TesterArmy (YC P26) – Agents that test web and mobile apps
Hey HN - we’re Oskar, Szymon, and Piotr, and we’re building TesterArmy (https://tester.army). TesterArmy is an agentic testing platform that runs end-to-end checks before deployment and in production. Instead of wasting hours on manual testing or maintaining static scripts, we let you specify your tests in natural language and handle everything in between. We've built the platform fully around agents. Our agent will reliably execute the tests, but your coding agent can manage everything in our platform, from defining tests in natural language to running them on your behalf.Check out our demo video: https://www.youtube.com/watch?v=291IkUbPrlk.We started TesterArmy because testing is still far too painful. AI coding tools have made it dramatically faster to write and ship code, but testing is still a bottleneck. Traditional E2E tests are slow to set up and expensive to maintain. Managing auth and test users is painful. Setting up staging environments is painful. Running tests reliably is painful.We think most teams do not actually want to spend their time writing selectors or maintaining test infrastructure. They just want confidence that their core flows work. With TesterArmy, an engineer can sign up, give an agent our CLI, and let it handle creating tests and running them on schedule or on GitHub.When something breaks, TesterArmy alerts your team through Slack or Discord.Over the past few months, we scaled from 0 to 30+ teams using our product every day. We caught bugs in critical flows, including onboarding, checkout, and AI chat. We've got many of our customers migrating from already established competitors to us because of the quality and reliability of our agents.Here are a few of the recent bugs that our agent found (there were quite a lot of them!):1) Timezone bug that affected the booking flow in one of our clients' apps, the dashboard was very complex and hard to catch by a human. 2) Regression in agent orchestration
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
AI agents that write and maintain end-to-end tests automatically, eliminating the manual testing bottleneck that slows down shipping.
The pain
The gap
Build angle
Strengths
- 30+ paying customers already using daily, real traction, not theoretical demand
- Concrete bug catches (timezone issues, regressions) prove the agent actually works and finds real problems
- YC backing provides credibility and network for enterprise sales
- Natural language interface removes friction for non-QA engineers
- Timing is perfect, AI coding tools have created a testing gap that needs filling
- Recurring revenue model with clear unit economics (per-test or per-run pricing)
Risks
- Agent reliability at scale, 30 teams is small; brittleness could emerge with 1000+ teams running complex flows
- Competitive response from Cypress, Playwright, or cloud testing platforms adding AI features
- Customer acquisition cost may be high if sales requires enterprise deals; SMB market may have low willingness to pay
- Dependency on LLM quality, if Claude or GPT degrades, product degrades; no moat around the agent itself
- Test coverage paradox, teams may use TesterArmy for happy paths but still need manual testing for edge cases, limiting TAM
- Churn risk if agents miss bugs that humans would catch, eroding trust quickly
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
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