Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

🚨 THIS IS ACTUALLY INSANE Python developers are still installing five different tools to manage one project. `pip` for packages. `virtualenv` for environments. `pyenv` for Python versions. `pipx` for CLI tools. `poetry` for project management. uv replaces all of them with one tool. And it’s written in Rust. The... headline number: 10–100× faster than pip. But speed isn't even the most interesting part. uv can: → Create and manage Python projects → Resolve dependencies with a universal lockfile → Create virtual environments → Install and switch between Python versions → Run standalone Python scripts with dependencies → Install CLI tools with `uvx` → Compile and sync requirements → Manage Cargo-style workspaces The workflow becomes ridiculously simple: `uv init` → create a project `uv add` → add dependencies `uv run` → run your code `uv lock` → lock dependencies `uv sync` → reproduce the environment And if you're already using pip? You don't have to throw your workflow away. The `uv pip` interface is designed as a drop-in replacement while giving you the speed boost. The other clever bit: uv uses a global cache to deduplicate dependencies and save disk space. So instead of assembling a Python toolchain piece by piece, you get one fast CLI that covers the whole workflow. And unlike a lot of newer developer tools, uv is already considered stable and production-ready. If you work with Python, this is one of those tools you should probably try once. You might not go back. #Python #DevTools #Rust #OpenSource #Programming #AIEngineeringshow more

Vikas gupta

11,354 subscribers

39,971 просмотров • 12 дней назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 23

Фото профиля Andy Smith

Andy Smith12 дней назад

Just use devenv by @cachix_org. Same for any language.

Фото профиля Sebastian Buzdugan

Sebastian Buzdugan11 дней назад

till wins when custom python builds matter

Фото профиля Subhan Qureshi

Subhan Qureshi12 дней назад

Very interesting concept. I especially like how this combines creativity with practical value. Definitely keeping an eye on this one. 👀🚀

Фото профиля Tanvir Hossain

Tanvir Hossain11 дней назад

Helpful one

Фото профиля Vinay Bharambe

Vinay Bharambe12 дней назад

Wow really amezing Share

Фото профиля David Marco

David Marco11 дней назад

Awesome

Фото профиля A S II F

A S II F12 дней назад

Wow, this sounds like a game-changer for Python devs! So much simpler. I'm curious to see how this stacks up.

Фото профиля AI Horizon

AI Horizon11 дней назад

Awesome

Фото профиля Crio Songo

Crio Songo11 дней назад

This really solves Python dependency mess, I'll go try it on my current project right away.

Фото профиля Zivlen Ai

Zivlen Ai12 дней назад

good share

Фото профиля Satendra Tiwari

Satendra Tiwari12 дней назад

Great share

Фото профиля Sakhil Khan

Sakhil Khan12 дней назад

It makes managing python projects way easier

Фото профиля orc8r

orc8r12 дней назад

🤯 why to bother with that in 2026?!

Фото профиля Safwan

Safwan12 дней назад

Impressive share

Фото профиля Lyra Irish

Lyra Irish11 дней назад

Great

Фото профиля Dylan Sutton Chavez

Dylan Sutton Chavez12 дней назад

uv resolves faster, sure, but it resolves differently than pip. that difference makes migration less straightforward for everyone.

Фото профиля Sultan U

Sultan U11 дней назад

Did you just find out?

Фото профиля Jiwoong Lee

Jiwoong Lee11 дней назад

Mending an already broken architecture while retaining compatibility is a remarkably difficult art.

Фото профиля Maxwell Orion

Maxwell Orion12 дней назад

Amazing share

Фото профиля Jims not Jim

Jims not Jim11 дней назад

Uv is hardly new at this point

Фото профиля NAYAK

NAYAK11 дней назад

great

Фото профиля Max

Max12 дней назад

Really liked the way you approached this. Simple, natural, and engaging.

Фото профиля Meenakshi Yadav

Meenakshi Yadav12 дней назад

One tool replacing so many pieces is a huge win for developers.

