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550gh🙋🏽‍♀️

How to use Copilot by @BojanRadojici10

Qwen Image Edit w/ Camera Control is wild 🤯 Quickly rotate the camera, switch between bird's eye and worm's eye views using just clicks. Here's how plus 7 wild examples:👇

Initiate phone calls from an AI agent via an API call

How to really piss somebody off

Somebody wrote a prompt that's supposed to reduce ChatGPT hallucinations. A Reality Filter Has a Google Gemini and Claude version too. it’s a directive scaffold that makes them more likely to admit when they don’t know. "Reduce hallucinations mechanically—through repeated instruction patterns, not by teaching them “truth.” The Reality Filter here is a permanent directive for GPT-4, Gemini Pro, Claude and a universal version. It requires labeling any content not directly verifiable with tags like [Unverified] or [Inference] and mandates “I cannot verify this” when lacking data. --- From r/PromptEngineering/

RETWEET THIS !! 🙌🏾 Free ChatGPT prompt for creating the perfect CV 🔥

Chat GPT secret codes!

Claude + mcp . so = 16,000+ mcps tools in one place.

Did TikTok really see it coming?🫣

Girl child 😭

LLM Evaluation: Practical Tips at Booking.com booking.ai/llm-evaluation-pr…

Self-hostable chat UI for LLMs with RAG and web search

RIP Market Research Firms. AI just killed them. A thread 🧵

brainwash your agents. context engineering doesn't have to be hard, there are so many low-hanging fruits. just keep the memory a holy place and drop the bs messages I just wrote a blog post on how we do it at @CamelAIOrg. these are simple to implement, must-have techniques for apps that use agents, which can optimize the accuracy and the cost without crazy code changes. 🎁 BONUS: I created a number of bite-sized issues that you can get on right now and start your open-source arc. just open a PR, or help review one. read here: shorturl.at/zyCv7

A simple trick cuts your LLM costs by 50%! Just stop using JSON and use this instead: TOON (Token-Oriented Object Notation) slashes your LLM token usage in half while keeping data perfectly readable. Here's why it works: TOON's sweet spot: uniform arrays with consistent fields per row. It merges YAML's indentation and CSV's tabular structure, optimized for minimal tokens. Look at the example below. JSON: { "𝘂𝘀𝗲𝗿𝘀": [ { "𝗶𝗱": 𝟭, "𝗻𝗮𝗺𝗲": "𝗔𝗹𝗶𝗰𝗲", "𝗿𝗼𝗹𝗲": "𝗮𝗱𝗺𝗶𝗻" }, { "𝗶𝗱": 𝟮, "𝗻𝗮𝗺𝗲": "𝗕𝗼𝗯", "𝗿𝗼𝗹𝗲": "𝘂𝘀𝗲𝗿" } ] } Toon: 𝘂𝘀𝗲𝗿𝘀[𝟮]{𝗶𝗱,𝗻𝗮𝗺𝗲,𝗿𝗼𝗹𝗲}: 𝟭,𝗔𝗹𝗶𝗰𝗲,𝗮𝗱𝗺𝗶𝗻 𝟮,𝗕𝗼𝗯,𝘂𝘀𝗲𝗿 It's obvious how few tokens are being used to represent the same information! To summarise, here are the key features: 💸 30–60% fewer tokens than JSON 🔄 Borrows the best from YAML & CSV 🤿 Built-in validation with explicit lengths & fields 🍱 Minimal syntax (no redundant braces, brackets, etc.) IMPORTANT!! That said, for deeply nested or non-uniform data, JSON might be more efficient. In the next tweet, I've shared some benchmark results demonstrating the effectiveness of this technique in reducing the number of tokens and improving retrieval accuracy with popular LLM providers. Where do you think this could be effective in your existing workflows? Find the relevant links in the next tweet!

🎯⚡𝗧𝗵𝗲𝘀𝗲 𝟮𝟬 𝘁𝗲𝗿𝗺𝘀 𝗺𝗮𝗸𝗲 𝗲𝘃𝗲𝗿𝘆 𝗳𝘂𝘁𝘂𝗿𝗲 𝗽𝗼𝘀𝘁 𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗰𝗹𝗲𝗮𝗿 𝗮𝗻𝗱 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹. Problem: When your engineer says "agent," your PM thinks "autonomous system," and your CEO thinks "chatbot." That misalignment? It's killing your AI initiatives. After 20+ years applying AI across industries and teaching AI Agents Mastery globally, I've watched miscommunication destroy more projects than bad code ever could. The solution? A shared vocabulary. 𝗜 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗲𝗱 𝟮𝟬 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗲𝗿𝗺𝘀 ▼ 》 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 ✸ 1. Large Language Models Neural networks trained to predict the next token ✸ 2.Tokenization Breaking text into discrete meaningful units for processing ✸ 3. Vectorization Mapping meaning into numerical coordinates in high-dimensional space ✸ 4. Attention Disambiguating context by examining nearby words 》 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ✸ 5. Self-Supervised Learning Scaling training without human-labeled examples ✸ 6. Transformers Stacking attention & feedforward layers for deep understanding ✸ 7. Fine-tuning Specializing base models for specific domains and use cases ✸ 8. Reinforcement Learning Optimizing model behavior through feedback and rewards 》 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 ✸ 9. Few-shot Prompting Adding example inputs and outputs inline for better responses ✸ 10. Retrieval Augmented Generation (RAG) Fetching relevant context on-demand from external sources ✸ 11. Vector Databases Enabling fast semantic search for contextually relevant documents ✸ 12. Context Engineering Managing long conversations, history, and user preferences strategically 》 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 ✸13. Model Context Protocol (MCP) Connecting LLMs with external systems and real-time data sources ✸ 14. Agents Orchestrating multi-step autonomous tasks across systems ✸ 15. Chain of Thought Breaking down reasoning into explicit step-by-step processes ✸ 16. Reasoning Models Adapting complexity and steps dynamically based on problem difficulty 》 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 & 𝗦𝗰𝗮𝗹𝗲 ✸ 17. Multi-modal Models ☆ Processing and generating text, images, video, and audio ✸18. Small Language Models (SLM) Specializing efficiently with 3-300M parameters for specific tasks ✸ 19. Distillation Compressing teacher model knowledge into smaller student models ✸ 20. Quantization Reducing memory and inference costs by lowering numerical precision Which of these 𝟮𝟬 𝘁𝗲𝗿𝗺𝘀 confuses you most? ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ want to Master AI agent in 30 days? ꆛ Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 trusted by 1,500+ builders worldwide! ➠ 9 Real-world Agents ➠ 5 frameworks: MCP · LangGraph · PydanticAI · CrewAI · Swarm ➠ Full Code included ✔ Basic Python is all you need. 👉 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟲% 𝗢𝗙𝗙): maryammiradi.com/ai-agents-m…

8 Types of LLMs used in AI Agents (Must know for Gen AI Data Scientists & AI Engineers): Here's what they are and what they do: 🧵

Your teacger wont tell you this...