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@kaptain shared a link, 5 months, 1 week ago
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Streamline Complex AI Inference on Kubernetes with NVIDIA Grove

NVIDIA releasedGrove, a Kubernetes API baked intoDynamo, to wrangle the chaos of modern AI inference. It pulls apart your big, messy model into clean, discrete chunks - prefill, decode, routing - and runs them like a single, orchestrated act. The trick?Custom hierarchical resources. They let Grove h.. read more  

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@kaptain shared a link, 5 months, 1 week ago
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Prepare for the Kubernetes Administrator Certification and Pass

A tight 2-hour YouTube course built for theCKA examgrind. It's all real-world tasks: cluster setup, upgrades, troubleshooting. No fluff, just shell commands and Kubernetes in action. It walks through the gritty bits:etcdbackup and restore, node affinity, tolerations, and how to set upIngresslike som.. read more  

Prepare for the Kubernetes Administrator Certification and Pass
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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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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@kala shared a link, 5 months, 1 week ago
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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  

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).