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The latest News and Information on API Development, Management, Monitoring, and related technologies.

Silent Failures: Why AI Code Breaks in Production

You ship a small “safe” change on Friday. The diff is tiny, the tests are green, and the AI assistant was confident. An hour after deploy, your on-call channel lights up. A downstream service is rejecting responses that look fine in code review. Now you’re rolling back and rewriting a fix that should have been obvious if you had real traffic in the loop. This isn’t a hypothetical.

How To Cut Your LLM Costs for Startups (Without Slowing Product)

In February 2026, most startups don't "adopt AI" in a neat, planned way. LLM usage spikes the week you ship a new feature, add an agent, or connect tools. Budgets don't spike with it. The good news is that the biggest savings usually come from smarter routing, caching, and workload design, not from ripping out your stack or rewriting everything.

API Testing Tools Best Practices Guide

Today’s software testing trends show the growing demand for more efficient and automated API testing. Manual testing is not only time-intensive for internal testing teams, it can also lead to poor customer experiences. When manual testing processes cannot proactively discover issues, your customers may inevitably be the ones finding them. Many of the current test automation solutions today focus on the UI, while most API-level testing is still done manually.

My AI Agent Stole My Crypto #speedscale #openclaw #aicoding #codingagent #security

I thought I found the ultimate coding shortcut: an autonomous AI agent. Turns out, I just bought a one-way ticket to a digital nightmare. A friendly reminder to my fellow devs: Validation isn't optional—it's survival. Your laptop shouldn't have a higher calling than your production environment. Validate now: speedscale.com.

Sync Your Users Into Icinga Notifications: Introducing the Contacts/Groups API

If you’ve ever onboarded a teammate at 4:57 PM on a Friday (or offboarded one at 4:58 PM… ), you know the pain: keeping notification contacts and groups up to date is work. With the Icinga Notifications REST API, you can automate that and avoid drift.

What AI Has Never Seen: The Context Gap in Code Generation

Your AI coding assistant has read the entire internet. It knows every programming language, every framework, every best practice documented in Stack Overflow answers and GitHub repositories. It can generate a REST API handler in seconds that looks perfect with clean code, proper error handling, following all the patterns. But here’s what it’s never seen: your production traffic. Data from a real API request. Someone filling out a form with messed up or incomplete data.

The Dangerous Power of Local AI Agents. #speedscale #proxymock #aiagents #openclaw #localai

I’ve been testing OpenClaw, a fully autonomous agent that lets you remote control your entire system via Signal. It’s incredibly powerful to text your computer from a coffee shop and have it execute tasks, but you’re essentially handing the keys to your digital kingdom to an LLM. The Golden Rule: Trust, but verify. I’m using Proxymock to sniff every single API call going in and out of the agent. If there’s a data leak or a "hallucination" that tries to wipe my drive, I see it first.

Refactor Safely with AI: Using MCP and Traffic Replay to Validate Code Changes

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

How to Choose the Right API Monitoring Tool for Production Environments

APIs are no longer just technical connectors between systems; they are production infrastructure. Customer-facing applications, partner integrations, payment flows, and internal microservices all depend on APIs working correctly, consistently, and at scale. When an API fails, the impact is rarely limited to a single endpoint; it can disrupt user journeys, compromise revenue, and breach service-level agreements (SLAs).