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Ana Sayfaya Dön

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 görüntüleme • 2 yıl önce •via X (Twitter)

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Akshay Agrawal profil fotoğrafı

Akshay Agrawal2 yıl önce

@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 profil fotoğrafı

Hesam2 yıl önce

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

Ravi | ML Engineer profil fotoğrafı

Ravi | ML Engineer2 yıl önce

This actually looks pretty cool

Rudraksh Karpe profil fotoğrafı

Rudraksh Karpe2 yıl önce

Looks pretty interesting!

Pawel Jastrzebski 🇵🇱 🇬🇧 profil fotoğrafı

Pawel Jastrzebski 🇵🇱 🇬🇧2 yıl önce

I like the setting of your studio/office.

Santiago profil fotoğrafı

Santiago2 yıl önce

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

Omer Gronich profil fotoğrafı

Omer Gronich2 yıl önce

Is the Zerve UI written in python or react? 😉

Lauren Neal profil fotoğrafı

Lauren Neal2 yıl önce

i'm intrigued

Okot profil fotoğrafı

Okot2 yıl önce

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

Ryan Lisse profil fotoğrafı

Ryan Lisse2 yıl önce

@memdotai mem it

Mem profil fotoğrafı

Mem2 yıl önce

@svpino Saved! Here's the compiled thread:

Benzer Videolar

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 görüntüleme • 1 yıl önce

This is going to change Data Analytics! A Dashboard UI inside the Jupyter Notebook which makes Visualizing Easy! Using just one line of code and is easy to deploy as well!

This is going to change Data Analytics! A Dashboard UI inside the Jupyter Notebook which makes Visualizing Easy! Using just one line of code and is easy to deploy as well!

Sasi 📊📈

92,180 görüntüleme • 2 yıl önce

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 görüntüleme • 7 ay önce

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

377,928 görüntüleme • 2 yıl önce

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

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

David Fowler

23,700 görüntüleme • 1 yıl önce

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 görüntüleme • 2 yıl önce

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):

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):

Andrew Ng

118,164 görüntüleme • 8 ay önce

Introducing MakeReal in jupyter-tldraw ✨ Now you can hand-draw a plot on a tldraw whiteboard and turn it into Matplotlib code directly in a notebook.

Introducing MakeReal in jupyter-tldraw ✨ Now you can hand-draw a plot on a tldraw whiteboard and turn it into Matplotlib code directly in a notebook.

Jan-Hendrik Müller

239,726 görüntüleme • 2 yıl önce

The era of manually analyzing data will come to an end. AI can now do a lot of this automatically. It's a huge time saver. I do data analysis for a living, and I'm a huge fan of writing Jupyter notebooks to do it all, but it's now hard to justify manually writing code that you can generate in a few seconds. I still check everything manually, but breaking down datasets into tables and charts is now 10x easier than it's ever been. Here is a video where I'm using Retool. I load a dataset and generate a few charts as quickly as I can think. The speed at which we can go from one idea to a working solution is astonishing.

The era of manually analyzing data will come to an end. AI can now do a lot of this automatically. It's a huge time saver. I do data analysis for a living, and I'm a huge fan of writing Jupyter notebooks to do it all, but it's now hard to justify manually writing code that you can generate in a few seconds. I still check everything manually, but breaking down datasets into tables and charts is now 10x easier than it's ever been. Here is a video where I'm using Retool. I load a dataset and generate a few charts as quickly as I can think. The speed at which we can go from one idea to a working solution is astonishing.

Santiago

70,786 görüntüleme • 9 ay önce

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,363 görüntüleme • 9 ay önce

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 görüntüleme • 11 ay önce

How to Create a Spinning Globe 🌍 with Your Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibre

How to Create a Spinning Globe 🌍 with Your Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibre

Qiusheng Wu

12,678 görüntüleme • 4 ay önce

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 görüntüleme • 4 ay önce

Did you know that you can use GitHub Copilot in notebooks? Accept and run generated code with just a click. You can even attach variables from the Jupyter kernel in your requests ✨

Did you know that you can use GitHub Copilot in notebooks? Accept and run generated code with just a click. You can even attach variables from the Jupyter kernel in your requests ✨

Visual Studio Code

44,371 görüntüleme • 1 yıl önce

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.

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.

Santiago

140,737 görüntüleme • 7 ay önce

🤓 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 görüntüleme • 1 yıl önce

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!

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!

Santiago

55,609 görüntüleme • 1 yıl önce

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 görüntüleme • 4 ay önce