ShapeSearch @SearchShape

Your data is worthless if you can’t find it . We turn unsearchable data into searchable data, using extraction, shape search, and magic. OK, maybe not magic. vizseek.com Worldwide and beyond Joined August 2019

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ShapeSearch @SearchShape

a day ago

Our new website is live as of yesterday. Pretty cool. vizseek.com Release the hashtags: #engineering #design #manufacturing #digitaltransformation #ecommerce #purchasing #aftermarket #visualsearch #datamanagement #automotive #injectionmolding #shipbuilding

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ShapeSearch @SearchShape

2 weeks ago

One of our areas for visual search is ecommerce, letting buyers find that thing they need by dragging and dropping a photo. We try to stay out of shipping. #ecommerce #tech

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Restaurant Parts @PartsTown

2 months ago

Stick around, we’ve been building smarter ways to find the right part. 📌✨🗒️ Meet PartPredictor, OEM AutoShip, SnapScan & PartSpin 3D 🚀 #RestaurantEquipment #OEMParts #KitchenInnovation #PartsTechnology #PartsTown

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Rest of World @restofworld

2 weeks ago

A growing number of Indian AI researchers in Silicon Valley are looking to return to India as U.S. layoffs and immigration hurdles chip away at the allure of Big Tech restofworld.org/2026/silicon-v…

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ShapeSearch @SearchShape

a month ago

@RepPatHarrigan We did a successful DoD project that allowed multiple military bases to share 3-D printing information via shape search. You could photograph a broken product, search for matches in the database, create a 2-D drawing, a 3-D model, and then print it. Our funding was abruptly cut.

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ShapeSearch @SearchShape

a month ago

Go home, folks. We have a winner.

tmuxvim @tmuxvim

a month ago

I put a prompt injection into my LinkedIn bio and recruiters are messaging me in Old English and calling me Lord.

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ShapeSearch @SearchShape

a month ago

Very useful compilation.

Tech Soft 3D @TechSoft3D

2 months ago

CAD. CAE. CAM. BIM. STL. The 3D engineering industry loves its acronyms. Keeping track of them all can be a challenge. 😵 We’ve made remembering them all a little easier with a handy reference guide featuring 59 key CAD and engineering terms. hubs.ly/Q04fZKKv0

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ShapeSearch @SearchShape

2 months ago

@MatthewAlbano8 @cosmosarcive At :10, he literally says AT LEAST 2.5 times.

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ShapeSearch @SearchShape

2 months ago

Very cool.

Bilim Dünyası @dunyasalbilim

2 months ago

Basit ve güzel bir anlatımla " tüm çokgenlerin dış açılarının toplamının neden 360 derce olduğunun ispatı. Hiç bir çocuk bu şekilde anlatıldığında bunu unutmaz.

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ShapeSearch @SearchShape

3 months ago

We regret that our visual search software could not see #ArtemisII when it was not visible. Because, you know, it was not visible.

HARRISFAULKNER @HARRISFAULKNER

3 months ago

Breathtaking. #artemisII NOW silent— out of comms, loss of signal for the approx 40 minutes.💪❤️ “Houston, copy we’ll see you on the other side.”

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ShapeSearch @SearchShape

3 months ago

Kind of hypnotic. #Shipbuilding

Ship Building TV @shipbuildingtv

3 months ago

With its massive hull, it challenges the narrow passages… 🚢 Paul J. Martin emerges from behind Comfort Island in Alexandria Bay like stepping onto a stage. A perfect harmony of the sea’s power and human engineering

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ShapeSearch @SearchShape

3 months ago

ShapeSearch: correcting falsehoods on Twitter since 1981.

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ShapeSearch @SearchShape

3 months ago

Anyone else who watched the Challenger live in 1986 having a little trouble breathing watching #artemisII?

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ShapeSearch @SearchShape

3 months ago

This is cool.

internet hall of fame @InternetH0F

3 months ago

Test if you can read at 900 words per minute with this technique

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ShapeSearch @SearchShape

3 months ago

@mechanical_4u RUD. Rapid Unscheduled Disassembly.

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ShapeSearch @SearchShape

3 months ago

@LensScientific Very cool. And all this time we thought those lines in the sky were contrails.

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ShapeSearch @SearchShape

3 months ago

@Jane_Doe82 @atrupar @Make_US_Equal Norsun (Norway) Hyundai (Korea) Roche (Switzerland) Samsung (Korea) Siemens (Germany) Paris Baguette (Korea) (hah) (no really) Sanko (Japan) LG (Korea) Hindalco (India) All building plants in various places in the US right now; partly to avoid the chaos of arbitrary tariffs.

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ShapeSearch @SearchShape

4 months ago

Important to have software that works from day one 🤣 #Engineering #Software

Nature is Amazing ☘️ @AMAZlNGNATURE

4 months ago

I like how Sheepdogs come with the default software installed

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Ihtesham Ali @ihtesham2005

4 months ago

🚨 Stanford researchers just exposed a weird side effect of AI that almost nobody is talking about. The paper is called “Artificial Hivemind.” And the core finding is unsettling. As language models get better, they also start sounding more and more the same. Not just within a single model. Across different models. Researchers built a dataset called INFINITY-CHAT with 26,000 real open-ended questions things like creative writing, brainstorming, opinions, and advice. Questions where there isn’t a single correct answer. In theory, these prompts should produce huge diversity. But the opposite happened. Two patterns showed up: 1) Intra-model repetition The same model keeps producing very similar answers across runs. 2) Inter-model homogeneity Completely different models generate strikingly similar responses. In other words: Instead of thousands of unique perspectives… We’re getting the same few ideas recycled over and over. The authors call this the “Artificial Hivemind.” It happens because most frontier models are trained on similar data, optimized with similar reward models, and aligned using similar human feedback. So even when you ask something open-ended like: • “Write a poem about time” • “Suggest creative startup ideas” • “Give life advice” Many models converge toward the same phrasing, metaphors, and reasoning patterns. The scary implication isn’t about AI quality. It’s about culture. If billions of people rely on the same systems for ideas, writing, brainstorming, and thinking… AI might slowly compress the diversity of human thought. Not because it’s trying to. But because the models themselves are drifting toward the same answers. That’s the real risk the paper highlights. Not that AI becomes smarter than humans. But that everyone starts thinking like the same machine.