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I Built a Demo to Finally Understand Idempotency - Here's What Happened

Last Friday I posted something on LinkedIn about idempotency. I'd hit the concept while reading about API design and it wouldn't let me go. The scenario is so specific and so consequential that once you understand it, you start seeing the problem everywhere. Here's the scenario: a user hits "Pay". T

Dev.to·2d ago

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TypeSafe's Jev Costs 400x Less Than an LLM. These 25 Lines of Python Do the Same Job for Free.

Your agent loop has a dirty secret: most of what it does all day is make tiny decisions. Is this input spam? Is this tool call dangerous? Should this ticket go to billing or support? And for every one of those coin-flip decisions, your harness fires up a full frontier LLM, generates a paragraph of r

Dev.to·2d ago

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GitHub MCP Server for Claude Code: Direct Repo, PR, and CI Access in 3 Minutes

A CI run fails, and you’re back to being the messenger: check which job failed, copy the details into Claude Code, then go fetch the issue it relates to and the PR that touched that file. Every piece of context has to pass through your clipboard. The GitHub MCP server cuts that out. It gives Claude

Dev.to·2d ago

dev

How I’m Adding Local AI Autocomplete to CanvasDesk: Laya, System One Models, and Node-Based Calculations

A blank canvas for a calculation diagram and instead of flow, your thinking gets stuck in manual routine. You open the editor to quickly sketch out a service architecture or unit-economics. But instead, you start recalling the exact formula syntax, the name of a variable from a neighboring block, an

Dev.to·2d ago

dev

AI Agents and the 'Code Exorcist' Pattern: How LLMs Are Redefining Debugging and DevOps Workflows in 2026

Originally published on tamiz.pro. The most frustrating part of software engineering isn't writing code — it's finding out why the code broke. For decades, debugging has been a human-dominated craft: a developer stares at logs, reproduces a stack trace locally, sprinkles console.log calls, and slowl

Dev.to·2d ago

dev

What if the app lived inside the conversation?

Most discussions about AI interfaces start with the model: what it can answer, which tools it can call, how much work it can do. I'm interested in a different question: where does the actual app go? Does it remain a dashboard with a chat panel attached? Or can the conversation become the place where

Dev.to·2d ago

dev

Organizations need decision-grade knowledge. AI makes it urgent.

AI can make the first part remarkably fast. It can find the page, the discussion and the person who might know. The harder work begins when those sources disagree, or when they become stale.

Stack Overflow Blog·3d ago

dev

NVIDIA OpenShell Explained: A Safer Runtime for AI Agents

AI agents are getting genuinely useful. They can read your files, install packages, call APIs, and run shell commands. That is exactly what makes them risky. If you have ever pasted an API key into an agent's environment and then held your breath while it ran, this article is for you. NVIDIA has an

Dev.to·3d ago

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