into one format and sends it to dealers that have gone through know-your-business (KYB) checks. We verify the company, who owns it and who is allowed to trade for it. Before the request goes out, the buyer confirms the
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A B2B marketplace that standardizes pricing and reduces friction in GPU/server trading by aggregating dealer quotes through a single request interface.
The pain
The gap
Build angle
Strengths
- Timing is perfect, GPU shortage and financing constraints are real pain points right now
- Team has domain credibility from brokering deals and understanding the manual process
- Clear revenue model: transaction fees from dealers, premium data/pricing to lenders, potential financing partnerships
- Solves a structural problem (information asymmetry) that affects a high-value market
- YC backing validates the thesis and provides credibility for KYB verification
- Defensible moat: network effects (more dealers = better prices for buyers = more buyers) and data (pricing history becomes proprietary)
Risks
- Dealer adoption is uncertain, brokers may resist a platform that commoditizes their margins and makes pricing transparent
- Liquidity chicken-and-egg: need enough dealers to attract buyers, enough buyers to attract dealers. Could take 12+ months to reach critical mass
- Regulatory complexity: KYB/AML requirements scale with volume; compliance costs could eat margins early on
- Hardware specs are genuinely complex (configuration, condition, warranty, location, delivery terms), standardizing these is harder than it looks and requires deep domain expertise
- Lender adoption is not guaranteed, they may prefer direct relationships with dealers or build internal pricing models
- Competitive risk: a hyperscaler (AWS, Google, Meta) could build this internally or a logistics company (Wyle, Arrow) could add it as a feature
- Market size is real but concentrated, most volume is probably in 5-10 major cities; geographic fragmentation could limit liquidity
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
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Stoa Markets (YC S26) – A Marketplace for GPUs and AI Servers”.