8 RAG architectures all AI Engineers should know:
MACHINE LEARNING
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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
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Share AI and Machine Learning Insights with Your Network
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If 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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Training on Test Labels vs Inputs: ARC Benchmark Ethics
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Training on test refers to using the *labels* in the test set. Not the *inputs*. Including test inputs is standard common in semi-supervised/self-supervised. Whether or not this approach is against the ARC rules, I don't know. But it is *not* what "training on test" means.
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AI Economy Impact on Data Privacy: Threat or Solution?
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Will The AI Economy Strike The Final Blow To Data Privacy Or Save It?
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @lexfridman @sama @kaifulee @ID_AA_Carmack @karpathy @2morrowknight @ylecun -
Unsupervised Learning Confusion: Training on Test Data Explained
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Man discovers unsupervised learning and confuses it with what "training on test" actually means.
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How AI Models Learn: Debunking Data Reproduction Myths
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I’m tired of explaining how AI models learn. They don’t copy or reproduce data in the way you think they do.
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Stanford Hiring Data Science Postdocs in Causal Inference
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We're hiring data science postdocs! Work on interdisciplinary projects in causal inference, ML, econometrics, and more. Recent PhDs with coding expertise and cross-disciplinary experience are encouraged to apply: https://
datascience.stanford.edu/programs/data-
science-fellows
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