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A security company scanned 1,072 public apps built with AI tools like Lovable, Bolt and Replit.
98% had at least one security flaw. 16% had a critical one.
In 172 apps, anyone could change or delete data without logging in. In 39, anyone could read it.
An AI builder makes the app work for its owner. What a stranger can do is the part that gets skipped.
Source: Symbiotic Security, June 2026.
Have you ever tested your app as a stranger?
Wow, 172 apps open to edits without login is rough. Testing logged out is now on my checklist.
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Lovely cohesive work, Alex. The motion is spot on!
SES AI is a battery technology company accelerating the energy transition through AI-powered material discovery, battery development, manufacturing, and management.
SES AI approached us with a website redesign driven by its new “All-in on AI” strategy. As AI became central to the company’s work across safety, manufacturing, and science, the existing website still focused primarily on its battery-hardware side. We redesigned the experience to clearly communicate the three AI pillars, while making the website simpler, more direct, and easier to navigate.
This looks insane. The robotic arm with the battery cell is such a strong visual. Love how clean you kept it despite it being AI heavy.
CrowdVision AI – Real-Time Crowd Monitoring & Intelligence System
An AI-powered computer vision platform that detects and counts people in real time, monitors crowd density and occupancy, tracks entry and exit movements, and provides actionable crowd intelligence through a live monitoring dashboard.
Overview
CrowdVision AI is an intelligent crowd monitoring and safety analytics platform designed to help organisations monitor public spaces, understand crowd behaviour, and identify potentially unsafe crowd conditions.
The system uses computer vision and machine learning inference to analyse camera feeds and video streams, detect people, estimate occupancy, classify crowd density, and monitor movement across designated zones.
The Problem:
Monitoring crowded environments manually can be difficult, time-consuming, and inefficient. Security personnel may struggle to maintain accurate occupancy counts, monitor multiple zones, and identify unusual crowd conditions quickly.
CrowdVision AI was developed to explore how computer vision and real-time analytics could improve crowd visibility and monitoring.
My Solution:
I developed a computer vision system integrating a pretrained YOLO object-detection model with OpenCV for video processing.
The platform includes:
Real-time people detection and counting.
Crowd occupancy estimation.
Crowd-density classification.
Entry and exit line counting.
Zone-based monitoring.
Incident and anomaly monitoring workflows.
Camera health and tamper monitoring.
Evidence management and reporting.
Telegram alert notifications.
Remote edge camera processing.
Live monitoring dashboard.
Technical Implementation:
Machine Learning & Computer Vision
Python
YOLO / Ultralytics
OpenCV
NumPy
Pandas
Backend:
Python
FastAPI
REST API
Frontend:
React
Vite
JavaScript
Other Technologies
Git
GitHub
Telegram Bot API
JSON/CSV data storage
My Role
Founder & Full-Stack Machine Learning Developer
For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?