I did some more experimentation with open-interpreter today. The first use case I tried was to create, organize and reorganize files. It didn't generate interesting content, but it was fluent at...
I read quote from a long tweet the other day that made me smile. Writing pure JavaScript is like trying to cut a watermelon with a chainsaw in the dark. It sounds fun and free and quite easy until...
Playing with Rivet and OpenInterpreter
It's much easier to test Temporal Workflow in Python by invoking the contents of the individual Activities first, in the shell or via a separate script, then composing them into a Workflow. I need to...
Language models and prompts are magic in a world of deterministic software. As prompts change and use cases evolve, it can be difficult to continue to have confidence in the output of a...
I've been doing a bit of work with Temporal using it's Python SDK. Temporal remains one of my favorite pieces of technology to work with. The team is very thoughtful with their API design and it...
🎧 Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product)
Simon wrote an excellent post on the current state of the world in LLMs.
It will be interested to see if or when we hit scaling limits to training more powerful models and what our new bottleneck becomes. For now, there appears to be a lot of greenfield.
While not an entirely unique perspective, I believe Apple is one of the best positioned companies to take advantage of the recent improvements in language models. I expect more generic chatbots will...