# Image Search Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/image-search
> Markdown URL: https://aitinkerers.org/technologies/image-search.md
> Technology record last updated: 2026-02-25T08:48:51Z
> Generated: 2026-09-21T12:40:59Z

Identify objects, translate text, and source products instantly using advanced computer vision.

Google Lens handles 12 billion visual queries every month: it uses neural networks to decode pixel data in real time. Point your camera at a 1956 Eames Lounge Chair to pull pricing or identify a rare Monstera deliciosa in seconds. The tech translates 100+ languages on the fly and catalogs 20 million biological species. It turns the physical world into a searchable database with sub-second latency.

- Official technology site: https://lens.google
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Let's talk about Embeddings](https://cologne.aitinkerers.org/talks/rsvp_NX6I1fvuINY)

I will talk about why embeddings are such a great thing. They can do so many tasks that we set out a huge LLM to do, but in a much more efficient and cost saving way. There are tons of use cases for embeddings, and in this talk, I just want to give a simple insight into some use cases of embeddings, beside RAG. I want to cover (not sure if this is the final list yet): - RAG - Image Search - Image Classifier - Advanced Image Classifier with an added MLP Head - Text Matching across languages - Getting Clear Text Input for Customer Intention Analysis (Main Focus) - And a short example of how you can use that clear text input to improve what you are offering as a company. (Main Focus) As the 5 Minute Time slot is very narrow, I will likely focus on the Clear Text Input Analysis part, as I think that is quite a nice use case for embedding based, customer facing search. While I will not show a lot of code in this presentation, coding this yourself is so easy, that anyone could do it without seeing any code. It's more about the idea and concept for this usecase.

- Event context: AI Tinkerers Cologne 3: Demos, Code, and Architecture — 2026-03-05 — Cologne
- Public talk page: https://cologne.aitinkerers.org/talks/rsvp_NX6I1fvuINY

## Related Technologies

- [CLIP](https://aitinkerers.org/technologies/clip) ([Markdown](https://aitinkerers.org/technologies/clip.md)) — 10 public demos
- [Embedding Models like CLIP](https://aitinkerers.org/technologies/embedding-models-like-clip) ([Markdown](https://aitinkerers.org/technologies/embedding-models-like-clip.md)) — 1 public demo
- [Image Classifier](https://aitinkerers.org/technologies/image-classifier) ([Markdown](https://aitinkerers.org/technologies/image-classifier.md)) — 2 public demos
- [or Google's multimodalembedding@001 Model](https://aitinkerers.org/technologies/or-google-s-multimodalembedding-001-model) ([Markdown](https://aitinkerers.org/technologies/or-google-s-multimodalembedding-001-model.md)) — 1 public demo
- [RAG](https://aitinkerers.org/technologies/rag) ([Markdown](https://aitinkerers.org/technologies/rag.md)) — 147 public demos