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Thoughts, ideas, and insights from our team.
OpenAI's AgentKit launch seems late to the agent party—but their track record suggests a strategic consolidation play that could dominate the $93B agentic AI market.
I gave Claude Code an XML backup of my 19-year-old WordPress blog and asked it to rebuild everything as a modern NextJS site. What happened next was like watching a swarm of expert developers work in parallel—spawning agents, debugging TypeScript errors, and shipping production-ready code. All in 26 minutes. For eight dollars.
After watching 40% of agentic AI deployments fail in production, I'm building Dagentic — a serverless-first framework designed for what AI agents actually are: unpredictable, spiky workloads that modify themselves mid-execution.
After 12 months of systematic optimization, I've documented 50-70% productivity gains with AI coding assistants. The secret isn't just using AI tools—it's teaching them to think like you do through carefully crafted configurations.
How multi-agent AI systems are revolutionizing document creation, turning complex research workflows into automated pipelines that generate comprehensive reports with rich formatting, citations, and visuals.
Navigating the complex world of carbon credits can be as daunting as deciphering a wine list. Using wine analogies—from vintage and terroir to blending and expert sommelier guidance—this guide demystifies carbon markets and reveals how to make informed climate action investments.
At 3 AM, I was manually cropping 47 personal photos for a LoRA model when I realized half were the wrong aspect ratio. Three hours wasted. So I built a simple Python app that does the same work in 15 minutes—and it changed how I think about AI tooling infrastructure.
Quick and easy fix for the Heartbleed OpenSSL vulnerability on CentOS systems using yum update and service restart commands.
Despite claims that Social TV is dead, data from 486,659 Zeebox tweets and 4.3M Miso tweets reveals a more complex reality in the second-screen battle.
The real problem with Big Data isn't volume—it's knowing what you want to achieve and starting with clear business challenges, not technology.
Key insights from IBM's Twitter discussion on virtualization ROI, complexity concerns, and data center simplification for midsize businesses.