FLUX (or FLUX. 1) is a suite of text-to-image models from Black Forest Labs, a new company set up by some of the AI researchers behind innovations and models like VQGAN, Stable Diffusion, Latent Diffusion, and Adversarial Diffusion Distillation
— Stelfie the Time Traveller (@StelfieTT) July 7, 2024
With LivePortrait you can just perform what you want the character on the screen to do. This clip is using cc0 footage from Pexels, but you can do this with all the AnimateDiff / Gen3 / Luma / Pika etc clips you've already made as well! pic.twitter.com/y2fLoC5183
An open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
1️⃣ 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜) – The broadest category, covering automation, reasoning, and decision-making. Early AI was rule-based, but today, it’s mainly data-driven. 2️⃣ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟) – AI that learns patterns from data without explicit programming. Includes decision trees, clustering, and regression models. 3️⃣ 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗡𝗡) – A subset of ML, inspired by the human brain, designed for pattern recognition and feature extraction. 4️⃣ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗗𝗟) – Multi-layered neural networks that drives a lot of modern AI advancements, for example enabling image recognition, speech processing, and more. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 – A revolutionary deep learning architecture introduced by Google in 2017 that allows models to understand and generate language efficiently. 6️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜) – AI that doesn’t just analyze data—it creates. From text and images to music and code, this layer powers today’s most advanced AI models. 7️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗣𝗿𝗲-𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 (𝗚𝗣𝗧) – A specific subset of Generative AI that uses transformers for text generation. 8️⃣ 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠) – Massive AI models trained on extensive datasets to understand and generate human-like language. 9️⃣ 𝗚𝗣𝗧-4 – One of the most advanced LLMs, built on transformer architecture, trained on vast datasets to generate human-like responses. 🔟 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 – A specific application of GPT-4, optimized for conversational AI and interactive use.
3. Generative AI Fundamentals: Earn a skill badge by demonstrating your understanding of foundational concepts in Generative AI. https://www.cloudskillsboost.google/paths
7. Transformer Models and BERT Model: Get a comprehensive introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. https://www.cloudskillsboost.google/course_templates/538
1. Communicate the Why 2. Explain the context (strategy, data) 3. Clearly state your objectives 4. Specify the key results (desired outcomes) 5. Provide an example or template 6. Define roles and use the thinking hats 7. Set constraints and limitations 8. Provide step-by-step instructions (CoT) 9. Ask to reverse-engineer the result to get a prompt 10. Use markdown or XML to clearly separate sections (e.g., examples)
Top 10 high-ROI use cases for PMs:
1. Get new product ideas 2. Identify hidden assumptions 3. Plan the right experiments 4. Summarize a customer interview 5. Summarize a meeting 6. Social listening (sentiment analysis) 7. Write user stories 8. Generate SQL queries for data analysis 9. Get help with PRD and other templates 10. Analyze your competitors
2- tune the caption with ChatGPT as suggested by Pixaroma: Craft detailed prompts for Al (image/video) generation, avoiding quotation marks. When I provide a description or image, translate it into a prompt that captures a cinematic, movie-like quality, focusing on elements like scene, style, mood, lighting, and specific visual details. Ensure that the prompt evokes a rich, immersive atmosphere, emphasizing textures, depth, and realism. Always incorporate (static/slow) camera or cinematic movement to enhance the feeling of fluidity and visual storytelling. Keep the wording precise yet descriptive, directly usable, and designed to achieve a high-quality, film-inspired result.
1. Use the 80/20 principle to learn faster Prompt: “I want to learn about [insert topic]. Identify and share the most important 20% of learnings from this topic that will help me understand 80% of it.”
2. Learn and develop any new skill Prompt: “I want to learn/get better at [insert desired skill]. I am a complete beginner. Create a 30-day learning plan that will help a beginner like me learn and improve this skill.”
3. Summarize long documents and articles Prompt: “Summarize the text below and give me a list of bullet points with key insights and the most important facts.” [Insert text]
4. Train ChatGPT to generate prompts for you Prompt: “You are an AI designed to help [insert profession]. Generate a list of the 10 best prompts for yourself. The prompts should be about [insert topic].”
5. Master any new skill Prompt: “I have 3 free days a week and 2 months. Design a crash study plan to master [insert desired skill].”
6. Simplify complex information Prompt: “Break down [insert topic] into smaller, easier-to-understand parts. Use analogies and real-life examples to simplify the concept and make it more relatable.”
“Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.”
“In addition to the Shutterstock clips, Meta also used 10 million video clips from this 100M video dataset from Microsoft Research Asia. It’s not mentioned on their GitHub, but if you dig into the paper, you learn that every clip came from over 3 million YouTube videos.”
“It’s become standard practice for technology companies working with AI to commercially use datasets and models collected and trained by non-commercial research entities like universities or non-profits.”
“Like with the artists, photographers, and other creators found in the 2.3 billion images that trained Stable Diffusion, I can’t help but wonder how the creators of those 3 million YouTube videos feel about Meta using their work to train their new model.”
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