sumanyusharma

Congratulations on the launch. Few qs:

How do your agents decide a suspected issue is a validated vulnerability, and what measured false-positive/false-negative rates can you share?

How is customer code and data isolated and encrypted throughout reconnaissance, exploitation, and patch generation (e.g., single-tenant VPC, data-retention policy)?

Do the agents ever apply patches automatically, or is human review required—and how does the workflow integrate with CI/CD to prevent regressions?

Ty!

Appreciate the info; I'll double-check Fidelity again!

I'm actually pretty interested in what you're building. Sure, Vanguard and Fidelity are well-established giants, but they've barely moved beyond standard ETFs for decades. Having the option to tweak weightings at a more granular level and do daily tax-loss harvesting at scale seems like a genuine step forward.

I also like that you're transparent about how you might eventually introduce additional revenue streams like margin lending or maybe even PFOF. Knowing that upfront is better than a sudden terms-of-service surprise down the road. Still, I'd hope you'll consider giving users some say over how their shares are handled — like opting out of lending — so your incentives stay aligned over the long run.

Congrats on hitting $10M AUM. I'm rooting for more low-fee alternatives that keep the user in the loop!

No plans for acquisition :)

Building product, talking to customers and making something people want!

We're focused on end-to-end evals focused on function-call accuracy, style, tone & latency of the conversations between our sims and your voice agent. Less focused on pure TTS evals at the moment!

Likely outsourced call centers since call complexity is low to medium. We also expect rapid adoption in industries like customer service, healthcare, and retail, where 24/7 availability could be high-impact for businesses and convenient for consumers!

Should be fixed now; could you try again please?

We forgot to enable non-US numbers in our config for the demo. (oops)

We're working on a fix right now!

I am curious - how was the team solving this at Kea?

I use Superwhisper (no affiliation, just a happy user), which runs a local Whisper model, to create most of my email drafts and post-meeting notes. I find Whisper more accurate than Mac’s built-in speech-to-text, plus I’m faster at speaking than typing.

Sometimes, I even ‘talk’ into Cursor’s chat window instead of typing. The only downside? It can get a bit annoying for others when you're talking to yourself all day.

I'm curious to learn more about what's blocking the widespread adoption of the LLM capabilities. Lack of knowledge, reliability, or something else?