Embedded the first frame with an encoder then arranged in 2d with UMAP

👋 Hiring teams working on 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁/𝗩𝗶𝗱𝗲𝗼 𝗔𝗜, 𝗜𝗘, 𝗼𝗿 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗖𝗩—I’m open to full‑time or freelance. DM or [email protected]

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: surfaces the weird, rare, and important—so labeling targets 𝘀𝗵𝗼𝘄 𝘂𝗽 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 and you get 𝗯𝗮𝗹𝗮𝗻𝗰𝗲𝗱 𝘁𝗿𝗮𝗶𝗻/𝘃𝗮𝗹/𝘁𝗲𝘀𝘁 𝘀𝗽𝗹𝗶𝘁𝘀 without scrubbing terabytes by hand.
𝗦𝘁𝗮𝗰𝗸: fastdup · UMAP · H.264 metadata
𝗨𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: anomaly mining, active learning, dataset curation for industrial vision.

𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝘁 (𝗥𝗜𝗢𝗦 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀):

1. 𝗞𝗲𝘆𝗳𝗿𝗮𝗺𝗲𝘀 from 15s clips
2. 𝗦𝘁𝗿𝗮𝘁𝗶𝗳𝗶𝗲𝗱 𝘀𝗮𝗺𝗽𝗹𝗶𝗻𝗴 over H.264 stats (oversample rare quantiles)
3. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 + 𝗰𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 (fastdup)
4. 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝘃𝗲 𝗽𝗶𝗰𝗸𝘀 per cluster → high‑signal frames
5. 𝗨𝗠𝗔𝗣 visualizations to audit coverage & outliers

🟢 𝗢𝗽𝗲𝗻 𝘁𝗼 𝗪𝗼𝗿𝗸 — 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 (𝗥𝗲𝗺𝗼𝘁𝗲 / 𝗔𝘂𝘀𝘁𝗶𝗻) → rexdouglass.com

𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗳𝗶𝗻𝗱 𝗮 𝟭‑𝗶𝗻‑𝟭,𝟬𝟬𝟬,𝟬𝟬𝟬 𝗲𝘃𝗲𝗻𝘁 𝗶𝗻 𝟭𝟬,𝟬𝟬𝟬+ 𝗵𝗼𝘂𝗿𝘀 𝗼𝗳 𝘃𝗶𝗱𝗲𝗼?
You 𝘀𝘁𝗿𝗮𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝗵𝗮𝘆𝘀𝘁𝗮𝗰𝗸.

#ComputerVision #ActiveLearning #DatasetCuration #MLOps #VideoAnalytics #Manufacturing #AppliedScience

🟢 𝗢𝗽𝗲𝗻 𝘁𝗼 𝗪𝗼𝗿𝗸 — 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 · Remote / Austin
🎞️ 𝗦𝗹𝗶𝗱𝗲𝘀 tinyurl.com/yxr7k5f8

🚧 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
Completely unsupervised segmentation + labeling of parts, tools, and people in manufacturing. I demo it on a random “How It’s Made” fire-extinguisher video.

🧰 𝗦𝘁𝗮𝗰𝗸
DINO · SAM-2 · Gemini · YOLO

📜 𝗕𝗮𝗰𝗸𝘀𝘁𝗼𝗿𝘆
This is the POC I built to land an offer at RIOS. I inferred their internal pipeline and automated the whole thing—end-to-end in ~10 days. The work is paused while VC funding sorts itself out, but it was a blast and we were on track to productionize and scale quickly.

🧠 𝗠𝗲𝘁𝗵𝗼𝗱
LLM drafts a scene narrative
LLM proposes label vocabularies
Grounding DINO generates bounding boxes
LLM filters boxes
SAM-2 propagates high-confidence masklets
Distilled YOLO runs at the edge
(Definitions—scene, frame, object instance, masklet—standardize the units.)

🟢 𝗢𝗽𝗲𝗻 𝘁𝗼 𝗪𝗼𝗿𝗸 — 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 · Remote / Austin
🎞️ 𝗦𝗹𝗶𝗱𝗲𝘀 tinyurl.com/yxr7k5f8

🚧 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
Completely unsupervised segmentation + labeling of parts, tools, and people in manufacturing. I demo it on a random “How It’s Made” fire-extinguisher video.

🧰 𝗦𝘁𝗮𝗰𝗸
DINO · SAM-2 · Gemini · YOLO

Help! ─ 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 open to work ─ Remote/Austin → www.rexdouglass.com
With new tools, I've been cooking:
▸ Machine-vision pipelines — RIOS Intelligent Machines
▸ Information-extraction pipelines — Microsoft
▸ Interactive SMS surveys — Pantheon Insights

Rex W. Douglas PhD Applied Scientist (Remote/Austin)

Looking for full time and freelance projects.

Hoping for somewhere stable. I've never been more productive in my life, but mass layoffs and funding collapses have been endemic.

Help! ─ 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 open to work ─ Remote/Austin → www.rexdouglass.com
With new tools, I've been cooking:
▸ Machine-vision pipelines — RIOS Intelligent Machines
▸ Information-extraction pipelines — Microsoft
▸ Interactive SMS surveys — Pantheon Insights

Rex W. Douglas PhD Applied Scientist (Remote/Austin)

Looking for full time and freelance projects.

Hoping for somewhere stable. I've never been more productive in my life, but mass layoffs and funding collapses have been endemic.

Portfolio: rexdouglass.com
Resume: rexdouglass.com/Douglass2025...

Some ML Engineer at YouTube must handle just kids home for the holidays unsubscribing their parents from all the right wing channels.

Follow for more pro tips about destroying old hard drives last minute for a move.

90% of machine learning is data cleaning.

Who turned America's military against Americans?

I'm tracking 313+ instances of support/opposition to the use of armed forces in domestic law enforcement in a new dataset:

"Public Positions on Militarization of Domestic Law Enforcement in the U.S."
docs.google.com/spreadsheets...

Public Positions on Militarization of Domestic Law Enforcement in the U.S.
t.co/P5HmsHF1fA

No clue, there are lots of other parameters to the bargaining model of war even before you get to the domestic political constraints. I'm just very annoyed that there's taboo about seriously measuring the costs, and finally got to it.

Just the direct material costs for each additional year of war to Ukraine are about $111.7B or just over half their entire GDP burned per year
🚩$50B (25% of prewar GDP) to wartime defense spending over peace time levels
🚩$61.7B (30.8%) in direct destruction of things/sectors of the economy by Russia

This is absolutely nuts:
🚩30% of Ukraine's population is either internally displaced, a refugee outside of the country, or under Russian occupation.
🚩About 14% of the population's homes have been destroyed. Each additional year of war destroys about another 3%.

Each additional year of war in Ukraine implies an additional:
7,953 Civilians Directly Killed
12,822 Civilians Directly Injured

The bargaining model of war only has a few parameters. I'm often annoyed that the cost of fighting receives less serious measurement than military weapons/strategy. Then I remembered I pay $700 a month for LLMs, so here's the first in a series on Ukraine's costs from fighting.

Please reach out if you ever want to talk shop about the intersection of
-manufacturing
-machine vision
-generative AI
-and automation/robotics