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This is the future of data science development. Zerve is a new web-based IDE. It's block-based like a Jupyter notebook but follows a different philosophy, and the experience is much different. Check the attached video. I'll walk you through a simple example. Zerve is not a fork of Jupyter.... It's a completely reimagined experience where collaboration is a first-class citizen. But there's something else: Unlike in a regular notebook, you can deploy your models directly from Zerve. They store and cache every asset we use and generate inside a notebook, so it takes seconds to deploy your work. A few additional notes about Zerve: 1. It's web-based, so you can access your work anywhere. 2. Collaboration is a first-class citizen. 3. You can deploy your code without going anywhere. 4. It promotes code modularity by using blocks. 5. You can use multiple languages together. 6. You can run code blocks in parallel. Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. Zerve is free. You can start using it right now by visiting Thanks to the team for answering my questions and sponsoring this post.show more

Santiago

452,483 subscribers

84,664 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

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11 条评论

Akshay Agrawal 的头像

Akshay Agrawal2 年前

@marimo_io is not a fork of Jupyter. It's a completely reimagined experience that models your code as a DAG, giving you reproducibility, maintainability (version with git!), and deployability as interactive apps or scripts. Plus, it's all open source

Hesam 的头像

Hesam2 年前

couple an LLM with this graphic interface and DS will be almost CODELESS. Cool one 👌

Ravi | ML Engineer 的头像

Ravi | ML Engineer2 年前

This actually looks pretty cool

Rudraksh Karpe 的头像

Rudraksh Karpe2 年前

Looks pretty interesting!

Pawel Jastrzebski 🇵🇱 🇬🇧 的头像

Pawel Jastrzebski 🇵🇱 🇬🇧2 年前

I like the setting of your studio/office.

Santiago 的头像

Santiago2 年前

Thanks! It's a lot messier than what you can see in the video.

Omer Gronich 的头像

Omer Gronich2 年前

Is the Zerve UI written in python or react? 😉

Lauren Neal 的头像

Lauren Neal2 年前

i'm intrigued

Okot 的头像

Okot2 年前

How I wish there was part showing visualizations from matplotlib, and other libraries with beautiful visualizations.

Ryan Lisse 的头像

Ryan Lisse2 年前

@memdotai mem it

Mem 的头像

Mem2 年前

@svpino Saved! Here's the compiled thread:

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Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Santiago

27,942 次观看 • 1 年前

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,399 次观看 • 7 个月前

Ok, so the new Midjourney Style Tuner is here and it's a MASSIVE update. There is so much to unpack, no way I can do it all in one post, but here is an overview. You start by typing in /tune followed by your prompt. MJ will generate a range of sample images (between 16-128) showing different visual styles based on your prompt. Then you choose your favorite images from the sample to generate a unique code you can use to customize the look of future jobs. (link below) So for example, I just tried it with the prompt: 💬 1990s, a photo of a woman dressed in fuzzy velour, shimmering midnight hues reflecting sadness by the lake, a contemporary nightmare on cross-processed film --ar 4:5 --c 4 I selected 10 images, which generated the code zlPhgLCHDXol64kPlwSUzu97. I can then add it to my prompt using --style {code} But it makes more sense if you try it yourself, so try playing around with my Style Tuner for that prompt and generate your own code from your selections: Some quick notes: -You can control the level of style applied by adding the --s parameter to your prompt. -You can combine multiple styles together by separating with a hyphen, i.e --style code1-code2 I will be obsessing over this all day tomorrow and sharing everything I learn along the way. This is an incredible update.

Ok, so the new Midjourney Style Tuner is here and it's a MASSIVE update. There is so much to unpack, no way I can do it all in one post, but here is an overview. You start by typing in /tune followed by your prompt. MJ will generate a range of sample images (between 16-128) showing different visual styles based on your prompt. Then you choose your favorite images from the sample to generate a unique code you can use to customize the look of future jobs. (link below) So for example, I just tried it with the prompt: 💬 1990s, a photo of a woman dressed in fuzzy velour, shimmering midnight hues reflecting sadness by the lake, a contemporary nightmare on cross-processed film --ar 4:5 --c 4 I selected 10 images, which generated the code zlPhgLCHDXol64kPlwSUzu97. I can then add it to my prompt using --style {code} But it makes more sense if you try it yourself, so try playing around with my Style Tuner for that prompt and generate your own code from your selections: Some quick notes: -You can control the level of style applied by adding the --s parameter to your prompt. -You can combine multiple styles together by separating with a hyphen, i.e --style code1-code2 I will be obsessing over this all day tomorrow and sharing everything I learn along the way. This is an incredible update.

