some predictions on the future of software by inference (llm)

Mon, 24 Nov 2025 03:35:01 +0000

In particular, we’ve built up a vast amount of tooling that assists humans in writing 1.0 code, such as powerful IDEs with features like syntax highlighting, debuggers, profilers, go to def, git integration, etc. In the 2.0 stack, the programming is done by accumulating, massaging and cleaning datasets. For example, when the network fails in some hard or rare cases, we do not fix those predictions by writing code, but by including more labeled examples of those cases. Who is going to develop the first Software 2.0 IDEs, which help with all of the workflows in accumulating, visualizing, cleaning, labeling, and sourcing datasets? Perhaps the IDE bubbles up images that the network suspects are mislabeled based on the per-example loss, or assists in labeling by seeding labels with predictions, or suggests useful examples to label based on the uncertainty of the network’s predictions.

watch the hands of AI not their mouths

Sun, 12 Oct 2025 18:15:32 +0000

Seven years from GPT-1 to the plateau. How many more until we stop trying to build intelligence and start trying to understand what we’ve already built? That’s the real work now - not training the next model, but figuring out what to do with the ones we have. Turns out the singularity looks less like transcendence and more like integration work. Endless, necessary integration work.

I think this is it. The last 5 years and especially 3 have been amazing to watch. From GPT-2 to today AI has only gotten better at helping me code basic things. But it still tries to run non-sensical commands and just recently added a “Utilties-2” folder to my project because I already had one?

Llm on Duly Noted

some predictions on the future of software by inference (llm)

watch the hands of AI not their mouths