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Content creation just leveled up with AI agents. I just tested Superagent by Airtable, and it’s insane… Instead of doing research + planning + writing manually, you can run multiple AI agents at the same time and get a complete Super Report at the end. What surprised me the...

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17,913 просмотров • 7 месяцев назад •via X (Twitter)

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This is the best AI tool I have seen so far this year, and everyone can use it. Normally, when you research a topic using tools like Perplexity, Gemini, or ChatGPT, they just give you a wall of text. Try Superagent, a newly launched product by Airtable. It literally builds you an interactive Super Report with high-quality data from a single prompt, ready to present as-is. You can click into research sections, scroll through dynamic graphs, and see visualizations that make complex topics easy to understand. 👉 Use code KUNAL2FREE and get free 2 month access: It’s not just a chatbot. It spins up a team of multiple specialized AI agents that build a research plan, divide the tasks, and execute them simultaneously. You can build presentations, university reports, websites, dashboards, you name it! Shoutout to them for sponsoring this post.
5:09

This is the best AI tool I have seen so far this year, and everyone can use it. Normally, when you research a topic using tools like Perplexity, Gemini, or ChatGPT, they just give you a wall of text. Try Superagent, a newly launched product by Airtable. It literally builds you an interactive Super Report with high-quality data from a single prompt, ready to present as-is. You can click into research sections, scroll through dynamic graphs, and see visualizations that make complex topics easy to understand. 👉 Use code KUNAL2FREE and get free 2 month access: It’s not just a chatbot. It spins up a team of multiple specialized AI agents that build a research plan, divide the tasks, and execute them simultaneously. You can build presentations, university reports, websites, dashboards, you name it! Shoutout to them for sponsoring this post.

Kunal Kushwaha

15,150 просмотров • 7 месяцев назад

Congrats! Today, your team gets stronger, faster, bigger, and better. Reintroducing - the AI work platform where people and agents work together as one team. You set the goals, and your agents get the work DONE: research, tickets, reports, follow-ups, budget tracking, and so (so, so) much more. ✅ Easy to build ✅ Always on ✅ Fully under your control And we're just getting started. Over the coming weeks, we'll walk you through new ideas on how to leverage agents in your work, build-along sessions, and handy tips from our agents experts.
0:40

Congrats! Today, your team gets stronger, faster, bigger, and better. Reintroducing - the AI work platform where people and agents work together as one team. You set the goals, and your agents get the work DONE: research, tickets, reports, follow-ups, budget tracking, and so (so, so) much more. ✅ Easy to build ✅ Always on ✅ Fully under your control And we're just getting started. Over the coming weeks, we'll walk you through new ideas on how to leverage agents in your work, build-along sessions, and handy tips from our agents experts.

monday.com

27,491 просмотров • 4 месяцев назад

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.
2:09

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,811 просмотров • 11 месяцев назад

Four AI agents just replaced my $250,000 marketing team. I'm not talking about basic chatbots. I'm talking about AI systems that: → Write custom newsletters like Morning Brew → Generate viral social content (3.9M views on one post) → Steal competitor's best-performing ads and rebuild them → Create SEO content that ranks on page 1 of Google All running 24/7 without sick days, vacation requests, or performance reviews. The problem? Most businesses are still hiring expensive marketing teams when AI can now handle 90% of the workload for less than the cost of one employee. I've been testing this for 6 months. The results are wild: ✅ Millions of impressions generated monthly ✅ Tens of thousands in revenue on autopilot ✅ Content creation at enterprise scale ✅ Zero manual research or writing These four agents handle content research, creation, paid advertising creative, and SEO content - work that typically requires a 5-7 person marketing team. The businesses adopting AI marketing agents like these will have an insurmountable advantage while everyone else is still hiring expensive teams and dealing with human limitations. Want the exact n8n templates for all four agents? 1. Like & RT this post 2. Follow me for more AI automation breakdowns 3. Comment "AGENTS" below I'll send you the complete system for free, plus access to my YouTube tutorials showing the step-by-step builds.
0:40

Four AI agents just replaced my $250,000 marketing team. I'm not talking about basic chatbots. I'm talking about AI systems that: → Write custom newsletters like Morning Brew → Generate viral social content (3.9M views on one post) → Steal competitor's best-performing ads and rebuild them → Create SEO content that ranks on page 1 of Google All running 24/7 without sick days, vacation requests, or performance reviews. The problem? Most businesses are still hiring expensive marketing teams when AI can now handle 90% of the workload for less than the cost of one employee. I've been testing this for 6 months. The results are wild: ✅ Millions of impressions generated monthly ✅ Tens of thousands in revenue on autopilot ✅ Content creation at enterprise scale ✅ Zero manual research or writing These four agents handle content research, creation, paid advertising creative, and SEO content - work that typically requires a 5-7 person marketing team. The businesses adopting AI marketing agents like these will have an insurmountable advantage while everyone else is still hiring expensive teams and dealing with human limitations. Want the exact n8n templates for all four agents? 1. Like & RT this post 2. Follow me for more AI automation breakdowns 3. Comment "AGENTS" below I'll send you the complete system for free, plus access to my YouTube tutorials showing the step-by-step builds.

