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Devin: web-based agent, designed to feel like hiring a software dev - used to cost $500/mo 😱, but now only $20/mo - autonomously completes tasks end-to-end, then opens PRs - easy to spin up multiple Devins simultaneously - Gumroad says Devin writes 41% of their PRs

Indie Hackers

147,748 subscribers

19,644 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

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13 Comments

Indie Hackers1 year ago

AI Coding Tools in a Nutshell 📕 April 2025, Developer Edition The 9 most powerful tools developers are using to go from 1x to 10x (bookmark this for your toolkit):

Indie Hackers1 year ago

Codex CLI: @OpenAI's new coding agent, runs in the terminal - released *YESTERDAY* so the jury is still out - uses OpenAI's powerful new o3 and o4-mini models 🔥 - completely open source - multimodal, supports passing in screenshots and diagrams

Indie Hackers1 year ago

: AI IDE, fork of VS Code - very popular, strong community + tutorials - gold standard AI tab completion, ⌘K inline editor 🥇 - powerful agent kicked off vibe coding trend - $20/mo unlimited plan, limits tokens to save $ (or usage-based costs w high-quality MAX mode)

Indie Hackers1 year ago

: VS Code extension, sidebar agent, 1.2M installs - install in Cursor/Windsurf/VS Code - sends every token to LLMs, quality is high but so are costs - top-tier MCP marketplace, simple one-click installs ⭐️ - agent can see your Chrome browser, inspect elements, read logs

Indie Hackers1 year ago

: VS Code extension, sidebar agent, 380k+ installs - fork of Cline, adds new features, but no MCP marketplace - create custom modes for fine-grained control in your repo, very powerful - Boomerang Tasks: breaks down complex projects, delegates subtasks to assistants 🔥

Indie Hackers1 year ago

: "code prompting app," alternative to code editors - super precise file and context selection ✂️ - pass tons of context, 1-shot tasks, no agent required - the future? devs are ditching IDEs/agents and just using RepoPrompt - native Mac app, Windows/Linux coming

Indie Hackers1 year ago

: fork of VS Code, 2.5M installs - getting acquired by OpenAI?? 😯 - competes with Cursor: uniquely implemented tab completion, inline editing, agent - built-in 1-click "deploy to the web" button - AI agent available as plugin for JetBrains, VS Code, Vim, Emacs, etc

Indie Hackers1 year ago

Claude Code: @AnthropicAI's coding agent that lives in your terminal - released in Feb 2025, only works with Claude models - AI coding from your terminal, no need for an IDE 🧑‍💻 - no learning curve, easy to install and use - great for anything in the terminal, not just code

Indie Hackers1 year ago

Aider: the original terminal-based coding agent, 2M installs - high appeal for devs who prefer the command line over IDEs - integrates with pretty much any LLM, not just OpenAI or Anthropic 🧩 - cleverly tuned to adopt the role of someone you're pair programming with

Indie Hackers1 year ago

Every week we study indie hackers to find out how they succeed: - the tools they use, how, and why - also: marketing channels, biz ideas, and growth tactics We publish everything we find in the Indie Hackers newsletter. You should subscribe 👉

Asad Dhamani1 year ago

@DevinAI Devin costs 4-5x per task compared to cline FYI. For the more hands off nature it might be worth it but it also cannot tackle tasks above a non trivial level of complexity.

LordsGod🥀1 year ago

@DevinAI And then it go mess up your whole code and now you must look for the problem

🇿🇦 PatriotRZA 🇿🇦1 year ago

@DevinAI I just built a similar tool for $0 using Notion + Tally + Zapier. Seeing a lot of overlap—thanks for this thread

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Modal

32,334 views • 5 months ago

Cognition CEO Scott Wu says a quarter of Cognition's pull requests come from Devin -- and it'll be more than half by year-end. Each engineer runs up to five Devins at once. “Devin lets engineers go from bricklayer to architect.” The future of software isn't writing code -- it’s directing fleets of agents.

