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#1 skill for developers in 2026: Automate everything you can using AI. I bet my lunch your team is dealing with all of these: • Stale documentation • Outdated dependencies • Poor test coverage • Deprecated APIs Every company I work with has these same problems. You can solve... all of these right now. Automatically. Using AI. Here are 3 examples. Watch the attached video: I'm using Ona Automations to tackle this. These are background agents that run in the cloud, in a fully configured dev environment with your toolchain, your dependencies, and your services. You can run an unlimited number of these agents in parallel and across all your repositories. Claude Code and Codex only run locally, so they are hard to scale, and you can't run them when your computer is closed. Ona runs in the cloud. Here are the three examples: 1. Test coverage Run a nightly automation to identify any untested code paths, generate candidate tests, verify they pass, and open draft PRs. You wake up every morning to PRs that improve your test coverage. 2. Dependency upgrades Configure a weekly automation that bumps a dependency version, runs your full test suite, and reports any regressions. If everything is clean, it opens a PR. If something breaks, it opens a report so you can decide what to do. 3. Documentation auditing Set up a weekly automation that checks recent commits against your README file and setup guides, identifies broken examples and outdated instructions, and opens a PR with fixes.show more

Santiago

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25,691 次观看 • 4 个月前 •via X (Twitter)

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Here is how to set up a team of AI agents to work on your project without disrupting anything. This is the top 1 upgrade you can do to your development environment: Start using dev containers to isolate agents and run them in parallel in their own cloud containers. Let me show you:

Here is how to set up a team of AI agents to work on your project without disrupting anything. This is the top 1 upgrade you can do to your development environment: Start using dev containers to isolate agents and run them in parallel in their own cloud containers. Let me show you:

Santiago

22,742 次观看 • 10 个月前

Today we are taking Vy to another dimension 🚀 and we can’t be more excited! You can now run multiple agents in parallel, all on your own device 🧠💻 That means: • You can kickoff a bunch of tasks in parallel • Vy will run them in the background • You can keep using your computer like nothing’s happening — no weird cloud browser, no giving your authentication to a third party, no purchasing a new license, nothing blocking your access or your IP Think of it like a team of AI coworkers 🧑‍💻 You give them jobs, they get to work, and come back when they’re done. You can check in any time, and they’ll update you when they finish. It’s smooth. It’s fast. It feels natural.

Today we are taking Vy to another dimension 🚀 and we can’t be more excited! You can now run multiple agents in parallel, all on your own device 🧠💻 That means: • You can kickoff a bunch of tasks in parallel • Vy will run them in the background • You can keep using your computer like nothing’s happening — no weird cloud browser, no giving your authentication to a third party, no purchasing a new license, nothing blocking your access or your IP Think of it like a team of AI coworkers 🧑‍💻 You give them jobs, they get to work, and come back when they’re done. You can check in any time, and they’ll update you when they finish. It’s smooth. It’s fast. It feels natural.

Vercept

10,668 次观看 • 11 个月前

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

Santiago

43,749 次观看 • 25 天前

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

Simular

14,362 次观看 • 2 个月前

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Santiago

13,712 次观看 • 3 个月前

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

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 次观看 • 7 个月前

Google's new Open Knowledge Format opens up so many possibilities (and opportunities)! It is a standardized format for storing your knowledge in a way that agents can easily use. Instead of giving agents ALL of your knowledge to stuff into its context window, you give agents a map so they can find specifically what they need. I've shared some examples in my latest blog post and video.

Google's new Open Knowledge Format opens up so many possibilities (and opportunities)! It is a standardized format for storing your knowledge in a way that agents can easily use. Instead of giving agents ALL of your knowledge to stuff into its context window, you give agents a map so they can find specifically what they need. I've shared some examples in my latest blog post and video.

