
45 Claude Code Tips: From Basics to Advanced github.com/ykdojo/claude-…
Filip Pyrek
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@FilipPyrek
AWS Hero 🚀 / Cloud Expert 🪄 / Engineering Innovator 🤖

45 Claude Code Tips: From Basics to Advanced github.com/ykdojo/claude-…




it’s christmas so you know what time it is, carp in the tub 🫧





We've spent 7 years building the data notebook for AI era. Today, we're open sourcing it. Deepnote Open Source is successor to the Jupyter notebook. It acts as a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. Single-player notebooks were great in 2013. 2025 needs reactive, collaborative, AI-ready projects that integrate into your existing stack seamlessly. That's why we're making Deepnote open source - to offer the community an open standard for AI-native data notebooks and data apps. We're standing on the shoulders of Jupyter — it changed how the world explores data. But at team scale, the papercuts stack up: brittle reproducibility, no native data connectors, weak collaboration, and bolted-on AI features. In the enterprise context, this gets very tough to manage - and we're seeing an increasing demand from large companies to move away from Jupyter. What’s new: - Reactive execution (downstream block auto-update) - Powerful blocks beyond code: SQL, interactive inputs, charts, KPIs, buttons - 100+ data integrations - Code in your favorite IDE: Cursor, Windsurf, or VS Code - No lock‑in: open standard; export to `.ipynb` whenever you need Once you're ready to scale in your team, transfer to Deepnote Cloud with one command for beefier compute, powerful data apps from notebooks and agentic data science. Ty it now: Repo ➔ vist.ly/4ctcq Deepnote in VS Code ➔vist.ly/4ctcm Docs -> vist.ly/4ctcf CLI ➔ `npx @deepnote/convert notebook.ipynb` P.S. This only works as an open standard. Tell us what’s missing, file issues, send PRs. Help define the data notebook for the AI era!





- fall in love - get married - have kids - work hard - be faithful - stay together - find meaning - be happy
