Vertumnus – printable posters of farmers' market produce seasonality
The origin of this project was me, as a casual farmers' market attendee, grossly misremembering the start date of early girl tomatoes. Ironically, I still haven't achieved that goal, because I couldn't find a suitable source with varietal level granularity online. I'm half dreading a "Why didn't you check X?" comment and discover there's some perfect local varietal data source, but that would really be the best case scenario.The poster is customizable per crop, and the pictures reposition heuristically. There's only two regions, SF and NY, with very different source data for each. See https://vertumnus.fyi/about/sources for methodology.A static poster is not the greatest form factor for a schedule with annual variation, and this whole project was a non serious side exercise. See https://vertumnus.fyi/about for more.The project was coded in a single 731 prompt Claude Opus 5 session over 22 days within a 46 day period, from July 20th to September 2nd, 2026. I've published the entire transcript, searchable and organized by chapter, at https://vertumnus.fyi/about/transcript. A write up, with the stuff that Claude was good at and its repeated code quality shortcomings, is at https://vertumnus.fyi/about/ai.
FL score
out of 100
Verdict
medium confidence
Competition
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Trend
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A printable poster showing when specific produce varieties are in season at farmers' markets in SF and NY.
The pain
The gap
Build angle
Strengths
- Solves a real micro-problem for farmers' market enthusiasts
- Transparent about methodology and data sources
- Documented the entire AI-assisted development process
- Niche audience exists (farmers' market regulars in two cities)
Risks
- Extremely limited addressable market—only two cities, only farmers' market shoppers
- No clear monetization path; printable posters have low willingness-to-pay
- Static poster format doesn't match the problem (annual variation requires updates)
- Data collection is manual and doesn't scale; no sustainable data pipeline
- Farmers' markets themselves could provide this information more credibly
- Creator's own lack of conviction ('half dreading', 'non serious side exercise') signals this isn't a real business
- No evidence of customer demand beyond the creator's own need
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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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
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