Nick St. Pierre

377,958 次观看 • 2 年前

Weekend project: A reusable deployment pipeline defined in C# that can take any aspire project and deploy it to a VM with ssh and docker. - Use of C# >>>> Shell scripts interleaved in YAML 🤮. - The pipeline code that runs over a model. This same code can work for any aspire app without changes. It's a dynamic pipeline template. - It can run locally or in the CI - This was built on top of upcoming APIs in Aspire 9.4 (you can build your own pipeline and deploy anywhere too!) #dotnet #aspire

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David Fowler

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Built an open-source Jupyter Notebook that brings the power of OpenAI's code interpreter into your local Python development environment. Watch how easy it is to use Thread to edit code I wrote, chat with my notebook, or generate charts just using prompts. We built Thread using Next.js, Chakra and Project Jupyter and you can install it by running: pip install thread-dev

Built an open-source Jupyter Notebook that brings the power of OpenAI's code interpreter into your local Python development environment. Watch how easy it is to use Thread to edit code I wrote, chat with my notebook, or generate charts just using prompts. We built Thread using Next.js, Chakra and Project Jupyter and you can install it by running: pip install thread-dev

Ali Shobeiri

112,638 次观看 • 2 年前

AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):

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Andrew Ng

118,164 次观看 • 8 个月前

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Santiago

70,786 次观看 • 9 个月前

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Santiago

296,363 次观看 • 9 个月前

This is an excellent feature of Grok4. The code you provide to Grok gets nicely formatted in a Markdown block. The play button lets you run your code in the browser. You can also easily copy or edit your code.

This is an excellent feature of Grok4. The code you provide to Grok gets nicely formatted in a Markdown block. The play button lets you run your code in the browser. You can also easily copy or edit your code.

tetsuo

1,054,866 次观看 • 11 个月前

Going from design to code is going from something built and usable to an unnecessary level of abstraction. It's like finishing a house then asking for the blueprints so someone else can rebuild it. Design is code. Code is design. Use a code-first canvas. Use MagicPath.

Going from design to code is going from something built and usable to an unnecessary level of abstraction. It's like finishing a house then asking for the blueprints so someone else can rebuild it. Design is code. Code is design. Use a code-first canvas. Use MagicPath.

Pietro Schirano

26,974 次观看 • 4 个月前

This is literally the easiest way to build an AI agent. Zero code. You only need to run a couple of commands. (I've said this before, but Google ADK is my favorite way to build code-first agents.) Here is what you need to do: 1. Open your terminal and create an empty folder 2. Run `uv init` inside the folder (if you don't have uv installed, you are really missing out!) 3. Install the google-adk library using the following command: `uv add google-adk`. 4. At this point, you can use the ask to create your agent with a single command: `uv run adk create --type=config my_agent`. This will ask you a couple of questions, including your GEMINI_API_KEY (you can create one online). 5. The command above will create a my_agent/root_agent.yaml file. Open it and add the following two lines at the end of the file so your agent can use Google to search for stuff: tools: - name: google_search 6. You have everything you need now. Run the adk web interface and start playing with your agent: `uv run adk web` That's it! Literally zero code. The agent works off the YAML configuration file.

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Santiago

140,737 次观看 • 7 个月前

🤓 I just published my notebook on the BlackBasta chat log leak! It’s a dynamic marimo notebook you can run in your browser—no setup needed. Be patient, though—it takes a few seconds to load as the notebook is heavy. Here is what I did 👇 ➡️ Generated diagrams to vizualise the data and extracted some stats. ➡️ I added a dynamic table with all extracted IOCs (not curated) so you can browse and filter them directly from your browser. ➡️ Then I built a hybrid RAG using FAISS (semantic search) + BM25 (keyword search) + ensemble retriever. ➡️ The retrieved data is shown with the LLM’s response for verification. ➡️ I also included context relevancy checks to reduce hallucinations. Everything is documented in the notebook! No API key needed—I provided mine (it will be disabled once the limit is reached). ✨ I used GPT-4o Mini as it is less expensive, but I got much better results with o1, so you may want to use the code locally with your own api key. 👉 Note: This is a personal work and it is not affiliated with my employer. If you like it, share it & let me know what you think! 😊 👉 Notebook:

