A good example would be: "My team used Claude Code Opus 4.5 to build and ship an iOS fitness app that now has 10k paying users." This shows that the results of your process found paying customers.
Less helpful example would be: "My team is closing tickets faster than ever" or "I finally finished the novel I have been working on and my friends say it's great!" These are less interesting because they do not give us any insight into the market response.
By making LLMs for people who want to make money with LLMs.
For me though I see ChatGPT take all the hype now. I'm seeing people get more and more bored with that and in quest of a step up or sideways from that.
That goes pretty slow outside of developers people are still trying to come to grips with OpenAI.
All earlier adopters have been builders interested in the technology for tech sake. The real consumers are veeery slow to ramp up.
I know the original email was something like "Alert: you have a new thing: X Thing"
And the new emails are a prompt something like "we know all of this about the user and all of this about the X thing, write an email alerting them to the new thing with these particular goals".
I really don't know much about it so I'm being pretty vague and generic.
She also uses it daily for all kinds of things. For example recording/transcribing/summarizing meetings, creating plans, writing emails, reviewing employee performance, and a bunch of other stuff. If it went away she would be devastated.
We ran into this ourselves when we needed to manage a growing volume of inquiries without scaling our support staff. By using LLMs to generate responses and categorize requests, we not only enhanced our response times but also maintained a level of quality that our users appreciated.
We ended up building Wyshbone to handle sales lead generation and outreach timing, integrating seamlessly with our CRM. This has helped us identify potential leads more effectively and optimize our follow-up strategies.
But i've found that it's just good enough that support and teams can handle addressing the systematic problems while the LLM deals with operational overhead.