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Google Cloud donates A2A to Linux Foundation- Google Developers Blog

IntroducingAgent2Agentand brace yourself for the heavyweights—AWS, Cisco, Google, and a few more, are in on it. Their mission? Crafting the universal lingo for AI agents. It's called theA2A protocol. Finally, they're smashing the silos holding AI back... read more  

Google Cloud donates A2A to Linux Foundation- Google Developers Blog
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MCP — The Missing Link Between AI Models and Your Applications

Model Context Protocol (MCP)tackles the "MxN problem" in AI by creating a universal handshake for tool interactions. It simplifies howLLMstap into external resources. MCP leans onJSON-RPC 2.0for streamlined dialogues, building modular, maintainable, and secure ecosystems that boast reusable and inte.. read more  

MCP — The Missing Link Between AI Models and Your Applications
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A non-anthropomorphized view of LLMs

CallingLLMssentient or ethical? That's a stretch. Behind the curtain, they're just fancy algorithms dressed up as text wizards. Humans? They're a whole mess of complexity... read more  

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Context Engineering for Agents

Context engineeringcranks an AI agent up to 11 by juggling memory like a slick OS. It writes, selects, compresses, and isolates—never missing a beat despite those pesky token limits. Nail the context, and you've got a dream team. Slip up, though, and you might trigger chaos, like when ChatGPT went r.. read more  

Context Engineering for Agents
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Massive study detects AI fingerprints in millions of scientific papers

Study finds 13.5% of 2024 PubMed papers bear LLM fingerprints, showcasing a shift to jazzy "stylistic" verbs over stodgy nouns.Upending stuffy academic norms!.. read more  

Massive study detects AI fingerprints in millions of scientific papers
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‘Shit in, shit out’: AI is coming for agriculture, but farmers aren’t convinced

Aussie farmers want "more automation, fewer bells and whistles"—technology should work like a tractor, not act like an app:straightforward, adaptable, and rock-solid... read more  

‘Shit in, shit out’: AI is coming for agriculture, but farmers aren’t convinced
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The Portable Memory Wallet Fallacy: 4 Fundamental Problems

Portable AI memory pods hit a brick wall—vendors cling to data control, users resist micromanagement, and technical snarls persist.So, steer regulation towards automating privacy and clarifying transparency. Make AI interaction sync with how people actually live... read more  

The Portable Memory Wallet Fallacy: 4 Fundamental Problems
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Document Search with NLP: What Actually Works (and Why)

NLP document search trounces old-school keyword hunting. It taps into scalable*vector databasesandsemantic vectorsto grasp meaning, not just parrot words.* Pictureword vector arithmetic: "King - Man + Woman = Queen." It's magic. Searches become lightning-fast and drenched in context... read more  

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LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI

Dump BLEU and ROUGE. Let LLM-as-a-judge tools like G-Eval propel you to pinpoint accuracy.The old scorers? They whiff on meaning, like a cat batting at a laser dot.DeepEval? It wrangles bleeding-edge metrics with five lines of neat code.Want a personal touch? G-Eval's got your back. DAG keeps benchm.. read more  

LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI
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Building tiny AI tools for developer productivity

Tiny AI scripts won't make you the next tech billionaire, but they're unbeatable for rescuing hours from the drudgery of repetitive tasks. Whether it's wrangling those dreadedGitHub rollupsor automating the minutiae, these little miracles grant engineers the luxury to actually think... read more  

INTELLECT-3 is a frontier-class 100B+ Mixture-of-Experts language model developed by Prime Intellect and trained end-to-end using their large-scale asynchronous RL framework, PRIME-RL. Built on the GLM-4.5-Air base model, INTELLECT-3 combines supervised fine-tuning with long-horizon reinforcement learning across hundreds of verifier-backed environments spanning math, code, science, logic, and agentic tasks.

The model was trained on a high-performance cluster of 512 NVIDIA H200 GPUs across 64 nodes, supported by Prime Intellect’s Sandboxes execution engine, deterministic compute orchestration, and Lustre-backed distributed storage. The result is a model that surpasses many larger systems in reasoning benchmarks while remaining fully open-source.

Prime Intellect released not only the model weights but also the full training recipe: PRIME-RL, Verifiers, the Environments Hub, datasets, and evaluation suites. INTELLECT-3 is positioned as a foundation for organizations seeking to post-train or customize their own frontier-grade models without relying on proprietary AI labs.