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vitrupo

14,898 views • 1 year ago

Since I joined Cognition I've been obsessed with learning how our eng team uses Devin themselves If we are building the best coding agent + we have the most cracked engineers + we've been fully AI-pilled from day one... it stands to reason that there is a lot to learn by just watching our technical staff work And yes there are a lot of tips & tricks. I recorded a video talking about my favorite... Agent Fan Out - asking your agent to break down the problem, spin up 10 more agents in parallel, and combine their results This is something I've seen everyone do - from our model research team spinning up 100 Devins to examine eval logs - or our product team using 5 child Devins to try out 5 different alternative implementations of the same thing If engineering is cheap and easy, why not build the product 10 times and choose the best one? Think of it in a master/slave context: Master Devin -> 10 Slave Devins -> Master Devin pulls their results There are two reasons this is useful 1. Agents are smartest when their context is small and their task is small & precise. Context windows are finite and too much becomes distracting 2. Agents are good at helping you break a large problem into independent & parallelizable chunks of work Every Devin is its own VM/computer so this also is just a great way to move faster. I've done a migration from React Native to Swift by having Devin break it up into 6 pieces then spin up new Devins to work in parallel In the video I build a greenfield project and try my best to show off this agent fan out concept. I also threw in a few other tricks that I've seen my coworkers do: - Let Devin write its own prompts (especially for creating child Devins). It's way better than us humans - Do tons of things at once. You should be absolutely frying your attention span. Your job should just be babysitting 38 different Devins - Don't be a blocker. Before letting the agent work I make sure to tell it to ask me any questions that would fill in ambiguities. Give your agent all the information it needs (and then some more) so that it can just cook without stopping to ask you questions every few minutes - Let Devin test itself. Integration sanity tests are pretty much solved Hope this is useful!!

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Jared Zoneraich

131,565 views • 1 month ago

New episode with Scott Wu (Scott Wu), CEO and co-founder of @cognition, the company behind Devin Unlike other AI coding tools, Devin works like an autonomous engineer that you interact with through Slack, Linear, and GitHub, just like with a remote engineer. Each of Cognition's 15 engineers works with a team of Devins, and these Devins are writing about 25% of Cognition’s code today. By the end of the year, they expect over 50% of their code to be written by Devin. In our conversation, we discuss: 🔸 How Devin has evolved from a high school CS student to a strong junior engineer just in the past year 🔸 Why software engineering will shift from “bricklayers” to “architects” 🔸 Why AI tools will lead to *more* engineering jobs rather than fewer 🔸 The eight pivots Cognition went through before landing on their current approach 🔸 The cultural shifts required to successfully adopt AI engineers like Devin at your company 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Enterpret — Transform customer feedback into product growth: 🏆 Paragon — Ship every SaaS integration your customers want: 🏆 Attio — The powerful, flexible CRM for fast-growing startups:

New episode with Scott Wu (Scott Wu), CEO and co-founder of @cognition, the company behind Devin Unlike other AI coding tools, Devin works like an autonomous engineer that you interact with through Slack, Linear, and GitHub, just like with a remote engineer. Each of Cognition's 15 engineers works with a team of Devins, and these Devins are writing about 25% of Cognition’s code today. By the end of the year, they expect over 50% of their code to be written by Devin. In our conversation, we discuss: 🔸 How Devin has evolved from a high school CS student to a strong junior engineer just in the past year 🔸 Why software engineering will shift from “bricklayers” to “architects” 🔸 Why AI tools will lead to more engineering jobs rather than fewer 🔸 The eight pivots Cognition went through before landing on their current approach 🔸 The cultural shifts required to successfully adopt AI engineers like Devin at your company 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Enterpret — Transform customer feedback into product growth: 🏆 Paragon — Ship every SaaS integration your customers want: 🏆 Attio — The powerful, flexible CRM for fast-growing startups:

Lenny Rachitsky

251,809 views • 1 year ago

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"When I was younger, around 22, 23, 24, I used to have multiple boyfriends. I had up to seven talking stages at once. But when I clocked 27, I lost interest. I’m 28 now, and I feel like having multiple boyfriends is a waste of time. Now I only want one man." — Lady says.

