# Foundation models Projects at AI Tinkerers

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> Markdown URL: https://aitinkerers.org/technologies/foundation-models.md
> Technology record last updated: 2026-02-25T05:30:48Z
> Generated: 2026-09-21T14:42:25Z

Liquid AI's 1.2B and 2.6B parameter models deliver high-density performance for edge devices using a memory-efficient, non-transformer architecture.

These Cactus models replace traditional Transformers with Liquid Foundation Models (LFMs) based on linear state-space designs. The 1.2B and 2.6B variants beat larger competitors (including Llama 3.2-3B) in benchmarks while maintaining a minimal memory footprint. They support 32k token context windows without the quadratic scaling costs of self-attention. This makes them the top choice for on-device AI, robotics, and secure enterprise applications where hardware resources are limited.

- Official technology site: https://www.liquid.ai/blog/liquid-foundation-models
- Public AI Tinkerers demos and talks: 5
- Result page: 1 of 1

## Recent Public Talks and Demos

### [out.sg's recommendation engine and kew(as a library)](https://singapore.aitinkerers.org/talks/rsvp_QUwTKUjD7yo)

Creating recommendation systems with foundation models!

- Event context: AI Tinkerers Singapore: 4th Meetup - January 10th, 2025 — 2025-01-10 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_QUwTKUjD7yo

### [Clinical Events Prediction engine to accelerate pharma trials](https://nyc.aitinkerers.org/talks/rsvp_drBRHOh35QY)

KolateAI provides patient-level Clinical Events Prediction to accelerate pharma trials and real-world studies. With our foundation models trained on clinical studies delivered through our Co-pilot, we predict major clinical events (drug response, adverse events, study end-points). This enables pharmas to target the best-performing patient segments and pro-actively manage study trajectory.

- Event context: AI Tinkerers July Meetup — 2024-07-24 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_drBRHOh35QY

### [Video Understanding: Human Level Performance Using Multimodal Foundation Models with a Video First Ethos](https://denver-boulder.aitinkerers.org/talks/rsvp_6aXMaNtN0UE)

Video has traditionally be processed as its constituents: frames and audio but this isn't how humans process video. Learn how video foundation models take a video first approach to enabling human level understand of video.

- Event context: AI Tinkerers Denver - November Meetup — 2023-11-23 — Denver
- Public talk page: https://denver-boulder.aitinkerers.org/talks/rsvp_6aXMaNtN0UE

### [Make Video Just as Easy as Text: Introduction to Twelve Labs Video Foundation Model](https://sf.aitinkerers.org/talks/rsvp_mWAtc7EvDtg)

Twelve Labs has been developing a general purpose video foundation model to enable multimodal, contextual video understanding so video can be as easy as text.

- Event context: 🤖🔄🧠 AI Tinkerers SF - August Meetup — 2023-08-10 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_mWAtc7EvDtg

### [autodistill - an open source framework for model distillation](https://sf.aitinkerers.org/talks/rsvp_bkDN_zUQGV4)

autodistill is an open source framework for distilling big, general, slower models into domain-specific, smaller, and faster models. Right now, it best supports computer vision tasks. With autodistill, users apply foundation models like SAM and CLIP to auto label datasets and train smaller models in fewer than 10 lines of code. Hackers want to adapt foundation models to domain-specific models that they own, often leveraging their proprietary data. autodistill presents one open source approach to this.

- Event context: 🤖🔄🧠 AI Tinkerers SF - August Meetup — 2023-08-10 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_bkDN_zUQGV4

## Related Technologies

- [autodistill](https://aitinkerers.org/technologies/autodistill) ([Markdown](https://aitinkerers.org/technologies/autodistill.md)) — 1 public demo
- [BERT](https://aitinkerers.org/technologies/bert) ([Markdown](https://aitinkerers.org/technologies/bert.md)) — 179 public demos
- [BLOOM](https://aitinkerers.org/technologies/bloom) ([Markdown](https://aitinkerers.org/technologies/bloom.md)) — 115 public demos
- [CLIP](https://aitinkerers.org/technologies/clip) ([Markdown](https://aitinkerers.org/technologies/clip.md)) — 10 public demos
- [Co-pilot](https://aitinkerers.org/technologies/co-pilot) ([Markdown](https://aitinkerers.org/technologies/co-pilot.md)) — 2 public demos
- [Generative AI](https://aitinkerers.org/technologies/generative-ai) ([Markdown](https://aitinkerers.org/technologies/generative-ai.md)) — 45 public demos
- [GPT-3](https://aitinkerers.org/technologies/gpt-3) ([Markdown](https://aitinkerers.org/technologies/gpt-3.md)) — 191 public demos
- [GPT-4](https://aitinkerers.org/technologies/gpt-4) ([Markdown](https://aitinkerers.org/technologies/gpt-4.md)) — 529 public demos
- [Kew](https://aitinkerers.org/technologies/kew) ([Markdown](https://aitinkerers.org/technologies/kew.md)) — 1 public demo
- [Llama-2](https://aitinkerers.org/technologies/llama-2) ([Markdown](https://aitinkerers.org/technologies/llama-2.md)) — 227 public demos
- [LLMs](https://aitinkerers.org/technologies/llms) ([Markdown](https://aitinkerers.org/technologies/llms.md)) — 83 public demos
- [Machine Learning](https://aitinkerers.org/technologies/machine-learning) ([Markdown](https://aitinkerers.org/technologies/machine-learning.md)) — 20 public demos
- [Multimodal Models](https://aitinkerers.org/technologies/multimodal-models) ([Markdown](https://aitinkerers.org/technologies/multimodal-models.md)) — 6 public demos
- [OpenAI](https://aitinkerers.org/technologies/openai) ([Markdown](https://aitinkerers.org/technologies/openai.md)) — 112 public demos
- [PaLM 2](https://aitinkerers.org/technologies/palm-2) ([Markdown](https://aitinkerers.org/technologies/palm-2.md)) — 116 public demos
- [RoBERTa](https://aitinkerers.org/technologies/roberta) ([Markdown](https://aitinkerers.org/technologies/roberta.md)) — 118 public demos
- [SAM](https://aitinkerers.org/technologies/sam) ([Markdown](https://aitinkerers.org/technologies/sam.md)) — 1 public demo
- [Synthetic data](https://aitinkerers.org/technologies/synthetic-data) ([Markdown](https://aitinkerers.org/technologies/synthetic-data.md)) — 3 public demos