I created this community from the lack of communities that discuss brand new techniques for creating complex systems that withstand the harsh test that society has for AI solutions. Let's face it... AI already has a controversial name. Right now we are split between hyper optimistic AI engineers, rightfully so (that was ironic), and the hyper pessimistic majority of the world. That means I will be creating courses and resources in this community that dive way deeper than explaining the 10 best nodes in n8n... I will be creating videos and resources that will help you take your AI engineer skills to the next level, which means, the competency to look at ANY system in the world, and having the ability to build a custom AI powered system that can 10x it. Recently I have been creating a production ready estimator for one of our clients and it has gotten me diving deep into operationalizing evaluation systems, that make it easy to twist and turn the knobs to get your system to perform up to its intended use 100% of the time. So I leave the decision up to you guys, would you want to see a course on Algorithmic System Building OR Evaluation System Building ➗ Algorithmic System Building: Building algorithmic systems takes fundamentally probabilistic LLM technology, and forces it to be more deterministic without losing the core strengths that AI gives you. This is all about looking at a problem that a business brings to you, and creating a repeatable equation and weighted scoring system that allows the LLMs to take messy unstructured data, and turn it into weighted variables that are plugged into an equation that ultimately makes the decision for the business. This is how to build a system that reasons like a human but executes like a calculator consistent, tunable, and reliable every single time. 📝 Evaluation System Building: A course on how to think and write evaluation tests so that you can guarantee to clients that you will get the system working to a measurable output. Building evals has been identified by Sam and Dario (open ai + anthropic hotshots) as one of the most important skills for engineers. They’re how you prove your build works instead of hoping it does. Without evals, you’re flying blind you don’t know if your estimator, agent, or workflow is getting closer to “accurate” or just spitting out pretty answers. An eval takes the goal you want (like predicting cost, scheduling correctly, classifying a task) and creates a measurable test that tells you exactly how far off your system is from perfect. It’s the difference between guessing and engineering.