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@kaptain shared a link, 4 months, 2 weeks ago
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How to Add MCP Servers to ChatGPT

ChatGPT leveled up with fullModel Context Protocol (MCP)support. It can now run real developer tasks, scraping, writing to a database, even making GitHub commits, through secure, containerized tools in Docker. TheDocker MCP Toolkitconnects ChatGPT’s language smarts to production-safe tools like Stri.. read more  

How to Add MCP Servers to ChatGPT
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@jamesmiller shared a post, 4 months, 2 weeks ago

Automating Penetration Testing in CI/CD: A Practical Guide for Developers

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Automating pentesting in CI/CD helps developers catch vulnerabilities early, reduce MTTR, and keep releases secure without slowing the pipeline. This guide breaks down why automation matters, the tools developers rely on, common mistakes to avoid, and practical steps to build a reliable pentesting workflow inside modern CI/CD pipelines.

Automating Penetration Testing in CI/CD
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@elenamia shared a post, 4 months, 2 weeks ago
Technical Consultant, Damco Solutions

Google Cloud Services: A Comprehensive Overview for Modern Businesses

Read this blog to learn about Google Cloud Platform services and its key features, pricing, and use cases across industries.

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@kala shared a link, 4 months, 2 weeks ago
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How to Create an Effective Prompt for Nano Banana Pro

The author details how to effectively prompt Google’s Nano Banana Pro, a visual reasoning model, emphasizing that success relies on structured design documents rather than vague requests. The method prioritizes four key steps: defining the Work Surface (e.g., dashboard or comic), specifying the prec.. read more  

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@kala shared a link, 4 months, 2 weeks ago
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So you wanna build a local RAG?

Skald spun up a full local RAG stack, withpgvector,Sentence Transformers,Docling, andllama.cpp, in under 10 minutes. The thing hums on English point queries. Benchmarks show open-source models and rerankers can go toe-to-toe with SaaS tools in most tasks. They stumble, though, on multilingual prompt.. read more  

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@kala shared a link, 4 months, 2 weeks ago
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Learning Collatz - The Mother of all Rabbit Holes

Researchers trained small transformer models to predict the "long Collatz step," an arithmetic rule for the infamous unsolved Collatz conjecture, achieving surprisingly high accuracy up to 99.8%. The models did not learn the universal algorithm, but instead showed quantized learning, mastering speci.. read more  

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@kala shared a link, 4 months, 2 weeks ago
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200k Tokens Is Plenty

Amp’s team isn’t chasing token limits. Even with ~200k available via Opus 4.5, they stick toshort, modular threads, around 80k tokens each. Why? Smaller threads are cheaper, more stable, and just work better. Instead of stuffing everything into a single mega-context, they slice big tasks into focuse.. read more  

200k Tokens Is Plenty
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@kala shared a link, 4 months, 2 weeks ago
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Google tests new Gemini 3 models on LM Arena

Google’s been quietly field-testing two shadow models,Fierce FalconandGhost Falcon, on LM Arena. Early signs? They're probably warm-ups for the next Gemini 3 Flash or Pro drop. Classic Google move: float a checkpoint, stir up curiosity, then go GA... read more  

Google tests new Gemini 3 models on LM Arena
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@kala shared a link, 4 months, 2 weeks ago
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A trillion dollars is a terrible thing to waste

OpenAI co-founder Ilya Sutskever just said the quiet part out loud: scaling laws are breaking down. Bigger models aren’t getting better at thinking, they’re getting worse at generalizing and reasoning. Now he’s eyeingneurosymbolic AIandinnate inductive constraints. Yep, the “just make it huge” era m.. read more  

A trillion dollars is a terrible thing to waste
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@kala shared a link, 4 months, 2 weeks ago
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Practical LLM Security Advice from the NVIDIA AI Red Team

NVIDIA’s AI Red Team nailed three security sinkholes in LLMs:reckless use ofexec/eval,RAG pipelines that grab too much data, andmarkdown that doesn't get cleaned. These cracks open doors to remote code execution, sneaky prompt injection, and link-based data leaks. The fix-it trend:App security’s lea.. read more  

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.