TLDR DevOps 2026-01-21

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Platform engineering transitioned into an industrial standard by 2025, enabling mature organizations to achieve 20:1 developer-to-platform-engineer ratios and halve time-to-market. Average salaries, now at $160,000 in North America and $104,000 in Europe, reflect the role's democratization rather than devaluation. AI literacy has become a survival-level competency for practitioners.

GitLab Duo Agent Platform is now in general availability. The platform enables orchestrated AI agents across the full software lifecycle to address delivery bottlenecks beyond coding. It offers agentic chat, prebuilt and custom agents, governance controls, and usage-based pricing via GitLab Credits.

This post discusses AI coding tools designed to protect developer flow with contextual, customizable help rather than chat-centered automation, prioritizing human judgment, learning, minimal interruptions, and refinement driven by feedback that removes tedium without disrupting creativity.

Unconventional PostgreSQL techniques such as constraint_exclusion, function-based indexes on lower-cardinality expressions, virtual generated columns, and hash-backed exclusion constraints can significantly reduce query execution time, index size, and unnecessary full-table scans. These approaches outperform standard full-table scans and B-tree indexing in analytics-heavy and ad-hoc query workloads by better aligning indexes and constraints with actual query needs.

The Manning book Platform Engineering on Kubernetes takes an in-depth look at the latest best practices and open source tools from the Kubernetes community, including:

• Multi-cloud infrastructure management with Crossplane
• Using Dapr for standard API distribution
• Implementing feature flags with OpenFeature

Grab a free copy of the ebook from Chronosphere

A new Claude Agent Skill for Terraform and OpenTofu has been introduced, offering comprehensive guidance on testing strategies, module patterns, CI/CD workflows, and production-ready infrastructure code for Claude Code users.

agent-lightning is an open-source framework for training and optimizing AI agents—enabling reinforcement learning (RL), automatic prompt optimization, supervised fine-tuning, and more—without requiring substantial changes to existing agent code. It works with virtually any agent framework (e.g., LangChain, OpenAI Agents SDK, and AutoGen) and provides modular components to collect agent execution data and iteratively improve agent performance via a decoupled RL training loop.

Small, local LLMs can be effectively leveraged for niche development tasks by implementing Retrieval Augmented Generation (RAG). This approach allows LLMs to generate accurate code snippets for the custom Nova Golang library, addressing limitations faced by larger commercial models unaware of such specialized projects.

Stakpak introduced an open-source DevOps agent aimed at making infrastructure more autonomous by securing secrets, preventing destructive actions with guardrails, and centralizing shared operational knowledge so AI can handle complex, non-coding DevOps tasks reliably.

IPv4 is effectively “post-exhaustion” by 2025: the globally allocated public pool is flat to slightly shrinking (~3.687B allocated at end-2025, down ~237k on the year), remaining free space sits mostly in APNIC/AFRINIC, and scarcity is managed via NAT/CGNAT plus an active—but cooling—transfer market whose prices fell sharply through 2025. IPv6 keeps growing in deployment, but 2025 saw fewer and much smaller allocations (big drop in total /32s allocated), and the Internet's trajectory is still shaped by the client/server + NAT model rather than a return to universal, publicly reachable end hosts.

mirrord for CI lets you run tests against your staging or pre-prod Kubernetes environment without deploying code or spinning up test environments. Get faster, more reliable CI feedback at a lower cost. Learn more

32.8% of practitioners identify observability as a main focus.

Cloudflare has acquired UK-based Human Native to expand licensed, high-quality AI data access and give content owners control over AI usage.

This comprehensive guide for engineers on the Kubernetes and cloud-native ecosystem curates high-signal resources from fundamentals to advanced operations for building Internal Developer Platforms and running AI/ML workloads.