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The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment...

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Santiago

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41,738 просмотров • 10 месяцев назад •via X (Twitter)

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Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.
0:38

Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.

Santiago

34,856 просмотров • 1 год назад

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.
0:31

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,799 просмотров • 1 год назад

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!
7:27

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

Santiago

83,805 просмотров • 1 год назад

Your coding agent may be lying to you. You ask it to write tests. It says they pass. It never ran them. You ask for a feature. It hits a wall and silently swaps in fake data. We built Vet to fix this. It’s open source. Get the code below.
1:41

Your coding agent may be lying to you. You ask it to write tests. It says they pass. It never ran them. You ask for a feature. It hits a wall and silently swaps in fake data. We built Vet to fix this. It’s open source. Get the code below.

Imbue

142,839 просмотров • 3 месяцев назад

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!
4:59

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

Santiago

43,782 просмотров • 1 год назад

Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.
6:17

Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.

Santiago

73,062 просмотров • 7 месяцев назад

A workflow I'm enjoying for managing coding agents on a kanban board: When an agent needs your input, it turns the task red to alert you that it's blocked! And then you can respond right there on the card to unblock it 😎
0:29

A workflow I'm enjoying for managing coding agents on a kanban board: When an agent needs your input, it turns the task red to alert you that it's blocked! And then you can respond right there on the card to unblock it 😎

Geoffrey Litt

474,369 просмотров • 5 месяцев назад

Today we open the Zapier SDK to everyone. If you're building with AI agents, this is for you. I've been using this for 2 months. It's totally changed how I do my job. You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through Zapier and can do anything those APIs can do. I think it’s the most powerful thing we’ve launched in years. Now in open beta. Just give this link right to your agent:
2:47

Today we open the Zapier SDK to everyone. If you're building with AI agents, this is for you. I've been using this for 2 months. It's totally changed how I do my job. You install it in your coding agent. Cursor, Claude Code, Codex, whatever you use. Now that agent has access to 8,000+ apps through Zapier and can do anything those APIs can do. I think it’s the most powerful thing we’ve launched in years. Now in open beta. Just give this link right to your agent:

Wade Foster

174,361 просмотров • 2 месяцев назад

Jack Dorsey’s company just released a free AI agent called Goose 🤯 It’s a local coding agent with 35,000+ GitHub stars that works with almost any AI model. Think of it like a free alternative to Claude Code that you can install, run forever, and customize however you want. This might be one of the best free AI coding tools available right now. 🚀
0:29

Jack Dorsey’s company just released a free AI agent called Goose 🤯 It’s a local coding agent with 35,000+ GitHub stars that works with almost any AI model. Think of it like a free alternative to Claude Code that you can install, run forever, and customize however you want. This might be one of the best free AI coding tools available right now. 🚀

Julian Goldie SEO

33,120 просмотров • 2 месяцев назад

I built a FREE AI Agent that can browse the web, code websites, and automate tasks WITHOUT any technical setup I literally have my own AI assistant that works 24/7 In this video I'll show you how to easily set it up No coding experience required (Trust me, you want to bookmark this)
11:32

I built a FREE AI Agent that can browse the web, code websites, and automate tasks WITHOUT any technical setup I literally have my own AI assistant that works 24/7 In this video I'll show you how to easily set it up No coding experience required (Trust me, you want to bookmark this)

Julian Goldie SEO

47,792 просмотров • 1 год назад

Excited to share the Agent Trainer and Agent Studio - coming soon to the HyperWrite⁩ platform. Teaching Agents to work the way you like is difficult. So we built a simple way for your Assistant to shadow you and learn. Just show it a task once, and the AI can repeat it.
1:10

Excited to share the Agent Trainer and Agent Studio - coming soon to the HyperWrite⁩ platform. Teaching Agents to work the way you like is difficult. So we built a simple way for your Assistant to shadow you and learn. Just show it a task once, and the AI can repeat it.

