Bramble – Local-first password manager
I'm currently working on Bramble, an open source password manager with P2P cross-device sync. Initially I released the Chrome extension, but recently I also published the Android app and iOS is pending Apple's approval. Besides that, the latest version also includes passkey storage for all platforms!About Bramble:It aims to be as feature-rich as all popular and a replacement for cloud-based providers. I don't think we need to store our data in the cloud and be at the whims of companies raising their prices every year. There's always a breach and then we find out that some fields aren't encrypted, metadata is visible, and so on. I'm frustrated with this and the increasing lack of transparency during these breaches.The P2P sync in Bramble uses a Nostr relay (which can be self-hosted) to keep your devices in sync. The relay just introduces the devices to each other; the data then flows directly over WebRTC, so there's no vault server and no cloud copy of your passwords anywhere. What leaves your device is end-to-end encrypted and your devices authenticate each other directly, so a snooping or MITM relay gets practically nothing.Crypto is all done in Rust so I can control exactly how key material lives and dies in memory (secrets get zeroed out, no GB leaving copies lying around). In Chromium it's a wasm module, on mobile it's native builds bridged over via uniffi.Android app:I'm still deciding whether to publish the app on Play store or simply provide the signed APK which users can sideload. Reason for that is Google's plan to lock down Android and take away ownership from its users. Read more about it here: https://keepandroidopen.com/The app uses no Play APIs whatsoever and runs perfectly on GrapheneOS, where I actually did all my testing.Questions, feedback, feature requests - all welcome!TL;DR: I dislike private-equity and venture funded companies messing with our security, so I created my own Pa
FL score
out of 100
Verdict
medium confidence
Competition
No competitor data yet
Trend
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Open-source, peer-to-peer password manager that eliminates cloud storage and vendor lock-in by syncing encrypted data directly between user devices.
The pain
The gap
Build angle
Strengths
- Technical architecture is genuinely differentiated, P2P sync with no vault server is harder to build but eliminates the single point of failure that plagues competitors
- Open-source foundation creates credibility in security-conscious segments and enables community auditing
- Multi-platform coverage (Chrome, Android, iOS) matches user expectations for password managers
- Passkey support positions the product ahead of the curve as the industry shifts away from passwords
- Founder has deep technical conviction and has already shipped working code across three platforms
Risks
- User acquisition is extremely difficult in password managers, switching costs are high, trust is low, and most users are already locked into a solution. No clear go-to-market strategy articulated.
- Monetization is undefined. Open-source + no cloud = no obvious revenue model. Freemium, paid tiers, or donations all conflict with the anti-VC positioning.
- Sync reliability at scale is unproven. P2P systems are notoriously fragile when devices are offline, networks are poor, or relay infrastructure fails. One bad sync experience loses users permanently.
- Security audits are expensive and necessary before enterprise or security-conscious users will switch. No mention of audit plans or funding for them.
- iOS approval is pending, Apple's app review process is unpredictable for privacy-focused tools. Rejection or restrictions could derail the multi-platform strategy.
- Nostr relay dependency creates a new trust assumption. Even if self-hosted, most users won't run their own relay, so they're trusting a third party (just not the password manager company). This undermines the core value proposition.
- Distribution through sideloading on Android severely limits addressable market. Most users won't sideload; Play Store rejection means missing 99% of Android users.
- Competing against well-funded, established players (Bitwarden, 1Password) with massive user bases and brand trust. Differentiation on architecture doesn't matter if users never hear about it.
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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