SocietyAI glossary
Open source / open-weight
A model whose weights are downloadable and usable by anyone (Llama, DeepSeek, Kimi…). You can self-host it, audit it, modify it.
In other words
The recipe published in the book, versus the dish served only at the restaurant.
Alyriaz articles that mention it
- A startup is valued at 1.4 billion and doesn't train any model from scratch
A young Chinese company reaches a $1.42 billion valuation in seven months by specialising open models rather than training them. We analyse this model and what it reveals.
- K2 Horizon: six open models, from 0.9 to 375 billion parameters
The Institute of Foundation Models has released six open models under Apache 2.0, spanning a range from 0.9 to 375 billion parameters. We explain why publishing a full range changes the game for those building.
- Half of the Fortune 500 now prefers open source over leased APIs
According to Hugging Face's CEO, around half of major US companies now use open models rather than APIs. We analyse this shift and its three drivers.
- Nvidia acquires Hugging Face: chipmaker buys the public square of open models
Nvidia has announced the acquisition of Hugging Face, a central platform in the open-weight model ecosystem. We analyse what this consolidation means for the independence of that ecosystem.
- GLM-5.3: an open model found over a thousand real flaws in widely used software
Z.ai's GLM-5.3 achieves top scores in vulnerability discovery. Unlike previous versions, its weights have not been released: the lab has withheld them for a security assessment. We analyse this precedent.
- Qwen 3.8 in the 27-billion version: the open model that fits on a single card and sees images
Alibaba has released a 27-billion-parameter version of Qwen 3.8 under the Apache 2.0 licence, with built-in vision and a native context of 262,000 tokens. We explain why this size is the real point of interest.