Voxel51

• Explore, tinker with, and push the boundaries of the latest AI and machine learning technologies to showcase what's possible with open-source tools and frameworks

• Build inspiring prototypes, proof-of-concepts, and example applications that demonstrate creative and impactful uses of AI to the developer community

• Contribute meaningfully to the open source ecosystem by creating and maintaining libraries, tools, datasets, tutorials and guides that enable developers to more easily leverage AI capabilities

• Curate, clean, annotate and publish unique multimodal datasets spanning text, images, audio, video, 3D and other emerging data types to facilitate novel machine learning research and applications

• Create engaging technical blog posts, social media content, videos, and presentations that educate and inspire developers and data scientists to build with AI

•  Collaborate openly with our internal teams and the broader AI/ML community to cross-pollinate ideas, share knowledge, and collectively advance the state-of-the-art

• Evangelize our technology and vision by representing the company at hackathons, conferences, meetups, universities, and other developer-focused events

•  Influence the product roadmap by providing input on the developer experience, uncovering opportunities for new features and improvements based on community feedback and your work

• Continuously learn, experiment with, and provide feedback on beta versions of our latest models and capabilities to help shape the future of our platform

Hacker-in-Residence

LinkedIn

1) Introductory computer vision course for LinkedIn Learning

2) Prompt Engineering with LangChain (releasing March 2024)

3) RetrievalAugemnted Generation with LlamaIndex (releasing Q2 2024)

4) Fine-tuning LLMs with Hugging Face TRL (est release Q3 2024)

5) Text Generation with Hugging Face Transformers (est release Q3 2024)

LinkedIn Learning Instructor

The Artists of Data Science

The ONLY personal growth and development podcast for Data Scientists

Host

Deci AI

Developer Relations Manager

Pachyderm Inc.

Senior Developer Advocate

Comet

ML Developer Advocate

Data Science Dream Job

Principal Data Science Educator

Price Industries

Lead Data Scientist

Bold Commerce

Senior Data Scientist

Emergent BioSolutions

Biostatistician

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 🥑

sam3 is seriously a step change improvement over sam2

the model is seriously good, glad they brought back text prompts. the automatic segmentation isn't as good as i hoped it to be...but it still works quite well. 

i also got it to output image embeddings and that was cool to visualize in the app

learn more here: https://lnkd.in/gzqJzj6G

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

qwen3vl is literally the best model i've used for video usecases. blows my mind

hosting a virtual event tomorrow about document ai

you may or may not find it interesting, but i guarantee you'll learn something

join me here: https://lnkd.in/dFXPimVc

sony ai released a pretty cool dataset called the fairness human centric image benchmark, super high quality labels

instructions for downloading and parsing the dataset into fiftyone format can be found here: https://lnkd.in/g_VwxJx4

physical AI is top of mind as the next frontier for deep learning. but it'll only succeed if you have good datasets

language models scaled with web-scale text. 

vision models got ImageNet. 

now Voxel51 and NVIDIA are trying to do the same thing for physical AI 

autonomous vehicles, humanoid robots, anything that moves around atoms.

building these systems means millions of hours of testing in high-fidelity sims before you can actually deploy anything. 

petabytes of multimodal sensor data from various modalities

 LiDAR, cameras, radar, IMUs. 

but over 50% of simulations end up completely unusable because the input data is bad.

even small calibration or timing errors between sensors cascade through your neural reconstructions. 

you waste weeks and burn through millions in compute costs before you realize what went wrong.

the new FiftyOne Physical AI Workbench. it integrates with NVIDIA Omniverse and NVIDIA Cosmos and basically sits at the start of your sim pipeline to validate, clean up, and prep your multimodal data before it hits neural reconstruction and synthetic data generation.

what it does:

- automatically audits physical AI inputs across 75+ critical checkpoints

- transforms raw sensor streams into structured, searchable datasets

- triggers accurate neural reconstructions and generates synthetic scene variations

this means fewer wasted compute hours. 

