Aleksander Brousseau

About

At GitBook, I handle Content, where I focus on creating and curating high-quality, engaging material that enhances our users' experience. My role involves crafting clear, informative content that not only helps our community understand and utilize our platform effectively but also aligns with our brand’s voice and goals. I’m committed to delivering valuable resources and insights, ensuring that every piece of content supports our mission of simplifying knowledge sharing and collaboration.

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Skim v1.0.22 — "Ask the MX, not the vibes" 🛂

The one where Skim stops pretending that Microsoft owns exactly four domains on the entire internet.

New

  • Work & school accounts are finally recognized. If your company lives on Exchange Online, Skim now asks your domain's MX records who really hosts the mail. Everything Microsoft runs answers on *.protection.outlook.com, so the sign-in button that actually works shows up on its own no more falling into the manual IMAP/SMTP form to guess hostnames that were never going to connect. (Yes, hotmail.de too. Sorry it took a whole minor version to notice Germany exists. )

  • Behind a mail gateway? Proofpoint and Mimecast tenants don't advertise Microsoft in MX at all, so unknown domains now get one quiet "Work or school account?" line instead of a shrug. One line. We counted.

  • The DNS probe goes through the machine's own resolver (DnsQuery_W) your domain never visits a third party, results are memoized, the whole thing is capped at 3 seconds, and if it fails the screen looks exactly like it did before.

Fixed

What is BAD vs. GOOD AI judgement?

Would love to hear thoughts.
Here's my perspective.
I'm against AI slop, but I'm not against AI. I believe AI is a great tool but it also needs greats pilots to use it well. This is what I'm working on. AI Fluency.

Biggest marker of AI fluency is AI judgement. Where and when to use AI and where and when not to.

BAD AI JUDGEMENT

1mo ago

How much should an AI fitness product remember about the user?

I ve been thinking a lot about the difference between personalization and real adaptation in fitness software.

A profile with age, goal and experience level is useful, but it still treats each workout request almost like a new session.

A more interesting model is longitudinal: remember what the person actually completed, which movements and muscle groups were trained recently, how difficult the exercises felt, what restrictions exist, and how nutrition has looked over the previous days.

Then the next recommendation is based on that history instead of starting from zero.

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