Orange (software)
Visual programming doesn’t replace coding — it defers the hard parts until they’re unavoidable.
Machine learning
Statistics at a scale that stopped behaving like statistics.
- 10
- in technology
- 9:56
- average
- 99 min
- in total
- 15
- across the network
All briefs
Diffusion model
Diffusion models don’t generate images—they reverse entropy, one noisy step at a time.
Generative adversarial network
GANs don’t model reality—they train a mimic to beat a critic.
LoRA (machine learning)
LoRA doesn’t shrink models—it shrinks the cost of changing them.
PyTorch
Facebook · 2016
PyTorch is a Python-first deep learning library built by Meta in 2016 as Torch's successor. It enables rapid model prototyping via dynamic computation graphs (Autograd), tensor abstractions, and CUDA-accelerated training. As of 2025, it remains one of the most popular deep learning libraries—alongside TensorFlow and Keras—but its design prioritises researcher agility over production robustness, scalability, or deployment tooling.
Reinforcement learning from human feedback
RLHF outsources ethics to annotators—and then optimises for their rankings, not their intent.
TensorFlow
Google · 2015
TensorFlow is a machine learning API developed by Google. It is used mainly for training and inference of neural networks. It is free and open-source software released under the Apache License 2.0. It was developed by the Google Brain team for Google's internal use in research and production. The initial version was released under the Apache License 2.0 in 2015. Google released an updated version, TensorFlow 2.0, in September 2019. TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java. It is one of the most popular deep learning frameworks, alongside others such as PyTorch.
AI data center
AI data centers don’t just run models—they starve the rest of computing of memory.
ChatGPT
ChatGPT is not intelligence — it’s a prompt-to-output relay, optimised for speed, not truth.
ImageNet
Fei-Fei Li
ImageNet is a foundational dataset infrastructure — not a model, not a tool, not a service. It succeeded by making scale measurable, not magical.