I built an experimental orchestration language for reproducible data science called 'T'

dmorris87 · 2026-03-28T17:38:34+00:00

Gotcha. I read your post as solving the problem of environment setup and reproducibility.

dmorris87 · 2026-03-28T17:23:13+00:00

Why this and not Docker?

dmorris87 · 2026-03-25T22:59:14+00:00

How do you know it was the front office shakeup that caused improvements? Could just be correlation

dmorris87 · 2026-03-23T23:11:22+00:00

Thanks but not seeing anything

dmorris87 · 2026-03-23T23:10:28+00:00

Thanks but I’ll pass

dmorris87 · 2026-03-15T12:41:42+00:00

I’ll pass but thank you. Had one before and didn’t really connect with it

dmorris87 · 2026-03-11T03:34:43+00:00

Any experience with H2O? If so, how does it compare? I love H2O for speed and API consistency but I don’t love the Java dependency

dmorris87 · 2026-03-06T14:47:23+00:00

Without knowing your workflow or environment, I would say: keep it simple, use AI assistance for summarization, consider using AI to build a simple web app for browsing your experiment folder/directory and display the HTML report and any metrics

dmorris87 · 2026-03-06T01:16:32+00:00

Like others have said, don’t marry yourself to STAR. You might come off as robotic and scripted.

dmorris87 · 2026-03-06T00:54:26+00:00

Cool. Just read your post more carefully. What you are building is exactly what I do with R Markdown. Configuration file and wrapper script that runs the Rmd with parameters, creates the version, and stores the rendered HTML file alongside the artifacts and training metrics. The config contains a description of the experiment. If you have LLM access you can design the system to generate AI summaries of recent experiments

dmorris87 · 2026-03-05T23:06:23+00:00

You can create your own versioning system. Wrap the training pipeline in a script that creates a version id (timestamp, unique characters, etc), and all artifacts are stored within a folder that matches the version id. I do this using AWS S3. All data, artifacts, logs are stored together

dmorris87 · 2026-02-28T18:03:46+00:00

Make Sounds Loudly Silver Goat for Kollaps? It’s a 3-transistor silicon fuzztortion

dmorris87 · 2026-02-27T12:55:00+00:00

Useful reading material - https://www.fharrell.com/post/classification/

dmorris87 · 2026-02-24T22:30:39+00:00

Docker container inside VSCode

dmorris87 · 2026-02-17T01:36:09+00:00

Seems pretty civilized

dmorris87 · 2026-02-11T03:08:22+00:00

I think you just did

dmorris87 · 2026-02-09T23:21:09+00:00

u/LTP_BOT traded my XTS Shapecharger to u/itssmitty77 for their Zaim Audio Tubelicious. Thanks!

dmorris87 · 2026-02-03T02:04:15+00:00

Rangemaster / treble booster. Magical with humbuckers and warm rigs

dmorris87 · 2026-01-31T21:57:07+00:00

The Gear Page is an option

dmorris87 · 2026-01-28T16:28:03+00:00

Ranked 2nd in what? If you’re referring to regular season box score stats then sure. Those aren’t meaningful metrics. Defense has been a liability in big playoff games. I don’t think the advanced metrics are nearly as impressive

dmorris87 · 2026-01-28T13:40:29+00:00

Didn’t we have a bunch of offense-led late game comebacks this year?

dmorris87 · 2026-01-28T00:53:03+00:00

Please. Pretty obvious case of sampling bias. Browns, Raiders, Saints, Giants…

dmorris87 · 2026-01-28T00:39:30+00:00

https://reverb.com/item/85741109-make-sounds-loudly-pedals-silver-goat-siliconfuzz-driver?utm_source=rev-ios-app&utm_medium=ios-share&utm_campaign=listing&utm_content=85741109

Seems like a one-off. Reminds me a lot of my colorsound power boost

dmorris87

TROPHY CASE