Pomerado (YC F26) – Turn any website into an MCP for agents
Hi HN, we’re Alan and Akshay from Pomerado (https://pomerado.ai/). We connect your agents to any website by turning sites into deterministic, self-healing APIs in minutes, fully self-serve.Agents are no longer limited by their intelligence, but rather by their ability to interact with systems. Airlines, banks, e-commerce, insurance portals, government sites, and legacy portals all lack APIs or MCPs and are common parts of personal assistant or business workflows. Agents instead rely on browser automation or computer use, which can be slow, expensive, and unreliable. And even if computer use were perfect, most companies have banned it out of security and compliance concerns.We discovered this problem while tinkering with our own personal assistants. When OpenClaw came out, we wanted to “live our lives through our agents” and have them do everything for us. We soon found their usefulness was restricted by what tools they could connect to. For example, when trying to book flights, they fell back on computer use, which failed to checkout. These issues got magnified on repeated workflows, like searching many airlines at once.Pomerado started off as a project to improve these personal assistant workflows. As we started building, we realized that just-in-time tool creation could have significant applications for AI builders in legacy verticals.We’re ex-McKinsey and Scale AI, where we drove agent deployments in healthcare, professional services, and telecom. Each time, the biggest bottleneck was getting agents to interface well with legacy websites and apps.Pomerado is what we built. It’s a self-serve platform where devs can create APIs for any website (even those they don’t own), in minutes. Users (or their coding agents) provide a short natural language description, answer a few questions about their workflow, and receive a MCP in less than an hour. Future tool calls use the deterministic MCP, which usually returns results in 5-30 seconds.Here’s a walkthrou
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Pomerado enables developers to quickly create reliable APIs for any website, unlocking AI agent workflows on legacy systems without direct access or cooperation from site owners.
The pain
The gap
Build angle
Strengths
- Clear and widespread pain point in AI agent integration
- Strong founder background in relevant industries
- Self-serve, fast API creation lowers barrier for developers
- Addresses security and compliance concerns by avoiding browser automation
Risks
- Technical complexity in maintaining deterministic, self-healing APIs
- Potential resistance from websites or legal issues around scraping
- Customer acquisition challenges in convincing enterprises to adopt
- Competition from evolving browser automation and API providers
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 90
- Solution gap
- 85
- Willingness to pay
- 75
- Buildability
- 78
The problem of legacy websites lacking APIs is clear and painful, the solution addresses a real gap, customers likely will pay for reliable integration, and the founders have relevant experience but the technical complexity is non-trivial.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a high-value pain point with a clear solution that can generate revenue, but customer acquisition and trust may require effort.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The market need is strong and the product fits well, though competition from browser automation and custom integrations exists.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept is innovative and scalable but depends on overcoming technical challenges and convincing enterprises to adopt a new integration method.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The founders have domain expertise and the product solves a bottleneck for AI agents interacting with legacy systems, fitting well with YC’s focus on developer tools and AI.
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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