Without using agnost, what are some basic SQL queries I can run on my data to find outliers I'd otherwise be missing?
How far can I get with just keywords, common phrases, boring traditional analysis?
Depending on what I measure there, when is the right time for me to consider upgrading to something like Agnost/what is a specific example of what it will find that traditional/rigid analytics approaches will miss?
keywords and sql rarely work - you can not find the repeated hidden feature requests, cause we don't know them at the first place yet, or a frustrated user puts vague signals as ugh, ahh, or just an 'f!' (and added modalities, accents and languages makes it much more challenging)
interestingly, even embeddings seem to bucket "no" and "nooo!" somewhat similar, but are pretty different when viewed from a user satisfaction perspective.
A sweet spot on moving to Agnost is the time when you get higher inflow of conversations you can't manually read or listen, and want to clusterize them into things which matter, with the outliers highlighted
Lovely name! I implemented profanity monitoring in my Hermes setup to identify "learning opportunities" for my agents. It is quite useful. If you are budget-conscious, one challenge is determining what is the smallest number of previous rounds that Hermes needs to correctly infer what it did wrong. Curiously, Claude Code is horrible at figuring out what it did wrong. I often read its memories, and they are rarely useful.
Well, good luck with the launch, this seems like an interesting product with potential.
However privacy is central in a service like this and I think you should probably beef up your representation of how you deal with that.
eg. "We use each customer’s data only for that customer" - well that customer may have hundreds of staff; how are they being consulted and onboarded wrt their own voices (or is that transcripts?) and messages being used in this way?
ofc you might argue that nothing in work is private but I do think you have some margin for improving the detail here.
codex is great for like a one-time/overview analysis on a handful of transcripts. we usually serve to companies where the volume is >10k messages & continuous ingestions + with claude/codex it messed up this + metadata linking of the user like what plan are they on, when is it expiring, etc.
although we had a few customers who come to us after running this for a while so at smaller volume it does work well.
I thought startups wrapping prompts would require something a more complex than semantic analysis, which is literally what this is. And for 500 bucks. Wow. Props for being able to sell this.
I don't get the appeal of the UI, why is it so complex/convoluted.
lol i wish it was just wrapping prompts but things got harder once our customers grew bigger, we had to build queues. we had to do context management for bigger conversations and bunch of metadata fields started coming in per customer.
I don't hate AI companies. The key value proposition is gather data > feed it to AI for semantic analysis (does the actual work, is a prompt) > display it in a UI
Without using agnost, what are some basic SQL queries I can run on my data to find outliers I'd otherwise be missing?
How far can I get with just keywords, common phrases, boring traditional analysis?
Depending on what I measure there, when is the right time for me to consider upgrading to something like Agnost/what is a specific example of what it will find that traditional/rigid analytics approaches will miss?
keywords and sql rarely work - you can not find the repeated hidden feature requests, cause we don't know them at the first place yet, or a frustrated user puts vague signals as ugh, ahh, or just an 'f!' (and added modalities, accents and languages makes it much more challenging)
interestingly, even embeddings seem to bucket "no" and "nooo!" somewhat similar, but are pretty different when viewed from a user satisfaction perspective.
A sweet spot on moving to Agnost is the time when you get higher inflow of conversations you can't manually read or listen, and want to clusterize them into things which matter, with the outliers highlighted
> Rageprompting
Lovely name! I implemented profanity monitoring in my Hermes setup to identify "learning opportunities" for my agents. It is quite useful. If you are budget-conscious, one challenge is determining what is the smallest number of previous rounds that Hermes needs to correctly infer what it did wrong. Curiously, Claude Code is horrible at figuring out what it did wrong. I often read its memories, and they are rarely useful.
haha yea, i even got the domain rageprompt dot com like a couple of days ago lol i love the name too.
for profanity, did you define keywords or just let the agent figure out rage stuff?
how many rounds did you set for the hermes? claude doesnt work yea on its own, one of my friends set us up for their claude lol
Well, good luck with the launch, this seems like an interesting product with potential.
However privacy is central in a service like this and I think you should probably beef up your representation of how you deal with that.
eg. "We use each customer’s data only for that customer" - well that customer may have hundreds of staff; how are they being consulted and onboarded wrt their own voices (or is that transcripts?) and messages being used in this way?
ofc you might argue that nothing in work is private but I do think you have some margin for improving the detail here.
why would i pay $499/month for this when codex costs $199/month and can do everything you described
codex is great for like a one-time/overview analysis on a handful of transcripts. we usually serve to companies where the volume is >10k messages & continuous ingestions + with claude/codex it messed up this + metadata linking of the user like what plan are they on, when is it expiring, etc.
although we had a few customers who come to us after running this for a while so at smaller volume it does work well.
i mean i would get codex to build everything you just described
Do it then.. the hubris of vibecoders is really something.
Would you?
Looking forward to your "show HN" post.
lol true but then you’re just building another us :D
I thought startups wrapping prompts would require something a more complex than semantic analysis, which is literally what this is. And for 500 bucks. Wow. Props for being able to sell this.
I don't get the appeal of the UI, why is it so complex/convoluted.
lol i wish it was just wrapping prompts but things got harder once our customers grew bigger, we had to build queues. we had to do context management for bigger conversations and bunch of metadata fields started coming in per customer.
It's still a prompt, it's just not a static one. Either way props for building a company from it.
we're still learning and so our the prompts haha, whats your take though
How is it just a prompt? Like hey, I hate AI companies with a passion but I think this is a lot more than just a prompt.
I don't hate AI companies. The key value proposition is gather data > feed it to AI for semantic analysis (does the actual work, is a prompt) > display it in a UI
on a satirical note: we also have an mcp server/api endpoint if you dont want the ui