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The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix

Researchers squeezed GPT-2-class performance out of a model trained on just1 billion tokens- 10× less data - by dialing in a sharp dataset mix:50% finePDFs, 30% DCLM-baseline, 20% FineWeb-Edu. Static mixing beat curriculum strategies. No catastrophic forgetting. No overfitting. And it hit90%+of GPT-.. read more  

The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix
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Jensen Huang's Stark Warning: China's 1 Million AI Workers vs America's 20,000

Nvidia CEO Jensen Huang, in some leaked comments, didn’t mince words: U.S. export bans aren’t hobbling China’s AI game - they’re fueling it. He pointed to Huawei’s 910C chip edging close to H100 territory, a forecast putting China ahead in AI compute by 2027, and a fast-growing local chip industry n.. read more  

Jensen Huang's Stark Warning: China's 1 Million AI Workers vs America's 20,000
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Context Management in Amp

Amp stretches the context window into something more useful. It pulls in system prompts, tool info, runtime metadata, even AGENTS.md files - fuel for agentic behavior. It gives devs serious control: edit messages, fork threads, drop in files with @mentions, hand off conversations, or link threads to.. read more  

Context Management in Amp
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Google to release Nano Banana Pro next week

Google dropsGemini 3and the newNano Banana Pronext week. Big swing at image generation - now tied tight to Gemini 3 Pro. Early glimpses in Google Vids hint Nano Banana Pro is built for sharper visuals in creative tools. System shift:Google’s stacking its apps behind a single backbone: Gemini 3 Pro. .. read more  

Google to release Nano Banana Pro next week
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Inside Cursor - Sixty days with the AI coding decacorn

Cursor is shaking up recruiting by treating the hiring process as more about the person than the job, resulting in a fast-growing team of exceptional individuals drawn in by the company's compelling mission and focus on challenging technical problems. Women in product and engineering roles are a kno.. read more  

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The Fatal Math Error Killing Every AI Architecture - Including The New Ones

LLMs are fading as JEPA (Joint Embedding Predictive Architecture) emerges with joint, embedding, predictive architecture. JEPA is a step towards true intelligence by avoiding the flat, finite spreadsheet trap of Euclidean space and opting for a toroidal model... read more  

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Introducing structured output for Custom Model Import in Amazon Bedrock

Amazon Bedrock’s Custom Model Import just got structured output support. Now LLMs can lock their responses to your JSON schema - no prompt hacks, no cleanup after... read more  

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LaTeX, LLMs and Boring Technology 

LLMs are tearing down LaTeX's old walls. Syntax hell, cryptic errors, clunky formatting - easier now. Whether baked into editors or running solo, these models smooth the pain. Why does it work so well? LaTeX has history. Mountains of examples. It's the perfect training set. That puts newer contender.. read more  

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Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac

NVIDIA just droppedIsaac for Healthcare v0.4, and it’s a big one. Headliner: the newSO-ARM starter workflow- a full-stack sim2real pipeline built for surgical robotics. It covers the whole loop: spin up synthetic and real-world data capture, train withGR00t N1.5, and deploy straight to 6-DOF hardwar.. read more  

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Visibility at Scale: How Detects Sensitive Data Exposure

Segment gutted its old permissions table—bloated, slow, tangled in logic - and replaced it with a lean, service-based setup. The new stack runs onPostgres,Redis, and a sharply tunedGo API, cutting query times from 1400ms to under 100ms. Clean, fast, and centralized... read more  

Visibility at Scale: How Detects Sensitive Data Exposure
k3d is an open-source utility designed to simplify running Kubernetes locally by wrapping K3s (Rancher’s lightweight Kubernetes distribution) inside Docker containers. Instead of creating virtual machines, k3d uses Docker as the execution layer, allowing developers to spin up multi-node Kubernetes clusters in seconds using minimal system resources.

k3d is especially popular for local development, CI pipelines, demos, and testing Kubernetes-native applications. It supports advanced setups such as multi-node clusters, load balancers, custom container registries, port mappings, and volume mounts, while remaining easy to tear down and recreate.

Because it uses K3s, k3d inherits a simplified control plane, bundled components, and reduced memory footprint compared to full Kubernetes distributions. This makes it ideal for developers who want a realistic Kubernetes environment without the overhead of tools like Minikube or full VM-based clusters.

k3d integrates cleanly with common Kubernetes workflows and tools such as kubectl, Helm, Skaffold, and Argo CD. It is frequently used to validate manifests, test Helm charts, and simulate production-like environments locally before deploying to cloud or on-prem clusters.