Gonçalo Silva is a seasoned technology leader and CTO based in Porto with 16 years of experience building and scaling products from native Android apps to backend infrastructure. At Doist he progressed from Android engineer to CTO, blending hands-on engineering with product and team leadership across distributed teams. A pragmatic full-stack developer, he has a strong open-source footprint—contributing performance and feature fixes to well-known projects such as Twitter4J, acts_as_paranoid, and RVM—that reflect deep expertise in Java and Ruby runtime internals. He pairs formal CS training (MSc) with focused upskilling in machine learning and cryptography, bringing both systems-level thinking and a curiosity for emerging AI and security topics. Colleagues would describe him as an engineer-operator who still dives into code to solve subtle performance and correctness problems.
16 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Computer Science, Master, Master of Science - MS, Computer Science, Master at Faculdade de Engenharia da Universidade do Porto
Cryptography, Cryptography, Cryptography, Cryptography at Coursera
Deep Learning Nanodegree, Machine Learning, Deep Learning Nanodegree, Machine Learning at Udacity
ActiveRecord plugin allowing you to hide and restore records without actually deleting them.
Role in this project:
Back-end Developer
Contributions:131 commits, 1 push, 1 comment in 9 years 2 months
Contributions summary:Gonçalo contributed to the `acts_as_paranoid` gem, a plugin for ActiveRecord that enables soft-deleting of records. Their initial contribution involved importing the plugin and implementing the core logic for soft deletion. The user added tests to verify the functionality of the soft-delete implementation and also refactored the code for improved performance. Further contributions included adding features like validations, along with fixes and improvements.
Contributions summary:Gonçalo contributed to the Ruby enVironment Manager (RVM) project by adding patches related to garbage collection (GC). These patches included enhancements and fixes for GC behavior across different Ruby versions, specifically targeting MRI 1.9.2 and updating patches for earlier versions such as 1.8.6 and 1.8.7. The contributions involved modifying the GC implementation to improve memory management and allocation tracking. The user's work demonstrates a focus on improving the performance and stability of Ruby's core runtime.
ruby-environmentmri-rubyrbxrailsruby
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