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Curated links from external sources — not 360Softy original articles.

ExternalSoftware Engineering
DEV Community

LLM Integration in CI/CD: Real Use Cases Beyond Code Completion

A GitLab API token appeared in a README.md file. It was committed, pushed, and reviewed by two engineers before merging. Nobody caught it. This was not a junior developer's mistake. Both reviewers were experienced. The token was on line 47 of a 200-line file, sandwiched between installation instructions. At the end of a sprint, with three other reviews queued, it looked like documentation. It was a secret sitting in plain sight. We caught it the next day during a routine audit. By then it had be

aidevopsinfrastructure
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ExternalSoftware Engineering
DEV Community

How to Write Content That AI Search Engines Actually Cite

Most content advice for AI search treats writing as an SEO problem with new rules. It is not. It is a citation problem with old rules, applied differently. Ranking on Google was about earning the click. Getting cited by ChatGPT, Perplexity, or Google’s AI Overviews is about earning the quote, a discipline known as answer engine optimization when it focuses on direct-answer citations. This guide breaks down what AI engines actually look for in a piece of content, the seven writing moves that turn

aiwebdevwriting
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ExternalSoftware Engineering
DEV Community

Eu decidi — de novo — me dedicar aos jogos.

Aos 12 anos, eu decidi que ia fazer jogos. Passei por volta de 5 a 6 anos usando RPG Maker. Comecei muitos jogos, tive várias ideias e aprendi bastante coisa, mas terminar um jogo? Não, isso não. Na pandemia de 20, voltei a programar e, junto com minha esposa, fiz um quiz sobre a cultura potiguar. Fomos aprovados em um edital público de incentivo aos jogos, e foi a primeira vez que ganhei dinheiro programando e fazendo um jogo. Foram dois cajados com um coelho só. Mas depois eu parei. Na mesma é

gamedevaiprogramming
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ExternalCloud
Google Cloud Blog

Supercharging the agentic era with Spanner’s multi-model architecture

In the agentic era, the role of the database has fundamentally changed. It is no longer a passive repository; it’s a critical context engine designed to ground generative AI apps, models and power autonomous workflows. To do this effectively, databases must move beyond fragmented architectures and embrace a unified, multi-model foundation, facilitating deep reasoning and transforming static data into a system of action.  Spanner is leading this charge, and as a foundational pillar of Google’s Ag

SpannerDatabases
Google Cloud BlogRead original
ExternalSoftware Engineering
DEV Community

Approval Queues Are the Runtime for Agentic AI Workflows | Focused Labs

An exception queue is the next agent interface to embed. Dull. An exception queue is fundamentally simple. All enterprise autonomy requires is for the agent to create a review item (e.g. an Approval item) and park its state. Then it waits for someone to make a decision, and resumes execution when done. If the AI agentic workflow depended on someone physically being present to “review” the AI (e.g. by staring at chat) then it would simply be another chat app, dressing up in a hard hat for work. L

programmingai
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ExternalSoftware Engineering
DEV Community

I replaced hooking libraries with one rust crate

TL;DR: I've shipped hooks with different libs before. I even neohook (v0.11.0) a Rust crate that does inline hooks, trampolines, mid-function hooks and more, I wanted to intercept a function call inside a running Windows process, run my own code, and still let the original do its thing. The textbook answer is function hooking, and the textbook tools are C++. I've shipped all common libs. They work, but the "just overwrite the first bytes with a jump" idea hides a surprising amount of sharp edges

rustreverseengineeringassembly
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ExternalSoftware Engineering
DEV Community

Google A2A Protocol in 2026: Adoption, Hype, and Reality

Google's Agent2Agent protocol, usually shortened to A2A, had a strange first year. When Google announced A2A in April 2025, the pitch was clear: AI agents built by different vendors, frameworks, and teams needed a standard way to communicate. The protocol promised agent discovery, task delegation, message exchange, streaming updates, and artifact sharing. The reaction, however, was considerably less clean than the announcement. Some developers saw A2A as the missing agent-to-agent layer for the

architecturellmai
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