Terraform Production Readiness Cheatsheet
Terraform working isn’t enough. Learn what it takes to make it production-ready — from backend design to security and automated pipelines.
Updates and recent posts about GPT-5.4.. Activity
@simme started using tool Grafana Loki , 5 days, 18 hours ago.
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@simme started using tool Claude Code , 5 days, 18 hours ago.
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@cloudsignals shared a post, 6 days, 7 hours ago
Director - Cloud Engineering, osttra
DevSecOps: Rapid & Secure Delivery
If security is your last step, you’re already too late. This guide shows how to build a DevSecOps pipeline where security is continuous, automated, and invisible to delivery speed.
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@varbear shared a link, 1 week ago
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GitHub backs down, kills Copilot PR ‘tips’ after backlash
GitHub revoked Copilot's ability to inject tips into other users' pull requests after reports that Copilot Review inserted aRaycastlink. They disabled agent tips in PR comments, blamed a programming-logic bug, and said they won't turn tips into ads... read more
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SQLite Features You Didn’t Know It Had: JSON, text search, CTE, STRICT, generated columns, WAL
SQLite packsJSONextraction, expression indexes,FTS5full-text search,CTEs, window functions, andWALinto a single file. It enforcesstrict tables, supportsgenerated columns, and indexes JSON expressions for fast semi-structured queries... read more
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@varbear shared a link, 1 week ago
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I told Claude Code to build me an executive assistant. This is what my work as CTO looks like now
CTO at ZAR shares his experience managing 10 engineers, shipping code, and operating at the C-level with an AI assistant named Claude Code. The system allows him to maintain context across multiple workstreams, automate tasks, and scale his productivity. In just three weeks, he has documented 82 mee.. read more
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@varbear shared a link, 1 week ago
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Python 3.3: The Version That Quietly Rewired Everything
Python 3.3 introduced three key features that have had a lasting impact on Python development. Firstly, yield from simplified the composition of generators by allowing easy delegation between them. Secondly, venv standardized virtual environments in Python, improving isolation and reproducibility of.. read more
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.
