My experience at GOTO Copenhagen 2019
This is a short summary of what I saw in the conference, what I liked and didn't like and what I think was most interesting or worth notice considering my personal preferences and needs.
It was my first international conference and also the biggest conference I've attended so far, so please take that into account when reading this.
Variety and quality of talks
The conference had a good amount of different topics, I was satisfied with the balance between topics related with soft and hard skills, although, some of the mandatory talks could be frustrating to some people due to the lack of technical points or a practical guides.
Talks presented by partners (sponsors) weren’t simply trying to sell their solution, but were tackling problems or showing general solutions that can benefit you or your company even if you don’t use the partner specific solution. It was great to see the power of Kafka, GraphQL and Erlang/Elixir.
I was happy to be able to catch up on some topics like AI and quantum computing, the talks in general were well presented, not too long or too short and relevant. I felt that some topics should have had less talks considering that talks on similar subject were already present as part of the mandatory talks (AI and machine learning for example). I believed that the topics could had been better distributed (maximum number of talks per topic). On another side I understand that some redundancy is built-in for the different days and for people that might not be able to attend certain talks due do conflicts with parallel sessions. I understand that it’s not an easy to solve problem, and even with these remarks the overall experience was incredibly positive.
On a personal note I really liked the talk from Saša Jurić about Erlang and Elixir, seeing what Erlang and Elixir can do really gave me the extra push needed to dive into it. The talk from Adam Tornhill "Prioritizing Technical Debt as if Time and Money Matters" gave me a new perspective and even actionable points (although not easy) to deal with the trade-off between improving existing code and adding new features. Finally I was surprised with the extreme digitization in China presented by Christina Boutrup, it gave great insights which I believe are not on the radar of standard western people.
Organization of the conference
I currently live in Switzerland and I have to say that the Danes seemed to me to be even more punctual and organized! No real issues with talks being late or running over time, the duration and number of breaks helping to keep your attention on each talk you attend. Registration and overall logistical organization was very good ( quality of the food and amount of options was great).
The area for partners was cool and well structured, good amount of swag and most partners were easy to interact and have a good conversation during the quick breaks between talks.
One point that I really didn’t like was the amount of talks that were not being recorded, I don’t have enough conference experience to know how much this is common practice but feeling forced to attend to certain talks that might, or not, be of interest just for fear of missing out isn’t a great feeling in my opinion.
I didn't know most of the speakers, but was satisfied with the quality of the talks. As celebrities go, it was cool to see Steve Wozniak in person!
Key takeaways
Most of the talks contributed more or less in the same way and a lot of them helped me into catching up with the current state of the industry (at least in a high-level view).
I'm less scared about AI and I think the field still has to evolve a lot before an AI tries to destroy humanity, still I would bet that many people will have their jobs taken by AI based solutions, but it's difficult to say how fast this will happen even for specialists in the field.
China is really a place to pay attention to, we can learn from them, as they seem to be able to foster innovation despite being very restrictive on their market and ideas.
Machine learning is finding more efficient and precise methods to do things in ways that humans can't understand, so it’s something I think everyone should pay more attention to as the results so far are already impressive (and seem to be less dangerous than what we normally associate with AI apocalypse, although strongly related).
Elixir is really awesome and I need to start experimenting and learning asap.
There are different ways to handle the trade-off between adding new functionality and improving existing code, I have much to learn!
Immediate use on my Swissquote role
The new perspective from Adam Tornhill is something that I believe will have an immediate positive impact on my daily work as in any fast paced and competitive business it’s part of the day to day job to weight the benefits of improving existing code versus the many new features that business requires in the short and long term. This talk gave me a different perspective on how to look at the problem and directions on what needs to be considered on a more technical and practical stand for me to be able to keep the best balance between business value added and the long term evolution of our platforms, while also keeping our 24/5 high frequency trading system competitive.
As a possible mid/long term contribution I think I would like to partner with our big data and BI teams and see what I can do using machine learning to improve the overall business. I now, more than before, believe that machine learning can really help businesses to take big leaps forward in ways that we humans simply can’t predict.
I wish I have the time and opportunity to put some Elixir in action at Swissquote as I believe that the very nature of the business can benefit immensely from the platform features (Erlang/Elixir) that support high availability, fast recovery and the benefits of the actor based model (processes in Elixir). Perfect for my needs in the Forex business line!
Thank you for you attention.
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