Похожие видео

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

140,922 просмотров • 10 месяцев назад

Finally, Apple's ML-Sharp will run directly in the upcoming Python scripting module in LichtFeld Studio as a plugin. This is not another ML-Sharp UI! The LichtFeld release 0.5 will transform LichtFeld Studio from mere splat software into a comprehensive platform. I'm developing a plugin system that will require uploading Python plugin scripts to GitHub with all dependencies specified. In LFS, you'll simply need to type the GitHub repo URL, and it will import the extension. Dependencies are managed by the integrated UV packet management. You'll be able to run Colmap, SAM3, Depth Models, and more directly in LFS, and even create your own UIs. This change will be transformative for the 3DGS software ecosystem. The custom LichtFeld tensor library is already fully interoperable with PyTorch via dlpack, bringing all the ML power into LFS if desired. I'm extremely hyped and excited about these upcoming features.

Finally, Apple's ML-Sharp will run directly in the upcoming Python scripting module in LichtFeld Studio as a plugin. This is not another ML-Sharp UI! The LichtFeld release 0.5 will transform LichtFeld Studio from mere splat software into a comprehensive platform. I'm developing a plugin system that will require uploading Python plugin scripts to GitHub with all dependencies specified. In LFS, you'll simply need to type the GitHub repo URL, and it will import the extension. Dependencies are managed by the integrated UV packet management. You'll be able to run Colmap, SAM3, Depth Models, and more directly in LFS, and even create your own UIs. This change will be transformative for the 3DGS software ecosystem. The custom LichtFeld tensor library is already fully interoperable with PyTorch via dlpack, bringing all the ML power into LFS if desired. I'm extremely hyped and excited about these upcoming features.

MrNeRF

10,404 просмотров • 9 месяцев назад

THIS IS F**KING INSANE. someone just built a free Claude skills library that gives you thousands of tools, agents, and personas all in one place. the GitHub repo already has 47,000+ stars. it gives you: → 290+ Python tools with zero pip installs → skills for executing most tasks → agents for handling different workflows → personas like senior architect, CTO, and security auditor → one command to convert the library to 11 different platforms and it's totally free. licensed. with the right setup, Claude can take on a lot of the work you’d normally need different tools or people for. i actually broke down how to build a workflow around this idea in my article below. save this, you will need it

THIS IS F**KING INSANE. someone just built a free Claude skills library that gives you thousands of tools, agents, and personas all in one place. the GitHub repo already has 47,000+ stars. it gives you: → 290+ Python tools with zero pip installs → skills for executing most tasks → agents for handling different workflows → personas like senior architect, CTO, and security auditor → one command to convert the library to 11 different platforms and it's totally free. licensed. with the right setup, Claude can take on a lot of the work you’d normally need different tools or people for. i actually broke down how to build a workflow around this idea in my article below. save this, you will need it

Innopro

34,290 просмотров • 17 дней назад

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Rohit

145,342 просмотров • 4 месяцев назад

This has been possible for months and barely anyone is doing it Months ago, Andrej Karpathy, a co-founder of OpenAI, open-sourced a project that gets you the best possible answers instead of trusting one model on its own. It's called LLM Council. Repo: /karpathy/llm-council The idea in two lines: your question goes to several models at once, Claude, GPT, Gemini, whatever you pick. They answer independently, then anonymously critique and rank each other (anonymous so they can't play favorites), and a chairman model merges the best of all of it into one final answer. One model just agrees with you; a council catches each other's mistakes. How to set it up: > Clone the repo > It uses uv for setup, install dependencies with uv sync > Get an OpenRouter API key and add it, this is what lets one app reach Claude, GPT, and Gemini together > Pick which models sit on your council in the config, then run it. It opens as a local web app, like ChatGPT but with a whole panel answering Bookmark this

This has been possible for months and barely anyone is doing it Months ago, Andrej Karpathy, a co-founder of OpenAI, open-sourced a project that gets you the best possible answers instead of trusting one model on its own. It's called LLM Council. Repo: /karpathy/llm-council The idea in two lines: your question goes to several models at once, Claude, GPT, Gemini, whatever you pick. They answer independently, then anonymously critique and rank each other (anonymous so they can't play favorites), and a chairman model merges the best of all of it into one final answer. One model just agrees with you; a council catches each other's mistakes. How to set it up: > Clone the repo > It uses uv for setup, install dependencies with uv sync > Get an OpenRouter API key and add it, this is what lets one app reach Claude, GPT, and Gemini together > Pick which models sit on your council in the config, then run it. It opens as a local web app, like ChatGPT but with a whole panel answering Bookmark this

Yarchi

21,488 просмотров • 3 месяцев назад

Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is the important part: You can now build an interface for your Python code without learning HTML, JavaScript, or CSS. Go to this link to try this: Remember to work smarter, not harder! Thanks to the team behind Taipy for partnering with me on this post.

Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is the important part: You can now build an interface for your Python code without learning HTML, JavaScript, or CSS. Go to this link to try this: Remember to work smarter, not harder! Thanks to the team behind Taipy for partnering with me on this post.

Santiago

155,254 просмотров • 2 лет назад

WHOEVER BUILT THIS WAS TIRED OF INSTALLING 50 DIFFERENT HACKING TOOLS Someone turned 183+ penetration testing and OSINT tools into ONE command line toolkit. —> Recon. —> Vulnerability scanning. —> Web app testing. —> iOS security. —> OSINT. All sitting behind a single CLI menu. Instead of spending hours installing tools, configuring dependencies and jumping between terminals, you can manage the whole stack from one place. And because it’s open source, you can actually inspect it, modify it and build your own workflows around it. Cybersecurity students are going to have a field day with this. Just remember: use it on systems you own or have explicit permission to test. This is basically a Swiss Army knife for security researchers. REPO BELOW

WHOEVER BUILT THIS WAS TIRED OF INSTALLING 50 DIFFERENT HACKING TOOLS Someone turned 183+ penetration testing and OSINT tools into ONE command line toolkit. —> Recon. —> Vulnerability scanning. —> Web app testing. —> iOS security. —> OSINT. All sitting behind a single CLI menu. Instead of spending hours installing tools, configuring dependencies and jumping between terminals, you can manage the whole stack from one place. And because it’s open source, you can actually inspect it, modify it and build your own workflows around it. Cybersecurity students are going to have a field day with this. Just remember: use it on systems you own or have explicit permission to test. This is basically a Swiss Army knife for security researchers. REPO BELOW

MAX

28,040 просмотров • 1 месяц назад

Exciting update on PantheonOS: Introducing Pantheon-Notebook & Pantheon-CLI — the first fully open-source, Python-based agentic tools that go beyond Claude Code in the field of data analysis. Pantheon-CLI runs entirely on your computer or server, supports 60+ tools and 50+ databases, and can call any Python, R, or Julia package alongside natural language. Chat with your data directly. It look like python-claude-code, but more appreciate for data analysis. Pantheon-Notebook brings the same agentic framework into Jupyter! Not just for writing code, it can also run and revise code automatically to generate the correct result, and even operate on files and study from website — beyond what any other tool can do! With Pantheon, you mix natural language + programming in one workflow, focusing on discovery instead of syntax barriers. We've applied Pantheon in some real-world cases: finance (customer explore), biology (Seurat, cell segmentation, annotation), sociology (survey analysis), and drug discovery (molecular docking). Pantheon is not just a CLI or a plugin — it's an agentic operating system for science, spanning both terminal and notebook. Why not try it now? We are actively preparing publications from this series of projects. Major contributors will be recognized in our GitHub repository and listed as key authors in these manuscripts. Feel free to reach out for collaborations, research assistant positions, visiting opportunities, rotation project or future PhD projects.

Exciting update on PantheonOS: Introducing Pantheon-Notebook & Pantheon-CLI — the first fully open-source, Python-based agentic tools that go beyond Claude Code in the field of data analysis. Pantheon-CLI runs entirely on your computer or server, supports 60+ tools and 50+ databases, and can call any Python, R, or Julia package alongside natural language. Chat with your data directly. It look like python-claude-code, but more appreciate for data analysis. Pantheon-Notebook brings the same agentic framework into Jupyter! Not just for writing code, it can also run and revise code automatically to generate the correct result, and even operate on files and study from website — beyond what any other tool can do! With Pantheon, you mix natural language + programming in one workflow, focusing on discovery instead of syntax barriers. We've applied Pantheon in some real-world cases: finance (customer explore), biology (Seurat, cell segmentation, annotation), sociology (survey analysis), and drug discovery (molecular docking). Pantheon is not just a CLI or a plugin — it's an agentic operating system for science, spanning both terminal and notebook. Why not try it now? We are actively preparing publications from this series of projects. Major contributors will be recognized in our GitHub repository and listed as key authors in these manuscripts. Feel free to reach out for collaborations, research assistant positions, visiting opportunities, rotation project or future PhD projects.

