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@cloudsignals shared a post, 1 week, 5 days ago
Director - Cloud Engineering, osttra

Terraform Production Readiness Cheatsheet

Terraform Terragrunt

Terraform working isn’t enough. Learn what it takes to make it production-ready — from backend design to security and automated pipelines.

Terraform Production Readiness Cheatsheet
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@cloudsignals shared a post, 1 week, 5 days ago
Director - Cloud Engineering, osttra

DevSecOps: Rapid & Secure Delivery

SonarQube Vault Kyverno Open Policy Agent (OPA) Trivy

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.

DevSecOps - Rapid & Secure Delivery
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@varbear shared a link, 1 week, 6 days ago
FAUN.dev()

Email address obfuscation: What works in 2026?

The article catalogs obfuscation methods:HTML entities,SVG in an object,display:none, JavaScript decoders, custom encodings, andAES‑256. It coversmailtoobfuscation, redirects (302/301,.htaccess), interaction-gated reveals, accessibility caveats, and ahoneypot-based spam-statistics system... read more  

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@varbear shared a link, 1 week, 6 days ago
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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, 6 days 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  

GitHub backs down, kills Copilot PR ‘tips’ after backlash
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@varbear shared a link, 1 week, 6 days 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, 6 days 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  

Python 3.3: The Version That Quietly Rewired Everything
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@kaptain shared a link, 1 week, 6 days ago
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Duolingo's Kubernetes Leap

Duolingo made a bold leap migrating 500+ services to Kubernetes, embracing Argo CD for blue-green deployments and leveraging GitOps for flexibility and control. This shift to a cellular architecture enabled them to isolate environments and manage developer trust while navigating AWS rate limits. Exc.. read more  

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@kaptain shared a link, 1 week, 6 days ago
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Kubernetes Monitoring Helm chart v4: Biggest update ever!

The Kubernetes Monitoring Helm chart version 4.0 is designed to solve real pain points that users have hit as their monitoring setups have grown. Destinations are now defined as a map instead of a list, making it easier to manage configurations for multiple clusters. Collectors are defined by the us.. read more  

Kubernetes Monitoring Helm chart v4: Biggest update ever!
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@kaptain shared a link, 1 week, 6 days ago
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How GitHub uses eBPF to improve deployment safety

GitHub hosts its own source code on github.com, creating a circular dependency. To mitigate this, GitHub maintains mirrors of its code and built assets. By using eBPF, GitHub can selectively monitor and block calls that create circular dependencies in their deployment system... read more  

How GitHub uses eBPF to improve deployment safety
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.