photon_collider

What a historic moment! It'll be interesting to see how much Anthropic can continue growing.

There will come a point where open-source models are good enough for most people. However, I do expect there to be demand for frontier models for things like cybersecurity (especially on the enterprise side) and scientific research.

Yeah, I was confused about why it was talking about subscriptions for enterprise. The company I work at is billed on API usage.

Nilay has been discussing these ideas on podcasts hosted by The Verge over the last few weeks. I can tell it's something that's been top of mind for him especially on his podcast series Decoder where he interviews CEOs about their approach to integrating AI in their products and how consumers feel about this.

I thought it was just me. I'm running a M2 MacBook Pro and scrolling down the article on Safari is quite stuttery.

Reading this news only leaves me worried about long-term future of these open source tools.

Fair point! When I first started on this I went down a deep rabbit hole exploring all the ways I could set this up. Ultimately, I decided to start simple with hardware that I had laying around.

I definitely will want to have a dedicated NAS machine and a separate server for compute in the future. Think I'll look more into this once RAM prices come back to normal.

Ah nice! Didn’t know that. I’ll try that out next time.

Nice to see a new release from Anthropic. Yet, this only makes me even more curious of when we'll see a new Claude Opus model.

Ah, I remember Grooveshark. I discovered so much good music there.

"Trust but verify" is still useful especially when you ask LLMs to do stuff you don't know. I've used LLMs to help me get started on tasks where I wasn't even sure of what a solution was. I would then inspect the code and review any relevant documentation to see if the proposed solution would work. This has been time consuming but I've learned a lot regardless.

Almost like keeping a personal write-ahead log. :)

Has Julia's popularity in scientific computing and data science continued to grow? I haven't heard much about it recently.

Astro is really good for this use case. Also easy to host on platforms like Netlify.

I agree that LLMs are less useful for smaller scale use cases. I’ve found success using them for creating mock data, reformatting unstructured text into structured data, and a few other menial tasks that would take me a while to do on my own.

I think so much of the hype is about potential larger scale applications but the models just don’t seem reliable enough yet for that.

CSS has had a similar issue with naming classes, which is one reason for how TailwindCSS’s design. I wonder if we’ll see more AI tools for these kinds of use cases.

Strangely, I've noticed mixed opinions from developers about whether learning vim is a productivity boost. Some people believe it is while others disagree.

I'll admit my FOMO was what originally got me to start learning vim. I still barely know the basic motions but I'm starting to think it could lead to a productivity boost once I get over the learning curve.

>The LLM is like another user. And it can surprise you just like a user can. All the things you've done over the years to sanitize user input apply to LLM responses.

I really like this analogy! That sums up my experiences with LLMs as well.

I learned this first-hand when I entered the profession with a master's degree. Much of the work required me to know how to munge tabular data and do some stats with R.

My impression is that many hiring managers desire candidates with a PhD because they are perceived to be more capable of working independently. Then again, the field isn't very standardized so every company has varying expectations of what they expect out of this role.

The website says these glasses have all-day battery life. Is that with the use of a portable charge? If so, I'm wondering what the battery life is without the charger.

This looks really cool! It's interesting that this compiler provides performance optimizations by looking for data diffs rather than diffs in the Virtual DOM. Is this intended to be an alternative to React Forget (still in development)?

It certainly feels like AI is a new tech bubble. Lots of AI services just seem to be thin wrappers over OpenAI’s offerings.

Tried running this on my iPhone and couldn’t hear anything despite pressing the play button. Is there a bug somewhere?