API calls, structured outputs, tool calling, agent loops, MCP servers.
Grab the free LLM APIs and MCP cheat sheet and keep all five on one page next to your editor.
https://t.co/HYJj1djv7F
"I know some Python basics. Where do I start with data?"
Start with this path. Set up a notebook → load and clean real data → make charts → finish with statistics.
https://t.co/78mSPXHSmV
Paused a video lesson because your terminal doesn't match the recording?
This quiz checks how well you use Mentor AI, Real Python's AI helper for video courses. How many can you get right?
https://t.co/1Jn6sJfUuM
Your Pygame jet dodges missiles. Can you make it collect fuel cans too?
The starter code has a fuel can that drifts in from the right. You write the code that puts it in the game.
https://t.co/h9BuJGG2tg
AI wrote the code in seconds. Now you have to decide if you trust it.
This free cheat sheet helps you review AI-generated Python code faster. Grab the PDF before your next review.
https://t.co/lnFdMIlQMu
Turn a folder of PDFs, Excel files and images into clean Markdown your LLM can actually read.
The MarkItDown course shows the Python code, the batch run and the MCP server. Plus where it falls short next to Pandoc.
https://t.co/UzuQrTbPKd
The GIL doesn't make threaded Python code safe. It can just make thread bugs very rare.
Nathan Goldbaum can't confirm it, but suspects that's behind some hard-to-debug flaky crashes. On the free-threaded build, he says, they usually show up right away.
https://t.co/Lcr55rc3ve
Pick a year and a round, and this Python app replays the F1 race on a drawn track, with live positions, tyres and the Safety Car.
The F1 data has no GPS for the Safety Car, so the app has to fake its position.
Built on FastF1 and Arcade by @tomshaw_dev
https://t.co/Rwrx9eHzKW
Using a list as a queue works fine. Until the queue gets big.
A deque gives you fast appends and pops at both ends. This course shows when to reach for it over a list.
https://t.co/tiixCDnMOr
5 lines I add to almost every prompt for Codex, or any coding agent 🧵
1. Ask me questions
2. Official docs only
3. Another developer
4. Only if confident
5. Report back
Which agent type do you use most, and which would your company block?
The tutorial charts all four by autonomy and interaction style, plus 3 mistakes to avoid.
https://t.co/YLLZ10U1iT
Claude Code, Cursor and GitHub Copilot each run in your editor, your terminal, your pull requests and the cloud.
Let the task pick:
1. Editor: edits you approve
2. Terminal: multi-file changes
3. PRs: work you don't watch
4. Cloud: scoped jobs
5. Where cloud agents run
🧵
5. Where cloud agents run depends on the vendor.
Claude Code on the web: Anthropic's servers.
GitHub Copilot cloud agent: a short-lived GitHub Actions environment.
Cursor Cloud Agents: optionally your own machines.
Check your company's policy for private code.
Your dataset is bigger than your machine's memory.
A generator hands you one item at a time instead of loading it all. Chain a few and you've got a data pipeline.
https://t.co/9D9t2wKNRe