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

Microsoft dropped a free AI Agent training for beginners. No paywall. No coding required. This repository provides a course on building AI Agents, covering 11 lessons that teach the fundamentals of AI agent development. The course includes code examples, multi-language support, and links to additional resources for further learning. 11 lessons covering the fundamentals of building AI Agents Code examples utilizing Azure AI Foundry and GitHub Model Catalogs https://github.com/microsoft/ai-agents-for-beginners #microsoft #aiagentsforbeginners #aiagent #agentops #azureai #github #aimodels #nocode #aitraining #machinelearning #deeplearning #learnai #foundry #modelcatalogs #automation #aitools

Andrej Karpathy discusses the evolution of software, introducing Software 1.0, 2.0, and 3.0, highlighting the rise of large language models (LLMs) as a new programming paradigm. He emphasizes the importance of adapting to these changes, exploring the potential of LLMs in various applications, and the need for a collaborative approach between humans and AI. https://youtu.be/LCEmiRjPEtQ?si=KlGcFZYTI9HeL1ho #Software1.0 #Software2.0 #Software3.0 #AndrejKarpathy #LLMs #LargeLanguageModels #AIProgramming #ArtificialIntelligence #MachineLearning #NeuralNetworks #DeepLearning #AICoding #AICollaboration #ProgrammingParadigm #Codegen

Apple is reportedly considering acquiring Perplexity AI, a deal potentially valued around $14 billion. This move signifies Apple's interest in building advanced AI capabilities, potentially including a chatbot, and marks a significant step in its AI strategy. Discussions have been held internally regarding this acquisition. The acquisition would allow Apple to build what could become the world's first comprehensive AI. #PerplexityAI #Apple #AIacquisition #Chatbot #ArtificialIntelligence #AIstrategy #BigTech #AIMerger #DeepLearning #MachineLearning #GenerativeAI #LLMs #SearchEngine #AIresearch #Siri #Bard #ChatGPT #Gemini https://techfundingnews.com/apple-considering-14b-perplexity-acquisition/

Check this Repo out ! Shubham Saboo's GitHub repository hosts a collection of LLM Apps. These applications leverage large language models (LLMs) from various providers, including OpenAI, Anthropic, and Google, and open-source models. The apps incorporate Retrieval-Augmented Generation (RAG), AI agents, multi-agent teams, and voice agent technologies. This curated list is a valuable resource for exploring the capabilities and applications of LLMs in different contexts. #AwesomeLLMApps #LLM #LargeLanguageModels #OpenAI #Anthropic #GoogleAI #RAG #RetrievalAugmentedGeneration #AIAgents #MultiAgentTeams #VoiceAgents #GitHub #MachineLearning #NLP #AI #Langchain #LlamaIndex #DeepLearning #AISolutions https://github.com/Shubhamsaboo/awesome-llm-apps

This is madness leonidas! So someone beat to the punch l tried working on something similar 3 years ago but yeah ... execution Leonidas ! modelplayground.ai is a platform for comparing and evaluating various AI models. It provides access to over 100 models through a "single subscription" (my mantra for the project ... l m deeply touched!), without any markup. The platform is in its early stages, but it seems geared towards facilitating the side-by-side comparison and analysis of different AI solutions, allowing users to find the best fit for their needs. The website is modelplayground.ai. It compares Image, Video and 3D Ai generators. https://modelplayground.ai/ https://www.linkedin.com/posts/craig-pickard-tech_i-dont-post-on-linkedin-very-often-but-activity-7343356563715178496-CfXX/ https://www.reddit.com/r/SideProject/comments/1lkabo0/we_built_a_playground_to_compare_100_genai_models/ #modelplaygroundai #aimodels #generativeai #machinelearning #ai #sideproject #imageai #videoai #3dai #modelcomparison #aievaluation #aicrossroads #huggingface #midjourney #stablediffusion #aitools #aiml #ainews

Tool: Cognition AI is the company behind Devin, which is an AI agent designed for writing code. The co-founders include Scott Wu and possibly Walden Yan and Steven J. as well. Cognition AI was founded in 2022 by three former coders from Susquehanna. Devin's announcement was made on March 12, 2024. So whats the big deal ... Devin isn’t just another “code completion” model. It behaves like a junior software engineer who can pick up a ticket and carry it all the way to a tested pull-request. Try it ! Check out Devin here: https://devin.ai/ officail youtube announcement from Cognition Labs: https://youtu.be/fjHtjT7GO1c?si=fJY9XiFBNrPBKouo #devin #cognitionai #aiagent #aicoding #softwareengineer #cursor #github #copilot #codeium #tabnine #codegen #pullrequest #juniorengineer #automation #aiassistant #programming #devtools #machinelearning #artificialintelligence #coding

