a16z published a piece last week arguing that everything at work is being recorded, and that companies who build AI on top of that context layer will win.
Compelling thesis. But it mostly frames recordings as a memory and retrieval problem. I think the more interesting question is what people are actually doing with them beyond transcripts, summaries, and action items.
I've seen teams use recordings for onboarding, async decision reviews, even as a source of truth when things go sideways. But I'm curious what's happening in practice.
A few things I'd love to discuss with the community:
I tested locally for two days. Everything worked. I deployed, and within an hour three users reported the same issue.
The difference was environment variables. One flag was set in development and not in production, and the code had a silent fallback that made it look like it worked when it didn't.
This all goes back a bit to the era of NFT, virtual reality, when, during COVID, we went to virtual events, had VR glasses and promised ourselves that we would spend more time in some digital parallel reality online.
Well. So Facebook, with a growing portfolio and a new vision, renamed itself Meta.
The App Store shows downloads, revenue, crashes. Fine as a pulse check. Not enough if you care what people do inside the app: which paywall they hit, whether they finish a session, whether a feature even got used.
For Briskly I built a small event tracker and a private dashboard for that. It works. I m just not sure it s the smartest use of solo time, or if there s a simpler setup most people use.
They help with launch copy, visuals, outreach, follow-ups basically most of what used to take days can now be done in hours.
But I keep thinking about something. Are AI agents actually improving product discovery on Product Hunt or are they just making launches look more polished?
Yes, AI speeds things up. You can test messaging faster, create better assets, prepare more efficiently.
Our tiny team got completely flooded with support tickets during our last major update. Critical bug reports ended up buried under simple stuff like password resets and basic account questions.
Setting up basic auto replies for common issues and organizing tickets by tags made a huge difference. It cut our response times way down and saved us from hitting total burnout.
Three things went wrong on Arteza this year and not one was reported by a user.
A spend function wrote bad balance events, so the ledger drifted and the cap I thought I had wasn't the cap. Nothing threw, nothing alerted. Our sign-in gate promised a credit amount that didn't match what the account actually got, and I only found it by opening every modal in the product one at a time and reading what it promised. And before launch I said the failure I expected from 111 one click apps wasn't a crash, it was an app returning something plausible and slightly wrong that nobody would bother to report.
Lately I ve been noticing something in my workflow:
Sometimes I jump into prompting AI before I fully understand the problem itself. Or I use AI to reduce friction instead of slowing down and thinking more deeply. And because the output often sounds convincing, verification can become weaker too without noticing.
