Fast, native Mac file manager (filters, fuzzy find, 9 MB, no Electron)
My Downloads folder had been left unkept for a really long time and cleaning it up using Finder was quite cumbersome. So I started creating a simple app to help me filter out and delete or move the files in the folder.It started out very basic and the filtering options genuinely helped me clean out the Downloads folder, then as I thought of more features I would like to see in a file manager I started to add them. Some of the features are:- Fuzzy go to folder/file where you only need to write a few letters of a full path, get suggestions and can jump to the correct path instantly - Hover over a file to preview an image or PDF without opening it - Dual-pane view and tabs that remember selected filters - Command palette to find actions fast - Batch rename (with regex support and presets for common operations), image conversion (HEIC/WebP/AVIF to JPG/PNG), zip creation - Bookmarks, Quick Look, single-click open, keyboard controlAnd many other small quality of life features.I used Claude Code for making the app and the tech stack is .NET/C# with AppKit. The app is compiled to Native AOT so the total app size is only around 9 MB. Since file operations are very important to get right (I don’t want to lose any important files) I put a lot of time into hardening file move/copy/delete operations. Copies are written to a temp file and atomically renamed into place. I also made a dedicated audit of move/copy/delete and verified each operation by hand.Another thing that’s important to me is apps that are privacy first so the only network request the app makes is to check if there’s a new version (this is not done at all for the trial version, the trial expiry date is enforced locally by just checking the date and trusting the user) and the only information that is sent is the current app version.The app is Apple Silicon, macOS 12+ and has a 30-day free trial with a one-time launch price of $19.99 (no subscription).
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Native Mac file manager with fuzzy search and batch operations targeting power users frustrated with Finder's limitations.
The pain
The gap
Build angle
Strengths
- Fully functional product with real-world validation (solved your own problem first)
- Technical execution is strong: Native AOT, 9 MB, AppKit, atomic file operations with audit trail
- Privacy-first approach is a genuine differentiator in 2024
- Feature set is comprehensive without bloat: fuzzy find, dual-pane, batch rename with regex, image conversion, Quick Look
- One-time $19.99 pricing with 30-day trial removes friction for adoption
- Built with Claude Code, demonstrates AI-assisted development is viable for complex native apps
Risks
- Market size is niche: power users and professionals only. Casual Mac users won't pay for file management. TAM is probably 50K-200K potential customers globally.
- Distribution is hard. No App Store (likely due to sandbox restrictions on file operations). Requires direct sales, word-of-mouth, or indie dev communities. Expect slow initial growth.
- Finder integration is limited by macOS APIs. Apple could add features that obsolete your advantages (they've done this before).
- Support burden for file operations is high. One data loss bug destroys reputation. You've mitigated this well, but ongoing testing across macOS versions is required.
- Competitive pressure from free alternatives (Total Commander, Commander One, even improved Finder in future OS versions) could limit willingness to pay.
- Single developer dependency. If you stop maintaining it, users abandon it 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!
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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
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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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