iteration loops go from weeks to hours. 

you build safer, more reliable systems on data you can actually trust.

read more about what we've been building: https://lnkd.in/gC6_zn-X

i recently integrated 4 ocr models into fiftyone as remote zoo models these handle text extraction and document parsing. 

all available as remote zoo sources, you can get started with a few lines of code

different approaches for different needs:

1. mineru-2.5

1.2b params, two-stage strategy: global layout on downsampled image, then fine-grained recognition on native-resolution crops. 

handles headers, footers, lists, code blocks. strong on complex math formulas (mixed chinese-english) and tables (rotated, borderless, partial-border).

good for: documents with complex layouts and mathematical content

https://lnkd.in/dSwimdvp

deepseek-ocr

dual-encoder (sam + clip) for "contextual optical compression." 

outputs structured markdown with bounding boxes. has five resolution modes (tiny/small/base/large/gundam). gundam mode is the default - uses multi-view processing (1024×1024 global + 640×640 patches for details). 

supports custom prompts for specific extraction tasks.

good for: complex pdfs and multi-column layouts where you need structured output

https://lnkd.in/dhJn2h-v

olmocr-2

built on qwen2.5-vl, 7b params. outputs markdown with yaml front matter containing metadata (language, rotation, table/diagram detection). 

converts equations to latex, tables to html. labels figures with markdown syntax. reads documents like a human would.

good for: academic papers and technical documents with equations and structured data

https://lnkd.in/dNdTQYKg

kosmos-2.5

microsoft's 1.37b param multimodal model. two modes: ocr (text with bounding boxes) or markdown generation. automatically optimizes hardware usage (bfloat16 for ampere+, float16 for older gpus, float32 for cpu). handles diverse document types including handwritten text.

good for: general-purpose ocr when you need either coordinates or clean markdown

https://lnkd.in/dCTfGfV8


two modes typical across these models: detection (bounding boxes) and extraction (text output)

i also built/revamped the caption viewer plugin for better text visualization in the app: 

https://lnkd.in/dBebWdga

i've also got two events poppin off for document visual ai:

- nov 6 (tomorrow) with a stellar line up of speakers: https://lnkd.in/gkBEY4_r

- a deep dive into document visual ai with just me: 

https://lnkd.in/dFXPimVc

Open article: Document Visual AI with FiftyOne - November 14, 2025 by voxel51.com

Document Visual AI with FiftyOne - November 14, 2025

hype for this new integration, and proud of the team for shipping it at lightning speed. big ups to Daniel and AdonAI for goin beast mode on this one

🚀 Introducing Physical AI Workbench — integrated with NVIDIA Omniverse NuRec and NVIDIA Cosmos to solve the biggest bottleneck in physical AI.  

More than half of all physical AI simulations fail — not from bad models, but from bad input data. A timestamp off by milliseconds or a calibration drift of a few degrees can cascade through your neural reconstructions, leaving you with unusable results after weeks and millions in wasted compute. 

FiftyOne Physical AI Workbench sits at the start of your simulation pipeline and provides turnkey access to NVIDIA technology to ensure every simulation run begins with trustworthy data: 

✅ Audits 75+ critical checkpoints to ensure every reconstruction starts with validated inputs 
🎯 Enriches raw sensor streams into structured, searchable datasets 
🔗 Integrates directly with NVIDIA Omniverse and NVIDIA Cosmos for high-fidelity reconstruction and synthetic data generation. 

The result: an end-to-end workflow that prevents downstream failures — cutting iteration cycles from weeks to hours and eliminating GPU waste on failed reconstructions. We’re excited to see this workflow power autonomous vehicles NVIDIA DRIVE and robotics NVIDIA Robotics development teams move faster with higher accuracy. 

🔗 Read the full technical breakdown: https://lnkd.in/gTy5-TNF

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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