It's a fast classifier you can use out-the-box, ~1.5bn tokens is about $40 (I've been hammering it)

It just works ... a whole bunch of low-level/low-importance workflow stuff that was getting farmed out to small/fast LLM models now has a competitive alternative ... and bits that hadn't even been considered to go into some external descision/classifier service can be tested/deployed at ~$0.00003/req

I don't get this wall of negativity on it, it's genuinely innovative/useful tech ... would expect HN to be more positive, regardless of whether it's the absolute best execution

have you ever held and read an old book?

(I had same issue, just asked it to check some code that 4.8 had modified earlier in day)

I had a look at getting same agent up in WhatsApp but it seems to need FB business acc to even start process, to get FB business you need an FB personal etc. ... looked like too much effort

Because MCPs solve the exact issue the whole post is about

It's easy to forget the product Anthropic are selling here, and throttling, is based on data they mostly pay little or no content fee for

6 months ago, "what temp is pork safe at?" was a few clicks, long SEO optimised blog post answers and usually all in F not C ... despite Google knowing location ... I used it as an example at the time of 'how hard can this be?'

First sentance of Google AI response right now: "Pork is safe to eat when cooked to an internal temperature of 145°F (63°C)"

How is it not the responsibility of senior management at a major retailer to ensure an exploit at a vendor can't take the whole house of cards down?

Many other major enterprise clients out there are all over vendor security/compliance ... auditing and reauditing vendors to minimise chance of this happening or worst-case, if does happen, containing it and recoverying quickly

But this particular project is not like a standard site and the CSS is in small fragments across 100s files and uses constants for some things like color values in places too

In that Loopple example you can see the conversion uses the Tailwind arbitrary value notation, the -[], so background-color:#afa8af gets converted to bg-[#afa8af], but I wanted nearest pure tailwind class bg-zinc-400, the agent seems to work out color distance fine so does all that in one-shot too

That was gemini-cli, I could see some mistakes on trial run so created a GEMINI.md with system prompt and project description (about 50 lines) which clarified some tricky source layout situations

Second run it was fine, ran for about an hour or so -- I had attempted to do it manually a while back but it started to look like it would take a week or two

Much of the criticism of AI on HN feels driven by devs who have not fully ingested what is going with MCP, tools etc. right now as not looked deeper than making API calls to an LLM

https://elevenlabs.io/

https://layercode.com/ (https://x.com/uselayercode has demos)

Have you used the live mode on the Gemini App (or stream on AI Studio)?