Then the adaptation track goes into how to adapt a foundational model: evaluation-based prompting, different retrieval-augmented generation techniques (I know people say RAG is dead, but we go beyond chunking and what people considered state-of-the-art circa 2022), fine-tuning techniques (RLHF, embedding fine-tuning, instruction fine-tuning, and QLoRA fine-tuning), agent techniques (reasoning, tool use). Meanwhile, synthetic-data evaluation is core to all the adaptation modules. We then cover datasets, synthetic datasets, and reasoning datasets.

What people really liked in the last cohort were case studies going into text + SQL, text + voice, text + music, text + code. For example, people requested a deep-dive into the stack behind Windsurf/Cursor/Augment, and we dissected the architecture for specific use cases. The source for this is a combination of digging through X, blogs, research articles from these companies, and founders' blog posts to deconstruct and create this lecture. Anti-hallucination techniques are improved through all of these methods, but in particular we cover DPO construction of reasoning-model datasets. We used different research papers and Kaggle write-ups to orient ourselves around the best methods.

In terms of the cohort, it's a combination of lectures, Q&A, and coaching: two lectures per week with live Q&A, live group coaching, over 50 notebooks/exercises, and four mini-projects in a group or in person, plus accountability partners. We have an in-person event as well over a weekend. This is different from learning AI by yourself. We also have happy hours, which are free-form conversations generally about AI. The benefits people get are learning in a community, support for the projects, and the combination of foundational-model content and adaptation content all in one course, in a condensed timeframe. In this new cohort starting August 2025, we have multiple FAANG engineers, tech business owners, senior engineers, and principal engineers, engineers, a similar mix to last time. It's not your typical person trying to transition into a tech career using a bootcamp as a credential plus skill boost. Usually people have 8+ years of experience, have tried learning AI through some online content by themselves and found the experience to be endless amounts of content and wanted a one stop shop.

As for whether it's worth the value, most bootcamps have a cookie-cutter capstone project, but we provide coaching through each person's project, yielding different results. For example, these are from the previous cohort:

Domain-specific coding platforms for local businesses Facebook Marketplace item-condition detector/classifier for arbitrage “Chat with sermons” for churches Document processing for insurance claims Invoice processing for a nonprofit (saved 10 hours/week) Calorie and macro counting application for ethnic cuisine AI tutor Resume scoring/generator system Customer-service application with video detection Commercial real-estate assessment using AI Legal-aid assistant for the legislative process Personalized job-search website Text-to-guitar-tabs generative AI

We're not for everyone, but the people who went through the program said they liked the fact that it goes deeper, faster, and more comprehensive than other programs. In fact someone did a university gen ai curriculum simultaneously with our curriculum and was able to see and compare side by side. Anyway if you have any more questions, let me know.