Sphinx – the Jupyter-native AI copilot for data scientists
Hey HN community! Over the past year, AI copilots like Cursor and Windsurf have fueled a dramatic shift in software engineering workflows. And yet, many technical users in adjacent fields like data science and analytics have been unable to reap the rewards of this revolution. It turns out that the existing tools are a poor match for analytical workloads. Beyond that Cursor and similar tools have very poor support for Jupyter notebooks, data science is a fundamentally different discipline from software engineering and we believe it requires a correspondingly different tool. We're excited to…
What it does
In the maker’s words, at launch
Hey HN community! Over the past year, AI copilots like Cursor and Windsurf have fueled a dramatic shift in software engineering workflows. And yet, many technical users in adjacent fields like data science and analytics have been unable to reap the rewards of this revolution. It turns out that the existing tools are a poor match for analytical workloads. Beyond that Cursor and similar tools have very poor support for Jupyter notebooks, data science is a fundamentally different discipline from software engineering and we believe it requires a correspondingly different tool. We're excited to announce the launch of Sphinx to fill this gap. Sphinx is a data-native AI copilot for Jupyter notebooks. Unlike existing copilots, Sphinx understands that data science often requires an experimental, exploratory approach. Some highlights of Sphinx's capabilities: - A fully agentic loop, able to break tasks into discrete steps and implement them cell-by-cell - Able to create and read charts and graphs to guide its next steps - Fluent in working with common data connectors, like Snowflake, Databricks, and databases - Full MCP integration, and large outputs can even be processed programmatically as data directly in the notebook To make it as easy as possible to use, Sphinx operates on ordinary Jupyter notebooks and is available as a simple VSCode extension with a generous free tier. Excited to hear what you all think!
Does the same job
all alternatives →- PIParabola.io – Automate your work with visual programming2018 · parabola.io · ▲291
Hi HN! I’m Alex from Parabola (https://parabola.io). Parabola is a visual programming tool for creating functional data flows that everyone can use. It’s entirely drag-and-drop, handles data sizes much larger than a traditional spreadsheet, calculates everything live, and can run your flows on a schedule of your choosing. I used to work in strategy consulting, doing data analytics for SMBs and Fortune 500 companies. The amount of time wasted on menial tasks was astounding. Things like cleaning data, generating custom reports, creating human workflows to solve shortcomings in third…
- TCThink, CL tool that makes AI your command line copilot (written in Go)2023 · github.com · ▲14
Hi HN, I'm thrilled to share with you think, a command-line tool that I developed while diving back into the programming scene. For the past few years, my focus had shifted from hands-on programming to managerial roles, my last one being at Affirm (I started Affirm's Polish office). However, I recently left my job to revisit my passion for coding and see if I could still write code that works. Not to mention that with what is currently going on in AI if you are a developer this probably the best possible time for a sabbatical in human history :) My first attempt was with LangChain, but I…
- GBGraph-Based Editor for LLM Workflows2024 · github.com · ▲8
Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
- RNRMOTR Notebooks – Free Online Data Science Environment2018 · ▲11
Hello HN. We're happy to show to the world a platform that we've created to help our students get started with Data Science: https://notebooks.rmotr.com/ We know how hard it is to start working with Data Science tools: setting up local environment, installing dependencies, keeping projects organized, etc. RMOTR Notebooks has Jupyter Lab already preinstalled + the ability to upload datasets. Our platform is now open to everybody for free, and we are thinking about adding paid tiers in the future with better hardware, GPU support, pro features, etc. We've also worked on a…
- DODataHub, open source datasets for Artificial Intelligence2017 · ▲14
Hi HN! I am an undergrad student trying to build interesting things with AI. Recently, I was looking for a dataset I could use for a new project. I realized that it is really frustrating to go through all the government websites (with terrible UX) just to find some usable dataset. I set out to build a GitHub for datasets, named DataHub. Right now, we have more than 1000 datasets from Montréal and New York City, with more cities coming soon (and possible government agencies). All of this is wrapped into a powerful search. It's a breeze to find a dataset to work on. I'd be interested to know…
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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.
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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…
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Launched alongside, September 2025
the whole month →
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I made a built-from scratch Wayland Compositor to display any GUI app* in the terminal! I think there is a lot of unexplored potential in custom Wayland compositors, a lot of really cool things you can embed existing applications into! So, I started with embedding apps into the terminal because that is the easiest input/output (output is just utf-8 and I use the great `chafa` library for that, and I just read from stdin for the input). If you have any other ideas for cool Wayland compositors, let me know. I purposedly wrote 80% the app in Typescript to appeal to the most developers and…
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Years ago I stumbled across a basic version of this concept and it stuck with me. I knew if I was ever going to take on such a project, it would need to be flawless, but without coding experience it was just another idea that would never happen. By the end of 2024, as AI coding tools exploded everywhere, I finally had a way to make it real. I started from zero knowledge and spent months collaborating with AI agents as a learning experience. Every pixel and every function went through me. The AI translated what I asked for into code, but every decision was human. I didn't use existing OS…
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