Liquid AI published a "cookbook" on GitHub this week—a batch of application tutorials written with their own LFM (Liquid Foundational Models, Liquid's foundational model series focused on small, efficient architectures) and the LEAP SDK (developer toolkit). The news itself is modest, but we're more interested in the signal behind it: small-model companies that can't compete with OpenAI and Anthropic on parameter scale are now seriously competing on developer ecosystems.
What this is
Liquid AI is an AI company spun out of MIT, founded by Ramin Hasani. His team takes the path of chasing efficiency over parameter counts. The core product is the LFM model series, paired with the LEAP SDK for developers. The "cookbook" they published this week compiles end-to-end tutorials and application examples for various LFM models—so developers can copy, paste, and get started. This is the standard playbook for open-source AI projects courting developer mindshare.
Industry view
Supporters argue that releasing the cookbook shows Liquid AI is serious about building an ecosystem. For small-model companies to compete with the giants, simply saying "our models are smaller and faster" isn't enough—they have to get developers to try them, contribute code, and deploy them in production projects.
But there are cooler voices. The cookbook was posted on r/LocalLLaMA—a community of local-deployment enthusiasts—which itself signals an extremely narrow audience. No amount of tutorials will amount to more than splashing around in a small pond. The more critical question: when GPT-4 and Claude APIs are already cheap enough to cost just pennies per call, why would enterprises bother with small models? If Liquid AI wants to answer that question, tutorials alone won't get there.
Impact on regular people
For enterprise IT: If your company is evaluating private-deployment AI solutions, small models like Liquid AI's belong on the shortlist—especially in scenarios where data can't leave internal networks and compute budgets are tight.
For individual careers: Limited short-term impact. But if the "small models run locally" trend holds, AI costs for SMBs could drop further.
For the consumer market: Indirectly positive. More diverse model supply means fiercer price competition at the consumer app layer, which means lower costs for regular users accessing AI services.