Reading up on data-extraction
41 deep · digging since nov 19, 25
- Social Media Scraping APIs for YouTube, TikTok, Instagram, Facebook, X & LinkedIn
SocialKit provides a unified API that extracts transcripts, summaries, comments, and stats from YouTube, TikTok, Instagram, Facebook, X, and LinkedIn as clean JSON.
- Fintech Engineering Handbook
Hacker News commenters debate whether monetary values must always be stored as integers, with quants arguing floats suffice for risk modeling but transfers require exact integer precision.
- Fintech Engineering Handbook
The handbook catalogs patterns for representing, recording, and executing money movements—precision handling, double-entry ledgers, immutability, and invariants—to prevent data loss or invention in fintech systems.
- Statistics that live in your SQL
the-stats-duck v0.6.0 adds OLS regression, bootstrap, expanded distributions, and a Vega-Lite plot grammar to DuckDB SQL.
- Show HN: StartupWiki – A Free Alternative to Crunchbase
StartupWiki is a free, community/AI-driven startup directory that faces widespread criticism over data accuracy and reliability.
- Show HN: I made Google Trends for Hacker News by indexing 18 years of comments
Hacker Trends indexes 18 years of Hacker News comments to let users compare term frequency over time, built on Upstash Redis Search.
- Connect — Exa
Exa Connect aggregates data from over a dozen third-party providers — including Crunchbase, Similarweb, and ZoomInfo — into a single API request for AI agents.
- I Stored a Website in a Favicon
The author encoded a 208-byte HTML page into a 9x9 pixel favicon by storing RGB values and decoded it with JavaScript, demonstrating data storage in unintended places.
- birdclaw — Local Twitter memory in SQLite
Birdclaw is a local-first Twitter workspace that imports archives and caches live reads into a single SQLite database, accessible via CLI and web app.
- Google Is Quietly Buying Code From Play Store Developers to Train AI
Google is secretly paying Android developers for their code to train AI coding tools, suggesting it lacks sufficient training data.
- Empty Screenings – Finds AMC movie screenings with few or no tickets sold
A tool surfaces AMC screenings with zero tickets sold, letting users find effectively private theater showings based on live seat data.
- Gemini API File Search is now multimodal
The Gemini API File Search tool now supports multimodal data, custom metadata, and page citations for building RAG systems.
- GitHub - vincentkoc/slacrawl: cli terminal app for slack with sqlite backend
Slacrawl is a Go CLI tool that mirrors Slack workspace data into local SQLite for offline search, querying, inspection, and git-backed archive sharing.
- Building a Fast Multilingual OCR Model with Synthetic Data
NVIDIA's Nemotron OCR v2 uses 12 million synthetic training images to achieve near-zero error rates across six languages at 34.7 pages per second on an A100.
- Show HN: Every CEO and CFO change at US public companies, live from SEC
A live dashboard extracts CEO, CFO, and board changes from SEC filings, showing compensation details and trends, with a premium subscription for full access.
- Cosmologically Unique IDs
Designing a universal ID system for all possible objects across all future cosmic eras may require ~800-bit identifiers to avoid collisions.
- [2602.02361] SWE-Universe: Scale Real-World Verifiable Environments to Millions
SWE-Universe automatically constructs 807,693 real-world software engineering environments from GitHub PRs via iterative self-verification and in-loop hacking detection.
- OpenAI's In-House Data Agent
OpenAI built an internal AI data agent using its own tools to let employees query 600PB of data via natural language in minutes.
- Inside OpenAI’s in-house data agent
OpenAI built an internal AI data agent using GPT-5, Codex, and memory to reason over massive datasets, delivering insights in minutes.
- Parallel Web Systems | Web Search & Research APIs Built for AI Agents
Parallel's Monitor API lets AI agents define custom JSON schemas to extract structured, validated data from scheduled web searches.
- Why DuckDB is my first choice for data processing
DuckDB is an excellent choice for data processing because it allows SQL queries directly on CSV, JSON, and Parquet files with high performance and simplicity.
