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@kala shared a link, 4 weeks ago
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I regret building this $3000 Pi AI cluster

A 10-node Raspberry Pi 5 cluster built with16GB CM5 Lite modulestopped out at325 Gflops- then got lapped by an $8K x86 Framework PC cluster running4x faster. On the bright side? The Pi setup edged out in energy efficiency when pushed to thermal limits. It came with160 GB total RAM, but that didn’t h.. read more  

I regret building this $3000 Pi AI cluster
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@kala shared a link, 4 weeks ago
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Why open source may not survive the rise of generative AI

Generative AI is snapping the attribution chain thatcopyleft licenseslike theGNU GPLrely on. Without clear provenance, license terms get lost. Compliance? Forget it. The give-and-take that powersFOSSstops giving - or taking... read more  

Why open source may not survive the rise of generative AI
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@kala shared a link, 4 weeks ago
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What Significance Testing is, Why it matters, Various Types and Interpreting the p-Value

Significance testing determines if observed differences are meaningful by calculating the likelihood of results happening by chance. The p-value indicates this likelihood, with values below 0.05 suggesting statistical significance. Different tests, such as t-tests, ANOVA, and chi-square, help analyz.. read more  

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Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference

Amazon rolled out fine-tuning and distillation forVision LLMslike Nova Lite viaBedrockandSageMaker. Translation: better doc parsing—think messy tax forms, receipts, invoices. Developers get two tuning paths:PEFTor full fine-tune. Then choose how to ship:on-demand inference (ODI)orProvisioned Through.. read more  

Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference
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Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

Generative recommender systems need more than just observed user behavior to make accurate recommendations. Introducing A-SFT algorithm improves alignment between pre-trained models and reward models for more effective post-training... read more  

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A FinOps Guide to Comparing Containers and Serverless Functions for Compute

AWS dropped a new cost-performance playbook pittingAmazon ECSagainstAWS Lambda. It's not just a tech choice - it’s a workload strategy. Go containers when you’ve got steady traffic, high CPU or memory needs, or sticky app state. Go serverless for spiky, event-driven bursts that don’t need a long lea.. read more  

A FinOps Guide to Comparing Containers and Serverless Functions for Compute
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How and Why Netflix Built a Real-Time Distributed Graph -  Ingesting and Processing Data Streams at Internet Scale

Netflix built a Real-Time Distributed Graph (RDG) to connect member interactions across different devices instantly. Using Apache Flink and Kafka, they process up to1 millionmessages per second for node and edge updates. Scaling Flink jobs individually reduced operational headaches and allowed for s.. read more  

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What is autonomous validation? The future of CI/CD in the AI era

CircleCI droppedautonomous validation, a smarter CI/CD that thinks on its feet. It scans your code, predicts breakage, runs only the tests that matter - and fixes the easy stuff on its own. If things get messy, it hands off full context so you’re not digging through logs. Bonus: it keeps learning fr.. read more  

What is autonomous validation? The future of CI/CD in the AI era
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Jump Starting Quantum Computing on Azure

Microsoft just pulled off full-stack quantum teleportation withAzure Quantum, wiring up Qiskit and Quantinuum’s simulator in the process. Entanglement? Check. Hadamard and CNOT gates set the stage. Classical control logic wrangles the flow. Validation lands cleanly on the backend... read more  

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@kala shared an update, 4 weeks ago
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FSF Talks GPL Compliance and AI Code at GNU Cauldron

The FSF's Licensing and Compliance Lab engaged with GNU toolchain maintainers at GNU Cauldron to discuss GPL compliance, AI-generated code, and attribution in containerized environments.

FSF Talks GPL Compliance and AI Code at GNU Cauldron
The "Nano Banana Pro" is an AI image generation and editing model. It is built on the Gemini 3.0 Pro reasoning engine. It is designed to create visuals from ideas. Nano Banana Pro plans scenes before rendering. This ensures high-quality results. It can generate text in multiple languages directly within the image.

The model offers advanced creative controls. Users can specify camera angles, lighting conditions, and depth of field. It has editing features, like "multi-image fusion". Up to 14 reference images can be combined. This maintains consistent branding, character identity, and style. Its "search grounding" capability uses real-time information from Google Search. This produces accurate infographics or diagrams.

The visuals are available in up to 4K resolution. They are suitable for social media posts to print materials. They are generated in seconds. Nano Banana Pro is available across the Google Gemini interface, Google Cloud for enterprise clients, and integrated into popular creative software like Adobe Photoshop.