🤓 I just published my notebook on the BlackBasta chat log leak! It’s a dynamic marimo notebook you can run in your browser—no setup needed. Be patient, though—it takes a few seconds to load as the notebook is heavy. Here is what I did 👇 ➡️ Generated diagrams to vizualise the data and extracted some stats. ➡️ I added a dynamic table with all extracted IOCs (not curated) so you can browse and filter them directly from your browser. ➡️ Then I built a hybrid RAG using FAISS (semantic search) + BM25 (keyword search) + ensemble retriever. ➡️ The retrieved data is shown with the LLM’s response for verification. ➡️ I also included context relevancy checks to reduce hallucinations. Everything is documented in the notebook! No API key needed—I provided mine (it will be disabled once the limit is reached). ✨ I used GPT-4o Mini as it is less expensive, but I got much better results with o1, so you may want to use the code locally with your own api key. 👉 Note: This is a personal work and it is not affiliated with my employer. If you like it, share it & let me know what you think! 😊 👉 Notebook:

Thomas Roccia 🤘

15,791 次观看 • 1 年前

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

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Santiago

55,609 次观看 • 1 年前

Devin is like Claude Code except it lives in the cloud and runs against all of your repos vs your local filesystem. So it never turns off, can be run from anywhere including your phone + Slack, and runs as many tasks as you can send it in parallel. It's complementary to all agentic IDEs and CLIs, and for the first time ever it's free to get started.

Devin is like Claude Code except it lives in the cloud and runs against all of your repos vs your local filesystem. So it never turns off, can be run from anywhere including your phone + Slack, and runs as many tasks as you can send it in parallel. It's complementary to all agentic IDEs and CLIs, and for the first time ever it's free to get started.

nader dabit

49,534 次观看 • 4 个月前

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,586 次观看 • 2 年前

I can't stop thinking about this... Michael Caine on his defining philosophy on life: Use the Difficulty As a young actor, he was rehearsing a play scene when a chair got stuck in the door and blocked his path. He told the other actor he couldn't get by the chair to enter the room in the scene. The actor's response: "Use the difficulty...if it's a comedy, fall over it, if it's a drama, pick it up and smash it." This idea—of using the difficulty—became a defining mantra for Michael Caine's life. "There's never anything so bad that you cannot use that difficulty...if you can use it a quarter of one percent to your advantage, you're ahead, you didn't let it get you down." This idea hit me hard... How can you use the difficulty you're currently facing? How can you embrace the struggle? How can you find flow through the friction? How can you sow the seeds of your light during the periods of darkness? As with everything in life, control the controllable: The difficulty is already there, you can't control it. You can control how you react to it. You can control your response to it. You can control your attitude towards it. Lesson: Difficulty is inevitable. Use it.

I can't stop thinking about this... Michael Caine on his defining philosophy on life: Use the Difficulty As a young actor, he was rehearsing a play scene when a chair got stuck in the door and blocked his path. He told the other actor he couldn't get by the chair to enter the room in the scene. The actor's response: "Use the difficulty...if it's a comedy, fall over it, if it's a drama, pick it up and smash it." This idea—of using the difficulty—became a defining mantra for Michael Caine's life. "There's never anything so bad that you cannot use that difficulty...if you can use it a quarter of one percent to your advantage, you're ahead, you didn't let it get you down." This idea hit me hard... How can you use the difficulty you're currently facing? How can you embrace the struggle? How can you find flow through the friction? How can you sow the seeds of your light during the periods of darkness? As with everything in life, control the controllable: The difficulty is already there, you can't control it. You can control how you react to it. You can control your response to it. You can control your attitude towards it. Lesson: Difficulty is inevitable. Use it.

Sahil Bloom

1,359,994 次观看 • 2 年前

Claude Code with Sonnet 4.5 is actually incredible I gave it a prompt for a super complex app, and it one shot the entire thing In this video I walk you through how to use Claude Code to build a prompt library app you can start using immediately (no coding experience required):

Claude Code with Sonnet 4.5 is actually incredible I gave it a prompt for a super complex app, and it one shot the entire thing In this video I walk you through how to use Claude Code to build a prompt library app you can start using immediately (no coding experience required):

Alex Finn

40,955 次观看 • 9 个月前

A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓

A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓

slash1s

383,466 次观看 • 1 个月前