Anonymous age verification with passkey-powered encryption: VALIDATE, 72/100 | Ideas Lab

A privacy-first age verification service that encrypts identity documents on the user's device so they can prove age without sharing personal data repeatedly.

The pain

Age-gated services (alcohol, gambling, adult content) currently require users to upload government IDs repeatedly to different platforms, creating privacy leaks and friction. Users distrust sharing documents. Platforms face regulatory burden and liability for storing sensitive data.

The gap

No existing solution lets users prove age without either: (1) sharing PII with the service, or (2) going through expensive KYC processes. The technical gap is that passkey-based encryption with PRF extension is new enough that no one has productized it for this use case yet.

Build angle

Start by targeting one high-volume, regulated vertical (alcohol e-commerce or online gambling) where the pain is acute and willingness-to-pay is clear. Prove that platforms will integrate it to reduce liability and improve conversion. Then expand horizontally to other age-gated categories.

Strengths

Solves a real compliance problem for platforms (storing less PII = less liability)
User experience is genuinely better than current alternatives (no re-uploading)
Technical implementation is novel and defensible via the passkey PRF approach
Founder has working prototype and understands the crypto/auth stack deeply
Open-source library angle creates network effects and developer adoption

Risks

Regulatory uncertainty: age verification rules vary by jurisdiction and may require specific document types or third-party verification that this approach can't provide
Chicken-and-egg problem: needs both user adoption and platform integration; hard to bootstrap either side first
Passkey adoption is still low; many users don't have compatible devices or understand the technology
Competing against entrenched KYC vendors who have regulatory relationships and compliance certifications already in place
Revenue model unclear: who pays? Users won't; platforms may resist if they can use cheaper alternatives
The core value prop (privacy) may not matter to platforms if they're already comfortable with KYC vendors

Fly Labs Method

Is the pain real, is there a gap, is it the right time, can one person build it.

72

Value Equation

Dream outcome and how likely it feels, against the time and effort it costs.

58

One-Person Business

Curiosity pull, identity fit, and a path from free value to paid for a solo creator.

75

Viral Frameworks

Hook strength, shareability, and how cheaply it can be tested.

68

Builder Lens

Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.

71

Five lenses, one composite. How scoring works

The angle

Start by targeting one high-volume, regulated vertical (alcohol e-commerce or online gambling) where the pain is acute and willingness-to-pay is clear. Prove that platforms will integrate it to reduce liability and improve conversion. Then expand horizontally to other age-gated categories.

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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