Current notes
- World models for agents: Qwen-AgentWorld, simulator fidelity, LLM-as-judge rewards, and sim-to-real transfer for agent rollouts.
- Multimodal architectures: two-tower AR + diffusion systems like Cosmos 3 / Nemotron TwoTower, especially feedback from generated states back into reasoning.
- Human signal: how to collect better eval, ranking, and preference data close to the agent workflow.
Research Notes
Small public experiments and explanations where the mechanism is inspectable: speech systems, multimodal models, biological learning rules, sparse expansion, and agent eval loops.
- Audio Is Computer Vision - a short note on why spectrograms make audio models feel familiar if you understand computer vision.
- The Fly's Trick: Sparse Expansion as a Learning Prior - a small MNIST experiment inspired by the fly olfactory system, with code, references, and measured visuals.
Candor — Human Signal for Agent Workflows
Candor collects human feedback where AI builders already work: the terminal. It supports pairwise comparisons, ratings, labeling, product feedback, and AI-moderated interviews, then returns structured signal for evals, training data, and agent workflows.
I’m especially interested in where human signal fits into agent RL loops: judge calibration, high-disagreement cases, preference data, and evaluation rubrics that survive contact with real users.
Writing
Most short-form writing is on X: world models, multimodal architectures, real-time speech systems, eval loops, and the human signal that makes those loops useful.
EzDubs on Ray-Ban Meta Glasses
A quick demo of Paddy and me talking through EzDubs using Ray-Ban Meta glasses. This is the kind of product surface I like: AI that disappears into a live interaction instead of becoming another dashboard.
More EzDubs Demos
A couple more demos from the same product arc: real-time voice translation moving from model work into live user-facing surfaces.
RL, Rollouts, and World Models
Implemented Soft Actor-Critic with an AtariNet-style convolutional encoder. Training leveraged replay, MC rollouts, and importance sampling. I’m still interested in the same core problem today: how agents learn from simulated experience, how those rollouts are scored, and when simulated feedback transfers to real environments.
Training code: gist
Smartmiq Desktop Assistant
Spotlight‑style voice assistant for desktop. Trigger actions hands‑free (search, reply to Slack, etc.). I trained my own ASR based on a transducer architecture for tighter control over latency and accuracy.
What I learned: voice on laptops/desktops is useful but often slower than typing in public; UX must bias toward zero‑friction hybrid input (keyboard + voice), and ASR needs strong endpointing.
Replying to Slack by voice:
Noise Suppression Neural Network
Learned a spectral mask to suppress non‑speech noise, inspired by Google’s VoiceFilter. The model improves intelligibility in common home/office environments.
Rover — Music Visualizer via GAN
Mapped audio frequency features to a smoothed latent walk through a pretrained art‑DCGAN to generate synchronized visuals in real time.
- Compute frequency components per frame
- Integrate with previous latent for temporal smoothness
- Render GAN output to framebuffer
Code: github.com/kareemn/rover
Meeting Voice Assistant (Zoom/Meet/Webex)
Before high‑quality meeting audio APIs existed, I emulated webcam/mic drivers in a Dockerized headless Chrome to capture >16kHz audio for wake‑word and ASR, then scaled it across a Kubernetes cluster. We also used a virtual webcam to demo the bot live, a surprisingly effective growth loop.
Poynt OS & Platform Integration
Led OS and platform integration for Poynt's Android-based payment terminals: Android OS fork work, kernel/HAL integration, OTA update framework, the Poynt App Store, and OS security changes for PCI PTS certification. This was building and shipping an operating system for real payment hardware, not app-layer product work.
The Nay-Nay video below was a stress test of the remote OS/app update pipeline: we pushed a music video to a fleet of test devices at once.