I used #notebooklm to generated a 35min podcast discussing 4 top stories on hacker news. The flow: pick top stories, stitch their audio overviews together, make smooth transitions between topics, add intro and outro music (generated by @suno_ai_ ). https://t.co/E6vN3Uu6MT
@paulg@fat Art is about what audience feels, not how awesomely an artist can do. I personally don’t feel relevant at all to the given art piece. I would rank it much lower than lots of recent work I’ve seen, like, from James Jean, Miyamura Gen and many many more.
@jimkxa Totally! We need more computing, not “smaller transistors+better instruction set+branch prediction”. 1000x can come from: Once a software is developed, it’s compiled to an ASIC design. Then foundry build the chip and host my software. “Serious software peeps build own hardware.”
@natfriedman This is great! We had internal scripts to run the comparison but the problem is the time spending on getting access to all the APIs. Now I can run a small test on your tool and decide models of interest. As a result, I just submit an access application to Anthropic. Thank you!
@erik_nijkamp@nathanbenaich If LLM API vendors are willing to provide read/customization access to the parameters of the last 2-3 layers of their models, would it be sufficient for RLHF?
@kevinyang@OpenAI Great work! If you fine tune models for each user, do you see that actually gives better personalization compared with in-context learning? Great to see llm is disrupting how we work. I’m experimenting it for analytics, and got amazed by how much it can do in many different ways.
@_paulshen Natto is a wonderful piece of software that I used to teach my kid programming. Whatever you decide on how much energy to spend on future Natto development, I would appreciate, even it’s 0.