I have been a content creator in the BJJ niche for a decade and a half now. When I started playing with Ai and Machine Learning my vision was very narrow - I wanted to teach a LLM how to do jiu jitsu! Ha! Not impossible but a task tantamount to it with the depth and breadth of the nearly infinite permutations of the way the human body moves in conjunction with another human's body trying to manipulate it with nearly infinite paths of resistance and compliance. But I digress... 🤓 I saw a friend building a project to set an agent free researching content in a specific niche and then generating short YouTube clips based on its findings. The best part - it was gathering statistics and analytics to use as fuel for self-improvement! He had it set up so each iteration started in a new session and used analytical data from the last to improve upon it. I was fascinated and decided to try to reproduce it myself. bjjDigest.com was born. Today I have a near-fully autonomous Content Creation Engine that I set free on the BJJ community. Calibrated to my "voice" from a decade and a half of my own content - it generates News Articles for real-world Current Events in the BJJ space, with a snarky, sarcastic, dry tone (me). It also writes Satire Articles - I'm pretty proud of this part. As LLMs aren't known for their comedic timing or judgement. The weakest link in this chain (in my opinion) was actually its impetus - the YouTube shorts. And yes - it closes the loop I saw in my friend's project, and honestly this is the part I'd point to first. The research never really stops. Every couple hours it scans the BJJ landscape - news, forums, RSS, event results - and scores what it finds, ranking the topics worth writing about and working out the angle on each one. That scored board is what feeds the pitch generator - nothing gets written that the research didn't surface first. Then the other half of the loop runs backward. It reads its own analytics - what actually landed on YouTube and the sites - and uses that to re-weight what it goes looking for and which topics it prioritizes next. Each run starts in a fresh session and inherits the last run's data, so it's not really "generate content" so much as "generate, watch how it did, adjust, repeat." Slowly, it's learning what this audience actually responds to.

I’m running into a massive bottleneck with corporate clients right now. Because they’re handling market-sensitive data, their IT compliance teams have them completely locked down into Microsoft. No external APIs, no Claude—nothing. Ideal world would be building solutions in CC. I’m convinced the only real solution here is building custom tools and agents natively inside their system (Azure OpenAI, Power Platform, Copilot Studio, etc.) rather than trying to pitch external pipelines they'll just reject anyway. Has anyone actually built robust, advanced agent workflows strictly inside an MS-only corporate environment? Curious to know how you handled the architectural constraints, and if it's worth the headache of building inside their sandbox. Let me know what you've found works.