Startup Engineering Rob De Feo

Building a fully distributed system is really hard. But a few compromises can go a long way. Hear how Paddy CTO and Co-Founder of Ably, would white board out complex problems and where needed centralise small pieces in a single region. It might seem simple or small but trade offs like this can go along way and still meets the requirement of a pier to pier region relationship where any region can fail and come back online at anytime.

What trade offs are hidden in your architecture that would simplify your stack?

Lots of systems store events that happen at moments in time. But what if the timestamps can have more than one meaning.

Tom the co-founder of Finbourne explains how they use bi-temporal data and event sourcing to build a consistent view of portfolio data where they can look back at any point in time and find out the two truths. What did system see at that time and what did the world see.
Find out the difference between "effective at" and "as at" and learn what happens when you need to make corrections to the timeline.

Developing deep learning models requires the latest in academic research. Yet the structure and pace of academia can't be found in the chaotic and ambiguous world of startups. SignalAI do research with in house and in collaboration with universities . Luca Grulla the CTO explains how SignalAI build their teams, tools, and datasets so researchers can directly implement the state of the art to build great products.

Computed Aided Engineering (CAE) allow engineers to run Computational Fluid Dynamics, Finite Element Analysis and Thermal simulations. The software is built and maintained over years with many contributors as open source in large C++ codebases. Simulation software was designed for running on a desktop client. SimScale run these in the cloud as part of a modern mircoservice architecture.

Anatol Dammer on of 5 co-founders takes us behind the scenes and explains how SimScale have taken large, difficult to scale, legacy codebases and built a microservice architecture using modern programming languages.

Working remotely is becoming the new norm with more companies adopting long term, yet little analysis has been completed to measure its benefits. Before scaling their team Echobox measured the impact on remote working on performance.

Marc Fletcher the CTO of Echobox explains how they analyzed remote working in their team by using data collected on productivity over a 2 year period.

Using the latest from machine learning research in enterprise products is hard. Research projects are built to advance research goals. Its not easy to convert papers, code, and scripts in products. They are difficult to maintain and scale.

Malte Pietsch is a Co-Founder of deepset explains their approach to scaling research into production ready enterprise scale applications.