ProvenMetal (YC S26) delivers circuit boards in days instead of weeks
Hey HN, we’re Will & Johnny from ProvenMetal (https://provenmetal.com). You send us design files or specs and we give you assembled boards domestically in days.The US produced 30% of PCBs globally in 2000, now they produce 4%. Chinese manufacturers have completely dominated this space at 55% of global production.Now, the need for a domestic PCB supply chain is higher than ever before, yet the infrastructure has been dissolving over the last 2 decades. What is left is mostly small family run manufacturers (CMs) that have been operating in largely the same, labor intensive way since the early 2000’s.When you place an order through a CM it typically takes several days to receive a quote and complete design for manufacture review, and then you have to source all of the components (the hardest part) and bare boards yourself, then wait anywhere from a few days to several weeks for the assembly and testing of those boards.We started off assembling circuit boards out of a garage with prosumer grade equipment (NeoDen YY1, Glenbrook X-ray, solder paste stencils, and manual rework stations). We believed that by owning the manufacturing process, we could turn all the front of house automations inwards. But here’s the thing… manufacturing circuit boards with prosumer grade equipment out of a garage takes a huge amount of time. Suddenly we were spending 90% of our time assembling circuit boards instead of growing the business. We were completely capacity constrained, with fancy software automations that are not the binding constraint at low volumes.Then we got our heads out of the weeds, took a step back, and realized that we were trying to solve the wrong bottlenecks.These manufacturers are good at manufacturing and terrible at the front of house (quoting, DFM review, and part procurement). When you look at the full process, you realize that assembly is not the bottleneck. So we stopped trying to solve the problem that we can’t solve at this stage.We measured the b
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Software layer that wraps fragmented US PCB manufacturers to deliver assembled boards in days by automating quoting, DFM review, and component sourcing, the actual bottlenecks.
The pain
The gap
Build angle
Strengths
- Founders already validated the pain by operating in the space and hitting capacity limits, they understand the real bottleneck
- Clear pivot from hardware manufacturing to software/operations shows good judgment and adaptability
- Large addressable market: any hardware company, defense contractor, or electronics manufacturer in US needs this
- Geopolitical tailwind: CHIPS Act, nearshoring, supply chain resilience are real policy drivers
- Existing US manufacturer base is fragmented and underserved, low competition from tech-native players
- Recurring revenue model: each order generates margin, high repeat rate in hardware development
Risks
- Execution risk on component sourcing: this is genuinely hard. Requires real-time inventory visibility across distributors, negotiation power, and handling allocation during shortages. Not a software problem alone.
- Manufacturer relationships are sticky but not defensible: competitors can replicate the same partnerships
- Margins compressed by existing CM ecosystem: if manufacturers are already operating at thin margins, there's limited room for a middleman
- Chicken-and-egg: need volume to negotiate better component pricing, but need better pricing to attract volume
- Quality/liability: if boards fail, who's responsible? Unclear contractual structure between ProvenMetal, manufacturers, and customers
- Incomplete description: post cuts off mid-sentence. Unclear what they actually measured or what their current traction is
- Market size validation missing: how many orders/month do they need to be profitable? What's the TAM in actual order volume?
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!
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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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