We Tried 14 AI Coding Agents for 30 Days. Only 3 Actually Ship Code.
Head-to-head against real production tasks — Cursor, Devin, Cline, Aider, and the surprise winner nobody's talking about.
Original reporting and long-form essays on the companies, tools, and technical decisions shaping the AI industry. Every piece is independently researched — no sponsored takes, no recycled announcements.
Benchmarks are easy to game. Real-world workloads aren't. This month we've focused on what actually ships — agents that complete tasks end-to-end, models that survive week-three of production use, and startups whose traction isn't manufactured. If a piece doesn't teach you something useful about how to build, ship, or evaluate AI systems, we didn't publish it.
— Jaleed Abdullah, Executive Editor
Context retention is quietly becoming the deciding factor in enterprise AI adoption. We ran 30-day evaluations across four frontier models and found the gap between "reads well" and "remembers well" is wider — and more consequential — than any leaderboard suggests.
Head-to-head against real production tasks — Cursor, Devin, Cline, Aider, and the surprise winner nobody's talking about.
While competitors chase headlines, Anthropic has been quietly embedded in the Fortune 500 stack. We mapped the footprint.
Inside the stealth-mode lab that poached four senior engineers from OpenAI and Anthropic to build differentiable physics.
The native agent runtime isn't a feature, it's a strategic pivot. We break down what it means for the ecosystem.
Google's long-context claims meet real workloads — codebase navigation, legal doc analysis, and the latency ceiling.
A look at Memphis, Colossus, and the contrarian infra bet that's letting xAI train at speeds no one else can match.
We surveyed 140 teams shipping agents. The results separate hype from what's paying for itself in real workflows.
Training gets the headlines. Inference gets the invoice. How batching, quantization, and caching reshape unit economics.
Llama 4, Qwen3, and DeepSeek R2 are closer to frontier performance than the leaderboards suggest. We ran the diff.
The ChatGPT Enterprise pricing playbook quietly targets the exact workloads Azure used to own outright.
We built a 200-task agent suite and ran it against every frontier model. Here's what Claude does that the others still don't.
Quiet ARR, minimal press, and enterprise contracts most growth-stage investors have zero visibility into.