Satya Mallick ✓

@LearnOpenCV · San Diego, CA · joined 08 Jun 2008

CEO, https://t.co/CzUdJlxzJM. CEO, https://t.co/et3i4a7oGF Entrepreneur. Ph.D. ( Computer Vision & Machine Learning ). Author: https://t.co/olraDEG5Ue

14 738Followers

808Following

3 836Posts total

1.3MViews on collected posts

Ultimi post

Satya Mallick ✓@LearnOpenCV8 Sep

Quick and dirty segmentation training (no human annotations) with GPT-6 Astra Ultra https://t.co/cgFRsCuUGt 7K views · 3 likes · 0 reposts · 1 replies Open on X →

Satya Mallick ✓@LearnOpenCV8 Sep

1/20 I trained two segmentation models using GPT-6 Astra Ultra as the orchestrator with no human labels supplied. My prompt was very lazy too as I had to catch a flight. RF-DETR-Seg-M predictions: Left: SAM3 training masks Right: Astra training masks Both videos held out. https:

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31.5K views · 186 likes · 11 reposts · 5 replies Open on X →

Satya Mallick ✓@LearnOpenCV7 Sep

Also, the instance segmentation is insane! https://t.co/fveFjXPWbi 23K views · 24 likes · 0 reposts · 2 replies Open on X →

Satya Mallick ✓@LearnOpenCV7 Sep

Near perfect instance segmentation by GPT-6 Astra Ultra. This is insane! Prompt used "Segment all microduck robots. I want a different colored back for each one of them." https://t.co/F28Ndf3Kc3

26.8K views · 2 likes · 0 reposts · 1 replies Open on X →

Satya Mallick ✓@LearnOpenCV7 Sep

@LearnOpenCV 7.8 million tokens to do what a 10-line OpenCV color mask and Kalman filter does at 120 FPS on a Raspberry Pi is peak modern AI irony 13.3K views · 108 likes · 5 reposts · 3 replies Open on X →

Satya Mallick ✓@LearnOpenCV7 Sep

Of course you cannot use this model directly. But can you use it for annotation? 41K views · 38 likes · 0 reposts · 6 replies Open on X →

Satya Mallick ✓@LearnOpenCV7 Sep

I used GPT-6 Astra Ultra to track a tennis ball. The results are impressive until I tell you the following. Ball annotation, using 3 parallel agents took 8m 35s and 17,611 tokens. Complete workflow: 11m 49s and 30,481 tokens. The complete workflow also processed 313,914 https

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1.1M views · 1.9K likes · 153 reposts · 161 replies Open on X →

Satya Mallick ✓@LearnOpenCV6 Sep

Not sure what's wrong. 171 views · 1 likes · 0 reposts · 0 replies Open on X →

Satya Mallick ✓@LearnOpenCV6 Sep

Every time I visit IIT Chennai, I am reminded of the beautiful coexistence of humans and wild animals within the campus. The whole campus is inside a forest and animals roam freely. Most people use bicycles and cars drive at 20 kilometers per hour or less https://t.co/IPsZUtcwj 586 views · 6 likes · 0 reposts · 2 replies Open on X →

Satya Mallick ✓@LearnOpenCV5 Sep

The best way frontier model companies can help accelerate open source software development is by donating large (say $1M) token donations to open source organizations. 545 views · 1 likes · 0 reposts · 2 replies Open on X →

Satya Mallick ✓@LearnOpenCV5 Sep

My first task using Astra. Fingers crossed. https://t.co/eq4xN3K0oT 4.5K views · 23 likes · 0 reposts · 1 replies Open on X →

Satya Mallick ✓@LearnOpenCV4 Sep

Robust Face Alignment at Extreme Head Angles Demoed HRFFA by PINTO: a fast, high-angle face alignment model that holds up at extreme poses. Runs ~20 fps via OpenCV DNN with ONNX Runtime. Uses a head detector (DEIMv2, HGNetV2 or DINOv3 backbone), then a CNN or tiny ViT aligner. h 9.1K views · 86 likes · 11 reposts · 0 replies Open on X →

Rispetto ad account della stessa dimensione

12 post degli ultimi 90 giorni, accanto alla fascia di 10K–100K follower. arriva a molti, ma pochi di loro reagiscono.

Visualizzazioni mediane11 210questo account924mediana per 10K–100K

Copertura, %76.06%questo account3.62%mediana per 10K–100K

Interazione, %0.54%questo account1.52%mediana per 10K–100K

Metrica	Questo account	Mediana per 10K–100K	Rapporto
Visualizzazioni mediane per post	11 210	924	12.1×
Copertura (visualizzazioni ÷ follower)	76.06%	3.62%	21.0×
Tasso di interazione	0.54%	1.52%	0.35×

Altri account di questa fascia → Confronta con un altro account → Come sono costruiti questi parametri →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

9.1K4 Sep

4.5K5 Sep

545

5866 Sep

171

1.1M7 Sep

41K

13.3K

26.8K

23K

31.5K8 Sep

Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.09%4 Sep

0.53%5 Sep

0.55%

1.37%6 Sep

0.58%

0.21%7 Sep

0.11%

0.87%

0.01%

0.11%

0.65%8 Sep

0.06%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes56.0%2 415 in total

Reposts4.2%180 in total

Replies4.3%184 in total

Quotes2.2%93 in total

Bookmarks33.4%1 443 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Ultimi post

Rispetto ad account della stessa dimensione

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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