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AI realism (part one)

Emotions are running high about AI technologies. In this 2-parter, I do my best to make a rational case on the reality of AI, and how we can respond to it. This is part one; part two next week. We seem to be struggling to have pragmatic discussions about advancements in Artificial Intelligence. It's hard to hear calmer voices over the detractors and breathless enthusiasts. Today, I want to make a reasoned, evidence-based case for the potential of this technology, glance at present and future applications, and offer some practical examples for implementing AI within an organization.

Speeding up AI Coding Assistants using Deterministic Feedback

AI coding assistants are transforming the way developers approach software development by automating routine tasks and enhancing code quality. These tools leverage artificial intelligence and machine learning to provide real-time code suggestions, auto-complete functions, and even debug existing code, making the development process faster and more accurate. Modern AI coding assistants integrate seamlessly with a wide range of programming languages and frameworks, including Java, Python, and C++.

COREDUMP #014: Why Your IoT Project Still Hasn't Taken Off - And How to Fix It

In today’s Coredump Session, the team takes a hard look at why some IoT projects stall before they ever hit scale. From organizational missteps to product-market fit challenges, they explore the hidden forces that derail even technically sound products. You’ll hear candid insights on why being “connected” isn’t enough—and what it really takes to succeed in IoT today. Key Takeaways Listen to the Podcast episode.

Vibe coding with the incident.io API

Many, many years ago, I was a computer science major at the University of Illinois, hoping someday I’d be able to write code for a living. I started my career in QA hoping to learn the ins and outs of software development. But it turns out I wasn’t very good at coding. I was just good enough to get a role as a sales engineer, where all I had to do was write code that could hold together for 30 minutes in a demo.

The Innovation vs. Control Syndrome: Unlocking Enterprise AI's Full Potential

From optimizing supply chains to personalizing customer experiences, artificial intelligence and machine learning models are no longer statistics-based revenue initiatives; they’re foundational to modern business strategy. Organizations are pouring resources into developing and deploying AI, driven by the promise of unprecedented efficiency, insight, and competitive advantage. Yet, beneath this surging wave of innovation lies a growing tension: the Innovation vs. Control Syndrome.

Unifying Autoregressive and Diffusion-Based Language Models

Welcome to the AI research bites. This series of short and informative talks showcases cutting-edge research work from ServiceNow AI Research team. The AI Research Bites are open to all, especially those interested in keeping up with the fast-paced AI research community. In this presentation, Pierre-André Noël will show that, if you squint hard enough, autoregressive language models are a special case of diffusion models. We make this idea more concrete by introducing hyperschedules, allowing different token positions to get different noise levels.

AI-Driven Risk Analysis in Seconds #AI #itsm

IT changes can be challenging. Hidden risks, costly mistakes. In Alloy Navigator, you’ve got an AI-driven risk analyst that tears through past tickets and mines your knowledge base, delivering risk analysis and tailored recommendations for every change — in seconds. Now everyone on your team has a personal Mentat, a strategic sidekick that turns data into sharp insight. In this short video, we used gpt-4.1-mini for completions and text-embedding-3-large for generating embeddings from OpenAI.