Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Those building AI coding agents will eventually hit the same wall: No matter how smart the model is, the moment you ask it to write production-ready code with the latest libraries, it starts hallucinating. Everyone is building coding agents these days, but most of them run into the same... show more

Jafar Najafov

61,479 subscribers

19,801 просмотров • 1 месяц назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Most AI coding agents can write code. But what happens when you give one agent an entire project and tell it to figure everything out? I put EvoX Agent to the test. Here’s what happened ↓

Most AI coding agents can write code. But what happens when you give one agent an entire project and tell it to figure everything out? I put EvoX Agent to the test. Here’s what happened ↓

GrowAIHub

55,857 просмотров • 6 дней назад

Meet Muse Spark 1.2 and Muse Code, the agent built to run it against your coding challenges. Now in beta. Together they take a task from idea to production, and deploy teams of agents that plan, write and refine across your repos. Start building now:

Meet Muse Spark 1.2 and Muse Code, the agent built to run it against your coding challenges. Now in beta. Together they take a task from idea to production, and deploy teams of agents that plan, write and refine across your repos. Start building now:

Meta for Developers

55,414 просмотров • 18 дней назад

AI coding agents are moving past “write me code.” They can now build, review, and improve the same project. So we connected Claude Code to Step 3.7 flash and gave it a real task.

AI coding agents are moving past “write me code.” They can now build, review, and improve the same project. So we connected Claude Code to Step 3.7 flash and gave it a real task.

Future Stacked

231,171 просмотров • 2 месяцев назад

Issue 4 of Interfaces just released! This issue is about building an animated sign-in dialog. It covers everything that goes into building it, from the animations to the small but important details. It's one of the most in-depth issues yet, full of code snippets and demos.

Issue 4 of Interfaces just released! This issue is about building an animated sign-in dialog. It covers everything that goes into building it, from the animations to the small but important details. It's one of the most in-depth issues yet, full of code snippets and demos.

Jakub Krehel

37,341 просмотров • 2 месяцев назад

NO WAY. Someone is basically building a MANAGER for your AI agents. You give it one objective. It briefs Claude, Codex + the agents you choose, keeps them on the same context and coordinates the work. This is Jarvix. And I had to dig into this: 🧵 #JarvixOS

NO WAY. Someone is basically building a MANAGER for your AI agents. You give it one objective. It briefs Claude, Codex + the agents you choose, keeps them on the same context and coordinates the work. This is Jarvix. And I had to dig into this: 🧵 #JarvixOS

EyeingAI

36,135 просмотров • 11 дней назад

AI agents are quickly becoming one of crypto’s biggest narratives in 2026 🤖 Coinbase calls it the future. Jensen Huang says they will “change everything.” At Bluwhale, you can already create your own AI agent with Bluprint ⚔️ No coding required. Start building 👇 👇 Source

AI agents are quickly becoming one of crypto’s biggest narratives in 2026 🤖 Coinbase calls it the future. Jensen Huang says they will “change everything.” At Bluwhale, you can already create your own AI agent with Bluprint ⚔️ No coding required. Start building 👇 👇 Source

Bluwhale

20,269 просмотров • 3 месяцев назад

How do you build AI agents that remember and learn from past conversations? Watch this episode of The Agent Factory for a deep dive into building agents with memory, including the fundamentals of agent memory, Vertex AI Memory Bank, and more →

How do you build AI agents that remember and learn from past conversations? Watch this episode of The Agent Factory for a deep dive into building agents with memory, including the fundamentals of agent memory, Vertex AI Memory Bank, and more →

Google Cloud Tech

12,952 просмотров • 1 год назад

Excited to launch a new way to upskill with AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.

Excited to launch a new way to upskill with AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.

elvis

19,058 просмотров • 2 месяцев назад

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Adarsh Chetan

269,248 просмотров • 8 месяцев назад

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,932 просмотров • 9 месяцев назад

Agents are the future of AI creative tools. And the new Grok Imagine Agent is a great one! It feels like a smart partner that you can brainstorm and iterate with - it does the heavy lifting of writing prompts + building out the canvas. Watch me build a froyo brand 👇

Justine Moore

31,174 просмотров • 3 месяцев назад

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

elvis

81,765 просмотров • 9 месяцев назад

It's true, most AI coding tools make you do the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.

It's true, most AI coding tools make you do the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.

Amjad Masad

109,857 просмотров • 8 месяцев назад

‼️found a site that ends AI design slop. superrr is an MCP server that gives your coding agent taste. they measured 132 real landing pages, verified 123 of them and pulled out 17 design directions with exact tokens. the agents still writes the code. superrr picks the direction, hands it the structure then grades what it built by scanning the live page after. no more generic purple gradient landing pages. their landing page was one-shotted with it. three lines to connect to Claude. link:

‼️found a site that ends AI design slop. superrr is an MCP server that gives your coding agent taste. they measured 132 real landing pages, verified 123 of them and pulled out 17 design directions with exact tokens. the agents still writes the code. superrr picks the direction, hands it the structure then grades what it built by scanning the live page after. no more generic purple gradient landing pages. their landing page was one-shotted with it. three lines to connect to Claude. link:

sui

25,411 просмотров • 11 дней назад

Today, AI can generate tons of code—but how do we know if it's good? That's why we built Sculptor: the first coding agent environment. Sculptor helps you catch issues, write tests, and improve your code—all while you work in your favorite editor.

Today, AI can generate tons of code—but how do we know if it's good? That's why we built Sculptor: the first coding agent environment. Sculptor helps you catch issues, write tests, and improve your code—all while you work in your favorite editor.

Kanjun 🐙

137,379 просмотров • 1 год назад

We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is **Lovie** Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like **Claude Code, Cursor, and Windsurf** through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat **$29/month**, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is Lovie Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like Claude Code, Cursor, and Windsurf through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat $29/month, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

Parul Gautam

41,751 просмотров • 1 месяц назад

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 просмотров • 4 дней назад

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

Atlas

501,277 просмотров • 1 месяц назад

The full potential of AI agents is still waiting to be unlocked! Want to see TikTok avatars or smart wallets in action? Build your own AI agent and take it to the moon! 🌕 Let's see how far you can take it.

The full potential of AI agents is still waiting to be unlocked! Want to see TikTok avatars or smart wallets in action? Build your own AI agent and take it to the moon! 🌕 Let's see how far you can take it.

AI Agent Layer | AIFUN

14,876 просмотров • 1 год назад