A free ESG stock screener that publishes its losses and methodology: VALIDATE, 72/100 | Ideas Lab

A transparent stock screener with documented methodology and published losses, solving the trust problem in retail investing tools by showing exactly how it works and when it fails.

The pain

Retail investors distrust stock screeners because vendors hide methodology, cherry-pick results, and overfit to past performance. They want to understand what they're buying into before committing capital. Most tools treat their algorithms as black boxes or marketing props.

The gap

The market has thousands of screeners but almost none publish their losses, explain their reasoning on specific stocks, or document their methodology in detail. Transparency is treated as a liability rather than a differentiator. Investors can't tell if a screener is genuinely useful or just lucky on a bull market run.

Build angle

The founder has already built the product and validated the core insight: transparency and honesty attract users who value rigor. The next phase is converting free users into a revenue model (premium tiers, API access, institutional licensing) while maintaining the transparency that creates trust. The 163% alpha claim needs real-world validation beyond backtests, and the business needs to prove users actually make money following it.

Strengths

Founder solved a real personal problem (bad stock picks) with systematic methodology, not theoretical speculation
Published losses and case studies create credibility that competitors can't easily copy without admitting their own failures
Genetic algorithm with multi-regime constraints shows technical rigor beyond typical retail tools
Free distribution with email watchlist removes friction and builds user base for monetization
Detailed methodology documentation lets users audit the approach and understand failure modes
Walk-forward validation across 25 hold periods reduces overfitting risk compared to single-window backtests

Risks

Backtested 163% alpha doesn't guarantee forward performance; 2012-2025 includes multiple bull markets where many strategies work
No clear monetization model yet; free tools with email distribution don't generate revenue without conversion funnel
Retail investor market is price-sensitive and attention-poor; users may not read methodology or case studies despite transparency
Regulatory risk: publishing specific stock picks and performance claims could trigger SEC scrutiny around investment advice
Competitive response: if the screener works, larger platforms (Bloomberg, Seeking Alpha, Finviz) can copy the transparency angle with more resources
User retention depends on forward performance; one bad year of live results destroys the trust advantage
ESG filtering narrows addressable market and may exclude high-performing stocks, limiting appeal to performance-focused investors
No evidence users actually follow recommendations or that the system works in real-time with slippage, commissions, and execution delays

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.

68

Viral Frameworks

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

75

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

The founder has already built the product and validated the core insight: transparency and honesty attract users who value rigor. The next phase is converting free users into a revenue model (premium tiers, API access, institutional licensing) while maintaining the transparency that creates trust. The 163% alpha claim needs real-world validation beyond backtests, and the business needs to prove users actually make money following it.

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

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