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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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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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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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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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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, 1 month, 3 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
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).