JavaScript web apps with WASM layout and GPU rendering in 280 KB
Over the past year, I have been working to find a better way to write cross-platform apps.The problems I am trying to solve:- Native frameworks only work on a subset of platforms, so a different implementation of the UI needs to be written per target. - They are updated at the same pace as the platform itself, which forces the developer to split the user base to be able to use new features, or wait a long time for bugs to get fixed. - They also force developers to use specific high-level languages, so they are not a good fit for apps with high performance requirements, like creative apps or games. - Cross-platform frameworks that rely on bundling a browser engine solve the problem of code reuse, but they inherit the limitations of HTML/CSS. - Those that take control over layout and rendering are not efficient and lightweight enough to be used on the web, and require the use of compiled languages.Ideally, we would be able to bundle the UI framework directly with the application. This would allow developers to use new features and fix issues in the UI framework immediately. It would also allow ideas for improvements to compete in a natural way, rather than being decided by a committee.Uniform is my idea for how we can solve these problems. This is the structure:- A layout and rendering engine written in C, compiled to WASM on the web and native code on all other platforms - A JavaScript API where each function call gets forwarded directly into the core, for best performance and efficiency - A native runtime (<2 MB) that makes it possible to run the same JavaScript code on macOS and iOS (more platforms to come). The runtime can also generate packaged apps to upload to app stores. The packaging steps are also described in JS.Uniform supports very fast iteration cycles via hot-reload on all platforms. Even on iOS, the JS source code of an app can be changed while the app is running. Changes are visible on the next display refresh. No connection to a computer or t
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A lightweight, high-performance cross-platform UI framework using WASM and GPU rendering with a unified JavaScript API.
The pain
The gap
Build angle
Strengths
- Addresses a well-defined developer pain with cross-platform UI frameworks
- Innovative use of WASM and GPU rendering for performance and size
- Supports fast iteration with hot-reload on multiple platforms
- Unified JavaScript API simplifies development and deployment
Risks
- High technical complexity and maintenance burden for cross-platform runtimes
- Strong competition from established frameworks and browser-based solutions
- Uncertain developer adoption due to ecosystem inertia
- Monetization and market size may be limited to niche developer segments
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
- 80
- Willingness to pay
- 70
- Buildability
- 75
The idea addresses a clear pain point in cross-platform app development with a novel approach, but market willingness and solo build feasibility have moderate uncertainty.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The value proposition is strong for developers needing performant cross-platform apps, though monetization and market size may be limited initially.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea solves a real developer pain with a technically sound solution, but adoption depends on ecosystem acceptance and competing frameworks.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept is innovative and technically feasible, but the challenge lies in convincing developers to switch from established tools and frameworks.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The project targets a niche with clear technical advantages and iteration speed, but scaling user base and monetization remain open questions.
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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