Subbu 👋 @subbuballa

This 2 hour Stanford lecture on AI careers will teach you more about winning in the AI race than every piece of AI content you have scrolled past this year. Bookmark this & give it 2 hours, no matter what. It'll be the most productive thing you could do this weekend.

Nano banana pro just solved the text and veo 3.1. Exciting times!!!!1

They say if you can visualize it, you can achieve it. 👁️✨ Here is the vision for 2028. The 100th Academy Awards. Best Director. @ssrajamouli Best Actor. @urstrulyMahesh We are planting the seed today. Let’s break the internet before we break the box office. #Manifest2028

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@GoogleAI @elevenlabs Edited using @AdobeAE

Made with @GoogleAI #Veo3 #gemini @elevenlabs

We as humanity has been running through the ages – from primal forests to vast civilizations, through industrial might to a world transformed by AI. Is this where our run end? #EvolutionShort #FutureIsNow #HumanEvolution #ShortFilm #CinematicJourney #TheLastRun

3️⃣ Lambda Architecture ⛰ Combines batch and real-time layers to serve fast and accurate results. 4️⃣ Kappa Architecture ♻️ Stream-only processing — no batch layer needed.

2️⃣ Event-Driven Architecture ⚡ Powered by events and message brokers like Kafka. Think real-time triggers and microservices that respond to data as it happens.

✨ Here’s a visual guide depicting 4 powerful data architectures: 1️⃣ Layered Architecture A classic! Data flows through layers: Source → Ingestion → Storage → Processing → Analytics Great for clarity and modular pipelines.

Google Gemini 2.5 Pro is the best AI coding model right now. People are finding insane ways to build apps, games, and supercharge productivity. 10 wild examples: 1. Office simulation game

@sama Just create me this infographic and it is mind blowing 🤯 This is with a basic prompt, what can it achieve with a detailed one

🚀 6 Core Responsibilities of a Modern Data Engineer 1️⃣ Data Pipelines – Kafka, Flink 2️⃣ Data Modeling – Snowflake, dbt 3️⃣ Data Quality – Great Expectations 4️⃣ Automation – Airflow, Prefect, Terraform 5️⃣ Collaboration –Feast, Databricks 6️⃣ Security –IAM, Purview

Microsoft is offering FREE courses in following areas: - AI - IOT - Data Science - Machine Learning A project-based pedagogy that allows you to learn while building! 🚀 Read More ...👇

Struggling with Machine Learning algorithms? 🤖 Then you better stay with me! 🤓 Today I am starting a new series of threads to simplify ML algorithms. ...and Linear Regression is the first one! 👇🏻

Really enjoyed NeurIPS! After attending great sessions around LLMs, I documented a huge list of interesting LLM papers that were either presented or mentioned. Here is a list of some of my favorite papers in no particular order. I have included papers that won awards and are pushing ideas that we will continue hearing more about: -- Chain of Code: Reasoning with a Language Model-Augmented Code Emulator - arxiv.org/abs/2312.04474 -- Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective - arxiv.org/abs/2305.15408 -- Scaling Data-Constrained Language Models - arxiv.org/abs/2305.16264 -- Language to Rewards for Robotic Skill Synthesis - arxiv.org/abs/2306.08647 -- Tree of Thoughts: Deliberate Problem Solving with Large Language Models - arxiv.org/abs/2305.10601 -- Why think step by step? Reasoning emerges from the locality of experience - arxiv.org/abs/2304.03843 -- Toolformer: Language Models Can Teach Themselves to Use Tools - arxiv.org/abs/2302.04761 -- Reasoning with Language Model is Planning with World Model - arxiv.org/abs/2305.14992 -- ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings - arxiv.org/abs/2305.11554 -- DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models - arxiv.org/abs/2306.11698 -- QLoRA: Efficient Finetuning of Quantized LLMs - arxiv.org/abs/2305.14314 -- Direct Preference Optimization: Your Language Model is Secretly a Reward Model - arxiv.org/abs/2305.18290 -- Are Emergent Abilities of Large Language Models a Mirage? - arxiv.org/abs/2304.15004 -- Reverse Engineering Self-Supervised Learning - arxiv.org/abs/2305.15614 -- Learning Transformer Programs - arxiv.org/abs/2306.01128 -- OpenAssistant Conversations -- Democratizing Large Language Model Alignment - arxiv.org/abs/2304.07327 -- Privacy Auditing with One (1) Training Run - arxiv.org/abs/2305.08846 -- Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning - arxiv.org/abs/2312.05230 -- Large Language Models as Zero-Shot Conversational Recommenders - arxiv.org/abs/2308.10053 -- Zephyr: Direct Distillation of LM Alignment - arxiv.org/abs/2310.16944 -- I am also putting together a year in review later in the week or early next week so you will see other cool and important LLM papers that were published throughout the year. Stay tuned! Feel free to comment with your favorite papers as well.

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Introducing the 𝗪𝗼𝗿𝗹𝗱’𝘀 𝗕𝗲𝘀𝘁 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝟳𝗕 𝗟𝗟𝗠 - OpenChat-3.5-1210, further surpassing ChatGPT and Grok models. This upgrade to the widely adopted OpenChat-3.5 is focused on increasing the performance in one of the most important areas for LLMs - Coding. We achieved a near 15 point increase on HumanEval, while maintaining or improving performance on other benchmarks, making OpenChat-3.5-1210 one of the most capable Generalist models to date. The model is now available on HuggingFace: openchat/openchat-3.5-1210 Try it today with Together AI’s (@TogetherCompute) optimized Inference API: api.together.xyz/playground/cha… We would like to thank RunPod (@runpod) for the support and sponsorship provided for the model!

Phi-2 by @MicrosoftAI is now the #1 trending model on @huggingface (hf.co/models). 2024 will be the year of smoll AI models!

🦜💬FastUI-Chat Want to get an easy frontend for your LangChain app? All written in Python? @Shroominic made an awesome example hooking up LangChain to FastUI (a new framework from the @pydantic team) Check it out here: github.com/shroominic/fas…

Huge day indeed for AI and LLMs, congrats to Meta 👏 This is now the most capable LLM available directly as weights to anyone from researchers to companies. The models look quite strong, e.g. Table 4 in the paper: MMLU is good to look at, the 70B model is just below GPT-3.5. But HumanEval (bad misnomer) shows coding capability is quite a bit lower (48.1 vs 29.9).

Yann LeCun @ylecun

3 years ago

This is huge: Llama-v2 is open source, with a license that authorizes commercial use! This is going to change the landscape of the LLM market. Llama-v2 is available on Microsoft Azure and will be available on AWS, Hugging Face and other providers Pretrained and fine-tuned

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#monsterjam