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Continents move at about the speed your fingernails grow, a few centimeters a year.
That slow drift is still lifting the Himalayas as India pushes into Eurasia, and it grinds the Pacific plate past North America along the San Andreas Fault.
Wegener proposed drift in 1912 and was dismissed because he could not name the force. Magnetic stripes on the seafloor settled it half a century later.
How the Earth works: learnhowtoscience.com/geolog… #Geology
AI video editors don't replace editing. They replace the tedious part of it.
Silence and filler removal, captions, vertical reframing and picking clips from long recordings are basically solved. Deciding what stays in is still your job.
Which tool fits which kind of video: aitools9.com/ai-video-editor…
#AITools #VideoEditing
4. Chrome extensions. Instant Data Scraper, Web Scraper and Data Miner run inside the page you are looking at. Zero setup, export to CSV in a click. Great for one table today, not for a job that runs every night.
6. AI scrapers. Firecrawl, Crawl4AI and similar tools read the page with a language model and extract from plain instructions instead of selectors, so a site redesign does not break the job overnight.
The quick rule: match the tool to how hard the target site is and how often the job runs, not to what is trending.
The full comparison, with pricing models and how to match a tool to your use case: webbrowserbot.com/web-scrapi… #WebScraping #Python
2. No code visual tools. Octoparse, ParseHub and Browse AI let you click the data you want inside a built in browser, then handle pagination and schedules for you. Built for analysts and researchers who never want to see a CSS selector.
Most scraping projects pick the tool first and the problem second. That is why so many of them get rebuilt six months later.
A thread on the six kinds of web scraping tools, and who each one is actually for:
1. Scraping APIs. You send a URL, they send back rendered HTML. Proxy rotation, retries and CAPTCHAs happen on their side. ScraperAPI, ScrapingBee and Scrape.do live here. Best for developers who want to write their own parsing but not run infrastructure.
Most automations fail quietly. The form stopped reaching the CRM three days ago and nobody knows why, because the tool only says "error".
A thread on building workflows you can actually debug, on hosting you already pay for:
The part that saves the most time is the run history. Every step is logged with resolved values, so a flow that took the wrong branch shows you the exact value that steered it, like "42.00" arriving where the flow expected "42".
Free, open source, MIT licensed. The full guide with the node reference, triggers and the GitHub repo: aiappsapi.com/Workflow-Chain… #WorkflowAutomation #OpenSource
Picture your 50th conversation with an AI assistant. It still doesn't know your name, your stack, or what you decided last week. That is not a model problem, it is an architecture problem. A thread on what separates useful assistants from forgetful ones:
When a tool fails, hand the model a clear error with context. Models recover well from readable errors and badly from cryptic ones. And run independent tool calls in parallel, users feel every second.
Full developer guide to building AI assistants that remember, use tools well, and improve over time: adaptiverecall.com/ai-assist… #AIAgents #LLM
