Graphene – Data analysis toolkit for your coding agent
My friend and I have worked at a number of BI companies and thought: Can’t a coding agent do most of this now?Almost. They just need a context/semantic layer to ensure query correctness and some kind of artifact for publishing findings and visualizations.We built an open source project called Graphene that provides these tools:- Semantic layer that’s more token-efficient than YAML, more deterministic than Markdown (eg, composable, callable metric macros), with a query API that’s well in-distribution (SQL). - MDX-like files for dashboards (Markdown with inlined SQL + HTML components for viz with support for CSS and Javascript) - Connects to popular data warehouses or local DuckDBYour coding agent will build really in-depth reports and can ofc leverage any other skills or context that you've made available to it. We dogfood it inside a monorepo with our website, app source code, planning docs, etc so the agent has access to a ton of context. It’s also nice that the agent can add instrumentation, adjust pipelines and transformations, and add dashboards all in one PR.If you want to try it out, just point your coding agent at https://github.com/graphene-data/graphene/blob/main/docs/set... and ask it to set up Graphene.If you don’t have data to play with, you can clone our example project:1. `git clone <https://github.com/graphene-data/example-flights.git`> 2. `cd example-flights && npm install`Would love any/all feedback!
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Graphene is an open source semantic layer and dashboard toolkit that enables coding agents to generate accurate, in-depth BI reports and visualizations.
The pain
The gap
Build angle
Strengths
- Founders have direct BI industry experience
- Open source lowers adoption friction
- Technical innovation in semantic layer design
- Integration with coding agents and data warehouses
Risks
- Unclear willingness to pay or monetize
- Competition from established BI tools and platforms
- Complexity of onboarding and user education
- Dependence on coding agent adoption and maturity
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 80
- Solution gap
- 70
- Willingness to pay
- 70
- Buildability
- 80
The pain of inefficient BI workflows and lack of semantic layers is clear, and the solution addresses a real gap, though market willingness and monetization remain uncertain.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a meaningful pain in BI automation with a technically sound solution, but customer acquisition and clear monetization paths need more definition.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The product fits a niche in data tooling with a strong technical foundation, but the market size and willingness to pay are moderate and require validation.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept is feasible and addresses a real problem, but the competitive landscape and product differentiation could be stronger.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The founders have relevant experience and built a functional open source tool that solves a real BI pain, though scaling and business model clarity are still 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!
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
GlycemicGPT – Open-source AI-powered diabetes management
I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)What the AI layer does:Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalationImportant: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-inStack:Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxedLooking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech spa
AI
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