I create content about deep learning | The DevRel and Deep Learning Dude

Hacking on LLMs, curating multimodal datasets, and writing a book about RAG.

Harpreet Sahota 🥑

i've literally been waiting for years to have an OPEN SOURCE model like qwen3-vl-embedding

i tested its multimodal retrieval capabilities on a corpus of 412 short video clips and the results literally blew my mind

here are the queries i tested:

1. a cartoon guy drinks merlot wine

i like this query because we see how it can retrieve based on semantics (a cartoon), text (the label merlot), and temporal action in the video (the cartoon guy drinks the wine mid-way through the video)

2. garfield runs out of the door

semantics are awesome here, the model knows i'm talking about the cartoon character...

3. a woman in a blue dress sleeping on a red bench

the skeptic in you might think that it's retrieving just based on the red color block in the 2/3 of the video...but notice the specific part of the query "a woman in a blue dress..." and this is only shown in 3s out of the full 10s

this is such a huge release 

and it's gonna open up SO much more for multimodal video retrieval this year

on my wishlist is natural language search of pcd datasets, who gonna ship that?

you can hack around with the model using the resources below

check out the docs: here: https://lnkd.in/gh7yfBNP

and the quickstart nbhere:  https://lnkd.in/gbinsx7p

🤖 Hacker-in-Residence @ Voxel51| 👨🏽‍💻 AI/ML Engineer | 👷🏽‍♀️ Technical Developer Advocate | Learn. Do. Write. Teach. Repeat.

for those who missed it, or were there in-person and know the trouble i was facing 😆, my ODSC webinar is now live on youtube!

in this session i discuss visual ai techniques for automotive damage detection. 

i used the cardd dataset, which is a public dataset built specifically for vehicle damage analysis and demonstrated how fiftyone helps you explore, visualize, and build models to detect dents, scratches, cracks, glass shatter, tire flats, and broken lamps.

i feel like its a super practical workshop with some insightful workflows for visual AI in automotive settings, but as the creator i might be biased.

only one way to find out for yourself, watch the webinar!

https://lnkd.in/ewfz5vZD

Open video: Mastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques by Harpreet Sahota on youtube.com

Mastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques by Harpreet Sahota

sometimes i look back at the shit i get done and i genuinely impress myself

how teams really build Visual AI in the wild? what's the he messy reality?  

i don't know...and maybe its because nobody’s asking practitioners and compiling their answers so we can find out the landscape actually looks like. 

so, we at Voxel51 are kickin off the...

wait for it...

 State of Visual AI survey...vey...vey

for those out ther building visual AI systems i want your hot takes

what’s the hardest part about making visual ai a reality? 

let us know in this survey

it'll take you 30 minutes and is a much better use of time than baby sitting your runs and staring at your loss curves

i can't offer much beyond my heartiest thanks, but you will get early access to the full report with benchmarks, common pain points, and where you stand within the landscape. 

here's a link: https://lnkd.in/gnsADACy

MedGemma 1.5 supports detection, but for best results, you'll need to fine-tune. 

there's a kaggle competition using the model, i created a starter notebook to give you a jump start on how to fine-tune it for detection

checkout the starter notebook and additonal resources 👇🏼

cc Omar Sanseviero Megan Risdal Logan Kilpatrick

in the asu area and wanna get started with open source contributions? then don't miss this hack

On March 21, 2026, we’re hosting Agents World: Visual AI Hackathon @Arizona State University, together with Pavan Turaga 🚀 

This is a hands-on day: meet people, build with AI, eat pizza, and leave with something you created after a short workshop and a full day of hacking. 
You’ll work with the FiftyOne ecosystem and real models used in practice, like Qwen, Gemini Vision, Sharp, and more. 

If you’re an ASU student who wants to build, learn by doing, and work with real Visual AI tools, this is for you.

Registration is open:
https://lnkd.in/gBCHW5Fg

Hope to see you at ASU. I’ll be there with the amazing Voxel51 team:  Sid Mehta, Ritchie Martori, Jimmy Guerrero ! 🙌

looking forward to the next meetup in boston!

Join our in-person meetup at the Microsoft Research Lab at the Massachusetts Institute of Technology in Boston, Massachusetts on Feb 18 to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Register here: https://lnkd.in/gi4kpSMB

Talks will include:

• SNAP: Towards Segmenting Anything in Any Point Cloud - Hanhui Wang at Northeastern University

• Culturally Adaptive AI - Anku R. at Massachusetts Institute of Technology

• Data Foundations for Vision-Language-Action Models - Harpreet Sahota 🥑 at Voxel51

• Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models - Sharut G. at Massachusetts Institute of Technology

• Neural Radiance Fields for Image Verification - Sage Simhon at Pasteur Labs & ISI

#computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

Syed Abdul Sami Rizvi, Matthew Ebisu and 118 other attendees

View event: Feb 18 - Boston AI, ML and Computer Vision Meetup

Feb 18 - Boston AI, ML and Computer Vision Meetup

Inspired by the amazing Hugging Face’s 2025 AI Timeline, we created one for the Fiftyone community. The FiftyOne Community Development Timeline highlights more than 131 contributions from 2025, including datasets, models, plugins, and integrations.

You can filter by type or modality and jump straight to each integration.
https://lnkd.in/ev2kpCGN

Big thanks to the entire Voxel51 community for building all of this together.
Harpreet Sahota 🥑, Daniel Gural, Paula Ramos, PhD, Jimmy Guerrero, Derek Van de Streek, Nicholas Lotz 🚀

Gonna be a great series of talks, lots of technical depth!

