MCP vs RAG vs AI Agents MCP → connects tools
RAG → connects knowledge
Agents → execute tasks Not competitors.
A stack. The advantage?
How you combine them. Where are you investing first? #AI #Agents #RAG #Tech
LLMS
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Integrating MCP, RAG, and AI Agents into an AI Stack
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Gemini API Update Adds Structured Timelines for Model Reasoning
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The old Gemini API worked like a vending machine. Prompt in, answer out, no visibility into what happened between. This update gives you a structured timeline of every step the model took: thinking, searching, tool calls, final output. Each one labeled and separated. You can now
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Speculative decoding speeds up LLMs by 6x
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The slowest part of running an LLM just got 6x faster without losing a single token.
— AlphaSignal AI (@AlphaSignalAI) 10 mai 2026
LLMs generate text one token at a time.
That sequential bottleneck wastes GPU power and slows everything down.
Speculative decoding fixes part of this.
A small draft model guesses ahead,… pic.twitter.com/y7YlqTWvrjThe slowest part of running an LLM just got 6x faster without losing a single token. LLMs generate text one token at a time. This sequential bottleneck wastes GPU power and slows everything down. Speculative decoding addresses part of this issue. A small draft model predicts ahead.
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AI Progress: From Basic Code to Engineering Tests
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A year ago, AI could write basic functions. Now it solves multi-step coding challenges and passes actual engineering tests. Of course, bigger models helped. But the bigger shift is in how models learn what a "good answer" means. We used to train AI using Reinforcement Learning
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Analyzing token consumption and efficiency for HTML in LLMs
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The most frequent objection in the comment section is this line: "HTML eats up so many tokens— is Anthropic indirectly fleecing us?" I've flipped this thought over in my mind, and maybe we can look at it this way. First, HTML does eat tokens—that's a fact—but if Anthropic
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Rediscovering Karpathy’s wikiLLM with Obsidian and Claude as a second brain
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I'd somehow completely forgotten that Karpathy introduced the wikiLLM a while back (obsidian + Claude code/codex). I'm sick in bed and set it up because I have nothing else to do. I love it. I have a second brain now. Amazing.
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Distinguishing Orchestrator and Harness in AI Task Completion
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unclear the diff between orchestrator and harness since they both have TASK_DONE
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ZeRO-Infinity: Breaking the GPU Memory Wall for Deep Learning
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ZeRO-Infinity: Breaking the GPU Memory Wall
for Extreme Scale Deep Learning! MichiGAN Hyperscale GPU Data Complete Nvidia cluster installation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS -
Building Agentic Systems with CrewAI and Amazon Bedrock
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Build Agentic System with CrewAI and Amazon Bedrock! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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DeepMind Develops AI Capable of Competitive Human-Level Coding
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DeepMind Has Made Writing #AI That Rivals Human Coders! #BigData #Analytics #DataScience #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/DeepMinder-Cod
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