hardware
MBK Geyser Monitor
An ESP32-based geyser temperature monitor with OLED display, local web interface, and remote dashboard — built from scratch with a 3D-printed enclosure.
Co-founder & CTO @ LearnOBots
Shamyl
Bin Mansoor
Technologist · Maker · Educator
Building Products that matter
SPECIALIZED IN ROBOTICS, STEAM EDUCATION, AND PRODUCTS THAT MATTER. SCROLL_Y TO INITIALIZE ▼
01
Featured Work
Selected projects across software, hardware, and research
software
Pakistan Petrol Updates Dashboard
A live dashboard tracking daily OGRA-notified petrol, diesel, kerosene, and LPG prices in Pakistan with historical charts and global oil comparisons.
software
LearnOBots Library Management System
An office book management system with catalog browsing, issue/return tracking, member management, and overdue reminders.
software
LearnOBots Meeting Room Booking App
A calendar-based meeting room booking system for LearnOBots with department filtering, dashboard view, and admin controls.
02
Latest Updates
Physical Intelligence Launches a Robotics Foundation Model API: What It Means for Robot Builders and Educators
Physical Intelligence released the Pi API beta on October 13, 2026, letting developers fine-tune robotics foundation models and run cloud-hosted inference on their own robots. Here is what the API does, how the underlying models evolved from π0 to π0.7, and what it means for product teams, educators, and emerging-market builders.
How to Choose an Educational Robotics Platform for Your School: A Practical Guide for Educators and Administrators
A practical guide for schools evaluating educational robotics platforms. Covers age-appropriateness, hardware vs. simulation, curriculum integration, budget realities, and what questions to ask before buying.
Sakana AI's MASS: Can a Model Teach Itself by Watching Its Own Agent Teams?
Sakana AI's MASS paper shows that a single language model can improve itself by running multi-agent teams, evaluating the results, and training on the best executions. After two cycles with Qwen3.6-27B, the model achieves 1.2-1.6x higher performance per output token on public benchmarks. Here is what the method does, where it falls short, and what it means for product teams and AI safety.