nbdeploy
Your Jupyter notebook, production-ready in minutes
What it does
Most tools help you write code. nbdeploy understands your entire notebook. It maps every cell dependency, detects what will break in production, and refactors everything into clean modular Python code, with the architecture you choose. You get a full project back. Modular code, Deployment guide, CI/CD scripts, deployment scripts, and a complete project structure. Push to GitHub in one click. Before anything applies, you review every AI fix in a diff view. You stay in control.
Does the same job
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- AMAdding Mistral Codestral and GPT-4o to Jupyter Notebooks2024 · github.com · ▲269
Hey HN! We’ve forked Jupyter Lab and added AI code generation features that feel native and have all the context about your notebook. You can see a demo video (2 min) here: https://www.tella.tv/video/clxt7ei4v00rr09i5gt1laop6/view Try a hosted version here: https://pretzelai.app Jupyter is by far the most used Data Science tool. Despite its popularity, it still lacks good code-generation extensions. The flagship AI extension jupyter-ai lags far behind in features and UX compared to modern AI code generation and understanding tools (like…
- ASA Spatial Environment for Python2022 · python.natto.dev · ▲169
Hi all! A little background: I've been working on natto.dev, a spatial environment for JavaScript. I'm really excited about new interfaces for code (leveraging metaphors we're good at, spatial reasoning, making state visible, design tools, etc). With all the buzz around PyScript, I discovered Pyodide and got it working inside natto. This Python version is a stripped down version of https://natto.dev (eg interactive outputs, multiplayer) so please check that out if this interests you. I'm excited to share this spatial environment for Python. Imagine Jupyter cells arranged on a 2D…
- DBDropbase – Build internal web apps with just Python2023 · github.com · ▲207
Hey HN, I’m Jimmy, co-founder of Dropbase (https://www.dropbase.io). We are an internal tools builder for Python developers. All you have to do is import any Python scripts/libraries, declare UI components, and layer app permissions so you can share them with others. We’re a middle ground between Airplane and Retool—simpler UI creation than Airplane, more code-centered than Retool. UI building is declarative and you can bind Python scripts/functions to UI components. You can write Python scripts/functions using our App Studio with support from a Python Language…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com