David Roberts

109,529 просмотров • 11 месяцев назад

I found an AI browser that is actually awesome FlowithOS is a web browser COMPLETELY powered by AI You give it tasks, the agents work for you in the background It's like an army of AI agents working for you 24/7 Here's a guide on how it works and how you can get started today
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I found an AI browser that is actually awesome FlowithOS is a web browser COMPLETELY powered by AI You give it tasks, the agents work for you in the background It's like an army of AI agents working for you 24/7 Here's a guide on how it works and how you can get started today

Alex Finn

25,490 просмотров • 8 месяцев назад

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:
0:35

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

Kawsar

31,812 просмотров • 6 месяцев назад

What if your trading strategy could run on autopilot? NOKS AI lets you build agents like this one: ✅ Scans for tokens created in the last 7 days ✅ Ensures liquidity > $10k, with some locked for at least a month ✅ Trades with defined stop loss (30%) and take profit (100%) ✅ Never buys the same token twice Crypto trading just got smarter. Get early access:
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What if your trading strategy could run on autopilot? NOKS AI lets you build agents like this one: ✅ Scans for tokens created in the last 7 days ✅ Ensures liquidity > $10k, with some locked for at least a month ✅ Trades with defined stop loss (30%) and take profit (100%) ✅ Never buys the same token twice Crypto trading just got smarter. Get early access:

NOKS AI

41,865 просмотров • 1 год назад

Welcome to the group chat, but it's all my AI agents. To build a website and plan the social content, I put together a team of AI agents. Each one had a different role. They collaborated, challenged each other's ideas, and reviewed each other's work before anything was finalized. Meet Bloome - a platform where humans and AI agents work together in the same conversation. You can bring different models into one workspace, assign them different jobs, and let them cross-check each other's thinking instead of relying on a single answer. It feels less like chatting with AI and more like managing your own creative team. Try it:
0:37

Welcome to the group chat, but it's all my AI agents. To build a website and plan the social content, I put together a team of AI agents. Each one had a different role. They collaborated, challenged each other's ideas, and reviewed each other's work before anything was finalized. Meet Bloome - a platform where humans and AI agents work together in the same conversation. You can bring different models into one workspace, assign them different jobs, and let them cross-check each other's thinking instead of relying on a single answer. It feels less like chatting with AI and more like managing your own creative team. Try it:

Gizem Akdag

99,348 просмотров • 2 месяцев назад

Sora 2 + n8n is an absolute cheat code for AI UGC 🤯 One Airtable form generates multiple videos with the exact same AI creator. Same face, same vibe. Every single clip. All inside n8n + Airtable. Perfect for DTC brands and agencies who need consistent AI creators across entire ad campaigns. You're trying to test 10 different hooks with the same AI "creator", but every time you prompt Sora 2 manually, you get a different person in every video. And even when you nail consistency, you're still downloading, organizing, and tracking everything by hand. This n8n automation handles all of it: → Fill out one Airtable form (character, scene, video count) → Claude auto-generates detailed Sora 2 prompts → Sora 2 creates videos with your consistent character cameo → Videos auto-upload to ImageKit → Everything lands in Airtable with shareable URLs No Frankenstein characters, manual prompting, or file management nightmare. What you get: > Multiple videos with the exact same AI creator > UGC-style formatting (selfie angles, natural delivery) > Full status tracking in Airtable (queued → generating → complete) > Production-ready clips in minutes Built 100% in n8n + Airtable. Want the full workflow for free? > Like this post > Comment "SORA" And I'll send it over (must be following so I can DM)
0:22

