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@kala shared a link, 1 month ago

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Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph

Netflix's Saish Sali, Nipun Kumar, and Sura Elamurugu describe the Metadata Service (MDS), a graph layer built to connect siloed ML tooling (model registry, pipeline orchestrator, experimentation platform, feature store, dataset platform, identity) across personalization, studio, payments, and ads. .. read more

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@kala shared a link, 1 month ago

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The AWS MCP Server is now generally available

AWS now offers AWS MCP Server as a managed remote MCP server in US East (N. Virginia) and Europe (Frankfurt). MCP-compatible clients can use existing IAM credentials to access more than 15,000 AWS API operations. For GA, AWS added IAM context keys, documentation retrieval without authentication, low.. read more

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@kala shared a link, 1 month ago

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Running local models on an M4 with 24GB memory

Local LLMs work best as supervised coding assistants. The writer ran Qwen 3.5 9B (Q4) in LM Studio on a 24GB MacBook Pro and got about 40 tokens per second, with thinking mode, tool use, and a 128K context window. The author saw mixed results: Qwen helped with simple Elixir linter edits, then failed.. read more

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@devopslinks shared a link, 1 month ago

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S3 Files and the changing face of S3

AWS launchedS3 Files, an EFS-backed feature that mounts any S3 bucket or prefix as an NFS filesystem on EC2, containers, or Lambda, with changes batched back to S3 roughly every 60 seconds. Rather than collapsing file and object semantics into a single model (an early design attempt called "EFS3" th.. read more

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@devopslinks shared a link, 1 month ago

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I Left Port 22 Open on the Internet for 54 Days. Here's Who Showed Up.

A 54-day SSH honeypot on port 22 logged 268,000+ login attempts from 7,556 IPs, with 99.6% of attackers running a single automated fingerprinting command and only 28 ever opening an interactive shell. The data shows hardcoded IoT credentials and Solana validator hunting dominating the password lists.. read more

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@devopslinks shared a link, 1 month ago

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Why Queues Don’t Fix Scaling Problems

Queues do not create capacity, they delay the moment insufficient capacity becomes visible, and sustained overload turns a queue from a smoothing buffer into a cascading failure that takes down databases, connection pools, and consumer instances before it ever hits the queue's own limits... read more

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@devopslinks shared a link, 1 month ago

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Migrating from DigitalOcean to Hetzner: From $1,432 to $233/month With Zero Downtime

A walkthrough of migrating 248 GB of MySQL across 30 databases, 34 Nginx sites, GitLab EE, and Neo4j from a $1,432/month DigitalOcean droplet to a $233/month Hetzner AX162-R dedicated box with no downtime. The path:mydumper/myloaderwith 32 threads for the bulk MySQL 5.7 to 8.0 import, master-to-repl.. read more

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@devopslinks shared a link, 1 month ago

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Incidents Will Happen. Are You (Actually) Prepared?

Joe Mckevitt, CTO of Uptime Labs, argues that incident prevention and incident preparation are not substitutes, and that organizations relying on the heroic engineer who knows the infrastructure at 2amhave a habit, not a strategy. The piece pushes for a deliberate playbook (practiced communication, .. read more

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@laura_garcia shared a post, 1 month ago

Software Developer, RELIANOID

𝗚𝗹𝗼𝗯𝗮𝗹 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗚𝗧𝗠)

🌍 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗹𝗼𝗯𝗮𝗹 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗚𝗧𝗠) Delivering fast, resilient applications globally isn’t optional anymore—it’s expected. Global Traffic Management (GTM) ensures users are always routed to the 𝗯𝗲𝘀𝘁-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿, improving performance, availability, and scal..

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@ozzuns created an organization How the Right Digital Marketing , 1 month ago.

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.