Getting an agent working via an existing Slack setup is fairly effortless and the control over output format is useful
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
You can still look at almost any codebase and ask 'why is this bit not using a state machine here?' ... the AASM repo readme is very accessible even if you don't know Ruby: https://github.com/aasm/aasm
But a small collective running that box, especially spanning timezones, could potentially be a viable alternative or will be soon -- with obv privacy gains too
Conversely, it's useful to get an immediate answer sometimes
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
For the trivial cases that's fine (just using LLM does same)
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
Last week stripped out all CSS from a fairly substantial project and replaced with Tailwind equivs, it got all but a few cases right
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
I expect in a few days there will be a new tool launched that returns word frequency/velocity in recent biomedical papers ... so next year's PhDs can level things using an MCP function
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
That's likely because OG devs have been seeing the hallucination stuff, unpredicability etc. and questioning how that fits with their carefully curated perfect system
What blocked me initially was watching NDA'd demos a year or two back from a couple of big software vendors on how Agents were going to transform enterprise ... what they were showing was a complete non-starter to anyone who had worked in a corporate because of security, compliance, HR, silos etc. so I dismissed it
This MCP stuff solves that, it gives you (the enterprise) control in your own walled garden, whilst getting the gains from LLMs, voice etc. ... the sum of the parts is massive
It more likely wraps existing apps than integrates directly with them, the legacy systems becoming data or function providers (I know you've heard that before ... but so far this feels different when you work with it)
I had similar skepticism initially, but I would recommend you dip toe in water on it before making judgement
The conversational/voice AI tech now dropping + the current LLMs + MCP/tools/functions to mix in vendor APIs and private data/services etc. really feels like a new frontier
It's not 100% but it's close enough for a lot of usecases now and going to change a lot of ways we build apps going forward
SaaS companies tended to need material engineering resource due to the software stacks and squad-style team structures in place -- also leaned heavily on costly metered infra
I'm not seeing anything like the same level of heads or stack complexity in this wave (Vercel, Firebase etc.), and the vendors involved get cheaper every day ... along with increasing ability to run models locally with no metered costs at all
(I had same issue, just asked it to check some code that 4.8 had modified earlier in day)