CLIP

Vision AI in 2025 — Peter Robicheaux, Roboflow

Aug 3, 2025 · 17:24

Peter Robicheaux, ML lead at Roboflow, argues vision models are not smart because they fail at basic visual tasks like telling time on a watch or identifying a school bus direction. He attributes this to saturated evals like ImageNet and COCO, and to vision models not leveraging large pre-training like language models. Roboflow introduces RFDetter, a real-time detector using a Dynav2 backbone, achieving second SOTA on COCO and large gains on their new RF100VL dataset of 100 diverse domains. The benchmark shows specialist models fine-tuned on 10-shot examples outperform large VLMs like Qwen2.5 VL 72B, indicating VLMs are strong linguistically but weak visually. Roboflow's platform is free for researchers contributing data back; dataset at rf100vl.org.

Building Multimodal AI Agents From Scratch — Apoorva Joshi, MongoDB

Jun 27, 2025 · 36:58

Apoorva Joshi, an AI developer advocate at MongoDB, leads a hands-on workshop on building multimodal AI agents from scratch. She defines agents as systems using LLMs to reason, plan, and execute tasks with tools, contrasting them with simple prompting and RAG. The workshop focuses on processing mixed-media documents (text and images) by converting each page to a screenshot, embedding it with Voyage AI's multimodal (VLM-based) model to avoid the modality gap of CLIP, and storing embeddings in MongoDB for vector search. The agent uses Google's Gemini 2.0 Flash as the multimodal LLM, implements REACT (reasoning and acting) for planning with feedback, and manages short-term memory via session IDs. Participants build a tool-calling agent that retrieves relevant page screenshots, passes them with conversation history to Gemini, and generates answers for queries like analyzing charts or extracting insights from documents.

120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson

Nov 22, 2023 · 15:59

Joseph Nelson (Roboflow CEO) details building paint.wtf, a viral AI Pictionary game using OpenAI CLIP that attracted 120,000 players in its first week, peaking at 7 submissions per second. The architecture uses GPT-3 to generate prompts, a browser canvas for drawing, and CLIP to judge similarity between text and image embeddings. Key lessons include CLIP's ability to read text, requiring a moderation hack that penalizes images more similar to handwriting than the prompt; CLIP's conservative similarity scores (ranging 8% to 48%); and the necessity of using CLIP itself to block NSFW content. Nelson also live-codes a minimal MVP in under 50 lines of Python using Roboflow's open-source inference server, demonstrating CLIP embedding and cosine similarity. The talk underscores foundation models' new paradigm of open-set understanding.

The Hidden Life of Embeddings: Linus Lee

Nov 7, 2023 · 18:15

Linus Lee, a Research Engineer at Notion, presents a tour of embedding visualization and manipulation at the AI Engineer Summit 2023. He demonstrates an encoder-decoder model fine-tuned from T5 that can reconstruct text from embeddings, allowing direct manipulation of features like length and sentiment by moving in latent space. Lee shows that mixing embeddings by splicing dimensions from two texts produces a semantic blend, and a linear adapter can decode text from OpenAI's text-embedding-ada-002 embedding space. Using CLIP, he interpolates between photographic and cartoon images and performs vector arithmetic to modify facial expressions. Lee releases these custom text embedding models on Hugging Face, enabling others to explore and interact with latent spaces. He argues that making model internals visible and manipulable fosters deeper understanding and more humane interfaces to generative AI.

Principles for Prompt Engineering - Karina Nguyen (Claude Instant @ Anthropic)

Oct 20, 2023 · 55:31

Karina Nguyen, an engineer on Anthropic's Claude, presents principles for effective prompt engineering, treating it as a creative writing process requiring iteration. She advises using XML tags, placing instructions at the end of long prompts (improving accuracy), and decomposing questions to improve faithfulness over chain-of-thought. She covers reducing hallucinations by asking Claude to hedge or quote sources, and using self-consistency and contrastive examples for labeling. Nguyen explains generating evaluation datasets with Claude by splitting documents into multiple-choice questions, and notes that for long-document QA, asking at the end outperforms the beginning. She distinguishes Claude (larger, smarter) from Claude Instant (cheaper, faster, better at math and code), and predicts prompt engineering will remain essential for complex tasks.