- Data is the only moat
The author argues that data is the only sustainable competitive advantage for AI products, using Cursor's coding-agent success and a quadrant analysis of adoption vs. technical complexity to make the case.
- Show HN: Self-host Reddit – 2.38B posts, works offline, yours forever
Redd-Archiver lets you self-host an offline browsable archive of 2.38B Reddit posts with full-text search and monthly incremental updates.
- We can't have nice things because of AI scrapers
AI scrapers ignoring robots.txt and bypassing bulk downloads are overloading volunteer-run open data projects like MetaBrainz, forcing them to restrict access and harming legitimate users.
- Upload data. Ask questions. Get answers and charts.
Livedocs is an AI agent that analyzes uploaded data from CSVs, spreadsheets, and databases to generate charts, metrics, and plain-English insights instantly.
- Databases in 2025: A Year in Review
Andy Pavlo's 2025 database review covers PostgreSQL's continued dominance, emerging trends like vibe coding, and commentary on SQLite, DuckDB, and MCP security risks.
- The Most Popular Blogs of Hacker News in 2025
Simon Willison ranked as the most popular blogger on Hacker News for the third consecutive year in 2025, based on analysis of domain score data.
- Show HN: 22 GB of Hacker News in SQLite
A tool serves 22 GB of Hacker News history via SQLite compiled to WASM, fetching only needed database shards in the browser.
- Google Workspace Updates: Transform sources into structured Data Tables in NotebookLM
NotebookLM introduces Data Tables that automatically extract scattered facts from sources into clean, structured tables exportable to Google Sheets.
- Show HN: Books mentioned on Hacker News in 2025
A curated list aggregates books most frequently mentioned on Hacker News in 2025, with SICP, Clean Code, and Crafting Interpreters topping programming mentions.
- UNIX V4 tape from University of Utah (raw) : Computer History Museum : Free Download, Borrow, and Streaming : Internet Archive
A UNIX V4 tape from 1974, found at the University of Utah, was digitized and released under a BSD license, preserving early Unix source code.
- 8M users' AI conversations sold for profit by "privacy" extensions
A security researcher discovered the Urban VPN Proxy Chrome extension, with millions of users and a Google Featured badge, secretly harvesting AI conversations from ten platforms via dedicated scripts.
- Show HN: I built a dashboard to compare mortgage rates across 120 credit unions
A daily-updated dashboard compares mortgage rates from 140+ credit unions, showing they consistently beat big bank rates by up to 79 basis points.
- Show HN: Epstein Files Organized and Searchable
Searchepsteinfiles.com indexes and tags 20,000 Epstein estate documents, but commenters say the searchable dump is poorly formatted and hard to use.
- Thread by @p0 on Thread Reader App – Thread Reader App
Parallel Extract, a new API from Agent Tools, fetches a page's full content and returns it as markdown in full or compressed form.
- Parallel Web Systems
Parallel's FindAll API turns natural language queries into structured datasets from the web, achieving 61% recall at 3x the performance of competitors.
