Drafted (YC P26) – Models for residential architecture
I’m Nick, founder of Drafted (https://www.drafted.ai). We’re training models that generate residential architecture from structured design constraints.Product demo: https://www.youtube.com/watch?v=8QkJ7jNU9y4Residential architecture is still one of the most expensive, slow, and inaccessible creative processes in the world. Designing a custom home typically costs $10,000–$50,000 or more, takes months, and requires making major decisions before most people can even visualize the outcome. As a result, the vast majority of homes are built without direct architectural involvement.Our goal is to teach computers how the built environment works so anyone can imagine, explore, and eventually create physical spaces tailored to them.Today, users can design homes using simple inputs such as: - Square footage targets - Footprint shapes - Lot boundaries - Room placement preferences - Spatial relationships and constraints.Our models generate complete floor plans and matching exterior elevations in seconds. Users can explore designs in both 2D and 3D, iterate instantly, furnish interiors, experiment with materials, and export CAD, PDF, and other files for the rest of the pre-construction process.One of our newest capabilities allows users to draw any footprint shape and generate a complete home layout inside it: https://www.youtube.com/watch?v=wZJhBm7-OHI.Over the past month, more than 120,000 people have used Drafted, generating over 325,000 home designs.If you're building a home, developing property, working in architecture, construction, or AI, we'd love to hear your feedback!
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
AI-generated floor plans and elevations that compress a $10k-50k, months-long architectural process into seconds of iteration.
The pain
The gap
Build angle
Strengths
- Massive TAM: 1.2M new homes built annually in US alone; global residential construction is $2T+ market
- Clear unit economics: free-to-paid conversion on design tools typically 2-5%; premium features (CAD export, architect review, permits) have high margin
- Defensible tech: training models on building codes, zoning, structural constraints is hard; competitors can't just fine-tune Midjourney
- Proven traction: 120k users in one month is strong viral coefficient; 325k designs shows engagement depth
- Multiple revenue paths: B2C (homeowners), B2B (builders/developers), B2B2C (real estate platforms), professional services (architect review)
Risks
- Regulatory friction: generated designs must pass local building codes, zoning, and permitting. Each jurisdiction has different rules. Liability if a design fails inspection or causes structural issues. May require architect sign-off in many states, which kills the 'no architect' value prop.
- Architect resistance: professional architects may lobby against unlicensed design tools. Some states regulate who can produce construction documents. Could face legal challenges.
- Quality at scale: 325k designs generated doesn't mean 325k are buildable. No data on how many pass code review or actually get built. Early users are likely tech-savvy; mainstream users will demand higher quality.
- Monetization unproven: free-to-paid conversion rate unknown. Homeowners may use free tier to explore, then hire cheap architect for final plans. Builders may use for ideation but need licensed architect anyway.
- Execution complexity: integrating with permitting systems, construction software, and real estate platforms requires deep domain partnerships. Building code compliance is a moving target.
- Competition: Autodesk, Trimble, and smaller startups (Floorplanner, Planner 5D) are moving into AI-assisted design. Larger players have distribution and capital.
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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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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