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Computational Thinking Is The New Programming

Software's entering its blurred-lines era. The new hybrid model fuses old-school code with natural language prompts and AI-generated logic. Frameworks likeDSPylet devs stitch together pipelines where logic flows through code, prompts, and outside data—like it's all one system. What’s changing:Progr.. read more  

Computational Thinking Is The New Programming
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Man-in-the-Middle Attack Prevention Guide

XM Cyber just dropped a guide on puttingContinuous Threat Exposure Management (CTEM)into practice with their platform. It maps out clear steps to bake exposure management into your 2025 security plans. Trend to watch:CTEM is leveling up—no longer just a buzzword, it's becoming a real security disci.. read more  

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4 Ways I am Encouraging My 4 Year Old Child to Help Learn Coding and Use Computer

GCompris, CodeMonkey, Microbit, and Raspberry Pi kits aren’t just toys. They’re a full tech ladder for tiny humans. Start with GCompris to get little fingers clicking. Add CodeMonkey for block logic basics. Then toss in Microbit or an Elecrow kit, and suddenly code makes LEDs blink and buzzers buzz... read more  

4 Ways I am Encouraging My 4 Year Old Child to Help Learn Coding and Use Computer
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MCP Security Issues Threatening AI Infrastructure

Docker just dropped theMCP ToolkitandMCP Gateway, tightening up the Model Context Protocol with serious armor. We're talking six major server-side holes patched—OAuth RCE, command injection, leaked creds—plugged. How? With container-wrapped isolation, real-time network filters, first-class OAuth ha.. read more  

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Next Gen Data Processing at Massive Scale At Pinterest With Moka

Pinterest kicked its creaky Hadoop system to the curb and embraced Moka, a shiny Kubernetes +*AWS EKS platform, to crank up scalability and security.* Graviton ARM EC2 instances, Spark Operator, and Apache YuniKorn unleashed a performance beast and sliced costs.They wrestled with memory monsters and.. read more  

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Forcing LLMs to be evil during training can make them nicer in the long run

Researchers built an automated pipeline to hunt down the neuron patterns behind bad LLM behavior—sycophancy,hallucinations,malice, the usual suspects. Then they trained models to watch for those patterns in real time. Anthropic didn’t just steer modelsaftertraining like most. They baked the correct.. read more  

Forcing LLMs to be evil during training can make them nicer in the long run
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Building an AI Home Security System Using .NET, Python, CLIP, Semantic Kernel, Telegram, and Raspberry Pi 4

The post details the process of creating an AI home security system using .NET, Python, Semantic Kernel, a Telegram Bot, Raspberry Pi 4, and Open AI. It covers the hardware and software requirements, as well as the steps to install and test the camera module and the PIR sensor. It also includes code.. read more  

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Event-Driven Agents in Action

Docker wired up an event-driven AI agent usingMastraand theDocker MCP Gatewayto handle tutorial PRs—comment, close, the works. It runs a crew of agents powered byQwen3andGemma3, synced through GitHub webhooks and MCP tools, all spun up with Docker Compose. System shift:Agentic frameworks are starti.. read more  

Event-Driven Agents in Action
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Introducing the Amazon DynamoDB data modeling MCP tool

Amazon just dropped theDynamoDB MCP data modeling tool—a natural language assistant that turns app specs into DynamoDB schemas without the boilerplate. It plugs intoAmazon QandVS Code, tracks access patterns, estimates costs, and throws in real-time design trade-offs... read more  

Introducing the Amazon DynamoDB data modeling MCP tool
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Manus AI Launches ‘Wide Research,’ Pitting 100-Agent Swarms Against ‘Deep Research‘ from Google and OpenAI

Manus just droppedWide Research—a swarm of 100+ AI agents, each spun up as a Turing-complete VM. They don’t follow orders. They solve massive tasks in parallel, straight from natural language prompts. Forget rigid chains of command. These agents don’t play roles—they run jobs. No hierarchies. No br.. read more  

Manus AI Launches ‘Wide Research,’ Pitting 100-Agent Swarms Against ‘Deep Research‘ from Google and OpenAI
GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.