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@kala shared a link, 1 month, 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

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@kala shared a link, 1 month, 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

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@kala shared a link, 1 month, 4 weeks ago

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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

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@kala shared a link, 1 month, 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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@kala shared a link, 1 month, 4 weeks ago

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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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@devopslinks shared a link, 1 month, 4 weeks ago

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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

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@devopslinks shared a link, 1 month, 4 weeks ago

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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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@devopslinks shared a link, 1 month, 4 weeks ago

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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

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@devopslinks shared a link, 1 month, 4 weeks ago

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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, 4 weeks ago

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FSF Talks GPL Compliance and AI Code at GNU Cauldron

#LLMs #Complia... #FSF #AI #GPL

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

Flask is an open-source web framework written in Python and created by Armin Ronacher in 2010. It is known as a microframework, not because it is weak or incomplete, but because it provides only the essential building blocks for developing web applications. Its core focuses on handling HTTP requests, defining routes, and rendering templates, while leaving decisions about databases, authentication, form handling, and other components to the developer. This minimalistic design makes Flask lightweight, flexible, and easy to learn, but also powerful enough to support complex systems when extended with the right tools.

At the heart of Flask are two libraries: Werkzeug, which is a WSGI utility library that handles the low-level details of communication between web servers and applications, and Jinja2, a templating engine that allows developers to write dynamic HTML pages with embedded Python logic. By combining these two, Flask provides a clean and pythonic way to create web applications without imposing strict architectural patterns.

One of the defining characteristics of Flask is its explicitness. Unlike larger frameworks such as Django, Flask does not try to hide complexity behind layers of abstraction or dictate how a project should be structured. Instead, it gives developers complete control over how they organize their code and which tools they integrate. This explicit nature makes applications easier to reason about and gives teams the freedom to design solutions that match their exact needs. At the same time, Flask benefits from a vast ecosystem of extensions contributed by the community. These extensions cover areas such as database integration through SQLAlchemy, user session and authentication management, form validation with CSRF protection, and database migration handling. This modular approach means a developer can start with a very simple application and gradually add only the pieces they require, avoiding the overhead of unused components.

Flask is also widely appreciated for its simplicity and approachability. Many developers write their first web application in Flask because the learning curve is gentle, the documentation is clear, and the framework itself avoids unnecessary complexity. It is particularly well suited for building prototypes, REST APIs, microservices, or small to medium-sized web applications. At the same time, production-grade deployments are supported by running Flask applications on WSGI servers such as Gunicorn or uWSGI, since the development server included with Flask is intended only for testing and debugging.

The strengths of Flask lie in its minimalism, flexibility, and extensibility. It gives developers the freedom to assemble their application architecture, choose their own libraries, and maintain tight control over how things work under the hood. This is attractive to experienced engineers who dislike being boxed in by heavy frameworks. However, the same freedom can become a limitation. Flask does not include features like an ORM, admin interface, or built-in authentication system, which means teams working on very large applications must take on more responsibility for enforcing patterns and maintaining consistency. In situations where a project requires an opinionated, all-in-one solution, Django or another full-stack framework may be a better fit.

In practice, Flask has grown far beyond its initial positioning as a lightweight tool. It has been used by startups for rapid prototypes and by large companies for production systems. Its design philosophy—keep the core simple, make extensions easy, and let developers decide—continues to attract both beginners and professionals. This balance between simplicity and power has made Flask one of the most enduring and widely used Python web frameworks.

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