Sora 2 + n8n is an absolute cheat code for AI UGC 🤯 One Airtable form generates multiple videos with the exact same AI creator. Same face, same vibe. Every single clip. All inside n8n + Airtable. Perfect for DTC brands and agencies who need consistent AI creators across entire ad campaigns. You're trying to test 10 different hooks with the same AI "creator", but every time you prompt Sora 2 manually, you get a different person in every video. And even when you nail consistency, you're still downloading, organizing, and tracking everything by hand. This n8n automation handles all of it: → Fill out one Airtable form (character, scene, video count) → Claude auto-generates detailed Sora 2 prompts → Sora 2 creates videos with your consistent character cameo → Videos auto-upload to ImageKit → Everything lands in Airtable with shareable URLs No Frankenstein characters, manual prompting, or file management nightmare. What you get: > Multiple videos with the exact same AI creator > UGC-style formatting (selfie angles, natural delivery) > Full status tracking in Airtable (queued → generating → complete) > Production-ready clips in minutes Built 100% in n8n + Airtable. Want the full workflow for free? > Like this post > Comment "SORA" And I'll send it over (must be following so I can DM)

Mike Futia

12,159 просмотров • 7 месяцев назад

Launch time 🫡 We are excited to announce that will go live on the 24th of Feb, on the XRP Ledger and The Root Network Generate memecoins in a single prompt ✅ Trade at lightning speed using our in-built wallet ✅ Configure and deploy AI agents with no code ✅ Use both networks with one wallet ✅ Gamified UX ✅ Trading Incentives ✅ Learn more here -
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Launch time 🫡 We are excited to announce that will go live on the 24th of Feb, on the XRP Ledger and The Root Network Generate memecoins in a single prompt ✅ Trade at lightning speed using our in-built wallet ✅ Configure and deploy AI agents with no code ✅ Use both networks with one wallet ✅ Gamified UX ✅ Trading Incentives ✅ Learn more here -

aigent.run

48,102 просмотров • 1 год назад

J-Cal’s framework for how to be "AI first". It’s not just a slogan 1. Every task… Ask AI first. “How can I do this better.” 2. Every repeated task… Let your agents take care of it so you can do MORE. 3. Give the agent your last 10 outputs, let it score them, run it daily, and do deep-research weekly on what the best are doing differently.
3:02

J-Cal’s framework for how to be "AI first". It’s not just a slogan 1. Every task… Ask AI first. “How can I do this better.” 2. Every repeated task… Let your agents take care of it so you can do MORE. 3. Give the agent your last 10 outputs, let it score them, run it daily, and do deep-research weekly on what the best are doing differently.

This Week in Startups

45,723 просмотров • 3 месяцев назад

when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.
2:41

when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.

outside five sigma

63,526 просмотров • 5 месяцев назад

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.
0:44

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

AI Breakfast

78,285 просмотров • 1 месяц назад

Coinbase CEO, Brian Armstrong: Some great insights on how they are using internally hosted AI Agents. "It’s connected to every Slack message, every Google Doc, and every Salesforce data confluence. Now, this is all linked up and the data is all aggregated, so you can ask these agents questions. Every team is using it—legal, finance, everything. It’s like the "Oracle of Coinbase." I’ve started to ask it things that go beyond just simple prompting, like "Hey, can you write this kind of memo for me?" I’m asking these AI agents now, as CEO, "What should I be aware of in the company that I might not be aware of?" It will tell me, "Did you know that there’s actually disagreement on this team about the strategy?" I realized I didn't know that, but the AI does because it can read every Slack message and every Google Doc. Tobi, who is on my board, calls this "reverse prompting." Instead of telling the AI agent what you want to do, you ask it what you should be thinking more about." --- From The All-In Podcast YT channel (link in comment)
1:20

Coinbase CEO, Brian Armstrong: Some great insights on how they are using internally hosted AI Agents. "It’s connected to every Slack message, every Google Doc, and every Salesforce data confluence. Now, this is all linked up and the data is all aggregated, so you can ask these agents questions. Every team is using it—legal, finance, everything. It’s like the "Oracle of Coinbase." I’ve started to ask it things that go beyond just simple prompting, like "Hey, can you write this kind of memo for me?" I’m asking these AI agents now, as CEO, "What should I be aware of in the company that I might not be aware of?" It will tell me, "Did you know that there’s actually disagreement on this team about the strategy?" I realized I didn't know that, but the AI does because it can read every Slack message and every Google Doc. Tobi, who is on my board, calls this "reverse prompting." Instead of telling the AI agent what you want to do, you ask it what you should be thinking more about." --- From The All-In Podcast YT channel (link in comment)

