Cannot say this enough Working in robotics right now is probably one of the highest ROI bets in history It’s like web in the 90s, mobile in 2007…

Love this explanation on why AI models always generate outputs that seem good but don’t get anybody excited. This is the main reason why custom models are the future. A one-size-fits-all solution actually fits nobody

ODML is tricky, MLX-> CUDA is a great first step.

When you fine-tune your AI model without a “map”, it will wander. Random tweaks = random outcomes. TrainLoop gives your fine-tuning process structure, direction, and control - so you land where you intend, not where the currents take you

The intern now runs one mile every day at 7 am with Mason Pierce and comes to the office by noon. We'll update when he hits the 1.1-mile target.

So many good-looking things in one frame. Also, here’s our new office in North Beach. If you’re around, come say hello.

Model evaluation is the broccoli of Machine Learning. 🥦🥦 At least we made it simpler -- our evals framework is now open-source → github.com/TrainLoop/evals

The coffee machine is in the house! And we have started running some experiments. We are a Research Lab after all!

How accurate is your model? Before you think about it, Here's another one - what is 'accuracy', really?

New office at @trainloop_ai Comes with cardboard and bubble wrap

Have you ever spotted a four-leaf clover? 🍀 We’d love to hear your story if you have!

Remember the last time someone brought up "Mean Squared Error" in a conversation, and you nodded your head like you knew what it meant? Learn the concept in depth -- trainloop.ai/blogs-landing #learn_the_basics_with_trainloop

AI model training isn’t about getting lucky -- your competitive advantage depends on it. With TrainLoop fine-tuning, you trade chance for control.

Precision vs. Recall 🤔 Always confused between the two? You’re not alone. We broke it down in plain English + a 2-min “Wild Fire” game 🔥 After this, you’ll never mix them up again : 👉 trainloop.ai/blogs/importin…

With TrainLoop , AI model training outcomes are predictable, repeatable, and sustained --- not blind trial-and-error.

Cut through the AI model post-training confusion with TrainLoop.

A bite-sized explainer on how LLMs learn - end to end. Core ideas without the math. Follow TrainLoop for our plain-language blog series that dives deeper into each concept.

New on the TrainLoop blog: MAE, MSE & R² — Making Sense of Model Errors We break down Mean Absolute Error (MAE), Mean Square Error (MSE), and R-Squared -- three core metrics that shape how we judge model performance. Link to blog -- trainloop.ai/blogs/simple-e…

Only the best companies use Superset had a lot of fun onboarding TrainLoop. If you are a cracked MLE and want to jump on a rocket ship with crazy high talent density hit them up 🚀

🧵 Labs and VC's are throwing cash at RL environments, especially for computer and browser use. Yet, with just 4 customers and over 30+ vendors, is cloning every website in the world really the path to scale? of course not. Introducing TRACE: Trajectory Recording and Capture of