Never expected Casey to be poasting about gaussian splatting but here we are
GENERATIVE AI
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AI Solves Impossible Math Problems Better Than Top Mathematicians
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AI is solving 'impossible' math problems. Can it beat the world's top mathematicians? | Live Science https://
share.google/jACbXTwJJIuKc3
ADE
… #AI #ArtificialInteligence #math #LLM #GenAI #GenerativeAI #mathematics -
Meta AI developing new Memories and Custom Prompts features
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Meta likely aims to bridge a feature gap with top-tier AI labs in one shot, and is working on Memories and Custom Prompts for Meta AI. Soon?
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Generative AI Impact on Hospitality and Restaurant Jobs
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How Generative AI Will Affect Jobs in Restaurants and Hospitality Generative AI is set to reshape the hospitality industry — from kitchen workflows to guest services — with both opportunities and challenges ahead. Read more https://
bernardmarr.com/how-generative
-ai-will-affect-jobs-in-restaurants-and-hospitality/
… #AI #Hospitality -
Using ChatGPT to generate personalized Sora holiday videos
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You can get a personalised Christmas Sora video on ChatGPT if you send emoji to the chat. Looking forward to ChatGPT wrapped
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AI Tools Reach Massive Scale: ChatGPT Leads with 4.7B Monthly Visits
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AI tool usage at scale is here. ChatGPT: 4.7B monthly visits (Jan 2025)
Canva: 887M
Google Translate: 595M
DeepSeek: 268M (massive surge) http://
Character.AI: 226M
Perplexity: 133M
Gemini: 118M
Claude: 105M AI isn’t a trend anymore — it’s infrastructure. What’s your #1 -
8 RAG Architectures Every AI Engineer Must Know
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8 RAG architectures all AI Engineers should know:
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Building RAG Applications on AWS: Ingestion and Querying Stages
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Building a RAG app on AWS is simpler than you might think.
— Akshay 🚀 (@akshay_pachaar) 20 décembre 2025
Let me explain how you can achieve this using services you already know:
At its core, RAG follows a two-stage pattern: ingestion and querying.
Here's how you can implement each stage on AWS:
1️⃣ Ingestion: Turning raw… pic.twitter.com/UOTBpoUcpeBuilding a RAG app on AWS is simpler than you might think. Let me explain how you can achieve this using services you already know: At its core, RAG follows a two-stage pattern: ingestion and querying. Here's how you can implement each stage on AWS: Ingestion: Turning raw
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The need for efficiency research in AI agent development
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AI agents are getting slower and it might be the time they all stop scaling and start doing some research to make them fast and accessible to everyone.
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AI 2025 Breakthroughs: RL, Reasoning Models, and Future Paradigms
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Things move very very fast in AI. 2025 was a year of RL with verifiable rewards(RLVR), LLM ghosts/jagged intelligence, Cursor-like LLM apps, claude code/codex, vibe-coding, NanoBanana showing early glimpse of LLM promptable graphical interfaces(PGI, i just coined this lol), LLM reasoning models crushing olympiad competitions (maths, physics, code). Most altering releases tend to come early in a year, jan-feb, and then scaling-up and small fixes begin. Eagerly looking forward to new paradigm shifts. What will next NanoBanana look like, just bigger or new capabilities no one thought before? There are several stages of training now, RL(VR) being the most recent. What will be the RL successor? And continual learning, will it be fixed in 2026, or this is a problem we will live with for long? There are also world models, agents that actually work reliably in the wild for hours. Andrej Karpathy (@karpathy) x.com/i/article/200211463822… — https://nitter.net/karpathy/status/2002118205729562949#m