Tell us three things: 1. What you do (job, industry, student, career-changer, whatever) 2. What brought you to Clief Notes 3. One thing you're trying to figure out right now related to computing or AI I'll respond to every single one. And read each other's intros too because the person who's stuck on the same problem as you might already be in this thread. I'll go first I am Jake, I have been working in tech for 15 Years, building with Generative AI for 3 Years straight now! Excited to teach and learn! That's it. Simple, scannable, gives you data on who's joining and what they need, and keeps the feed clear for content that retains people past week one.

For the last couple of years, I spend two weeks each year teaching AI fundamentals, patterns and solutions to aspiring cloud architects from this one FAANG (or MAANG, MAMAA, what have you). The syllabus is already set, but I make it a point to add value in each cohort by imparting relevant, but game changing advice, particularly relating to their upcoming certifications and examinations. TLDR; this year, the cohort is a little more engineering-savvy. Most engineers tend to overthink problems and over-engineer solutions, in addition to being susceptible to shiny object syndrome. This is relevant to most of us who build on shiny new tech. We are often distracted and tempted to dive into solution building before we the problem is understood. We end up in a spiral refactoring or building the next feature, not quiet knowing when to stop. The game-changing advice I had for them was ask (before diving into architectures, and prescribing solutions): "what is the problem statement that best captures this question or issue?". 1. The problem in broad terms: what is it that you can't do or can't do well enough? What needs to change or improve? 2. Scope or criteria: what aspects of the organisation or problem needs solving, and what is the criteria for done? 3. Break it down into sub-problems if needed: articulate the problem statement, scope and criteria for each one. 4. Avoid producing any architectural diagrams, or committing to solutions and components before the problem statement is clear and unambiguous. 5. If unclear or ambiguous, keep questioning until clarity emerges. Now, this two-week intensive program is intended to prepare these cloud architect aspirants with most of the basic knowledge about data engineering, as well as cloud and AI solutions. It is highly modular, but the the syllabus don't encourage application or synthesis of knowledge across domains. My solution to this was a hypothetical case study drawn from real-world use-cases: an early stage startup has had some traction with their B2C SaaS. They now have 20K monthly active users globally, but have been receiving complaints and feedback from users in EU of a lack of GDPR compliance. Their web-based app is currently hosted on a different cloud provider and running a particular stack. It needs to be GDPR compliant.