Studied Electronics & Computing, with a masters in Maths & AI, worked as an ML Software Engineer, then an AI Research Engineer, now Building an Open-Source framework for edge ML (Cactus), with funding from YCombinator, Oxford Seed Fund and Google for Startups
- 2025-Present: Cactus Compute (YC S25) - Founder & CTO
- 2024-25: Deep Render - AI Research Engineer (Tiny AI models for realtime video compression)
- 2021-24: Wisdm - ML Software Engineer (Perception AI for Maxar Defence satelite imagery)
- 2019-21: Open-source activities during MSc (NanoDl, SuperLazyLLM, CUDARepo, etc.)
- 2018-19: Google Africa Developer Scholarship Programme with Andela (pre-MSc)
- 2014-18: Uni coursework in computing, electronics, data structures, algorithms, maths, physics.
- My research with previous employers were all proprietary, but you'd like this and this.
- I wrote This ML Handbook. and executable code for maths, ML, and computing, ideal for diving into the depth of ML foundations
- Kevin Murphy (DeepMind Principal), Daniel Holtz (Mid Journey Founder), Steve Messina (IBM CTO) followed back on X after NanoDL.
- After CUDARepo, Nvidia reached out, I did 7 technical rounds, got a verbal offer, back-and-forth over YOE/pay, then I got YC.
- Did MSc at QMUL, just to work with Prof Matt Purver (Ex-Stanford Researcher on CALO), did my project/thesis with his team.
- Did BEng under Prof Onyema Uzoamaka (Rumoured first Nigerian CS grad from MIT), he taught computing architecture off-head!
- I contribute to the JAX ecosystem, and am a Google Developer Expert in AI and JAX.
- Recieved the British Talent Immigration within 24hrs of application (no prority appeal or anything).
- I co-host this monthly dinner for AI researchers, engineers and founders in London.
- I gave this lecture to a small ML group in Nigeria, on optimising large-scale ML in JAX.
- When the talented fail to work hard, the hardworking beat the talented.
- Everything should be an adventure, not a race, everyone gets their moment someday.
- Make the best of your situation, complaining and pointing fingers do nothing.
- It often takes 120% effort, focus and passion, failure often results from giving less.
- Realtime EEG-to-Instruction AI for Brain-Machine Interfaces (MS patients could prompt agents to execute tasks).
- RL Algorithm for Distilling a User's Cognitive Signatures into a Model (imagine your proxy, need to define "signatures").
- Realtime Visual-Action World Models for drones, aerial robots, phones, tiny robots etc.
- Decentralised Compute Grid from Tiny Edge devices like phones, drones, robots (world models need more compute).
