Updates and recent posts about GPT-5.4..

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@faun shared a link, 8 months, 3 weeks ago

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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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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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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

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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

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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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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

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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

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Cursor AI Code Editor Fixed Flaw Allowing Attackers to Run Commands via Prompt Injection

XM Cyber dropped a practical guide for rolling outContinuous Threat Exposure Management (CTEM)with its platform—geared for those eyeing 2025 readiness. It dives into wiring up real-time exposure visibility, validating actual risk, and tightening up remediation across complex enterprise setups. Why .. read more

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GPT-5 is here

GPT-5 tightens reasoning and lands cleaner hits inmath,science,finance, andlaw. It outpaces GPT-4—not just wider, but deeper... read more

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Anthropic says OpenAI engineers using Claude Code ahead of GPT-5 launch

Anthropic just shut the door on OpenAI, yanking access to theClaude Code APIafter spotting ChatGPT engineers poking around—likely prepping forGPT-5. Claude Codeisn’t just an internal toy. It’s a serious coding co-pilot, used in the wild by devs who want answers without babysitting a model. Market .. read more

GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.