It is a new world model. Simulates all realities
MACHINE LEARNING
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Upcoming video shows deterministic AI for enterprise
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I will have a video up soon that shows a new deterministic AI for the enterprise. 🙂 Maybe tomorrow.
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Share AI Insights and Follow for LLM Tutorials
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 20 décembre 2025
Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!https://t.co/vwPMkQAl12If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Meta’s Query-Only Test-Time Training Improves Long Context Performance
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This paper by Meta introduces Query-Only Test-Time Training for long contexts So by using a small amount of inference-time training that retunes how the model attends to the given input, it works far better than generating extra reasoning tokens
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Casey discusses gaussian splatting technology developments
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Never expected Casey to be poasting about gaussian splatting but here we are
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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
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… #AI #ArtificialInteligence #math #LLM #GenAI #GenerativeAI #mathematics -
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