𝐀𝐬𝐚𝐤𝐲𝐆𝐑𝐍

945,467 views • 7 months ago

Day 3/5 of #MiniMaxWeek: MiniMax Agent — Code is Cheap, Show Me the Requirement Today, we’re officially launching MiniMax Agent: a general intelligent agent built to tackle long-horizon, complex tasks. From expert-level multi-step planning to flexible task breakdown and end-to-end execution — it’s designed to act like a reliable teammate, with strengths in: -Programming & tool use -Multimodal understanding & generation -Seamless MCP integration Already in internal use for 60 days, it’s become a daily tool for over 50% of our team. Here’s a classic saying: “Talk is cheap, show me the code.” But with intelligent agents, something shifts. Now we say: “Code is cheap, show me the requirement.” Try it now:

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MiniMax (official)

459,894 views • 1 year ago

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claire vo 🖤

41,177 views • 4 months ago

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The Disturbing Evidence Found Inside Michael Jackson’s Bedroom “That new biopic has earned almost $1 billion in a month. All it does is chronicle the rise of Michael Jackson from childhood to 1987, which is six years before the first child mo scandal. It just kind of lobs off what happened after 1987, which would include multiple people accusing him of child mo. They originally had a piece on the end of it that addressed the scandal and tried to dismiss it, but then somebody called to their attention that they had signed a bunch of mutual nondisclosure agreements with his accusers, which barred not only the accuser from speaking, but the Michael Jackson estate.” Megyn Kelly

Shawn Ryan

566,136 views • 2 months ago

How I get shit done, Episode 001 I've set up a playbook called ‘land’, which is triggered automatically when I drag an issue into the merging column in Linear. That reliably runs CI and merges any green PRs. This has allowed me to ship way faster than before. I think the key takeaway here is you can try to build your own code factory and your own agent orchestration layer, but it is a huge amount of work. The truth is there are entire companies with massive funding that are already tackling this and it's just easier to use their platform. I think this is a lot like if you were a carpenter: you could build your own generator, fuel it, wire it up, and then build a plug and then you could plug your saw into it. Or you could just plug your saw into the wall. Because the electricity company has already done all the work in the infrastructure and investment to make that plug work. I think more of us who are building companies should just be plugging into the wall instead of trying to build all this tooling ourselves. As a dev it's so tempting to build your own dev tools but I think a lot of times, even though you can build fast with agents now, it's a complete waste of time. It probably sounds like I'm being paid by Devin or something but I have zero financial interest here. They don't give me credits. I'm not an investor. I'm not being paid. I just think the tooling is really damn good. If you used Devin a long time ago and wrote it off, you really should have another look - for $500/month it's pretty obscene what you can get done.

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Ryan Carson

14,059 views • 4 months ago

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

Swami Sivasubramanian

28,946 views • 1 year ago

Linear CEO Karri Saarinen says throwing out SaaS entirely can send companies on a "long journey" back to the exact same workflows: "This idea of like, 'let's throw everything out, no SaaS at all'... I think that's an option. But then you start inventing stuff back from first principles, and you maybe end up in the same spot again." "Everyone's running their agent, and now they have 10 agents running. So then they put them on a Kanban board, which Linear has, and then it's like, 'Oh, now I invented agent orchestration.' Like, no, you invented the Kanban board, which has been around for like 30 years." "So, kudos to connecting those two topics. But some things don't need to be reinvented. Some things can still work. But if you just throw everything out, you kind of start from the beginning, and then I think it's a long journey to figure everything out again."

Linear CEO Karri Saarinen says throwing out SaaS entirely can send companies on a "long journey" back to the exact same workflows: "This idea of like, 'let's throw everything out, no SaaS at all'... I think that's an option. But then you start inventing stuff back from first principles, and you maybe end up in the same spot again." "Everyone's running their agent, and now they have 10 agents running. So then they put them on a Kanban board, which Linear has, and then it's like, 'Oh, now I invented agent orchestration.' Like, no, you invented the Kanban board, which has been around for like 30 years." "So, kudos to connecting those two topics. But some things don't need to be reinvented. Some things can still work. But if you just throw everything out, you kind of start from the beginning, and then I think it's a long journey to figure everything out again."

TBPN

38,083 views • 4 months ago

1) Full end-to-end AI software automation is near. At Nexus, we have the goal of achieving full end-to-end automation of our software production system by end of year. We are experimenting with this via a fast iteration closed-loop self-improving system. I think it's quite interesting, so I want to share publicly as I haven't seen too many engineering teams talk publicly about findings like this yet. In our tests, each autonomous improvement cycle has the following structure: - 1. Pre-planning: High-level architectural goals - 2. Planning: Medium-level planning and spec authoring, including specs for all tests, benchmarks, regression tests, etc. - 3. Execution: Full end-to-end agentic implementation cycle spanning 1-2hrs of autonomous work. This is the bulk of the productive session where the actual code is written, tested, and progressed. - 4. Evaluation: Complete running of tests, benchmarks, telemetry and profiling of the post-execution state system - 5. Retrospective: Thorough retrospective of the end-to-end run, including telemetry of the agent itself, such as cost, tokens used, times context limits were hit, etc. - 6. Self-improvement: Reasoning based on execution metrics and meta-level metrics about the agentic system itself The view below shows a 3D visualization of 29 generations of a self-improving codebase authored by our E2E automation system

