Line9 – A Mermaid rendering engine with its own layout
Line9 is a new rendering engine for Mermaid flowchart diagrams that aims to remove the need for manual adjustment of layout. Mermaid is a popular text-based language for describing multiple types of diagrams.Several Mermaid rendering engines already exist. Most, like the original mermaid.js, use Dagre or ELK graph drawing libraries to automatically lay out their diagrams. I’ve used Mermaid for the past four years but often recreated my flowcharts in a diagram editor so that I could modify the layout to something that better suited my needs.Use Line9 when you want an automatic flowchart arrangement that optimises for clear communication. It seeks to layout flowcharts with regular grid spacing, closer positioning of related nodes, shorter, straighter edges and targets a page-like aspect ratio. The Compare button in the /diagram page (share panel on the left) shows you the Line9 and mermaid.js renders next to each other.Use for free, no account required. A CLI tool can also be downloaded for Mac, Windows and Linux. Free-use diagrams carry a watermark. Commercial use of the CLI requires a paid account and you also get diagrams without watermarks. Line9 is not open source (I need to create an income and the business model is being tested).I’ve tried to encode and automate the layout principles I use when creating flowcharts by hand. All rendering takes place in the browser, so no private data is sent to the web service. The CLI is written in Rust and embeds the renderer so you get a single binary install and all data and processing remains local. It’s currently a little slower than mermaid.js, due to the difficulty of solving node positioning and edge routing simultaneously to find the layout that best meets the design principles.Output is produced in SVG or PNG formats. Diagrams can also be shared via a URL with a fragment that embeds the Mermaid text.Things I know still need improvement: positioning and rotating subgraphs, folding graphs to hit good aspect ratios
FL score
out of 100
Verdict
medium confidence
Competition
No competitor data yet
Trend
No signal yet
A specialized diagram layout engine that solves a specific frustration within an existing tool ecosystem, but faces uncertain monetization and limited total addressable market.
The pain
The gap
Build angle
Strengths
- Solves a real, specific problem the creator experienced repeatedly over four years
- Working product with browser-based rendering (no data privacy concerns) and CLI tool
- Clear technical differentiation: custom layout algorithm vs. existing libraries
- Low distribution friction: free web version requires no signup; CLI is single binary
- Embedded in Rust CLI means no external dependencies or server costs
Risks
- Mermaid is free and open-source; users may not pay for incremental layout improvements when they can use free alternatives or switch to paid tools like Lucidchart
- Total addressable market is narrow: only Mermaid users who care enough about layout to pay; excludes teams already using dedicated diagramming software
- Monetization unproven: no disclosed metrics on free-to-paid conversion, pricing elasticity, or customer acquisition cost
- Performance is slower than mermaid.js; speed matters for large diagrams and batch processing
- Known limitations (subgraph positioning, aspect ratio folding) suggest the algorithm isn't fully solved
- Competing against free, well-funded open-source projects with larger communities
- Single-person business with no team mentioned; scaling support or product development is constrained
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!
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 “Line9 – A Mermaid rendering engine with its own layout”.