Takes
The Log Is the Agent
@ishaansehgal
So... "ai, explain what I just did": we ingested 9M+ directed edges from X into a weighted influence graph, then ran a personalized PageRank variant — tuned for human accounts — to surface eigenvector centrality at scale. once you know the high-centrality nodes, you watch their signal propagation before it hits the long tail. beta soon. @digg @basic_in_
@kevinrose
I've been in the process of building a custom home for 5 years. Bought the land in 2021. Got the building permit this year. Haven't started construction yet. During those 5 years, I accumulated thousands of emails with dozens of architects, engineers, surveyors, contractors, government agencies, title companies, and others. Hundreds of PDFs I opened once and never found again. My project management system was email search and my own memory. I could always find individual emails when I needed them. What I couldn't do was see the project. How much money have we actually spent, and on what? Who are all the contractors we talked to, and how did we find each one? What happened with the easement, not one email about it, but the full arc across three years? Why did we stop using the original surveyor? The answers were all in my inbox. But they were spread across hundreds of threads. No single email contained the story. The story only existed in the connections between them. So I tried something. I pointed OpenClaw at my full email inbox and said: read all my emails in chronological order and figure out what happened with this project over the last 5 years. Build me a timeline. Find all the documents. Track the money. Map the people. That's it. I didn't sort anything. I didn't classify anything. I didn't tell it which threads mattered. I just pointed at the inbox and let it work. And it worked way better than I expected. It found 1,850 emails across 450 threads involving 58 people at 35 organizations. From that, it produced 511 timeline events describing what actually happened over 5 years. Not "Daniel emailed the architect" but "Easement delay threatens grading permit" or "architect warns the entire permit depends on securing the neighbor's access agreement." Real project history in PM language. It identified 690 documents and classified each one: invoice, permit, survey map, legal agreement, environmental report, estimate, and so on, and it linked them to the timeline events that referenced them. It extracted 170 finance records from email bodies and PDF attachments. Invoices, payments, estimates, and receipts with amounts, dates, and payees pulled from messy documents. It mapped out 58 contacts with their roles, their organizations, and how they related to the project over time. All interlinked. Click a timeline event, see the emails that produced it and the documents attached. Click a payment, trace it back to the invoice and the email thread. Click a person, see every event they were involved in. It built a dashboard on top of it and for the first time in 5 years, I could actually see the whole project. The full arc. Every dollar. Every person. Every decision. Stitched together from raw correspondence into something I can sit down and browse. The key insight for me was realizing this is basically an ETL process: Extract, Transform, and Load. The emails are the source data. The agent does the extraction from emails and loading into a database. But the really powerful part is the Transform: the LLM reads the raw correspondence with enough context to do intelligent enrichment across hundreds of threads spanning months and years. And by enrichment I don't mean summarization. I mean it actually reconstructed the narrative of the project. It traced how we almost hired the wrong well driller. We originally hired one company, paid a deposit, and were ready to go. Then the architect heard from someone in his network that they weren't reliable. We pivoted to a different driller who came recommended through a chain of referrals the agent traced back to its origin. The new company came out, drilled 140 feet, hit an artesian well with water pressure above ground level, and finished in two weeks. The original deposit got refunded. The agent reconstructed that entire sequence from first contact to final invoice, across dozens of emails and multiple contractors, and presented it as one coherent story. It reconstructed the full permit saga. Four separate permits with the county, each with its own cycle of applications, reviews, correction letters, resubmissions, and approvals. Years of back and forth. The agent built the complete timeline for each permit and linked every document and payment to the right stage. It tracked the money flow end to end. Not just "we paid the architect X." It found every invoice, matched them to the work described in the email threads, categorized the spending, and produced a financial history of the entire project broken down by architect, engineer, surveyor, contractor, county fees, and everything else. It mapped out relationships between people that I had half-forgotten. Which engineer referred which surveyor. Which contractor's crew member later became a separate vendor. Which county reviewer handled which permit. All of it was in the email, I just never had the time to stitch it together myself. One of the most fun things it did was writing honest personality profiles for each contact based purely on their communication style. How responsive they are. How they handle pushback. Whether they tend to over-promise. Whether they're the kind of person who answers at 11pm or takes five days to reply. Reading an AI's unfiltered take on the people you've been doing business with for years, based on nothing but their emails, is surprisingly entertaining and uncomfortably accurate. The thing that surprised me most is how much structure was already hiding in the email. I didn't add information. The agent found what was already there. The timeline, the document graph, the money flows, the cast of characters. It was all latent in the correspondence. Five years of decisions and negotiations and payments, all recorded in email, just never connected. I think a lot of people are sitting on projects like this without realizing it. Your renovation emails are a project database waiting to be assembled. Your legal correspondence is a case file. Your immigration threads are an application history. The raw material has been accumulating for months or years. It's rich, timestamped, and complete. It's just in a format designed for messaging, not for understanding. Point an agent at it. Let it read everything. Let it do the transform. The whole story was in my inbox the entire time. I just needed something that could read all of it at once.
@dvassallo
https://t.co/koRUvkFila
@phoebeyao
https://t.co/rc5GHb38Tq time
@thekitze