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If it Wanted to, it Would: The Bitter Lesson for LLM Users

There’s a viral saying folks use about flaky crushes, spouses, and forgetful friends: "if he wanted to, he would." The idea is straightforward: when someone cares, they make the effort. As it turns out, the same principle applies surprisingly well to AI. Systems, like people, have things they "want" to do. Each model has patterns of reasoning and synthesis it performs naturally.

Conquer Complexity, Accelerate Resolution with the AI Troubleshooting Agent in Splunk Observability Cloud

The digital landscape has transformed dramatically, and with it, the demands on our systems have grown exponentially. Traditional monitoring tools struggle to provide sufficient insight into complex, distributed, cloud-native environments. Observability is the answer, moving beyond merely knowing "what" is happening to understanding "why" it's happening, and its impact on user experience and business outcomes.

Our Engineering in the Age of AI: 2026 Benchmark Report finds AI is making engineering faster, but not necessarily better

Everyone's talking about how AI is transforming software development. Teams are shipping more code, deploying more frequently, and getting features to market faster than they could a year ago. The productivity gains are real. But we kept hearing a different story from engineering leaders. Yes, velocity is up. But incidents are climbing, resolution times are getting longer, and code review processes are struggling to keep up.

The Hidden Bottlenecks in AI Infrastructure (and How to Fix Them)

Artificial intelligence has entered an era where infrastructure is the real moat. Teams spend millions on GPUs, yet models still stall, latency spikes unpredictably, and throughput flatlines at 20% of what spec sheets promise. These hidden bottlenecks lurk far beneath the surface - in power grids, network fabrics, memory bandwidth, orchestration layers, and even governance policies. In this guide, we uncover where AI infrastructure actually breaks, what the emerging data and research reveal, and how Clarifai's reasoning and orchestration stack helps eliminate these unseen friction points.

AI and Style: How Artificial Intelligence Shapes the Future of Fashion

AI transforms fashion by predicting trends, analyzing customer preferences, and creating a fully personalized shopping experience. Instead of guessing, AI uses data to recommend outfits, colors, and styles that match a person's taste, lifestyle, and current fashion trends.

Zebra Study: 88% of Retailers in Europe Believe Gen AI to Have Significant Impact on Loss Prevention

Retailers are relying on automation and AI to address shrinkage, inventory challenges and demand for seamless retail experiences as shopper satisfaction continues to decline.

Open source vs commercial AI: choosing the right path for your business

This blog is based on a presentation by Guillaume, Field Chief Technology Officer at Upsun, and Robert from Ilwiin Technology during the AI Action Summit. The original French presentation has been translated and edited for clarity and accuracy. The AI field is advancing significantly, presenting organizations with the question: Should they choose open-source or commercial AI models? This choice impacts everything from costs and data privacy to long-term business strategy.

Harness AI October 2025 Updates: Smarter Pipelines, Instant Troubleshooting, and Memories

The AI Velocity Paradox is real. While teams are writing code faster than ever, they're hitting a wall downstream. Deployments are failing. Security vulnerabilities are slipping through. Manual toil is eating up whatever time developers saved with AI-assisted coding. The speed boost from one part of the software delivery lifecycle is being strangled by legacy processes in another. Harness is solving this the only way that works: by bringing intelligent AI deeper into the delivery process itself.