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Yesterday I told you about the panic that hit me the night before my son's SAT. I'd given an unidentified company access to my Google account. I had no idea what it could see or do with it. I mentioned this briefly yesterday, but I'd built this before. The agent I built took hours of trial and error to wire up and lived inside Telegram, a messaging app. I gave it access to my Gmail and my Calendar. It could read, draft, edit, and delete emails. It could create and cancel calendar invites. All from a chat window I already had open all day, because I already use Telegram for everything else. No external service sat in the middle. My bot talked straight to Google's own systems, using my own credentials. Every permission it had showed up right on my myaccount.google.com/security page, because I was the one who connected it. That's the real difference between what I built and what instinct.co is offering right now. Instinct hands you a finished product and removes the friction. You just don't get to see who is on the other end of it. Building your own removes the mystery but you pay for it in hours instead. If you want to try your own version, here's where I'd start.
I slept fine the night I built that bot because nothing could go wrong, but because if it did, I'd know exactly who to blame. Alex |
As an AI Coach, Advisor, and Agent Builder, I help organizations and business leaders harness the power of artificial intelligence to boost productivity and streamline operations. I enable organizations to navigate the transformative landscape of AI, educating teams, identifying operational and strategic opportunities with AI and creating a framework for safe and transparent use of data in the organization.
Last week a client texted me a screenshot of her Claude usage meter, hit her limit by 2pm, and said “I asked like ten questions today. How is this possible?” I went looking for the answer, and it led me to a piece by a builder named Nate who tracks his own AI token usage obsessively. He opened his tracker after a long day and found 3.77 billion tokens. Most of it was “reused” input: material he never typed. Why? Because every time you message the model, the LLM is reloading all the previous...
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