Systems | Development | Analytics | API | Testing

Your AI agent is fixing the wrong service

Everyone wants an AI agent factory in 2026. Autonomous agents fixing bugs and shipping features while you sleep. I’ve been building toward that myself. But the error rates don’t support the fantasy. The best AI coding agents in the world fix about 50% of real bugs on SWE-bench verified. Half the time they fail. And AI-generated code produces 1.7x more issues than human-written code.

Brand an Embedded Analytics App in Minutes with AI Theme Builder

It's the day before your POC, and the embedded analytics demo still looks like it belongs to someone else. Your designer handed over a brand guide last week. Your developer has been buried in CSS variables ever since: cross-referencing token names, mapping changes across components, reloading the page after every tweak to see what broke. The UI is almost right. The nav color is close. The typography still isn't matching, but there's no time left.

The Real Reason Your AI Project Is Stuck in Pilot Mode

Ever wonder why so many enterprise AI projects never make it past the pilot stage? It’s not the AI—it’s the foundation. In this video, we break down why rushing into complex models without fixing inconsistent data, fragile pipelines, and afterthought governance is a recipe for failure. Fix the basics first!

Multimodal AI Applications, Use cases and Everything Else you need to know

Forget everything you thought you knew about AI! Literally! Yes, we are not lying because a new era has already begun. A technology is emerging that doesn’t just compute… it perceives. It listens, observes, reads, and interprets the world with a blend of senses much closer to our own. It’s the age of multimodal AI, where intelligence is no longer limited to a single stream of data, but fuelled by the combined power of text, images, audio, and video.

Why AI Agents Need a Semantic Layer (and What That Actually Means in 2026)

Everyone is racing to put an AI agent on top of their data. Almost nobody is asking whether the agent can be trusted to act on what it sees. That is the wrong order. And the way most teams are trying to fix it — bigger context windows, more reasoning, another eval — is also wrong. The generative model stopped being the hard part of agentic analytics months ago. Wiring an LLM to a warehouse is a weekend project.