正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

love the microsite from Lou touching on the false summit of coding agents, and how to address the system bottlenecks standing between you and a self-driving codebase background-agents . com

Johannes Landgraf

2,608 subscribers

16,175 次观看 • 6 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

We deployed 10,000 background agents to security-scan our codebase. The system is simple, scales with compute, and runs on publicly available models. From the scan, we fixed several high-severity vulnerabilities.

We deployed 10,000 background agents to security-scan our codebase. The system is simple, scales with compute, and runs on publicly available models. From the scan, we fixed several high-severity vulnerabilities.

Ramp Labs

74,602 次观看 • 3 个月前

When agents start opening PRs, code review has to change. Join the free Background Agents Summit for practical talks on agent review, risk, audit trails, and how engineering teams can adopt background agents without losing trust. Register for May 6-7.

When agents start opening PRs, code review has to change. Join the free Background Agents Summit for practical talks on agent review, risk, audit trails, and how engineering teams can adopt background agents without losing trust. Register for May 6-7.

Ona

1,220,198 次观看 • 4 个月前

OpenAI Agents SDK + MCP = Vibe Coding Agents I now have a CRACKED workflow to develop agents, Cursor can not only write the code but look at the logs at LangWatch, so it can debug the interaction between agents and write the fix itself

OpenAI Agents SDK + MCP = Vibe Coding Agents I now have a CRACKED workflow to develop agents, Cursor can not only write the code but look at the logs at LangWatch, so it can debug the interaction between agents and write the fix itself

Rogerio Chaves

51,352 次观看 • 1 年前

shadow is an open-source background coding agent with a real-time interface and codebase context system here's a breakdown of how it works ⬇️

shadow is an open-source background coding agent with a real-time interface and codebase context system here's a breakdown of how it works ⬇️

ishaan dey

21,582 次观看 • 1 年前

Local coding agents can't do end-to-end work at your company. Proliferate is the open-source platform for company-specific agents that can. Provision secure access to your tools, set up automations, and let agents work in the background. Congrats on the launch, Pablo Hansen!

Local coding agents can't do end-to-end work at your company. Proliferate is the open-source platform for company-specific agents that can. Provision secure access to your tools, set up automations, and let agents work in the background. Congrats on the launch, Pablo Hansen!

Y Combinator

73,498 次观看 • 6 个月前

Illia presenting IronClaw at BUIDL Conference. "Think of IronClaw as iOS for agents - secure by default." From sandboxed execution and encrypted credential vaults to confidential inference and self-improving skills, IronClaw is building the secure operating system agents need. The future of private, sovereign AI agents is here.

Illia presenting IronClaw at BUIDL Conference. "Think of IronClaw as iOS for agents - secure by default." From sandboxed execution and encrypted credential vaults to confidential inference and self-improving skills, IronClaw is building the secure operating system agents need. The future of private, sovereign AI agents is here.

NEAR Legion

19,563 次观看 • 3 个月前

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Jurij Skornik

11,802 次观看 • 5 个月前

Walz: How is this coordinated? And some of you watch the videos on their total chaos… federal agents milling around, touching the vehicle at a crime scene

Walz: How is this coordinated? And some of you watch the videos on their total chaos… federal agents milling around, touching the vehicle at a crime scene

Acyn

17,158 次观看 • 8 个月前

DHS agents were rammed by the occupants of a car tailing them. Both were pulled from the vehicle and forcibly arrested. The driver thinks being a US citizen allows you to commit felonies against federal agents. Crazy Karen is yelling and screaming in the background.

DHS agents were rammed by the occupants of a car tailing them. Both were pulled from the vehicle and forcibly arrested. The driver thinks being a US citizen allows you to commit felonies against federal agents. Crazy Karen is yelling and screaming in the background.

Kim "Katie" USA

875,576 次观看 • 10 个月前

Awesome discussion with Varun Mohan & Kevin Hou from Google DeepMind on Builders talking about the future of coding agents! Lots of insights here for both building in & investing in AI broadly. A few things we discussed - how the IDE is rapidly decreasing in value for an increasing number of developers, the importance of structuring your codebase so that agents can debug, test, and build (without constant hand-holding), and how quickly these models & tools will continue to improve in the future 🚀 Link in the comments.

