APPropriate Behaviour
The book about all the things software writers do that aren't writing software.
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Category Archives: AI
Is spec-driven development the end of Agile software development?
A claim that I’ve seen on software social media is that spec-driven development is evidence that agile was a dark path, poorly chosen. The argument goes that Agile software development is about eschewing detailed designs and specifications, in favour of experimentation and … Continue reading
Posted in agile, AI
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On the value of old principles
People using AI coding assistants typically wrestle with three problems (assuming they know what they’re trying to get the model to do, and that that’s the correct thing to try to get it to do): (It’s important to bear in … Continue reading
Posted in AI, design, OOP
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Vibe coding and BASIC
In Vibe Coding: What is it Good For? Absolutely Nothing (Sorry, Linus), The Register makes a comparison between vibe coding today and the BASIC programming of the first generation of home microcomputers: In one respect, though, vibe coding does have … Continue reading
Posted in AI, history
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Essence and accident in language model-assisted coding
In 1986, Fred Brooks posited that there was “no silver bullet” in software engineering—no tool or process that would yield an order-of-magnitude improvement in productivity. He based this assertion on the division of complexity into that which is essential to … Continue reading
Posted in AI, software-engineering, tool-support
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LLMs and reinforcement learning
My reflection on the Richard Sutton interview with Dwarkesh Patel was that it was interesting how much the two participants talk past each other, and fail to find common ground. Particularly that they couldn’t agree on the power of reinforcement learning, when … Continue reading
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Prompting software or supporting engineering
As we learn to operate these new generative predictive transformers, those of us in the world of software need to work out what we’re doing it for. The way in which we use them, the results we get—and the direction … Continue reading
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Input-Output Maps are Strongly Biased Towards Simple Outputs
About this paper Input-Output Maps are Strongly Biased Towards Simple Outputs, Kamaludin Dingle, Chico Q. Camargo and Ard A. Louis, Nature Communications 9, 761 (2018). Notes On Saturday I went to my alma mater’s Morning of Theoretical Physics, which was … Continue reading
Posted in academia, AI
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Structured Pruning of Deep Convolutional Neural Networks
Structured Pruning of Deep Convolutional Neural Networks, Sajid Anwar et al. In the ACM Journal on Emerging Technologies in Computing special issue on hardware and algorithms for learning-on-a-chip, May 2017. Notes Quick, a software engineer mentions a “performance” problem to … Continue reading
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On the continuous history of approximation
The Difference Engine – the Charles Babbage machine, not the steampunk novel – is a device for finding successive solutions to polynomial equations by adding up the differences introduced by each term between the successive input values. This sounds like … Continue reading
Impossibility and Uncertainty in AI
About this paper Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function), Peter Eckersley. Submitted to the ArXiV on December 31, 2018. Notes Ethical considerations in artificial intelligence applications have arguably … Continue reading
Posted in academia, AI
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