Marie Haynes

31,648 次观看 • 28 天前

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

Santiago

79,974 次观看 • 1 年前

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

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

Santiago

64,399 次观看 • 7 个月前

Today we're launching Brand Skills in Claude. This is a set of skills /gooseworks that gives Claude full context on your brand — logo, colors, voice, positioning — so anyone can make on-brand ads and content directly in Claude, Claude Code, Cowork, or Codex. Here's how it works: 1. Install the skills: npx gooseworks install --all 2. Run this prompt: /gooseworks --skill brand-intel-branddev [your-website] — pulls your logo, brand colors + company data in ~15 seconds 3. Run brand-voice-extractor, brand-research and update-brand-kit — Claude learns how you sound, your positioning and audience, and saves it all as a reusable brand kit Every other skill reads your brand kit automatically. So every ad and post you generate is accurate to your brand's messaging, logo, assets, etc. By default. It's that easy. But this is just the start. We have created a library with 100+ open-source skills for growth that some of the fastest growing startups in the world are using every day to run ads, content, competitor research, gtm, seo and more. Comment Goose and I'll DM you the full open-source skill library.

Soham Mehta

139,017 次观看 • 12 天前

1-click ai agent coming soon to a mini-app near you 📍 Imagine a library of ready-made ai apps that you can deploy and start using with a click of a button. Crestal team is leveraging Intent Kit and our latest API suite to design everyday agents that you can use in your business, trading, automation or simply for fun. What agents do you want to see in this app?

1-click ai agent coming soon to a mini-app near you 📍 Imagine a library of ready-made ai apps that you can deploy and start using with a click of a button. Crestal team is leveraging Intent Kit and our latest API suite to design everyday agents that you can use in your business, trading, automation or simply for fun. What agents do you want to see in this app?

Crestal

3,117,014 次观看 • 1 年前

Top 1 upgrade for your development environment: Start using Dev Containers. During every cohort of my Machine Learning Engineering program, 100+ developers try to run a complex system on their computers. I was wasting 10+ hours every week troubleshooting individual configurations. It was exhausting! I started using Dev Container, and now people are up and running in 2 minutes with no issues. Dev Containers are probably one of the best upgrades you'll make to your environment. They asked me to record a video in class to show them how to create a Dev Container for any project. Here is that video.

Top 1 upgrade for your development environment: Start using Dev Containers. During every cohort of my Machine Learning Engineering program, 100+ developers try to run a complex system on their computers. I was wasting 10+ hours every week troubleshooting individual configurations. It was exhausting! I started using Dev Container, and now people are up and running in 2 minutes with no issues. Dev Containers are probably one of the best upgrades you'll make to your environment. They asked me to record a video in class to show them how to create a Dev Container for any project. Here is that video.

Santiago

63,246 次观看 • 1 年前

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,772 次观看 • 1 年前

Hi GoBid as a proudly South African company we at View4You disagree with the fact that people cannot run diagnostics, check the car to see if it starts check for any warning lights, check fluids on your cars. We understand moving them is not doable like all other auctions. Our founder recently reached out to your ambassador mumbo repairs and asked Him why your cars are not auctioned with a report stating the truth about the car as we have come across cases of possible mileage fraud and a case of a cloned car at GoBid. The response we received was that it would be difficult as the quantities are large and it would be costly. He also stated these cars are from various banks insurers etc. He further stated that GoBid will allow us to do our own inspections before we bid. His answers are acceptable and understandable. However, they do depict or show a lack of or no interest in protecting the consumer. Now today we were stopped from running a diagnostic, we were not allowed to get the car started and we were also not allowed to check the fluids. How can people bid confidently with these limitations? Now our humble request is, please permit us to run our checks thoroughly to ensure we know what we are bidding on. All other auctions permit such and it’s only you who does not allow it. A diagnostic test is crucial in the process of practicing due diligence, if you do not want us to run a diagnostic, can you please run it and place the report on the windscreen of your cars that can be diagnosed? You can contact us at info@view4you.co.za and we can run it on all your cars if you do not have the capacity to do so. Thank You.

Hi GoBid as a proudly South African company we at View4You disagree with the fact that people cannot run diagnostics, check the car to see if it starts check for any warning lights, check fluids on your cars. We understand moving them is not doable like all other auctions. Our founder recently reached out to your ambassador mumbo repairs and asked Him why your cars are not auctioned with a report stating the truth about the car as we have come across cases of possible mileage fraud and a case of a cloned car at GoBid. The response we received was that it would be difficult as the quantities are large and it would be costly. He also stated these cars are from various banks insurers etc. He further stated that GoBid will allow us to do our own inspections before we bid. His answers are acceptable and understandable. However, they do depict or show a lack of or no interest in protecting the consumer. Now today we were stopped from running a diagnostic, we were not allowed to get the car started and we were also not allowed to check the fluids. How can people bid confidently with these limitations? Now our humble request is, please permit us to run our checks thoroughly to ensure we know what we are bidding on. All other auctions permit such and it’s only you who does not allow it. A diagnostic test is crucial in the process of practicing due diligence, if you do not want us to run a diagnostic, can you please run it and place the report on the windscreen of your cars that can be diagnosed? You can contact us at [email protected] and we can run it on all your cars if you do not have the capacity to do so. Thank You.

