Augment Code is developer AI for teams that integrates your organization's specific knowledge and practices into every interaction. We put your team's collective coding knowledge at your fingertips, helping every developer work smarter and faster. 🚀 Why it's a game-changer: #1 Open-source agent on SWE-Bench Verified (65.4% success rate!) Competes directly with Cursor & Windsurf — but faster, smarter, and memory-aware Supports VSCode, JetBrains, Neovim, and Vim Native support for MCP (Multi-Component Prompting) — plug in APIs, SQL, CLI tools Powerful features like Next Edit, persistent memory, and visual debugging from screenshots Handles huge codebases with 39,000+ lines, no context limit issues - https://www.augmentcode.com/ - https://docs.augmentcode.com/introduction - https://youtu.be/NszE7xKpI5Y?si=Nr6LAFuMWgBFMH90 - #1 SWE Bench Test: https://www.augmentcode.com/blog/1-open-source-agent-on-swe-bench-verified-by-combining-claude-3-7-and-o1 - Blog Post: https://www.augmentcode.com/blog/meet-augment-agent - Demo Video: https://x.com/DataChaz/status/1907497648325800118 #AugmentAgent #aicoding #agenticai #cursoralternative #opensourceai #NeovimAI #VSCodeAI #claude37 #openai #AIDeveloperTools #NextEdit #VisualDebugging #aiprogramming #softwareengineering #mcp

Today, we introduce VantAI's first foundational model: Neo-1. Neo-1 unifies structure prediction and molecular design at an atomic level, allowing prompting with multimodal and fine-grained structural information both for individual molecules and their interactions. In addition to designing biomolecules, this programmability allows Neo-1 to accelerate the collection of structural data when combined with our cross-linking mass spectrometry (XLMS) platform, NeoLink https://www.vant.ai

https://x.com/boltdotnew/status/1889706307613073508 For the first time ever, you can create production-ready mobile apps just by prompting. Through our partnership with Expo, we're eliminating the traditional barriers of mobile development by combining the power of React Native and Bolt's frontier AI agent. What once required specialized expertise is now possible for anyone — turn your ideas into real iOS and Android apps, no development experience needed. Preview in real-time from any device, iterate on the fly, and deploy straight to the App Store. It's that simple. Check out our latest announcement on X for more details. Happy building and let's go 🚀

https://www.promptingguide.ai/agents/introduction In this guide, we refer to an agent as an LLM-powered system designed to take actions and solve complex tasks autonomously. Unlike traditional LLMs, AI agents go beyond simple text generation. They are equipped with additional capabilities, including: Planning and reflection: - AI agents can analyze a problem, break it down into steps, and adjust their approach based on new information. - Tool access: They can interact with external tools and resources, such as databases, APIs, and software applications, to gather information and execute actions. - Memory: AI agents can store and retrieve information, allowing them to learn from past experiences and make more informed decisions. This lecture discusses the concept of AI agents and their significance in the realm of artificial intelligence.

https://docs.lovable.dev/user-guides/video-tutorials Build real project on Lovable with video tutorials whilst learning Prompting ! #lovable #nocode #nocodedev #promptguide #aicoding #documentation

https://youtu.be/c0zhLzcVJRI List of prompting strategies and approaches. (Lovable Video) To help you make with prompting AI COding Agents, we compiled a list of prompting strategies and approaches. Some of these were collected from our team’s experience, and others were shared with us by our community members.

https://x.com/AndrewYNg/status/1882125891821822398 Our first short course with @AnthropicAI! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, @Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here: https://deeplearning.ai/short-courses/building-towards-computer-use-with-anthropic

https://www.coursera.org/learn/google-prompting-essentials#modules