1) Full end-to-end AI software automation is near. At Nexus, we have the goal of achieving full end-to-end automation of our software production system by end of year. We are experimenting with this via a fast iteration closed-loop self-improving system. I think it's quite interesting, so I want to share publicly as I haven't seen too many engineering teams talk publicly about findings like this yet. In our tests, each autonomous improvement cycle has the following structure: - 1. Pre-planning: High-level architectural goals - 2. Planning: Medium-level planning and spec authoring, including specs for all tests, benchmarks, regression tests, etc. - 3. Execution: Full end-to-end agentic implementation cycle spanning 1-2hrs of autonomous work. This is the bulk of the productive session where the actual code is written, tested, and progressed. - 4. Evaluation: Complete running of tests, benchmarks, telemetry and profiling of the post-execution state system - 5. Retrospective: Thorough retrospective of the end-to-end run, including telemetry of the agent itself, such as cost, tokens used, times context limits were hit, etc. - 6. Self-improvement: Reasoning based on execution metrics and meta-level metrics about the agentic system itself The view below shows a 3D visualization of 29 generations of a self-improving codebase authored by our E2E automation system

Daniel Marin | nexus.xyz

42,408 views • 2 months ago

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!

Onur Solmaz

149,337 views • 4 months ago

AD fatigue is REAL... I used to LOVE Pinterest but it's almost impossible to use now because of all the pop up ads. It's worse in the political space. You don't know who's getting paid to post what, you don't know if these people really feel this way or if it's just a ploy for engagement . It's all advertising for someone else at the end of the day. I feel like a walk away movement is coming because of Ad fatigue.

AD fatigue is REAL... I used to LOVE Pinterest but it's almost impossible to use now because of all the pop up ads. It's worse in the political space. You don't know who's getting paid to post what, you don't know if these people really feel this way or if it's just a ploy for engagement . It's all advertising for someone else at the end of the day. I feel like a walk away movement is coming because of Ad fatigue.

Shipwreck

26,989 views • 8 months ago

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

103,312 views • 2 months ago

🚨 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 views • 1 year ago

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,133 views • 1 year ago

CLAUDE + CURSOR + REPLIT = MAGIC (FULL VIDEO TUTORIAL) You can now write create complex web apps with just english (No copy - paste) using the BEST AI Code generation tool on the market - Cursor. The only problem with cursor for a coding beginner like me, is that its not that easy to deploy web apps. Well, Replit ⠕ makes deploying web apps and creating templates incredibly easy. In this video goes over how to sync Cursor and Replit. And then using our new template we made an AI version of Trello by Atlassian in like 6 or 7 cursor composer prompts. --------------------- In this video: 00:00 - Intro 04:08 - Syncing Cursor and Replit 07:40 - setting up Firebase 10:27 - Using Cursor and Replit to Create Trello Front End 19:41 - Setting up Backend API endpoint (we will make dedicated video on this soon w/template) 21:09 - Adding AI features to Trello!

CLAUDE + CURSOR + REPLIT = MAGIC (FULL VIDEO TUTORIAL) You can now write create complex web apps with just english (No copy - paste) using the BEST AI Code generation tool on the market - Cursor. The only problem with cursor for a coding beginner like me, is that its not that easy to deploy web apps. Well, Replit ⠕ makes deploying web apps and creating templates incredibly easy. In this video goes over how to sync Cursor and Replit. And then using our new template we made an AI version of Trello by Atlassian in like 6 or 7 cursor composer prompts. --------------------- In this video: 00:00 - Intro 04:08 - Syncing Cursor and Replit 07:40 - setting up Firebase 10:27 - Using Cursor and Replit to Create Trello Front End 19:41 - Setting up Backend API endpoint (we will make dedicated video on this soon w/template) 21:09 - Adding AI features to Trello!

Riley Brown

309,237 views • 1 year ago