Rohan Paul

174,953 просмотров • 5 месяцев назад

We're launching the best way to sell with AI. Sales reps are drowning in grunt work. CRM updates. Follow-ups. Research. Meeting prep. We built a superagent that connects across your entire GTM stack and just gets things done. Give it to your reps → watch them claim 50% of their time back. But here's what sets us apart: You can move work from chat into autopilot agent teams - triggered by signals, backed by evals, self-improving. We're the only platform that truly decouples growth from headcount. The four levels of autonomy: > Level 1 - Assisted: Superagent eradicates grunt work Teams move 10x faster > Level 2 - Copilot: Agents run playbooks with oversight Reps get blocks of time back > Level 3 - Autopilot: Agent teams automate entire functions Growth decouples from headcount > Level 4 - Self-Driving: AI workforce self-improves and builds new agents Business grows at the pace of ideas In closed beta for enterprise customers. Book a demo:
2:29

We're launching the best way to sell with AI. Sales reps are drowning in grunt work. CRM updates. Follow-ups. Research. Meeting prep. We built a superagent that connects across your entire GTM stack and just gets things done. Give it to your reps → watch them claim 50% of their time back. But here's what sets us apart: You can move work from chat into autopilot agent teams - triggered by signals, backed by evals, self-improving. We're the only platform that truly decouples growth from headcount. The four levels of autonomy: > Level 1 - Assisted: Superagent eradicates grunt work Teams move 10x faster > Level 2 - Copilot: Agents run playbooks with oversight Reps get blocks of time back > Level 3 - Autopilot: Agent teams automate entire functions Growth decouples from headcount > Level 4 - Self-Driving: AI workforce self-improves and builds new agents Business grows at the pace of ideas In closed beta for enterprise customers. Book a demo:

Relevance AI

18,755 просмотров • 5 месяцев назад

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 просмотров • 1 год назад

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!
44:33

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

Dan Shipper 📧

145,487 просмотров • 1 год назад

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:
2:02

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:

ericosiu

11,868 просмотров • 1 месяц назад

2025 is the year of the CLI, not 𝘥𝘦𝘴𝘱𝘪𝘵𝘦 its simplicity but 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of it. I’m thrilled to share today’s announcement. It’s a milestone for Qodo’s mission, and for every team that wants to put AI to work across the SDLC. Let’s dive in! 𝐌𝐞𝐞𝐭 𝐐𝐨𝐝𝐨 𝐆𝐞𝐧 𝐂𝐋𝐈 Built for creating, running, and managing AI agents that automate critical development tasks across the SDLC, from analyzing CI tests and logs to triaging production errors and more. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ✅ Runs everywhere ✅ Designed for task execution, not just Q&A ✅ Trigger from CI, hooks, or cloud functions—you choose ✅ Works with all major models ✅ Fully customizable agents with their own tools, credentials, and goals And yes, it plays nicely with our other Qodo agents too (e.g., Qodo Merge, the AI code-review tool). This isn’t just about writing code—it’s about catching bugs earlier, automating deeper, and shipping higher-quality software. 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐨𝐝𝐚𝐲 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 Make sure you have npm installed, then run: npm install -g @qodo/gen Oh, one more thing- 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐨𝐮𝐫 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐐𝐨𝐝𝐨 𝐀𝐠𝐞𝐧𝐭𝐬, and contribute your own so others can benefit!
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2025 is the year of the CLI, not 𝘥𝘦𝘴𝘱𝘪𝘵𝘦 its simplicity but 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of it. I’m thrilled to share today’s announcement. It’s a milestone for Qodo’s mission, and for every team that wants to put AI to work across the SDLC. Let’s dive in! 𝐌𝐞𝐞𝐭 𝐐𝐨𝐝𝐨 𝐆𝐞𝐧 𝐂𝐋𝐈 Built for creating, running, and managing AI agents that automate critical development tasks across the SDLC, from analyzing CI tests and logs to triaging production errors and more. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ✅ Runs everywhere ✅ Designed for task execution, not just Q&A ✅ Trigger from CI, hooks, or cloud functions—you choose ✅ Works with all major models ✅ Fully customizable agents with their own tools, credentials, and goals And yes, it plays nicely with our other Qodo agents too (e.g., Qodo Merge, the AI code-review tool). This isn’t just about writing code—it’s about catching bugs earlier, automating deeper, and shipping higher-quality software. 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐨𝐝𝐚𝐲 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 Make sure you have npm installed, then run: npm install -g @qodo/gen Oh, one more thing- 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐨𝐮𝐫 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐐𝐨𝐝𝐨 𝐀𝐠𝐞𝐧𝐭𝐬, and contribute your own so others can benefit!

Itamar Friedman

11,811 просмотров • 1 год назад