AI Agent in Jupyter – Runcell

Hacker News

I build runcell, an AI Agent in Jupyter Lab. It can understand context (data, charts, code, etc) in your jupyterlab and write code for you. Runcell has built-in tools that can edit or execute cells, read/write files, search web, etc. Comparing with AI IDE like cursor, runcell focus on building context for code agent in jupyter environment, which means the agent can understand different types of information in jupyter notebook, access kernel state, edit/execute specific cells instead of handling jupyter as static ipynb file. Comparing with jupyter ai, runcell is more like an agent instead of a chatbot. It have access to lots of tools to work and take actions by its own. You can use runcell with simple "pip install runcell" to start. Any comments and suggestions are welcome.

Visit product View on Hacker NewsAug 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, context · Missing: mac, agents, macos

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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