Awesome discussion with Varun Mohan & Kevin Hou from Google DeepMind on Builders talking about the future of coding agents! Lots of insights here for both building in & investing in AI broadly. A few things we discussed - how the IDE is rapidly decreasing in value for an increasing number of developers, the importance of structuring your codebase so that agents can debug, test, and build (without constant hand-holding), and how quickly these models & tools will continue to improve in the future 🚀 Link in the comments.

"Leigh Marie" Braswell

17,696 次观看 • 5 个月前

SpaceXAI engineer (ex. Cursor): "at SpaceXAI, 95% of our code is written by AI agents. Most of engineers are running 10-20+ agents in parallel. I'm running 20+ GrokBot agents with /loop, /goal, /swarm inside pstack, which lets agents fully own, verify and ship tasks." in a 1-hour workshop, SpaceXAI engineer showed how they build self-improving coding agent team from scratch worth more than 10 paid courses on building agents watch today, then read how to build a team of self-learning GrokBot agents from scratch in article below

SpaceXAI engineer (ex. Cursor): "at SpaceXAI, 95% of our code is written by AI agents. Most of engineers are running 10-20+ agents in parallel. I'm running 20+ GrokBot agents with /loop, /goal, /swarm inside pstack, which lets agents fully own, verify and ship tasks." in a 1-hour workshop, SpaceXAI engineer showed how they build self-improving coding agent team from scratch worth more than 10 paid courses on building agents watch today, then read how to build a team of self-learning GrokBot agents from scratch in article below

Codez

25,063 次观看 • 12 天前

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

DeepLearning.AI

23,255 次观看 • 1 年前

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

LangChain

30,744 次观看 • 6 个月前

Introducing Duet CLI. Now coding agents like Claude Code and Codex can call Duet (our agent-building agent) directly from the terminal to build and modify Decagon agents. Duet handles work within Decagon and returns the result while your coding agent stays focused on the broader task.

Introducing Duet CLI. Now coding agents like Claude Code and Codex can call Duet (our agent-building agent) directly from the terminal to build and modify Decagon agents. Duet handles work within Decagon and returns the result while your coding agent stays focused on the broader task.

Decagon

770,006 次观看 • 29 天前

JetBrains is no longer behind. Firebender just launched the first-ever background coding agents for all JetBrains IDEs. These coding agents are incredibly intelligent, have isolated workspaces, and don’t require any cloud setup.

JetBrains is no longer behind. Firebender just launched the first-ever background coding agents for all JetBrains IDEs. These coding agents are incredibly intelligent, have isolated workspaces, and don’t require any cloud setup.

Y Combinator

48,147 次观看 • 1 年前

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 次观看 • 5 个月前

Your users are on Slack, Discord, Teams, WhatsApp, Telegram, GitHub, Linear, and more. Your agents should be too. Chat SDK lets your agents run on every platform from a single codebase. Watch the announcement ↓

Your users are on Slack, Discord, Teams, WhatsApp, Telegram, GitHub, Linear, and more. Your agents should be too. Chat SDK lets your agents run on every platform from a single codebase. Watch the announcement ↓

Vercel

98,432 次观看 • 5 个月前

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.

Latent.Space

62,269 次观看 • 2 个月前

Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Y Combinator

61,650 次观看 • 6 个月前

🚀 Announcing LangSmith Skills + CLI 🚀 Agent improvements are increasingly driven by coding agents themselves. We're releasing LangSmith Skills alongside the LangSmith CLI to make coding agents experts at the agent engineering lifecycle. LangSmith Skills enable agents to debug traces, create datasets, and run experiments - and thanks to the CLI, agents are able to do it all natively through the terminal, where they're most comfortable. Try out LangSmith Skills and the CLI with your own coding agents! ➡️ Skills: ➡️ CLI:

🚀 Announcing LangSmith Skills + CLI 🚀 Agent improvements are increasingly driven by coding agents themselves. We're releasing LangSmith Skills alongside the LangSmith CLI to make coding agents experts at the agent engineering lifecycle. LangSmith Skills enable agents to debug traces, create datasets, and run experiments - and thanks to the CLI, agents are able to do it all natively through the terminal, where they're most comfortable. Try out LangSmith Skills and the CLI with your own coding agents! ➡️ Skills: ➡️ CLI:

LangChain

49,572 次观看 • 6 个月前