By now most of us have heard of, or used, Python type hints. Since being added to Python in 3.5, they've spread to every corner of the Python ecosystem. These days many libraries are built from the…

💻 Full-Stack Developer focused on building scalable web applications. 🛠️ Tech Stack: C, C++, C#, Python, Typescript, .NET 🚀Currently building:- Code-Editor - sanskarIN

After two decades of trying, the director and former Monty Python member has finally managed to make ‘The Man Who Killed Don Quixote’. But he’d rather talk to Alexandra Pollard about #MeToo, the trial...

Discover Reachy Mini 2026, the compact open-source AI robot with a camera, 4 microphones, expressive movement, Python programming, and Hugging Face integration.

Attached: 2 images ah yes, here we are. the largest companies in the world releasing a flagship product they are betting their entire competitiveness on that is built on bash scripts that have hardcoded string interpolation python scripts and in fact hardcoded string interpolated bash scripts that seems to be the machinery that manages runaway agent spawning and concurrency. edit: this is the bring-up routine that allows the agent to persistently modify the image it runs in which is even more awesome.

YouTube video by Blender

I wanted a small research database to grow from one subreddit to ten communities where people talk about building with Claude Code and automation. Doing it properly was estimated at about $118. It cost about $34, and the corpus went from roughly 1,000 chunks to 5,846. Overnight, while I slept. The scraping ran through a paid provider (Apify) that charges per delivered record. The cleaning, tagging and indexing were my own Python. Around all of it sat one PowerShell script, which Claude wrote for me. That script is what this post is about, because the agent isn't the hard part. Anyone can make an agent do something impressive while they're watching. The hard part is the wrapper that makes it safe to stop watching. Three rules. ## 1. Every phase is resumable The script keeps its own state: chunks before and after, spend at the start and end, what it recovered and what it couldn't. All of it is set before the work starts and read again at the end. Scraping is the phase that fails. Reddit blocks a scraper that hits too many communities in one session, with a 403. My first version asked for all ten subreddits at once. Four listings came back empty, including every Claude community, and the run still said it succeeded. The fix wasn't a smarter agent. It was batching: three subreddits at a time, and anything that comes up short goes into a retry queue and comes back on its own after a pause. All four blocked listings came back that way. Because the provider only bills for delivered records, the failed attempts cost nothing. Everything after scraping (cleaning, tagging, indexing) can run again on the same input without doubling anything, and the paid tagging step skips whatever it has already done. That's what lets the script retry without asking me. If it needed my permission to re-run, I'd have to be awake. ## 2. Judge success by the provider's number, not yours The obvious check: count your rows before the scrape, count them after, and if the number went up, it worked. It's wrong, and it's wrong in a way that looks exactly like a failure. The pipeline deduplicates on the way in. A second pass over a community pulls the day's top posts, and many of those are already stored from the first pass. So the provider can deliver hundreds of good records while your database barely moves. A check on your side calls that a dead run and pays to scrape it again. So success is the provider's delivered-record count, checked against a minimum I set for each community. My side can legitimately show no change and still be fine. Money works the same way. Before every paid attempt, the script reads the account's actual spend for the month from the provider and stops at a hard cap. It doesn't keep its own estimate. If something in my pipeline miscounts, the budget check can't be fooled, because it isn't counting anything. It asks the account. ## 3. Write the report even if it crashes The end of the script is a `finally` block. Whatever goes wrong above it (an error, a failed scrape, the provider going down), it still builds a coverage table, writes a status banner, saves the report and opens it in Notepad. One caveat: `finally` covers errors inside the script. If the whole process is killed, nothing runs. The first real report said **PARTIAL** : one community, one listing not recovered, named, with the command to re-run it. Not a failure, a to-do. The mop-up the next day brought back 47 records, which was everything that community had that day. When you're asleep for the whole run, silence is the worst output. Silence can mean everything worked, or that it died in the first 30 seconds, and from the couch those look the same. The report is what tells them apart. ## What went wrong anyway Two things, both the same lesson. **Headless Claude Code holds back its output.** Launched from a script, it prints nothing until it exits unless you tell it to stream. So the log file sits at zero bytes for the whole run, and a healthy agent looks exactly like a dead one. I now judge an agent by the timestamps on the files it's writing and its CPU time climbing, never by the log. **My summary tables were one row long.** The script that builds them had a nested database cursor that reset the outer loop after the first row. Every table came out formatted and plausible, and wrong. Now I count the rows I expect instead of eyeballing the top of the file. A run that reports success and is wrong is scarier than a run that crashes. The crash tells you. The plausible report doesn't. ## Get the script The script is free on GitHub, with a header marking which lines to adapt: https://github.com/lgcreativestudios/buildwithaihub/blob/main/scripts/v1/phaseC_overnight_orchestrator.ps1 It runs locally in PowerShell on Windows, reads your own Apify and OpenRouter keys from your user environment variables, calls those two services with them, and writes only local files. Read it before you run it. Here's the 7-minute walkthrough, with the real report on screen: I'm building the whole system in public. If you want the next pieces as they land, there's a waitlist: https://lgcreativestudios.github.io/buildwithaihub/ _Drafted with Claude's help from the video script. The run, the code and the numbers are real._

Monty Python star Eric Idle has opened up about mortality, his battle with pancreatic cancer, his enduring connection with the French countryside and his life away from the spotlight. At 83, the comed...

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Python API reference documentation for Astro and Starlight, extracted with Griffe.

Most computer vision systems stop at object detection. We’re working on the harder challenge of understanding context, relationships, and behavior across video in real-world environments. If that’s the kind of perception work you like solving, I’d like to talk. Opportunity for a strong Computer Vision / ML Engineer interested in helping solve a challenging perception problem. This is not a typical CRUD SaaS problem Ideal experience includes: • Computer vision / AI-ML model development • Python, PyTorch, and OpenCV • Object detection, segmentation, pose estimation, and multi-object tracking • Video understanding and spatiotemporal analysis • Fine-tuning and evaluating open-source vision models • C++ and/or C# for production computer vision systems • Real-time inference, model optimization, or edge deployment is a plus A great fit would be engineers who enjoy taking difficult perception problems from model → experiment → measurable performance → production system. If this sounds like your area of expertise, or you know someone who may be interested, fill out the form below or share this link: Computer Vision / ML Engineer