You’ve used ChatGPT through the web interface. Now you want to build it into your own app - a customer support bot, a code review tool, a content generator. You open the OpenAI docs, see 47 pages of API reference, and close the tab. It’s actually simpler than it looks. One endpoint, a few lines of code, and you’re calling GPT from your app. This guide covers everything from your first API call to running it in production. ...
Several open-weight models now match or exceed GPT-4o on specific benchmarks while running on hardware you can rent for $2/hour. Here is what that means in practice.
MMLU scores tell you almost nothing about which LLM to use in production. Here is an evaluation framework based on tasks that engineers actually care about.
Everyone talks about the per-token pricing. Nobody talks about the infrastructure, latency, retry logic, and prompt engineering costs that triple your real bill.
Training a frontier model on 100,000 GPUs is not just a bigger cluster. It requires solving distributed systems problems that push the limits of what networking hardware can do.
Both companies say they care about safe AI. But their approaches to what that means, how to measure it, and how to balance it against capability are genuinely different.
AI coding tools have revived the “junior developers are obsolete” panic. The argument is wrong, but not for the reasons most people give.
GitHub Copilot has been in widespread use for three years. The productivity claims were partly right. The concerns about code quality were partly right. Here is the real data.