Randall Thomas
Hacker. Musician. Bon Vivant
Randall Thomas (@daksis) is a classically trained musician that took one too many calculus classes and got sucked into geekery: computers, robots, video games, high energy physics - the usual suspects.
Afflicted with rabid technology ADD, Randall has built companies in various industries with numerous startups -- everything from robotics to digital video to cloud computing. After escaping a high-security military prison and driving a heavily armed conversion van filled with ex spec-ops soldiers for hire around the Los Angeles underground,
Randall founded Thunderbolt Labs - a software consultancy that teaches companies how to build better software by embedding with their teams and building it beside them.Randall is an internationally recognized speaker on practical data techniques and the insanely nonsensical business of startups.When not glued to a computer Randall is likely lost in a book or on a running trail wondering if he will get to the end of either. He has a fondness for good food and weakness for great whiskey and will happily discuss either at length.
Past Activities
Code BEAM V America
13.10 - 13.50
Forum over Functions
What 20+ years of bad languages, awful frameworks, and half-assed implementations tell us about Elixir, Erlang, and the future of BEAM?
He who cannot remember history is doomed to repeat it—an apt aphorism summing up the last 30 years of software development if ever there was one. We will take a walk down memory lane and identify the mistakes languages and frameworks seem to make over and over again and why languages like Elixir might just help us get out of the vicious cycle.
Code BEAM V Europe
15.40 - 16.30
Panel on Machine Learning on the BEAM
Machine learning is a method of programming where software is generated by other software. It's taught, or "learned" using examples, statistics, and iterative improvements to create useful functions. Nx, Axon, and other projects in Elixir and Erlang are bringing the benefits of machine learning to the BEAM. In this discussion, a panel of machine learning experts consider recent development in ML and BEAM languages and explore future work to help release the promise of machine learning for scalable, fault tolerant systems. If you're new to machine learning or a seasoned expert, this discussion will bring you up to speed on ML and the BEAM and will inspire your work in data-enabled application development.
Code BEAM SF
16.15 - 17.00
(Un)Learning Elixir
Elixir is a well thought out language with a rapidly evolving ecosystem of libraries and tools. Few languages offer so many useful solutions across so many domains: web applications, embedded systems, distributed systems, just to name a few.
However with great power can come great heaping mounds of frustration. If you're new to Elixir... or coming back after giving up the first few times... it can be tough to figure out what to do and more importantly what _not_ to do.
In this talk we'll take a step back and rethink the way we think about our - er -thinking ;) Learning a functional quickly requires a different approach than picking up the latest dialect with C inspired syntax.
For developers new to Elixir, we'll help get you pointed in the right direction. For Rubyists thinking about dipping a toe (or diving) into the thread pool, we can help you identify those early-stage "gotchas" that keep Rubyists from making an easy transition into the Elixir ecosystem.
THIS TALK IN THREE WORDS
Learn
Elixir
Faster
OBJECTIVES
- Give attendees the tools they need to lower the Elixir learning curve
- Detail what's different about learning a functional language vs a procedural language and why it requires a different approach
- How to learn Elixir (or any functional language) faster with less frustration
TARGET AUDIENCE
Those new to Elixir; those that are struggling to get better at Elixir