Ember, a native iOS Hacker News reader I built around accessibility
I read HN on my phone every day and never really settled on a reader, so I wrote my own and finally tidied it up enough to put out there.It's plain SwiftUI with no third-party dependencies. A few things I spent the most time on:Comments are parsed and drawn as native text instead of being dumped into a web view. Links, italics, quotes and code blocks behave like the rest of the OS, text selection works, and threads collapse instantly. The whole comment tree comes back from the Algolia API in a single request, which felt a lot nicer than walking the Firebase API node by node.Accessibility. Nothing depends on color on its own, so points, read state and selection all carry a shape or an icon too. VoiceOver reads each story as one coherent element with proper actions, Dynamic Type and Reduce Motion are respected, and there's a color-blind mode. The first-run setup actually looks at your device's accessibility settings, switches on the matching options, and tells you what it changed instead of making you hunt for them.Then the usual things you'd expect: Top/New/Best/Ask/Show/Jobs, search, saved stories, read tracking, an in-app reader, light and dark, and a handful of accent colors.It only talks to the public HN APIs, there's no account and nothing is tracked. Source and screenshots are in the repo.I'd most like feedback on the comment rendering and the accessibility choices, since those were the parts I cared about getting right. Happy to answer anything about how it's put together.
FL score
out of 100
Verdict
medium confidence
Competition
No competitor data yet
Trend
No signal yet
A polished iOS Hacker News reader optimized for accessibility, built by someone who solved their own problem but hasn't validated whether others share it enough to sustain a business.
The pain
The gap
Build angle
Strengths
- Genuinely thoughtful engineering: native text rendering instead of webviews, single API request for comment trees, zero dependencies
- Accessibility isn't an afterthought, it's baked into the design from first principles (shape + color, auto-detection of device settings, proper VoiceOver implementation)
- No tracking, no account required, uses only public APIs, removes friction and builds trust with privacy-conscious users
- Solves a real problem the builder experiences daily, which usually correlates with product quality
- Clean, maintainable codebase (SwiftUI, no dependencies) makes it easy to iterate or hand off
Risks
- Market size: HN's daily active users are estimated at 50k-100k. Even capturing 5% is a small user base. Most won't pay.
- No clear monetization path. Ads alienate the audience. Subscriptions ($2-5/month) don't scale to meaningful revenue with this TAM.
- Accessibility focus, while admirable, doesn't expand TAM, it serves existing users better but doesn't create new demand
- Competing against free alternatives (official HN site, web readers, other free apps). Switching costs are zero.
- Dependency on Algolia and HN APIs, no defensibility if either changes terms or shuts down
- Solo builder with no distribution channel or marketing plan mentioned. Getting users requires either App Store prominence (unlikely for a niche reader) or community evangelism (slow, uncertain)
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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