Matt Shumer

135,641 просмотров • 2 лет назад

Remote coding agents are now general purpose productivity tools Here I'm using the Todoist MCP server with Replit ⠕ Agent to clean up the reference section of my task list I'm also using it daily to add labels and keep things organized As it would turn out, coding agents are good for more than coding
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Remote coding agents are now general purpose productivity tools Here I'm using the Todoist MCP server with Replit ⠕ Agent to clean up the reference section of my task list I'm also using it daily to add labels and keep things organized As it would turn out, coding agents are good for more than coding

matt palmer

35,917 просмотров • 6 месяцев назад

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base
1:47

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

124,713 просмотров • 5 месяцев назад

Forget Roo, Cline, Windsurf, Cursor. Augment Code keeps raising the bar. Tried it on a fun project I hadn’t touched in months. Instead of random refactors, it rebuilt the architecture coherently, understanding the whole codebase → structure, dependencies + Git history. And where tools like Cursor often struggle with large repos or lose track of cross-file relationships... Augment mapped everything. Instantly 🤯 Once the full context is loaded, you can move ridiculously fast: → Restructure / rename components across repos → Swap between GPT-5, Claude Sonnet 4 / 4.5, and Claude Haiku via their Model Picker → Offload builds, tests, deploys to Remote Agents A few extras I really loved: → I could stay in VS Code, JetBrains IDEs... even Vim, which is v. cool! → If you prefer the terminal, the Auggie CLI now gives you the same experience headless. → It also handled a bunch of massive repos I threw at it smoothly, while Cursor struggled to even index them! → It always previews planned refactors before running them 🫶 ____ You can give Augment Code a try here → Big thx to the Augment Code team for partnering with me on this post, I learned a ton along the way! 🙏
0:31

Forget Roo, Cline, Windsurf, Cursor. Augment Code keeps raising the bar. Tried it on a fun project I hadn’t touched in months. Instead of random refactors, it rebuilt the architecture coherently, understanding the whole codebase → structure, dependencies + Git history. And where tools like Cursor often struggle with large repos or lose track of cross-file relationships... Augment mapped everything. Instantly 🤯 Once the full context is loaded, you can move ridiculously fast: → Restructure / rename components across repos → Swap between GPT-5, Claude Sonnet 4 / 4.5, and Claude Haiku via their Model Picker → Offload builds, tests, deploys to Remote Agents A few extras I really loved: → I could stay in VS Code, JetBrains IDEs... even Vim, which is v. cool! → If you prefer the terminal, the Auggie CLI now gives you the same experience headless. → It also handled a bunch of massive repos I threw at it smoothly, while Cursor struggled to even index them! → It always previews planned refactors before running them 🫶 ____ You can give Augment Code a try here → Big thx to the Augment Code team for partnering with me on this post, I learned a ton along the way! 🙏

Charly Wargnier

31,025 просмотров • 7 месяцев назад

This is incredibly useful for those using Claude Code! I have two computers, and sharing your Claude Code sessions is almost impossible without this. I often start working on something on my desktop, and then want to continue working on the same session (same context, conversation, etc.) on my laptop. The folks at Depot built an integration that solves this problem: You can now start a session on one computer and resume it anywhere else in the world! You can even share it with other team members so they can also contribute to it. Here is an article showing you how the Depot team uses this feature with Claude Code to automate their GitHub Actions Runner updates: Thanks to the team for the effort in building this integration and collaborating with me on this post.
4:29

This is incredibly useful for those using Claude Code! I have two computers, and sharing your Claude Code sessions is almost impossible without this. I often start working on something on my desktop, and then want to continue working on the same session (same context, conversation, etc.) on my laptop. The folks at Depot built an integration that solves this problem: You can now start a session on one computer and resume it anywhere else in the world! You can even share it with other team members so they can also contribute to it. Here is an article showing you how the Depot team uses this feature with Claude Code to automate their GitHub Actions Runner updates: Thanks to the team for the effort in building this integration and collaborating with me on this post.

Santiago

43,931 просмотров • 7 месяцев назад

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @pauleveritt@fosstodon.org. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!
2:51

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

Andrew Ng

454,644 просмотров • 1 месяц назад

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.
1:33

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

140,722 просмотров • 6 месяцев назад

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.
3:25

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

64,398 просмотров • 6 месяцев назад

Claude Code is the best coding agent on the market, but there is a catch: you have to keep your terminal open. This is why we built Terragon: use Claude Code as a background agent.
0:48

Claude Code is the best coding agent on the market, but there is a catch: you have to keep your terminal open. This is why we built Terragon: use Claude Code as a background agent.

Sawyer Hood

150,965 просмотров • 11 месяцев назад

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.
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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.

Santiago

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