evo-devo

45,656 просмотров • 1 год назад

🔥 UEFN Python Verse Fields Tool is finally here! Direct Python Tool: Claude/Codex Skill: This tool lets you create, manage, and bind Verse fields, either individually or in batches, making UI workflows much faster and easier. I'd love for you to put it through its paces! If you have ideas for improvements, whether it's the UI, workflow, or anything else, feel free to leave feedback or open a PR on the repository. Current limitations: - Event bindings and event Verse fields are not supported. - Bindings that require conversion functions (such as ToVisibility, MakeBrush, etc.) are not supported. It really shines for Verse Field to Verse Field bindings. For example, if you have a slot widget with fields like Name and Image, and a parent widget containing 10 of those slots, you can quickly generate the corresponding fields in the parent widget and bind each slot's fields to them automatically. 🔥 If you have any questions, feel free to DM me or leave a comment below. I'd love to hear your feedback! 🙏 #UEFN #Python #Verse Fortnite Developers

🔥 UEFN Python Verse Fields Tool is finally here! Direct Python Tool: Claude/Codex Skill: This tool lets you create, manage, and bind Verse fields, either individually or in batches, making UI workflows much faster and easier. I'd love for you to put it through its paces! If you have ideas for improvements, whether it's the UI, workflow, or anything else, feel free to leave feedback or open a PR on the repository. Current limitations: - Event bindings and event Verse fields are not supported. - Bindings that require conversion functions (such as ToVisibility, MakeBrush, etc.) are not supported. It really shines for Verse Field to Verse Field bindings. For example, if you have a slot widget with fields like Name and Image, and a parent widget containing 10 of those slots, you can quickly generate the corresponding fields in the parent widget and bind each slot's fields to them automatically. 🔥 If you have any questions, feel free to DM me or leave a comment below. I'd love to hear your feedback! 🙏 #UEFN #Python #Verse Fortnite Developers

Spookreme 🎃

30,054 просмотров • 3 месяцев назад

I'm putting a lot of attention into getting a high quality transfer for the Face Proxy Mesh we're adding to the Fondant Custom Normals Blender addon. Even if the proxy has great Normals, it will not matter if the transfer is distorted. A basic world space Nearest Surface (aka Nearest Face Interpolated) transfer can get screwed up if there are gaps between the meshes. And if they are different shapes and poly counts, there will be! So the first big piece of the puzzle is getting a clean transfer. Before transferring Normals, a transfer space is made and a UV map is transferred from the proxy. Then any other data needed can be transferred in that UV space. As long as it is right, any other data will be clean! (Normals or anything else you might want.) And a UV map is easy to debug because you can put a grid texture on it. If the grid is evenly spaced, then distortion is minimal. This clip shows the differences in distortion between a basic World Space transfer and a better transfer space made by blending Sphere projection and a simple projection of the face forward onto the proxy (used down the center line.) Different faces will need slightly different configuration. Most of this is automatic, but the addon will have some simple controls to adjust it to different face shapes.

I'm putting a lot of attention into getting a high quality transfer for the Face Proxy Mesh we're adding to the Fondant Custom Normals Blender addon. Even if the proxy has great Normals, it will not matter if the transfer is distorted. A basic world space Nearest Surface (aka Nearest Face Interpolated) transfer can get screwed up if there are gaps between the meshes. And if they are different shapes and poly counts, there will be! So the first big piece of the puzzle is getting a clean transfer. Before transferring Normals, a transfer space is made and a UV map is transferred from the proxy. Then any other data needed can be transferred in that UV space. As long as it is right, any other data will be clean! (Normals or anything else you might want.) And a UV map is easy to debug because you can put a grid texture on it. If the grid is evenly spaced, then distortion is minimal. This clip shows the differences in distortion between a basic World Space transfer and a better transfer space made by blending Sphere projection and a simple projection of the face forward onto the proxy (used down the center line.) Different faces will need slightly different configuration. Most of this is automatic, but the addon will have some simple controls to adjust it to different face shapes.

