Hello World! We are on Product Hunt.
AI agents are only as good as the work context they're given. Right now there's no layer that tracks cause and effect across your work data over time.
Weavable is that layer. One MCP endpoint, continuously updated.
https://t.co/MUc9joYaBW
Just launched Weavable on Product Hunt.
AI agents are only as good as the work context they're given. Right now there's no layer that tracks cause and effect across your work data over time.
Weavable is that layer. One MCP endpoint, continuously updated. https://t.co/ZBtoGQOghR
When using foundation models for product features, the bottleneck shifted from "how do I train a model?" to "how do I prepare data that actually works?"
@boztank Boz, it’s amazing to see all the things that were just ideas a few years ago in various meetings become reality. Well done to all (and all those reorgs were clearly worth it 😁)
@vedikaja_in This is great. A few adds:
* Pack everything into the prompt but GIGO still applies. The more relevant the data, the better the output.
* Set up a product push loop. It builds habits fast.
* Different parts of the product should aim for different LLM quality/latency tradeoffs.
@gdibner Definitely. Although similar to writing, there’s a different kind of thinking that happens when thinking and typing simultaneously. I sometimes rewrite generated code because it helps me get to a result that’s even better.