ChatGPT Health keeps your health chats, files, and memories in a separate dedicated space.
Health conversations appear in your history, but their info never flows into your regular chats.
View or delete Health memories anytime in Health or Settings > Personalization.
Idea to App Store TestFlight in 1 minute 🤯
AI builds web apps well. Mobile apps have been harder.
Now, the inventor of React (the technology that AI uses to build apps), has a new announcement.
Context engineering isn't just about throwing more data at your LLM - it's about giving it the right context at the right time.
In this talk for @OReillyMedia, our Developer Relations Engineer @tuanacelik walks through how memory blocks help you build agents that maintain structured context for complex tasks. She demonstrates artifact memory blocks using a restaurant order tracking bot - showing how to distill entire conversations down to just the essential structured information (pizza type, toppings, address) rather than processing the full chat history.
Key concepts covered:
· Different types of memory blocks (static, fact extraction, vector, artifact)
· Context ratio management - balancing chat history vs system prompts vs memory
· Using agent workflows to construct and optimize context step-by-step
The example shows how artifact memory blocks can transform a meandering conversation into a clean, structured order - exactly what you need for production agents handling real-world tasks.
Watch the full talk: youtube.com/watch?v=POO3ck…
Because the system harnesses internal activations already happening within a model, and reserves heavier computation only for potentially harmful exchanges, it adds only ~1% compute overhead.
It’s also more accurate, with an 87% drop in refusal rates on harmless requests.
Your AI retrieves based on semantics but highlights based on keywords. See the problem?
You search for "iPhone performance" and it only marks those exact words—missing all the good stuff like "A15 Bionic chip, zero lag." The answer's right there, just invisible.
This is especially frustrating in RAG systems where retrieval gets the semantics right, but users still have to hunt through long documents.
We tackled this head-on and 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞𝐝 the solution: 𝐚 𝐛𝐢𝐥𝐢𝐧𝐠𝐮𝐚𝐥 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐡𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 that actually understands context. It outperforms existing options (Provence, OpenSearch, etc.) with an 8K token window and works in both English and Chinese.
It's MIT-licensed and ready to use. Thought folkmilvus.io/blog/zilliz-tr…might find it ushuggingface.co/zilliz/semanti…JwwmZUW
Hugging face: https://t.co/dEBPDs1kXH
———
👉 Follow Milvus, created by Zilliz, for everything related to unstructured data!
