Claims are awash on X about how the latest Kimi Model K2 beats Grok 4. My verdict ... try it for yourself ! l also noticed that some models are better in certain contexts than others but fail in other context where others excel. So my take is to know which is which for yourself .. don' t just rely on Benchmarks, social media posters and such ... Now back to Kimi AI the chinese controversial release: Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities. Key Features Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability. MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up. Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving. Model Variants Kimi-K2-Base: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions. Kimi-K2-Instruct: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking. https://github.com/MoonshotAI/Kimi-K2 https://www.moonshot.ai/ https://platform.moonshot.ai/docs/introduction#text-generation-model https://github.com/MoonshotAI/Kimi-K2/blob/main/docs/deploy_guidance.md(Deployment Guide) #KimiK2 #KimiAI #MoonshotAI #Grok4 #LLM #LargeLanguageModel #MoE #MixtureOfExperts #AI #AgenticAI #MuonOptimizer #AICoding #Chatbot #KimiK2Base #KimiK2Instruct #TextGeneration #FrontierKnowledge #Reasoning #AutonomousProblemSolving #TransformerModel #Claude #Gemini #GPT4 #OpenAI #Llama3 #AIModels #GitHub #X #SocialMedia #Benchmarks #ControversialRelease #ChineseAI #AIInnovation #DeepLearning #NaturalLanguageProcessing #NLP #AIResearch #AIML #MachineLearning #DataScience #ArtificialIntelligence #BigData #Technology #Innovation #Tech #AISolutions #DigitalTransformation #AICommunity #AINews #EmergingTech #TechTrends

Full Stack PRD Guide for Vibe Coders 📝 https://github.com/cpjet64/vibecoding/blob/main/prd-guide.md Hey Vibe Coders! To help with product definition and avoid scope creep, here’s a guide for creating effective Product Requirements Documents (PRDs). Step 1: Create a "prd.md" Document 📋 Make a markdown file named "prd.md" as your product roadmap. A template is available at the end. Step 2: Document Each Product Component ⚙️ For each component (user flows, features, interfaces): - Key functionality (e.g., authentication) - User stories/acceptance criteria - Technical constraints - Priority level (must-have, should-have, nice-to-have) Step 3: Add Overall Product Metrics 📊 Key success metrics to document: - Key Performance Indicators (KPIs) - User acquisition and retention rates - Conversion goals - Engagement benchmarks Step 4: Consult Advanced AI 🤖 Engage in detailed discussions with AI like ChatGPT 4.5 or Claude 3.7. Discuss various aspects of your PRD, challenge assumptions, and gather insights. PRD Principles to Remember 🔑 - Focus on the WHAT, not the HOW - Requirements should be measurable - Link each feature to a user need - Prioritize to prevent scope creep Recommended PRD Components 🛠️ Include sections such as: - Product vision/goals - User personas/maps - Feature breakdowns - Non-functional requirements - Metrics/analytics plans - User research insights User Research Integration 💻👥 Incorporate user research by documenting: - User pain points/needs - User quotes/inspiration - User segments and distinct requirements - Edge cases and accessibility requirements - Testing plans for validation Feature Prioritization 🎯 Use frameworks like MoSCoW, RICE scoring, and ROI analysis to prioritize features. Stakeholder Management 👥 Document approval processes, feedback loops, and change management procedures. Establish communication plans. Product Analytics & Measurement 📊 Define success metrics, instrument tracking, and set up reporting for user behavior. User Experience Design 🎨 Link your PRD to user experience through flow diagrams, UI requirements, accessibility, and performance expectations. Technical Considerations 🔧 Align product requirements with technical planning, covering API needs, security, and third-party dependencies. Release Planning & Timeline 📅 Plan your release strategy, milestones, timelines, testing phases, and post-launch monitoring. Keep your products well-defined and focused! ✌️ Document Versions Latest versions of this guide are available on GitHub and X.com.