Pocketty – iPhone SSH terminal that pings you when an agent is blocked
Hello~! pocketty is an SSH terminal for iPhone and iPad, made for herdr. herdr keeps your agent panes alive on your computer and knows the state of each one: working, needs you, or done.I made this in anger/desperation for the latter half of my recent paternity leave. Nap traps are sweet, but there's only so much doom-scrolling and movie-watching I can handle... In any event, I've been using it for the last couple months and no longer have to be my desk anymore to be productive. Now the nap-traps are still productive (when i want them to be) !How it works:- The app talks to your computer directly over plain SSH. Tailscale is the easy way to reach it from anywhere but any SSH host you can reach works.- A small Rust daemon on the host watches herdr. When a pane needs you, it seals the alert to your phone's key with HPKE (X25519, ChaCha20-Poly1305). A stateless relay (pocketty's) on Cloudflare Workers passes the sealed bytes to APNs (Apple Push Notification servers), and a notification extension opens them on the phone. The relay can't read them and keeps nothing.- Your SSH key is made in the Secure Enclave and can't be exported.- The terminal uses libghostty-vt for state and draws with wgpu on Metal, so full-screen TUIs look like they do on your desk, albeit narrower.- Diffs for each agent turn, a file browser, and previews of `localhost` dev servers your agent starts, all through the same SSH connection. No port forwarding to set up.herdr and the daemon are optional. Without them, it's a normal SSH client.There's no account to set up and no analytics or tracking in the app. The app runs a 14-day free trial, with full feature access. Then, if you're as happy as I am with it, then it can be yours forever with a one-time purchase: $99 for the first two weeks (launch promo) then $129 after that.Happy to answer anything about the sealed push setup or running libghostty on iOS.
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Pocketty is a secure iOS SSH terminal that alerts developers when remote agents need attention, enabling productive remote work without being tied to a desk.
The pain
The gap
Build angle
Strengths
- Clear and specific pain point for remote developers
- Strong technical implementation with security and privacy
- No account or tracking lowers friction and privacy concerns
- One-time purchase model simplifies monetization
Risks
- Niche market limits growth potential
- High price point may deter casual users
- Dependence on herdr and Rust daemon adds setup complexity
- Limited appeal outside advanced developer users
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 85
- Solution gap
- 75
- Willingness to pay
- 70
- Buildability
- 80
The product addresses a clear pain for remote developers needing real-time alerts on SSH agents, fills a niche gap with a secure and integrated solution, has moderate willingness to pay given the price, and is technically feasible for a solo developer.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The value proposition is strong for a specific user base but the high price and niche market limit scale and immediate revenue potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea solves a real problem with a technically sound approach but faces challenges in market size and user acquisition outside a small technical community.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The product fits a defined user need and has a defensible technical moat but may struggle with broader adoption and pricing sensitivity.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The startup idea is focused, solves a real developer pain, and has a clear go-to-market path, but the niche focus and pricing could limit growth.
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
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Pocketty – iPhone SSH terminal that pings you when an agent is blocked”.