Justif – Knuth-Plass justification and microtypography for the web
Justif is a drop-in JavaScript library that progressively enhances web pages to TeX-level text justification. Installation is a single <script> line, standard text and accessibility affordances are unchanged, and users with JS disabled get native browser rendering.I made justif because I've long been a fan of justified text. I think it looks clean and elegant, and makes reading more enjoyable. But bad justification is the opposite, with gaping spaces that distract me to the point of making the text near unreadable.Browsers have got better recently at handling justified text [0][1], but still use suboptimal greedy algorithms for the most part, and are not near that TeX/InDesign level of quality that I crave. `text-wrap: pretty` exists but is far from a panacea, as you can see for yourself in the demo.Justif also has the benefit of providing more consistent text layout across browsers. Blink (Chrome), Gecko (Firefox) and WebKit (Safari) all justify text differently, so normally what the user sees depends on what browser they use.Take a look at the demo, play around with it, see if you can break anything. I'm open to improvements of the API design as well, so please let me know your thoughts.For more details, see the README: https://github.com/lyallcooper/justif0: https://cloudfour.com/thinks/justified-text-better-than-expe...1: https://owickstrom.github.io/the-proportional-web/
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A JavaScript library that applies TeX-quality text justification to web pages, solving the problem of ugly spacing in browser-rendered justified text.
The pain
The gap
Build angle
Strengths
- Solves a real problem that affects visual quality and consistency across browsers
- Low friction to install (single script tag) and gracefully degrades
- Addresses a gap that major browsers haven't solved despite years of development
- Creator has clear passion and understanding of typography
- Technically sound approach (Knuth-Plass algorithm is proven)
Risks
- Extremely niche audience, most web designers don't prioritize justified text, and many actively avoid it for mobile/responsive reasons
- Performance risk: JavaScript-based text layout is slower than native rendering and could cause layout shift or jank on large pages
- Maintenance burden: needs constant testing across browsers and devices as web standards evolve
- No clear monetization path, this is a free utility that solves a problem most people don't have
- Accessibility concerns: justified text can harm readability for dyslexic users; the library doesn't address this
- Market size is tiny: only applies to sites that use justified text, which is already a small subset of the web
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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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
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