CaudalLabs

The new release of github.com/caudal-labs/ca… supports image attachments in chat messages

@karpathy github.com/caudal-labs/ca…

@satyanadella Your AI chat is a dead-end hallway. Mine is a mind map. Branch. Explore. Merge. All on one canvas. github.com/caudal-labs/ca…

Introducing Critique, a new multi-model deep research system in M365 Copilot. You can use multiple models together to generate optimal responses and reports.

Your AI chat is a dead-end hallway. Mine is a mind map. Branch. Explore. Merge. All on one canvas. github.com/caudal-labs/ca…

@xbxnxdxcxtx @xai I am introducing the concept of an attention engine for AI agents. This approach is inspired by neurobiology and the way biological systems control and direct attention. caudal-labs.com

Your agent needs more than memory. Recall ≠ Relevance Your agent can retrieve everything. It just can't tell what matters right now. 🚀 Open-source attention engine. Agents send events. Caudal learns what's relevant. Old context fades naturally. caudal-labs.com

@contextkingceo I am curious to know what do you think about my approach. Introducing the concept of attention engine for AI agents. It is neuro-inspired attention control and biological attention mechanisms. caudal-labs.com

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️