Chief Community Officer @ Voxel51 | Startup Advisor

Join us on Feb 18 for the Boston AI, ML and Computer Vision Meetup! Pre-registration is mandatory to clear building security - https://lnkd.in/gHtBuH49

Talks will include:

* SNAP: Towards Segmenting Anything in Any Point Cloud - Hanhui Wang at Northeastern University

* Culturally Adaptive AI - Anku R. at MIT

* Data Foundations for Vision-Language-Action Models - Harpreet Sahota 🥑 at Voxel51

* Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models - Sharut G. at MIT

* Neural Radiance Fields for Image Verification - Sage Simhon at Pasteur Labs & ISI

***********

Want to build better computer vision models? FiftyOne is an open source toolkit from Voxel51 (our Meetup sponsor) that helps you curate datasets, evaluate model performance, visualize embeddings, catch annotation errors, and eliminate duplicate images—all in one place.

“pip install fiftyone” is all it takes to get started - https://docs.voxel51.com/

#computervision #ai #artificialintelligence #machinevision
#machinelearning #datascience #physicalai

Management in Times of War

In our recent project translating "Management In Times of War" into English, a pivotal element was the integration of LangChain, a cutting-edge language model toolchain. This sophisticated technology played a crucial role in enhancing our translation process, bringing together the best of both human expertise and advanced AI.

LangChain's capabilities were instrumental in streamlining complex linguistic tasks. Its architecture, designed to facilitate a seamless interaction between different language models, allowed us to handle diverse aspects of the translation process more efficiently. By leveraging LangChain, we were able to integrate various AI models, each excelling in specific linguistic functions such as semantic analysis, context understanding, and syntactic accuracy. This enabled a more nuanced and accurate translation, closely aligned with the intricacies of both Ukrainian and English languages and using the combined strengths of different AI models while integrating human oversight. The toolchain allowed us to create a feedback loop where initial translations generated by AI were reviewed and refined by our bilingual experts. This collaborative human-in-the-loop approach ensured that the final translation was not only linguistically precise but also culturally and contextually relevant.

LangChain's modular design also enabled us to customize the translation process according to the specific requirements of our project. This level of customization was vital in maintaining the original tone and message of the book while ensuring the translation's accuracy and readability.

Moreover, LangChain facilitated an efficient management of large volumes of text. Its ability to process and analyze extensive data at remarkable speeds significantly accelerated our translation timeline without compromising quality. This efficiency was crucial in meeting our project deadlines and maintaining a high standard of output.

AI Lead & Machine Translation Project Manager

eDopomoga

Generative AI to support the eDopomoga platform. eDopomoga is a digital platform where the state, humanitarian organizations, and volunteers from around the world unite to provide assistance to those in need. It's a hub where information on how to receive or give support is centralized. This includes help for internally displaced persons (IDPs) in Ukraine, support for Ukrainian families with necessities, and a way to reach out to volunteer benefactors by submitting applications.

Legion of Benefactors

Congressman Doug Lamborn

- Maintained and updated Congressman’s website
- Fielded and logged constituent phone calls to the Congressman’s  office, tracking metrics of public opinion on legislative issues
- Maintained constituent database
- Provided guided tours of the U.S. Capitol to Colorado business associates and constituents

Intern

David Gee for FL Senate

- Migrated website from a static frontend that required a full-time web developer to operate and maintain to NationBuilder, a platform that integrates voter and financial data tools seamlessly into a uniform and professional front-end, increasing campaign speed and agility. 
- Utilized data tools to target likely supporters and to successfully execute a successful ballot access petition campaign, managed all campaign communications; identified, motivated, and trained volunteer field staff; opened campaign HQ in Leesburg, FL.

Campaign Manager

August Wolf for Senate

- Rebuilt staff from ground up; hired/trained/managed team of paid, full-time staffers and dozens of volunteers while reducing the campaign burn rate and keeping operational expenses in check.; 
- Implemented data-driven small town delegate strategy
- Conducted market research and negotiated lease for the campaign’s first field office in Torrington, CT. 
- Set-up, managed, and mastered voter database systems (i360, TrailBlazer, NationBuilder). 
- Developed relationships with local RTC delegates to the party convention, the first of the three part nominating process, and with political reporters for the Connecticut Mirror, Hartford Courant, and CT Post.
-Promoted from State-wide Field Director in October 2015.

Field Director for August Wolf for U.S. Senate campaign

Field Director

Ossining Town Republican Committee

- Represented local GOP and led modernization effort of website and voter management data repository. 
- Executed GOTV Election Day strategy
- Managed campaign communications
- Used campaign data tool, NationBuilder, to organize phone banks and generate targeted walk lists for door-to-door canvassing. 
- Personally knocked on hundreds and hundreds of doors, speaking directly to constituents about local impact issues.

Chairman

Software Engineer and graduate of the Flatiron School with experience using frontend and backend technologies: React | JavaScript | CSS | HTML | Python | Flask | SQL | SQLAlchemy

I hold multiple certifications, including:

Certified Kubernetes Administrator (CKA)
Google Professional Cloud DevOps Engineer
Google Professional Cloud Architect
Google Professional Machine Learning Engineer
Google Professional Data Engineer
Google Associate Cloud Engineer
AWS Machine Learning Specialty
IIBA Entry Certificate in Business Analysis
AWS Solutions Architect Associate
IASSC Lean Six Sigma Green Belt.

Always eager to explore and develop, I look forward to contributing substantially to Industry 4.0. For further discussions regarding my experience and skills, or for potential collaboration opportunities, please feel free to connect with me on LinkedIn or contact me via email.

https://github.com/matthewrobertmac

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