Jules is a freelance consultant specialising in safety-critical systems, mathematical software, and compilers and languages. He has been writing, teaching and speaking for 25 years, and conducts frequent lectures and workshops. He is the author of “Extreme Reliability: Programming like your life depends on it”, and is the originator of Problem Space Analysis.
As developers, we all know how damaging technical debt can be: it decreases velocity, reliability, and daily joy. We know that true agile working requires constant refactoring to bring technical debt down. And yet, in the constant drive to develop new features and (if we’re lucky) fix old bugs, our lords and masters urge us ever onwards, faster and faster, feature after feature, until the codebase collapses into a sticky mess.
Technical debt is not merely a matter of programmer-aesthetics, it genuinely goes the heart of what quality development is all about. But non-technical managers don’t get that. To them, code is just code: if it works: it works, and if it doesn’t: there are plenty of hackers looking for work who are more competent than you.
In order to align our bosses’ needs with ours, we need to be able to express what technical debt is in language which is familiar to them. That is the language of finance. In this talk, we will explore how to quantify the cash value of code, how to measure technical debt, and how to find the actual interest rate the business is paying for it. This is how you can beat the bean-counters at their own game, and put the joy back into your code.
How would you like to see an actual quantum computer, actually working?
Everyone's heard about quantum computers - how they'll be able to solve every computational problem in the blink of an eye, decrypting every coded message, and spilling our secrets across the internet. That’s if they ever get delivered: for all the talk, nobody seems able to construct a working quantum computer. So is the whole idea nothing more than fairy dust?
Actually, quantum computers do exist, and we can use them to run real algorithms. Within a few years, quantum computers are going to be a useful part of the programmer’s armoury, routinely solving problems in optimisation, recognition, machine learning, simulation, and cryptography that no other technology can handle.
(Optionally) Quantum computers are worth studying now because they’re just about ready for commoditisation and large-scale adoption, in the same way that AI systems are now being commoditised. And the kind of techniques that are used to tame uncertainty in quantum systems can also be used to tame unreliability in networks of conventional computers.
This session explains what a quantum computer is, why it is so different from a conventional computer, and how we design quantum algorithms. Finally, it will show a simple, “Hello, Quantum World” program running on real quantum hardware.
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