vkatsuba

Deep walkthrough of Erlang-RED from TADSummit, led by Gerrit Riessen (creator of Erlang-RED), with contributions from Vance Shipley (SigScale Global Inc.), Jonathan Eisenzopf (Talkmap), and Viacheslav Katsuba (Scalicon Inc.), hosted by Alan Quayle. The session demonstrates building a Diameter AAA authorization flow with visual state machines, unit testing, and documentation - making telecom protocols more accessible while leveraging Erlang/OTP’s concurrency and fault-tolerance.

An interview with Vance Shipley (SigScale Global Inc.) and Viacheslav Katsuba (Scalicon Inc.), discussing how Erlang/OTP powers fault-tolerant and massively scalable systems in telecom.

We touched on open source protocol stacks (SIGTRAN, TCAP, CAP, MAP, NGAP, RADIUS, EAP) and briefly on how Erlang could support modern AI-driven use cases.

Erlang/OTP continues to power some of the world’s most reliable, scalable real-time systems - from telecom and fintech platforms to Voice AI and IoT. With built-in fault tolerance, hot code upgrades, and seamless distribution, it remains the go-to choice where uptime and resilience are critical. Companies like WhatsApp, Facebook Chat, Zoom, IBM, Goldman Sachs, Nintendo, and more rely on it to handle millions of concurrent users without breaking a sweat.

Fascinating project! The approach to enhancing the dataset and exploring feature engineering for something as dynamic as Dota 2 must have been a serious challenge, but the results sound promising. It’s inspiring to see machine learning applied to gaming analytics like this—gives a whole new perspective on how we can quantify and predict complex, real-time events. Looking forward to seeing how this evolves!

You're welcome! It was a great read, and I'm happy to share it. Looking forward to more of your insights in the future! Keep up the fantastic work!

I recently came across a fantastic article detailing the journey of building a Dota 2 Match Outcome Predictor using machine learning. The author dives deep into data analysis, feature engineering, and predictive modeling, sharing both successes and challenges along the way. If you're into gaming, data science, or machine learning, it's a great read full of real-world insights.