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AssemblyAI scales production Voice AI with Datadog's unified observability
AssemblyAI is a leading Voice AI platform that provides speech-to-text models and AI infrastructure developers use to build real-time voice applications. AssemblyAI uses Datadog to unify observability across its AI inference pipelines and multi-cloud GPU infrastructure, enabling the team to optimize performance and costs, accelerate model releases, and confidently deliver fast, reliable AI experiences at scale.
Building with AI: Our Approach to Responsible Agentic Development in Open Source
The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we’re seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve.
Agentic Pipelines | Bitbucket Blitz | Atlassian
Most CI/CD pipelines are fragile bash scripts that break when things change. What if your pipeline could think? Agentic Pipelines lets you add AI agents as steps in Bitbucket Pipelines. In this video, I show an agent that reads a design spec from Confluence, generates frontend code, runs tests, and opens a PR, all inside a pipeline. With Agentic Pipelines, Bitbucket goes from a CI/CD platform to a full workflow and automation engine you can use far beyond builds and deploys.
Switching Between AI Agents Like This Is a Game Changer #ai #productivity
AI didn't just change how fast code gets written. It exposed a new bottleneck: everything around the code. Reviews slow down. Context gets lost. Planning drifts from implementation. Teams move fast and still feel stuck. That's the problem GitKraken is built to solve, and this Friday we're going live to walk through what's changed. We'll cover the latest Code Flow Company features we've shipped, how they connect developers, AI agents, and production into one system, and what it actually looks like to go from plan to main without the chaos.
How to Build a Reliable Review Gate for AI Generated 3D Assets
A creative team generates twenty 3D props in an afternoon. The files look acceptable in preview images, so they are placed directly into the shared asset library. Days later, developers discover inconsistent scale, missing textures, reversed normals and several models with unclear ownership or revision status. The generation process worked. The production process did not.
Imaginary Test Data. Real Token Bill.
Ask an AI for K-pop concert advice without saying the group, city, date, or budget. It may confidently send you to a BLACKPINK tribute night in Cleveland with a $400 resale ticket. The AI was plenty confident. It just had nothing real to go on. That is exactly what happens when developers test AI applications with invented traffic. The test may look reasonable. The result may even pass.
The three questions every CFO should be asking about AI spend
Uber ran out of its entire 2026 AI budget by April. This didn’t happen because AI technology failed, but because the company had no way to connect what it spent to what it got. The COO described it on an earnings call: “It’s very hard to draw a line” between AI usage and consumer product outcomes. And with that one sentence, we have the CFO problem of 2026.
Shipped: See what Claude Code actually costs
Your engineers are running Claude Code every day, and every prompt burns tokens you’re paying for. Until now, that spend was hard to see. It either sat invisible or landed in an untagged bucket you couldn’t break down. Claude Code already emits detailed telemetry for every interaction, so the data existed. You just had nowhere to send it that would turn it into a cost.
3 Things IT Leaders Are Learning About AI-First Operations: Key Takeaways From PagerDuty on Tour 2026
In December 2025, an AI coding agent at AWS suddenly decided to delete and rebuild an entire production environment, causing a 13-hour service disruption and a PR headache for Amazon. As rapid adoption of AI leads to more high-profile, revenue-impacting incidents, resilience has moved from a technical concern to a board-level financial risk.