aVersionOfReality

14,935 просмотров • 1 год назад

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

Chris Long

13,210 просмотров • 2 лет назад

🚨 Unity AI is finally opening up, and this is a big one for Unity developers 🚀 You now get access to an in-project agentic assistant, AI Gateway, MCP server support, and deeper editor context so AI tools can understand your scene, assets, packages, hierarchy, settings, and target platforms. This is the kind of workflow I’ve been waiting for, AI that actually understands your Unity project and helps you build faster.

🚨 Unity AI is finally opening up, and this is a big one for Unity developers 🚀 You now get access to an in-project agentic assistant, AI Gateway, MCP server support, and deeper editor context so AI tools can understand your scene, assets, packages, hierarchy, settings, and target platforms. This is the kind of workflow I’ve been waiting for, AI that actually understands your Unity project and helps you build faster.

Dilmer

17,837 просмотров • 5 месяцев назад

someone just rebuilt claude code in pure python and made it 230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 860 stars. basically nobody is looking yet bookmark this before it gone

someone just rebuilt claude code in pure python and made it 230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 860 stars. basically nobody is looking yet bookmark this before it gone

alex

14,212 просмотров • 1 месяц назад

someone just rebuilt claude code in pure python and made it 230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 677 stars. basically nobody is looking yet bookmark this before it gone

someone just rebuilt claude code in pure python and made it 230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 677 stars. basically nobody is looking yet bookmark this before it gone

alex

158,896 просмотров • 2 месяцев назад

someone just rebuilt claude code in pure python and made it ~230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 677 stars. basically nobody is looking yet bookmark this before it gone

someone just rebuilt claude code in pure python and made it ~230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 677 stars. basically nobody is looking yet bookmark this before it gone

Argona

51,369 просмотров • 3 месяцев назад

New week new demo! Today I want to showcase the CLI to control the Superlogical multiplexer. The CLI is an extremely powerful tool for automation and hooking into other tools like editors, agentic coding tools, and more. And, it's super fun! Everything you can do in the GUI, you can do via the CLI. Actually, you can do more in the CLI. You can create sessions, run commands, make splits, move things around, change focus, simulate any input device (key/mouse/etc.), wait on scripts, and more. There are a couple unique parts of Rex I'm showing off here too: our streaming event system that any client can hook into (or script). And the terminal-specific API to retrieve information like a JSON object of the running process. I hope this demo lets your imagination run free of where you might slot something like this into your existing tools.

New week new demo! Today I want to showcase the CLI to control the Superlogical multiplexer. The CLI is an extremely powerful tool for automation and hooking into other tools like editors, agentic coding tools, and more. And, it's super fun! Everything you can do in the GUI, you can do via the CLI. Actually, you can do more in the CLI. You can create sessions, run commands, make splits, move things around, change focus, simulate any input device (key/mouse/etc.), wait on scripts, and more. There are a couple unique parts of Rex I'm showing off here too: our streaming event system that any client can hook into (or script). And the terminal-specific API to retrieve information like a JSON object of the running process. I hope this demo lets your imagination run free of where you might slot something like this into your existing tools.

Mitchell Hashimoto

204,281 просмотров • 28 дней назад

🚀 Visualize Geospatial Data Instantly from the Command Line! You can now interactively explore raster and vector datasets without opening a desktop GIS or Jupyter Notebook. Simply run: pip install "leafmap[viewer]" Then visualize your data with one line: view-raster /path/to/raster.tif view-vector /path/to/vector.geojson Customize your visualization easily: view-raster /path/to/raster.tif --band 1 --colormap coolwarm view-vector /path/to/vector.geojson --style liberty These CLI tools are powered by Leafmap, MapLibre, and LocalTileserver, supporting all geospatial formats compatible with rasterio and geopandas. 👉 Check it out: #geospatial #datascience #mapping #python

