We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers.LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that:- Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with nested arrays and complex schemas. One bad bracket and your pipeline crashes. - Relative URLs, markdown-escaped links, tracking parameters — the "small" URL issues compound fast when you're processing thousands of pages. - You end up writing the same boilerplate: HTML cleanup → markdown conversion → LLM call → JSON parsing → error recovery → schema validation. Over and over.We got tired of rebuilding this stack for every project, so we extracted it into a library.Lightfeed Extractor is a TypeScript library that handles the full pipeline from raw HTML to validated, structured data:- Converts HTML to LLM-ready markdown with main content extraction (strips nav, headers, footers), optional image inclusion, and URL cleaning - Works with any LangChain-compatible LLM (OpenAI, Gemini, Claude, Ollama, etc.) - Uses Zod schemas for type-safe extraction with real validation - Recovers partial data from malformed LLM output instead of failing entirely — if 19 out of 20 products parsed correctly, you get those 19 - Built-in browser automation via Playwright (local, serverless, or remote) with anti-bot patches - Pairs with our browser agent (@lightfeed/browser-agent) for AI-driven page navigation before extractionWe use this ourselves in production at Lightfeed, and it's been solid enough that we decided to open-source it.GitHub: https://githu
FL score
out of 100
Verdict
high confidence
Competition
12
competitors found, emerging market, funded players
Trend
No signal yet
A TypeScript library for robust LLM-powered web data extraction, solving major pain points for data engineers and developers.
The pain
The gap
Build angle
Strengths
Questions about this idea?
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Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
The idea addresses a real, specific, and severe pain for developers struggling with robust LLM-based data extraction. There's a clear gap for a developer-centric, open-source TypeScript library providing this, differentiating it from existing no-code or generic API solutions. However, the direct monetization path for an open-source library can be challenging, and while it's 'built,' ongoing maintenance and turning it into a sustainable solo business requires further thought.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea has strong market pain and a clear value proposition for developers, operating in a growing market. The challenge lies in converting an open-source library into a sustainable business with strong pricing power and a clear monetization path for a solo builder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
This idea has excellent problem clarity and creator fit for a technical niche. The main challenge for a solo builder is translating a powerful open-source library into a sustainable, low-leverage business model without becoming an ongoing support burden.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The idea targets a very specific and reachable audience with a clear, measurable value proposition. The main hurdle for micro-SaaS viability is defining a robust business model beyond the free open-source library and validating willingness to pay for commercialized offerings.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
This idea has strong demand from a specific, desperate user, directly addressing their current painful status quo with a clear, usable solution. The library itself serves as a viable, narrow wedge, with strong internal validation.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
Gumloop is a no-code automation tool that allows users to automate any tool within their tech stack by connecting nodes with AI models like ChatGPT, Claude, and Llama.
Pricing: Free: up to 2,000 credits/month; Solo: starts at $37/month for 10,000+ credits; Team: starts at $244/month for 60,000+ credits; Enterprise: custom pricing.
Octoparse is a no-code web scraping solution that enables users to scrape any website and turn it into structured data.
Pricing: Free plan available, then starts at $37/month. Base plan starts at $119/month.
Firecrawl allows users to input any URL and convert websites into LLM-ready data, offering output in formats like Markdown, JSON, and screenshots.
Pricing: Not explicitly stated, but offers no-code access through a Playground for testing.
Browse AI is an AI-powered web scraping and monitoring platform that allows users to scrape, extract, monitor, and turn any website into a live dataset with no coding required.
Pricing: Starts at $48/month.
Diffbot uses AI to build and expand a universal database of structured information, providing 'Knowledge as a Service' with on-demand APIs for automatic data extraction.
Pricing: Free tier: up to 10,000 credits/month; Startup Plan: $299/month for 250,000 credits; Plus Plan: $899/month for 1,000,000 credits; Enterprise Plan: custom pricing.
Apify is a full-stack web scraping and automation platform that provides a cloud runtime and a marketplace of pre-built scrapers and automations.
Pricing: Entry-level plan costs $29/month, including platform credits for unlimited access to features and scrapers.
Zyte is a web scraping platform offering smart proxy management, APIs, and AI-based data extraction, with solutions for large-scale crawling and managed data services.
Pricing: Not explicitly stated, but generally considered one of the pricier tools in its category, with complex tier-based pricing for multiple products.
ScrapingBee provides a web scraping API to handle headless browsers, proxy rotation, and CAPTCHA circumvention with a focus on developer-friendly features.
Pricing: API access for $49/month. Lite Plan at $0.001 per call.
Scrapfly offers a unified API for web scraping, screenshots, AI extraction, and crawling, focusing on high success rates on protected sites.
Pricing: Starts at $30/month. Offers 1,000 free API credits.
SOAX is a data extraction platform offering scraping APIs, a proxy network, and an AI Scraper that uses natural language to intelligently scrape any website.
Pricing: Not explicitly stated, but offers flexible plans and pricing suitable for individuals and businesses.
No-Code Scraper is an AI-powered tool that leverages LLMs to extract data from websites effortlessly, offering reusable scraping templates and automated data cleaning.
Pricing: Not explicitly stated, but pricing is competitive, and some key features are still lacking.
Thunderbit is an AI-powered Chrome extension designed for non-technical users to perform web scraping with 2-click scraping and instant exports.
Pricing: Starts at $15/month for 500 rows, with a free tier for small projects.
What they charge
Recent news
Gumloop, Not specified, but article refers to 2026
Scrape.do, October 02 2025
G2, Not specified, but refers to 2026
Thunderbit, May 20 2025
Firecrawl, February 24 2026
Market signals
The market for LLM extractors for websites is growing, driven by the increasing need for structured data from the web. Recent trends indicate a strong move towards AI-powered, no-code solutions that simplify data extraction and adapt to website changes. The web scraping market saw significant growth and funding activity in 2023.
What frustrates people
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
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI