Matt | MechE→ML @QuiverAndError
MechE → ML. Stage calibration & metrology. Building in public. DMs open. Joined August 2025-
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AI Copilot for hardware/R&D engineers: Prompt the interface with info (language-based or geometric data) about a design/challenge/test, and the service designs experiments, analysis scripts, loans test equipment, DAQ tools & sensors, jigs, etc -- all shipped out next day. Build a moat around successfully executed projects and productize test report templates, electronics/optical toolkits, etc
@KhadarHOsman1 @probnstat Almost (I think)--CLT is about the distribution of estimates approaching a Guassian. A consistent estimator is about convergence to the true value.
@probnstat I’ve used it in the context of Bayesian optimization for 3D printing… it can quickly add A LOT of dimensions to your dataset, though.
@deredleritt3r Definitely a good sounding board, at minimum
@haydenfield This will be awesome. Job descriptions should be posted with a second, behind-the-scenes description intended for LLMs to understand the needs of the company/role. And the same for candidates when searching for a role.
@JamesIvings For me it has been a tool that enables me to attempt and solve more difficult problems, rather than produce more commits.
@MicahJanke @sama @OpenAI Or delete/undo a prompt. Like ask the quick side question, get your answer, then undo and resume the main convo thread.
@hyhieu226 Easier to write a recipe than to cook the dish
@MechanizeWork $500k for a few years should buy a little bit of runway when you develop yourself out of a job, right?
@TivadarDanka Can you suggest any less expensive (used) textbooks—preferably with ML/robotics applications in mind?
More dots = more data… but also more time ⏱️ A denser grid can reveal stage errors, but does 4× the data really mean 4× better calibration? Spoiler: not usually. I’ll simulate that soon 👇 Follow for more on #Python #MachineLearning #DataViz #Metrology
Tighter grids = more data. With more dots, subtle stage errors become clearer — you can see bias accumulate across the field:
Up next: better stage error models (right now the implementation is clunky), plus calibration methods to estimate and correct these errors. Follow for more #Python #MachineLearning #DataViz #PhysicsTwitter — I’ll be sharing as I build 👇
Even small non-orthogonality (say, axes 89.9° instead of 90°) blows up into huge errors at the edges. This is why calibration isn’t optional.
Ever wonder how precise machines really are when they think they’re moving in straight lines? 🤔 I’ve been building a Python simulator to explore calibration, stage errors, and measurement accuracy. Demo thread ⬇️ #Python #MachineLearning #Metrology
Fortunately, squareness error is one of the easier ones to calibrate. It doesn’t hurt repeatability, and its linear nature makes it predictable and correctable. Here is what pure squareness error looks like (the y-axis is off 0.1° counterclockwise):
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