Introduction Some weeks in AI feel like a neat row of press releases. This week felt like someone kicked the table, then handed us a spreadsheet of consequences. We got open models that finally talk like engineers, not mascots. We got one-step ImageNet and diffusion language models that can edit themselves mid-thought. We got research … Read more
Introduction The weirdest part of “AI coding agents” in 2026 is not that they can fix bugs. It’s that they can fix bugs expensively. You watch an agent do the right thing, then you watch the invoice do its own little victory lap. MiniMax M2.5 is interesting because it shifts the conversation from “Which model … Read more
Introduction If you’ve watched AI “solve” math lately, you’ve probably felt the same whiplash I have. One day it’s confidently inventing a theorem. The next day it’s quietly nailing a proof that would have made your younger self sweat through three notebooks. The interesting part is not that models got better at talking about math. … Read more
Updated on 13 February 2026 Introduction Here’s my current test for a model: give it a task that involves a terminal, a half-broken repo, and a goal that takes 30 steps. If it still knows what it’s doing at step 25, I care. If it faceplants into a loop, it’s just fancy autocomplete. That’s the … Read more
Introduction Most model install guides have two failure modes. They’re either “paste this and pray,” or they’re a graduate seminar disguised as a README. This one is neither. You’re going to install LLaDA2.1-mini, run it once, watch it chew almost an entire 48 GB GPU, and then verify it’s actually thinking by making it solve … Read more
See our chatgpt archives for more Introduction Developers can argue about anything. Tabs vs spaces. Monolith vs microservices. Now we’ve upgraded to the premium argument: Codex 5.3 vs Opus 4.6. The irony is that most of these debates happen instead of coding. So let’s do the respectful thing and get you to a decision fast. … Read more
Introduction Most “feature selection” stories start the same way. You have a model, you have a mountain of inputs, and you suspect half of them are doing more harm than good. Then you open a paper, see “NP-hard,” and quietly decide you will just throw everything into the network and let SGD sort it out. … Read more