try , download https://openai.com/codex/

In the **AGENTIC ERA**, and already entering the **LOOPy ERA**, Vercel is launching a framework for creating agents from a folder-based structure. ## What Is Vercel EVE? **Vercel EVE** is an open-source framework for building and deploying AI agents using an architecture based on files and folders. In practice, the entire agent is organized inside a single folder: ```text agent/ ├── agent.ts ├── instructions.md ├── skills/ ├── tools/ ├── subagents/ ├── connections/ ├── channels/ ├── schedules/ ├── sandbox/ └── evals/ ``` Each directory represents one of the agent’s capabilities: * `agent.ts`: defines the model and the main configuration; * `instructions.md`: contains the system prompt and global rules; * `skills/`: specialized procedures and knowledge; * `tools/`: functions the agent can execute; * `subagents/`: specialized agents for more complex tasks; * `connections/`: APIs, databases, and MCP servers; * `channels/`: Slack, Discord, an API, or a custom interface; * `schedules/`: automated and scheduled tasks; * `sandbox/`: isolated code execution; * `evals/`: behavior and security tests. EVE’s main differentiator is automatic component discovery. You do not need to manually import every skill, tool, or subagent into the main file. The framework scans the folder structure, identifies the components, and generates a manifest with everything connected. This turns the file system into a kind of declarative configuration for the agent. Instead of writing one massive central file: ```ts import toolA from "./tools/toolA"; import toolB from "./tools/toolB"; import skillA from "./skills/skillA"; import investigator from "./subagents/investigator"; ``` You simply place each component in the correct folder, and EVE handles the integration during compilation. In addition to organization, EVE adds capabilities required for production: * durable sessions; * recovery after failures or redeployments; * sandboxed code execution; * human approval for risky actions; * testing before deployment;

I love running Qwen on my VPSs. I’m currently using version 3.6 and now testing 3.8. We’re living through an incredible moment—learning the foundations of what makes us special. Qwen @Alibaba_Qwen Qwen3.8 is launching and going open-weight soon! With a massive 2.4T parameters, this model is continuously evolving. We believe it’s one of the most powerful model available today, compatible to leading frontier AI models , second only to Fable 5. You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork. Be among the very first to try it out. Can't wait to hear what you build. Stay tuned! Token Plan international：https://qwencloud.com/pricing/token-plan

Kimi K3 has reshaped the AI race by bringing Chinese open models closer to the best American closed models. Key points: - Kimi K3 is said to have achieved approximately 98% of Fable 5’s capabilities, but at a price similar to Sonnet 5. - In some programming benchmarks, it reportedly outperformed GPT‑5.6 and even Fable 5. - In practical comparisons, it delivered similar or superior quality in web pages, 3D experiences, WebGL, maps, galaxies, and minigames. - Its main disadvantage appears to be speed: in one test, it took 240 seconds, compared with 80 seconds for Fable 5. - On the other hand, it could cost between one-third and one-fifth as much. - This is reportedly already leading companies—including major corporations—to consider migrating from OpenAI and Anthropic to Kimi. - If its model weights are fully released, companies will be able to run it on their own infrastructure, reducing their dependence on closed providers. The video’s most important reflection is not simply about which model won. It is about the rapidly falling cost of intelligence. According to the creator, the cost of obtaining the same level of capability is decreasing by a factor of 30 to 40 each year. With advanced intelligence becoming cheap and widely available, we could deploy dozens or even hundreds of agents to perform intellectual tasks. This would transform: - The value of human labor. - The service economy. - Software production. - Consulting and research. - Scientific development. - The structure of many jobs. - Even if AI models stopped improving today, society would still take decades to fully explore everything they can already do. Therefore, the new bottleneck would no longer be model intelligence, but its distribution and integration into society. In one sentence: Kimi K3 is not just another model; it suggests that intelligence approaching the best closed systems could become open, affordable, and available to almost everyone—disrupting the AI market and the value of intellectual labor.

NOBODY KNOWS THIS YET. After nearly 70 real tests with Agnes AI, we discovered something incredible: you can turn a story into a complete animated movie—with images, video, and narration—for practically $0. But there are secrets the official documentation doesn’t reveal: - Prompts work better in English. - Two reference images dramatically improve character consistency. - The video model supports real keyframe transitions. - Each clip can be up to 18.4 seconds long. - Images, videos, and text are currently free. - The main limit we found was 5 video requests per minute. - The API fails often, but automatic retries recover almost everything. We built a pipeline that receives a story and delivers a finished narrated movie. This could completely change the cost of AI video production. And the craziest part? Almost nobody is talking about it yet.