The Mind AI

OpenAI published a detailed case study describing how a small team shipped the initial Sora Android production app in a 28-day sprint by treating an AI coding agent (Codex) like a new teammate: give it context, define rules, review everything, and parallelize responsibly. This post turns those ideas into a checklist you can actually follow for your own fast Android launch—without relying on miracles. Disclaimer: This guide is for general information only and is not legal, HR, security, or compliance advice. Timelines vary by team, product complexity, and risk requirements. Always follow your organization’s review, privacy, and security policies, and validate app-store requirements and third-party licenses. Tools and platform policies can change over time. TL;DR Speed comes from structure: lock scope, build a thin “golden path,” then let the agent parallelize the rest. Context beats prompting: success depends on giving the agent architecture rules, st...

Temporal hardware baseline This overview is informational only (not professional advice) and reflects CUDA benchmarking and tooling practices as understood in early November 2025. Decisions and accountability remain with your engineering team. Toolchains, GPU architectures, and benchmark suites change over time, so validate findings in your own build environment before adopting any workflow as “standard.” CUDA is the place where software optimism goes to die. A kernel can compile, run, and still be “wrong” in the only way that matters in high-performance computing: it leaves most of the GPU unused. That’s why evaluating coding assistants in CUDA is fundamentally different from evaluating assistants in general programming. In late 2025, the question isn’t whether a model can write working code. The question is whether it can write code that respects the physics of the machine: memory bandwidth, synchronization cost, occupancy, and the relentless math of throughput. C...

Engineering & Responsibility Warning: This post is informational only and reflects large-model training practices as of its publication window. Real training outcomes depend on your data, hardware, software stack, and governance controls. Large-scale training can fail silently (numerics, data quality, evaluation gaps), and it can create real-world costs (energy, access concentration). Please validate designs with qualified experts; implementation decisions and accountability remain with the deploying team. The development of AI models with billions—or even trillions—of parameters is often described as a technical triumph. It is that, but it’s also something else: a stress test for engineering discipline and institutional responsibility. At small scale, a training run can be “mostly fine” and still produce something useful. At massive scale, “mostly fine” becomes expensive noise—because every inefficiency, every brittle assumption, and every blind spot is multiplied b...