... notetakers are dumb... like, they just transcribe and summarize... I'm trying to build something that's actually an online assistant that, being wired into your calendar, emails, and knowledge base, and with that it can actually support you during meetings with things like: - live capture tasks - find available times for the next meeting - live capture tasks (and even prepare prompts before and hand off prompts to Claude before you hang up Do you guys have any other cool ...

... conviction, and produces the same fix with the variable names shuffled. The apology is what makes it maddening. It saw the failure. It acknowledged the failure. Attempt five is already running. The explanation isn't that the model is dumb. It's that the model is stateless: every turn, it re-reads the entire session transcript, and that transcript is the whole world it can see. Count what's in that world after four failed attempts. The dominant pattern ...

... mobile logging, full CSV/JSON export, $39 once, no subscription ever. Nothing exists yet, just this idea and a waitlist (link below). Would genuinely rather kill it now than after a month of building, so tell me if this is dumb. If you already track workouts: what do you use, what s actually annoying about it? https://setflash-waitlist.vercel... ...

... same accuracy. One survey of 650 tech leaders found 78% had an AI agent pilot running and only 14% had actually scaled one to real, org-wide use. The pilots aren't failing because the models are dumb. They're failing because "smart on a benchmark" and "reliable with a real user" are not the same property, and we keep buying the first one hoping it's the second. The failure mode that should scare you isn't the crash ... ... transcripts starts accurate and then decays, not because the model changed but because your users did. Which means it has to be refreshed from live traffic on a schedule, or in a year you have rebuilt the exact lab-versus

... police them without triggering churn. So they push the responsibility back onto you. "Generate engagement." "Improve your sending practices." "Here's a documentation link." This is why their support is so bad. It's not because the agents are dumb. It's because there's no actual product to point you at. Their deliverability team is a cost center keeping the platform's IP pools alive not a service designed to make you successful. You walk away from the ticket ...

... people who actually matter: family, close friends, the relationships that get forgotten in the noise of work tools. The problem: I kept forgetting birthdays, missing check-ins, losing track of conversations. Notion is too cold. Reminders are too dumb. Salesforce-style CRMs are absurd for personal life. So I built Vellaci Notion-inspired, AI-first, Swiss-made: Log a Moment: capture meetings, calls, voice notes in seconds Calendar: birthdays, anniversaries, and AI-suggested check-ins Trilingual (FR/DE/IT) built for Switzerland ...

... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor pricing pages. We guessed. Here's what we missed: Procurement cycles. Enterprise deals take 6-12 months. We had 30-day sales cycles. We weren't built for that. Security reviews. We needed SOC2. We didn't have it. Customers asked ... ... using. They "sounded interested." That's not the same thing. Your rule "no enterprise features until someone pays first, not asks" is exactly right, and it applies to every feature at every tier. The 47 API rate limit requests vs

... literally in the docs" also me: answering the same question for the 6th time So I gave up. Instead of trying to make people smarter, I made the software dumber. Like, you-can-just-talk-to-it dumb. So I built pitch31.ai . You drop in an OpenAPI file or Postman collection, and it spits out a little AI agent that speaks human. You can type stuff like: Add a new user with admin rights Get all invoices from this month ...

... from it rather than just relying on it to do everything for us. One way I make sure I stay engaged is by not just accepting AI s first response. Instead, I challenge it asking follow-up questions, seeking alternative perspectives, and verifying information. This keeps my critical thinking sharp and helps me refine my decision-making. How do you personally balance AI use while ensuring you stay engaged in the learning process? Comment from Mark Maxwell(@john_melano07): @busmark ... ... conversation partner rather than just an answer machine can help keep our critical thinking sharp. I try to do this when working on my site if AI generates content, I question it, tweak it, and even ask for alternative