Pulpie – Models for Cleaning the Web: VALIDATE, 72/100 | Ideas Lab

A cheaper, faster HTML content extraction model that undercuts existing solutions by 20x on cost through better architecture choices.

The pain

Companies building search, RAG, and data pipelines waste money on content extraction. Current tools like Dripper cost $159k to clean 1 billion pages. Boilerplate (ads, footers, navigation) contaminates downstream LLM outputs, degrading answer quality. The founders experienced this directly when an ad ended up in their search results.

The gap

Existing extractors use decoder architectures that are memory-bound, they read the full model repeatedly to generate one token at a time. This forces expensive GPU usage. Pulpie uses an encoder that labels HTML blocks in a single forward pass, making it compute-bound instead. Cheaper GPUs have more compute relative to memory bandwidth, so Pulpie runs efficiently on commodity hardware.

Build angle

Start by selling to companies running RAG pipelines and search products that already know they have a data quality problem. They're already paying for extraction or doing it poorly. Offer a drop-in replacement with clear ROI (20x cheaper). Open source the models to build trust and reduce friction. Expand to a managed API service once you have proof of product-market fit and understand customer deployment patterns.

Strengths

Concrete 20x cost advantage with measurable metrics ($7,900 vs $159,000)
Technical insight is sound, encoder vs decoder tradeoff is real and well-explained
Open source models lower barrier to adoption and build credibility
Founders have domain expertise from building their own search product and hitting the problem firsthand
Clear use case with existing customers (anyone doing RAG, search, or web scraping at scale)
Demonstrated working product with side-by-side comparisons

Risks

Extraction quality must match SOTA, any regression in accuracy kills the value prop. Dripper has years of refinement; Pulpie is new
Market may be smaller than it appears. Many companies either accept noisy data or use cheaper heuristics (regex, CSS selectors). Hard to know TAM without customer conversations
Dripper and similar tools may quickly adopt encoder architectures if the advantage is real, collapsing the moat
Monetization unclear. Open source models are free. Managed API service faces competition from Dripper and others offering similar services. Margins may compress fast
Dependency on Hugging Face for distribution. Platform changes or policy shifts could affect reach
No evidence of customer traction yet. This reads as a technical launch, not a business launch

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.

68

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.

70

Builder Lens

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

76

Five lenses, one composite. How scoring works

The angle

Start by selling to companies running RAG pipelines and search products that already know they have a data quality problem. They're already paying for extraction or doing it poorly. Offer a drop-in replacement with clear ROI (20x cheaper). Open source the models to build trust and reduce friction. Expand to a managed API service once you have proof of product-market fit and understand customer deployment patterns.

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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