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@kala shared a link, 3 months, 1 week ago

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FinePDFs: Liberating 3T of the finest tokens from PDFs - a Hugging Face Space by HuggingFaceFW

Hugging Face introduces FinePDFs, a large open dataset built by extracting and cleaning text from millions of PDF documents, reaching trillions of tokens across many languages. The post explains how the pipeline handles messy PDF structure, layout noise, duplication, and low-quality content to produ.. read more

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@kala shared a link, 3 months, 1 week ago

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Recursive Language Models: the paradigm of 2026

Prime Intellect dropped a fresh take on long-range LLM workflows with itsRecursive Language Model (RLM)scaffold. It pulls off two smart moves: folds context to free up tokens and spins off sub-LLMs to handle chunkier tasks. Think persistent Python REPL meets lightweight agent swarm... read more

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@kala shared a link, 3 months, 1 week ago

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Reading across books with Claude Code

A custom LLM agent, built withClaude Codeand some hard-working CLI tools, chewed through 100+ nonfiction books by slicing them into 500-word semantic chunks - and then threading excerpt trails by topic. Under the hood: Chunk-topic indexes lived inSQLite. Topic embeddings flowed throughUMAPfor clust.. read more

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@kala shared a link, 3 months, 1 week ago

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The Complete Guide to CLAUDE.md

Claude Code just got smarter withCLAUDE.md- a project-level file that loads every time a session starts. Drop in your team's coding quirks, custom commands, naming rules, or traps to avoid. Claude reads it, remembers it, and quietly tailors responses to fit. Think of it likeAGENTS.md, seen in Cursor.. read more

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@devopslinks shared a link, 3 months, 1 week ago

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What I Really Mean When I Say “Good Communication” in Incident Response

In the world of incidents,communication is key. Tailor messages for different audiences: be clear for business stakeholders, factual for IT management, and detailed for fellow responders. Don't let vagueness derail incident response - keep stakeholders informed with precise updates and clear expecta.. read more

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@devopslinks shared a link, 3 months, 1 week ago

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Monitoring & Observability: Using Logs, Metrics, Traces, and Alerts to Understand System Failures

Railway just leveled up its observability game. Now logs, metrics, and alerts all live in one tidy dashboard - clean and connected. Structured logs flow straight from stdout/stderr. Metrics pulse in real time. Alerts plug into monitors or deployment webhooks so teams catch firesbeforethey rage... read more

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@devopslinks shared a link, 3 months, 1 week ago

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Year in Review: Lessons From 12 Projects Patreon Shipped in 2025

Patreon engineers made massive bets in 2025, shipping code across all areas of the system and enabling impactful features like Autopilot's growth tools suite. Expanding Autopilot's scope, reach, and effectiveness was a challenge, especially guaranteeing recipient redemption after email delivery in a.. read more

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@devopslinks shared a link, 3 months, 1 week ago

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Making a micro Linux distro

A dev dives into building a barebones Linux distro for RISC-V using QEMU. Starts at the metal: compiles the kernel, wires up a no-frills init process, packs it all into an initramfs. Then levels up, drops inu-rootto swap out raw shell scripts for Go-powered userland tools. Adds network. Now it’s a f.. read more

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@devopslinks shared an update, 3 months, 1 week ago

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Canonical Introduces Minimal Ubuntu Pro: Smaller Images and Secure Cloud Workloads at Scale

#Canonic... #Securit... #cloud #Ubuntu #Minimal...

Canonical has launched Minimal Ubuntu Pro, enhancing cloud security with lightweight images and robust features. Available on AWS, Azure, and Google Cloud, it offers minimized attack surfaces and long-term support.

Canonical Introduces Minimal Ubuntu Pro: Smaller Images and Secure Cloud Workloads at Scale

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@varbear shared an update, 3 months, 1 week ago

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AI's Dependence on Python Deepens as Anthropic Funds Core Ecosystem Work

#Python #Anthrop... #Python ... #CPython #PyPI

Anthropic invests $1.5 million in the Python Software Foundation to boost Python ecosystem security. The funding targets improvements in CPython and PyPI, including new tools for package review and malware datasets. It also supports the PSF's core activities and community initiatives.

AI's Dependence on Python Deepens as Anthropic Funds Core Ecosystem Work

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.