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marimo
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marimo
@marimo_io
An open-source AI-native Python notebook: reactive, git-friendly, execute as scripts, share as apps 🌐 https://t.co/YMoQiRxnLW 💬 https://t.co/2bEOclmIoI
Katılım Mayıs 2022
9 Takip Edilen6.9K Takipçiler
Meet Kiran Gadhave, who developed the extension! Read more here:
marimo.io/blog/pycharm
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Ask and you shall receive: marimo now runs natively in PyCharm.
Open, edit, and run reactive notebooks in @JetBrains without leaving your IDE.
Install from the JetBrains Marketplace 👇
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That's a wrap on our second notebook competition with @askalphaxiv! Thank you to everyone who submitted, joined the call, or helped spread the word. Winners below🧵
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Meet Kiran Gadhave, who built marimo-Glance. Grab it for Chrome or Firefox, install links and a quick demo are in the repo: github.com/marimo-team/ma…
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Meet Péter Gyarmati, who built mdx-marimo. He walks through how marimo-anywhere came together: marimo.io/blog/marimo-an…
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how we built it: marimo.io/blog/notebooks…
shoutout @trevmanz for this feature and write-up!
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happy official launch day 1! launch week opens with updates to marimo-pair, now in VS Code. pair opens up a new medium for agent-native data and computational work: you + your agent, collaborating over one interactive, flexible canvas.
more below ⬇️
marimo.io/pair
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SciPy was a blast! Missed @trevmanz's anywidget talk or @DylanMadisetti's caching poster?
Not to fear, for our first ever launch week is here...5 new announcements next week. What are you hoping it'll be?
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ArXiv paper in -> interactive, reproducible marimo notebook out!
We're thrilled to be partnering with @askalphaxiv to bring papers to life; all runnable and editable, not just static figures
alphaXiv@askalphaxiv
GPT 5.6 Sol can one-shot convert an arXiv paper into an interactive Marimo notebook! Great for papers best understood hands on (lots of fun examples in interpretability, inference engineering, agent harnesses, benchmarking, and more) Play around with the notebook, inspect the code, or try the same workflow with your own agents below
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marimo retweetledi
Cheap custom classifiers for production workloads
Define the function you want in natural language and this model will give you a "program" that cheaply behaves as you want - without having to fine-tune or do anything fancy
This is great for defining lots and lots of classifiers or simple "fuzzy functions" that you would typically use LoRAs for, but this has much less operational overhead
I reproduced the inference pipeline on @marimo_io cloud here with some GPUs to do the "compiling" too
alphaXiv@askalphaxiv
“Program-as-Weights” LLMs are great at fuzzy functions like log triage, JSON repair, and intent classification, but calling a big model on every input is slow, expensive, and not local. This paper compiles the fuzzy function once from a natural language spec into a small neural program, a pseudo-program plus a LoRA adapter. A frozen 0.6B interpreter then runs it locally, matching Qwen3 32B prompting on FuzzyBench while using about 50x less inference memory and running around 30 tok/s on a MacBook M3!
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molab has free GPUs and we've been exploring the different ways that you could use them. And there's plenty to do outside of deep learning!
You can use them to tackle TSP problems too. But it helps to rethink your CPU-based algorithms when you go here.
youtu.be/1-5wkzdP1Jc
YouTube
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72 hours left to win a @FrameworkPuter Laptop or Mac Mini + @claudeai subscriptions. Not too late to start: last round, almost 100 entries came in on the final day.
Pick any @askalphaxiv paper, build in molab, & submit by July 9, 11:59 PM PT 👀
marimo.io/pages/events/n…
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1 week left in our competition with @askalphaxiv. Don't know where to start? Pick a research area and check out the recommended papers below.
Details: marimo.io/pages/events/n…
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