VIEW4YOU

30,358 次观看 • 2 个月前

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

Akshay 🚀

165,936 次观看 • 9 天前

The first open-source implementation of the paper that will change automatic test generation is now available! In February, Meta published a paper introducing a tool to automatically increase test coverage, guaranteeing improvements over an existing code base. This is a big deal, but Meta didn't release the code. Fortunately, we now have Cover-Agent, an open-source tool you can install that implements Meta's paper to generate unit tests automatically: I recorded a quick video showing Cover-Agent in action. There are two things I want to mention: 1. Automatically generating unit tests is not new, but doing it right is difficult. If you ask ChatGPT to do it, you'll get duplicate, non-working, and meaningless tests that don't improve your code. Meta's solution only generates unique tests that run and increase code coverage. 2. People who write tests before writing the code (TDD) will find this less helpful. That's okay. Not everyone does TDD, but we all need to improve test coverage. There are many good and bad applications of AI, but this is one I'm looking forward to make part of my life.

The first open-source implementation of the paper that will change automatic test generation is now available! In February, Meta published a paper introducing a tool to automatically increase test coverage, guaranteeing improvements over an existing code base. This is a big deal, but Meta didn't release the code. Fortunately, we now have Cover-Agent, an open-source tool you can install that implements Meta's paper to generate unit tests automatically: I recorded a quick video showing Cover-Agent in action. There are two things I want to mention: 1. Automatically generating unit tests is not new, but doing it right is difficult. If you ask ChatGPT to do it, you'll get duplicate, non-working, and meaningless tests that don't improve your code. Meta's solution only generates unique tests that run and increase code coverage. 2. People who write tests before writing the code (TDD) will find this less helpful. That's okay. Not everyone does TDD, but we all need to improve test coverage. There are many good and bad applications of AI, but this is one I'm looking forward to make part of my life.

Santiago

774,488 次观看 • 2 年前

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

Santiago

215,727 次观看 • 1 年前

Over the past 6 weeks, I've automated dozens of hours of grunt, repetitive work. Every Friday, I spend a few minutes writing down everything I do more than once during the week: 1. I automate what I can automate 2. I improve what I can't automate Two levels of unlock here: 1. Start using voice 2. Start using voice-to-action agents The team reached out to partner with me. I've been testing them, and this is precisely what they do. First, if you are still typing your prompts, stop. Start using voice. Go from typing to dictation on your computer. This alone will save you hours every year. Second, move from asking questions to using an agent that automates tasks for you. Lemon is a VOICE-TO-ACTION agent (instead of a voice-to-text model): • You press a button • You say what you need • Lemon executes the task in whatever app you are using Lemon works across Mac apps, so you can drive them all with your voice. Lemon is currently free. 100% worth trying.

Over the past 6 weeks, I've automated dozens of hours of grunt, repetitive work. Every Friday, I spend a few minutes writing down everything I do more than once during the week: 1. I automate what I can automate 2. I improve what I can't automate Two levels of unlock here: 1. Start using voice 2. Start using voice-to-action agents The team reached out to partner with me. I've been testing them, and this is precisely what they do. First, if you are still typing your prompts, stop. Start using voice. Go from typing to dictation on your computer. This alone will save you hours every year. Second, move from asking questions to using an agent that automates tasks for you. Lemon is a VOICE-TO-ACTION agent (instead of a voice-to-text model): • You press a button • You say what you need • Lemon executes the task in whatever app you are using Lemon works across Mac apps, so you can drive them all with your voice. Lemon is currently free. 100% worth trying.

Santiago

20,566 次观看 • 4 个月前

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Santiago

20,744 次观看 • 1 个月前