So whats the next big fad .... "Context Engineering". So WHAT IS CONTEXT ENGINEERING? 𝟭. It’s Feature Engineering—but for AI Agents. (*Check what's feature engineering down below) 𝟮. The art of filling the context window with exactly what’s needed. 𝟯. You’re managing “working memory” like an operating system manages RAM. 𝟰. Agents need engineered context: instructions, tools, memories, examples, feedback. 𝟱. Poor context = forgotten steps, broken tools, bad decisions. 𝟲. Long-running agents hit context limits fast—engineering is essential. 𝟳. Vibe coding doesn’t scale—context engineering does. In the context of AI prompting, feature engineering refers to the process of creating, selecting, and transforming input data (features) to improve the performance of a machine learning model. It involves using domain expertise and various techniques to make the data more suitable for the AI model to learn from and generate better outputs. Check the following links for context: - Langchain Explanation on what it is: https://youtu.be/4GiqzUHD5AA?si=BEIThE_HOT-i3I9T - First post l saw talking about this was by Andrej Karpathy (@karpathy): https://x.com/karpathy/status/1937902205765607626 - And you may wish to follow this lady she dove right into it detail ... never mind hte course it hella expensive !: https://x.com/MaryamMiradi/status/1940810454013518178 #contextengineering #featureengineering #aiagents #langchain #promptengineering #aioptimization #machinelearning #contextwindow #workingmemory #aiprompting #vibecoding #aiperformance #contextualization #aitraining #aiscaling

NVIDIA's Nemotron family delivers enterprise-grade multimodal AI models designed for complex reasoning tasks across scientific research, advanced mathematics, coding, and visual analysis. The lineup includes three variants optimized for different deployment scenarios: Nano for edge computing and cost-sensitive applications, Super for single-GPU workloads balancing performance and efficiency, and Ultra for maximum accuracy in data center environments. Unlike many AI models with restrictive licensing, Nemotron offers commercial viability with an open license that allows organizations to customize the models while maintaining control over their data and deployments. #Nemotron #NVIDIANemotron #NVIDIA #MultimodalAI #EnterpriseAI #AICoding #AIScience #AIReasoning #OpenSourceAI #EdgeAI #ComputerVision #AIModels #MachineLearning #ArtificialIntelligence #TechInnovation https://build.nvidia.com/nvidia/llama-3_1-nemotron-ultra-253b-v1 https://github.com/NVIDIA/GenerativeAIExamples

Alison offers free online courses covering machine learning. These courses are designed for individuals with a background in computer programming and science. The courses utilize Python and cover topics like different algorithms, KNN, and random. These courses are suitable for students and anyone seeking to learn more about the evolving field of machine learning. #alison #alisoncourses #machinelearning #onlinecourses #python #knn #randomforest #datascience #coding #programming #learntocode #freecourses #education #mlalgorithms #deeplearning #tensorflow #pytorch #scikitlearn https://alison.com/course/machine-learning-with-artificial-intelligence https://www.edupstairs.org/free-online-course-diploma-in-machine-learning-with-python/

Introducing Gemma 3N: Enhancing AI Development with Google Gemma 3N is Google’s latest innovation designed to simplify the development of AI applications. This comprehensive tool offers a developer-friendly environment that facilitates building, training, and deploying AI models with ease. With features such as a streamlined user interface and robust integration capabilities, Gemma 3N aims to reduce the complexity of AI development processes, making it accessible to both experienced developers and those new to the field. Targeting a wide range of users, from tech startups to established enterprises, Gemma 3N provides extensive documentation and support to ensure that all users can take full advantage of its powerful capabilities. By enhancing efficiency and accessibility in AI development, Gemma 3N positions itself as a valuable resource for anyone looking to harness the potential of artificial intelligence. Hashtags #Gemma3N #GoogleAI #AIDevelopment #MachineLearning #TechTools #DeveloperResources #ArtificialIntelligence #AIEmpowerment #SoftwareDevelopment #DataScience #CloudComputing https://developers.googleblog.com/ja/introducing-gemma-3n-developer-guide/

The Ongoing Copyright Debate: The New York Times vs. OpenAI and Microsoft Recently, the New York Times (NYT) filed a lawsuit against OpenAI and Microsoft, alleging that they used millions of copyrighted NYT articles to train their AI models without permission. The lawsuit claims that OpenAI’s models sometimes generate content that closely resembles NYT articles, raising important questions about copyright infringement and the implications for generative AI technologies. While the NYT aims to protect journalistic integrity, critics argue the lawsuit lacks clarity in establishing how exactly the models regurgitate content, leaving many confused about the relationship between AI training and output. Experts suggest that the AI's responses may not solely derive from their training data but could also involve mechanisms like retrieval-augmented generation (RAG), which allows models to pull real-time data from the internet. If this is the case, limiting the regurgitation of copyrighted material might be technically feasible without completely hindering AI capabilities. As AI models evolve and adjustments are made to minimize such occurrences, the industry collectively continues to search for balanced solutions to address copyright concerns while enabling innovation in AI technologies. Hashtags #NewYorkTimes #OpenAI #Microsoft #CopyrightLawsuit #GenerativeAI #AIEthics #MachineLearning #DataPrivacy #TechNews #AIRegulation #DigitalMedia https://www.deeplearning.ai/the-batch/the-new-york-times-versus-openai-and-microsoft/