🚀 Visualize Geospatial Data Instantly from the Command Line! You can now interactively explore raster and vector datasets without opening a desktop GIS or Jupyter Notebook. Simply run: pip install "leafmap[viewer]" Then visualize your data with one line: view-raster /path/to/raster.tif view-vector /path/to/vector.geojson Customize your visualization easily: view-raster /path/to/raster.tif --band 1 --colormap coolwarm view-vector /path/to/vector.geojson --style liberty These CLI tools are powered by Leafmap, MapLibre, and LocalTileserver, supporting all geospatial formats compatible with rasterio and geopandas. 👉 Check it out: #geospatial #datascience #mapping #python

Qiusheng Wu

13,504 просмотров • 1 год назад

🐍 Attention Python devs who have wrestled with dependency management! 🚨 Wolf Vollprecht, the creator of Mamba 🔥, is back with Pixi 📦✨, the package manager that will make you forget your package management woes. 😌 In this live demo (full Outerbounds fireside chat in comment), Wolf showcases Pixi's incredible features: 🌿 Effortlessly install packages globally with a single command 🎨 Enjoy a colorful and user-friendly experience when viewing Pixi toml files with bat 📝 Define and manage dependencies, tasks, and metadata with ease using Pixi TOML ⚡ Resolve multiple environments in parallel, saving you precious time 🔧 Seamlessly handle development and production environments using solve groups 🗣️ Harness the power of AI with Whisper CPP integration, including live transcription and multi-language support But the real magic happens when Wolf removes the Pixi lock file and runs pixi run start. 🪄 Over 10 environments are resolved in parallel, showcasing Pixi's incredible speed. 🚀 No more waiting for ages! 🕑 Wolf also dives into the upcoming Rattler Build, which will simplify building conda packages, making it more accessible for everyone. 🛠️🎉 Ready to bid farewell to your package management headaches? Give Pixi a try! 🌠 👉 Visit to download Pixi and explore the documentation 👩‍💻👨‍💻 Join the Pixi community on Discord to learn, collaborate, and get support #PixiPackageManager #PackageManagement #DataScience

🐍 Attention Python devs who have wrestled with dependency management! 🚨 Wolf Vollprecht, the creator of Mamba 🔥, is back with Pixi 📦✨, the package manager that will make you forget your package management woes. 😌 In this live demo (full Outerbounds fireside chat in comment), Wolf showcases Pixi's incredible features: 🌿 Effortlessly install packages globally with a single command 🎨 Enjoy a colorful and user-friendly experience when viewing Pixi toml files with bat 📝 Define and manage dependencies, tasks, and metadata with ease using Pixi TOML ⚡ Resolve multiple environments in parallel, saving you precious time 🔧 Seamlessly handle development and production environments using solve groups 🗣️ Harness the power of AI with Whisper CPP integration, including live transcription and multi-language support But the real magic happens when Wolf removes the Pixi lock file and runs pixi run start. 🪄 Over 10 environments are resolved in parallel, showcasing Pixi's incredible speed. 🚀 No more waiting for ages! 🕑 Wolf also dives into the upcoming Rattler Build, which will simplify building conda packages, making it more accessible for everyone. 🛠️🎉 Ready to bid farewell to your package management headaches? Give Pixi a try! 🌠 👉 Visit to download Pixi and explore the documentation 👩‍💻👨‍💻 Join the Pixi community on Discord to learn, collaborate, and get support #PixiPackageManager #PackageManagement #DataScience

Hugo Bowne-Anderson

14,192 просмотров • 2 лет назад

you installed it with whichever command needed the least typing that command had a scope, and the short one is the widest one user scope does not live in the project, it lives in you, and it loads in every repository you open for the rest of the year a repo you touched once in march is still being told about your database tools i counted mine: four servers, eight thousand nine hundred tokens, riding into projects that have no database and no browser you did not install a tool you set a default for every piece of work you have not started yet the shortest command in the docs and the longest reach

you installed it with whichever command needed the least typing that command had a scope, and the short one is the widest one user scope does not live in the project, it lives in you, and it loads in every repository you open for the rest of the year a repo you touched once in march is still being told about your database tools i counted mine: four servers, eight thousand nine hundred tokens, riding into projects that have no database and no browser you did not install a tool you set a default for every piece of work you have not started yet the shortest command in the docs and the longest reach

Lagosaur

21,006 просмотров • 1 месяц назад