Coverage Cat (YC S22) – Umbrella insurance via your personal agent
Hey HN! We’re Max and Gabriel the co-founders of Coverage Cat. We’ve been friends for over a decade, met in college then hung out mostly on the internet. We love building products that help people optimize the crufty corners of their lives. Max is a former Google/Microsoft/Two Sigma PM and Gabriel has been working in startups for over a decade.We’re building Coverage Cat: a licensed insurance brokerage that helps you compare umbrella and home coverage side by side — with straight pricing, no sold leads, and a real broker on the other end. Our current focus is helping tech folks (think L3-L8) buy umbrella insurance.Coverage Cat pairs AI-guided intake with a licensed brokerage team, so you can size up coverage, see honest price ranges, and compare real carrier options without handing your details to five agents overnight. Folks who use personal AI agents (think Muse, Instinct, Town, Openclaw, etc.) can drive the same flow through the Agent API/MCP.We’d love your feedback so please do try it out. Just feed the prompt: “Shop for umbrella insurance with Coverage Cat” to your personal agent and let us know what you think!If you want to watch a couple of agents navigate the flow before you use it there’s a video here: https://youtu.be/1BUkgAn6s-IFor those who are unfamiliar with this type of coverage, here’s a blurb from one of our insurance agents: “Umbrella liability insurance is a policy that provides additional coverage above the limits of existing auto, homeowners, renters, or landlord policies. It kicks in after the underlying policy limits are exhausted and may also cover personal injury claims like libel, slander, or defamation that standard policies often exclude. Policies typically start at $1 million in additional liability coverage and can protect you, your spouse, dependents, and even pets in your household.”Buying insurance online is a miserable experience. You fill out a bunch of forms that say they’re going to give you quotes.
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A licensed insurance brokerage that uses AI intake and agent APIs to simplify umbrella insurance shopping for high-income tech workers, but faces regulatory friction and narrow market size.
The pain
The gap
Build angle
Strengths
- Founders have PM and startup experience, not just insurance domain knowledge.
- Targeting high-income tech workers who have money, understand digital products, and face real umbrella insurance gaps.
- Licensed brokerage status creates regulatory moat that competitors cannot easily replicate.
- AI agent API distribution is novel and could reduce customer acquisition cost if adoption accelerates.
- Clear problem statement with measurable friction points in the current market.
Risks
- Insurance brokerage is capital intensive, requires sustained compliance overhead, and does not follow typical SaaS unit economics or scaling curves.
- Market size is narrow. Only high-net-worth individuals and small business owners actively buy umbrella insurance. Expansion beyond tech workers is unclear.
- Commission-based revenue model creates misaligned incentives. Brokers profit more from higher premiums, not better customer outcomes.
- AI agent adoption is unproven. Personal agent platforms (Muse, Instinct, Town) have minimal user bases. Distribution risk is real.
- Regulatory risk is high. Insurance licensing varies by state. One compliance failure or bad actor could shut down the entire operation.
- Established brokers and aggregators (Insurify, The Zebra, local agents) have brand recognition and existing customer relationships. Differentiation must be sustained.
- Customer acquisition cost may be high if relying on agent API distribution before those platforms reach scale.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 78
- Solution gap
- 75
- Willingness to pay
- 68
- Buildability
- 68
The pain of buying umbrella insurance is real and underserved, but the willingness to pay and buildability face friction from regulatory complexity and a narrow initial market.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The business targets a specific niche with money (high earners) and solves a clear problem, but the market size is constrained and the unit economics depend heavily on commission structures that may not scale.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The founders have relevant experience and understand the problem space, but the regulatory and operational complexity of insurance brokerage requires sustained capital and compliance infrastructure that goes beyond typical software scaling.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The idea has a defensible moat through licensing and relationships, but the competitive landscape includes established brokers and aggregators, and the AI agent distribution channel is unproven at scale.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Strong founder pedigree, clear problem statement, and timely AI integration position this well, but the regulatory burden and capital intensity of insurance operations create execution risk that typical SaaS playbooks do not address.
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
GlycemicGPT – Open-source AI-powered diabetes management
I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)What the AI layer does:Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalationImportant: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-inStack:Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxedLooking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech spa
AI
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