... data stays mine, and I can drop into Python whenever. What's there today: - AI Loop for equity, options, and cross-sectional strategies - Monte Carlo significance testing (p-values, deflated Sharpe) so I can tell real edge from overfitting - Visual Flow Builder for non-coders - Paper and live trading via Alpaca Excited to be here. Would love to meet other indie builders in fintech, dev tools, or AI. Also genuinely curious: what's worked for you in pre-launch hustle ...

... Here's the dirty secret of trading: almost every "edge" you see online is fake. Not intentionally people just don't know how to test properly. They overfit to historical data. They don't correct for multiple testing. They cherry-pick the one timeframe where it worked. They look at in-sample results and think they found something. I saw this from the inside. I'm a quant I spent years watching even smart traders make the same statistical mistakes ... ... over and over. And the tools out there? They let you do it. Pine Script doesn't stop you from overfitting

... actively renewing assumptions. Curious to hear from this crowd: What part of your AI system needs the most refreshing today? Comment from Ghost Kitty(@ghost-kitty-9381936): Comment Deleted Comment from Musa Molla(@musa_molla): @dougli   Prompt overfitting is a great way to describe it. Prompts silently bind themselves to model behavior, so upgrades can regress quality even when capability improves. This is why evals need to track distribution shifts, not just accuracy. Comment from Musa Molla(@musa_molla ... ... review outputs to make sure quality hasn t shifted. We use a synthetic data pipeline, but even small changes in how a model writes can subtly change the output distribution. A lot of this feels like prompt overfitting