To make the analogy explicit, in Software 1.0, human-engineered source code (e.g. some .cpp files) is compiled into a binary that does useful work. In Software 2.0 most often the source code comprises 1) the dataset that defines the desirable behavior and 2) the neural net architecture that gives the rough skeleton of the code, but with many details (the weights) to be filled in. The process of training the neural network compiles the dataset into the binary — the final neural network. In most practical applications today, the neural net architectures and the training systems are increasingly standardized into a commodity, so most of the active “software development” takes the form of curating, growing, massaging and cleaning labeled datasets.
Recent articles
- Trying out llama.cpp's new vision support - 10th May 2025
- Saying "hi" to Microsoft's Phi-4-reasoning - 6th May 2025
- Feed a video to a vision LLM as a sequence of JPEG frames on the CLI (also LLM 0.25) - 5th May 2025