The Most Complete #AI Agentic Engineering System — Step-by-step guide to build, optimize, and scale LLM agents: http://
amzn.to/3L5p3BS
LLMS
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Step-by-step Guide to Build, Optimize, and Scale LLM Agents
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Practical Guide to Building AI Agents with LLMs and Knowledge Graphs
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"Building AI Agents with LLMs, RAG, and Knowledge Graphs — A practical guide to autonomous and modern AI agents" See it at http://
amzn.to/4622k2h via @PacktDataML -
From flying cars to 2M token context windows: a commentary on AI progress
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We were promised flying cars but all we got was 2m token context windows. :/
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Scaling Laws and Compute in AI Model Development
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a lot of energy over the past few years went into trying to solve problems that would naturally resolve themselves with larger models/memory/compute.
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Lean Community Infrastructure Enables LLM Advancement in Proof
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Honestly, a huge part of this is thanks to the incredible Lean community @leanprover ! Mathlib, proof tooling, and years of infrastructure work made this possible —Lean is far more usable today because of that collective effort. And yes, LLMs have gotten much better at Lean lately 🙂 definitely helps accelerate learning and prototyping. Evan Chipman (@evanchipman) This is the 3rd time this week I thought “someone ought to make x” then open this app and see a team announce x. The speed of this era is disorienting. — https://nitter.net/evanchipman/status/2028157017597374837#m
→ View original post on X — @animaanandkumar, 2026-03-01 22:57 UTC
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The Future of AI: Models as Commodities vs. Proprietary Data
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Eventually software will be commodity – models will have some value, but ultimately it’ll all be wrappers around unique / proprietary data
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Kling 3.0 tops text-to-video benchmarks
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Kling 3.0 by @Kling_ai took a top spots on Artificial Analysis arena in text-to-video benchmark with both Kling 3.0 1080p (Pro) and Kling 3.0 Omni 1080p (Pro) models.
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Top AI models stay close in a fast-moving packed peloton
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Funny how the top models from different labs stay within a tiny distance from each other. The peloton is moving extremely fast, but stays quite packed, no breakaway so far. And that is despite large differences, e.g. in available compute. What's the forcing function that makes
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The Evolution of AI Agents: From RAG to Memory-Based Systems
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RAG was never the end goal.
— Akshay 🚀 (@akshay_pachaar) 1 mars 2026
Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.
RAG (2020-2023):
– Retrieve info once, generate response
– No decision-making, just fetch and answer
– Problem: Often retrieves… pic.twitter.com/nU36jH3yVMRAG was never the end goal. Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible. RAG (2020-2023): – Retrieve info once, generate response
– No decision-making, just fetch and answer
– Problem: Often retrieves -
Tips for Using AI Coding Assistants for Frontend Development
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glad you’re enjoying codex! for frontend specifically, what helps is: 1. give codex examples of what kind of front ends you want it to build, the more it knows, the better it is! 2. have codex use playwright or agent browser skill, close the loop between what you see and what