Загрузка видео...

Не удалось загрузить видео

На главную

Background Agent (Preview) "a remote, asynchronous agent that runs outside the main Cursor editor in a remote containerized environment. It allows you to offload long-running or complex task" works best for tasks that require less human interaction like fixing simple bugs, small features, or long changes more information about...

81,372 просмотров • 1 год назад •via X (Twitter)

Комментарии: 13

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

cursor v0.50 is here lots of juicy features here's all the features you need to know 🧵 👇

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Simpler, unified pricing for @cursor_ai – All model usage now on request-based pricing – Max Mode adopts token-based pricing (like model APIs) – Premium tool calls & long context mode removed

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Max Mode for all top models (token-based) max mode is ideal for harder problems which require more context, intelligence and tool use @cursor_ai

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Include your entire codebase in context use @ folders to add your entire codebase into context @cursor_ai

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Inline Edit(Cmd/Ctrl+K) Refresh Supports full file edits. Full file edits makes it easy to do scope changes to a file without using agent You can also send the code block to the agent for multi-file edits @cursor_ai

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Fast edits for long files with Agent Agent can now find the exact place where edits should occur and change only that part @cursor_ai

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Workspaces Work across multiple codebases in one session

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Exporting Chat You can now export chats to a markdown file. This is useful when you want to get advise from other AI about your code using the conversation you already have with @cursor_ai

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Duplicate Chat Useful when you want to fork a chat and explore different options @cursor_ai

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

like this update? support me by reposting this thread 👇 you can also follow me @donvito for more AI

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

FYI, this is dropping in cursor 0.50 as well — added after I posted this thread Tab to edit other files — perfect for refactoring Tab suggestions also now have syntax highlighting!

Фото профиля Melvin Vivas
Melvin Vivas1 год назад

Here's how you enable Background Agents

Фото профиля MightyBot
MightyBot1 год назад

🧠 Unified Search. Smarter Meetings. Effortless CRM. MightyBot is your AI agent platform for seamless workflows—record meetings, automate CRM updates, and find answers across apps in seconds. 🌟 Focus on what matters. We'll handle the grind.

Похожие видео

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 просмотров • 2 месяцев назад

Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.

Santiago

80,792 просмотров • 25 дней назад