OnKey – I'm a bad singer, so I built an app to show where I'm off-key
Hi allI was pretty terrible at singing, now I’d say I’m bad but not terrible. This is because I’ve been using a singing app (won’t name it here) that scores me on my pitch and timing throughout the song so that I can improve.Lately however, the app I’ve been using has flooded their song list with songs that aren’t originals. I didn’t like this at all as for me, it’s easier to try to improve when you’re singing along to the original.So, I asked an agent (Claude Code) if it could write some code to listen to Spotify and also listen to me and see if it could detect both pitches and score me. It impressed me by writing some Python and showing me my pitch in the terminal!It couldn’t go all the way though and detect the singer’s pitch in real time.So I found the next best setup and built OnKey, a Mac app that needs to listen to the song once (it doesn’t save it) and then when it’s learnt the song (takes a couple of minutes), you can sing along to the pitch track that it’s learnt. OnKey is free to use and open source under the MIT license.It can listen to Spotify, Apple Music, and YouTube playing in your browser with the right permissions.During the build I found that someone had done something similar in Nightingale, but this requires a library of songs exported to disk and who has this in 2026 (maybe some of you ;) - but we need to help the normies too).You can check out a demo video on the site of me singing (warning given) here: https://singonkey.appThe app sends out to find the song info (short Shazam fingerprint, and looks up song details), lyrics (from LRCLIB), and checks for updates.The app never sends the music (it doesn’t save this to disk) and your voice. Everything you’d expect to stay private does.It’s still weak in a few places:- when the song’s backing is noisy it’s hard to pick the pitch track out- sometimes it gets a note an octave off- it’s limited to Apple Silicon Macs, M1 or later, and only macOS 14.2 up because extra drivers aren’t required p
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
OnKey is a Mac app that helps casual singers improve by scoring their pitch against original songs streamed live without storing music or voice data.
The pain
The gap
Build angle
Strengths
- Clear user pain around singing improvement with original tracks
- Privacy-conscious design that does not store user audio or music
- Open source and free, encouraging community involvement
- Technical innovation in real-time pitch detection from streaming
Risks
- Limited to Apple Silicon Macs and latest macOS, restricting user base
- Pitch detection accuracy issues with noisy backing tracks and octave errors
- Unclear monetization strategy and user willingness to pay
- Competition from existing apps and potential streaming service restrictions
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 80
- Solution gap
- 75
- Willingness to pay
- 60
- Buildability
- 75
The app addresses a clear pain for casual singers wanting to improve with original tracks, fills a gap left by existing apps, but monetization and user base size are uncertain, while the technical build is feasible.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
OnKey solves a specific problem with a unique approach but faces challenges in market size, user willingness to pay, and platform limitations that reduce its immediate growth potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea has a clear user pain and a novel technical solution, but the niche market and dependency on Apple Silicon Macs limit scalability and broad adoption.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The product fits a defined user need and leverages AI effectively, but the restricted platform support and imperfect pitch detection reduce its competitive edge.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
OnKey shows technical innovation and a real user problem but lacks a clear path to monetization and broad market traction, making it a modest early-stage idea.
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
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “OnKey – I'm a bad singer, so I built an app to show where I'm off-key”.