Chris Loy

Software engineering is a collaborative discipline, in which a single engineer typically works closely with others in a cross-functional team.

When it comes to using AI to write code, I have been thinking a lot about porridge.

I recently became a parent for the first time, as my wife and I welcomed a beautiful baby boy into our lives in January of this year.

> _Industrial_ > > _adj.

As AI assistants become more dominant in the software industry, one best practice that I have recommended(/post/2025/09/28/the-ai-coding-trap) is to invest time and tooling in using AI across the whole SDLC (software development lifecycle).

If you ever watch someone “coding”, you might see them spending far more time staring into space than typing on their keyboard.

I recently built a small flashcard web app(https://unthink.chrisloy.dev/) to help me learn and avoid various cognitive biases and logical fallacies.

The advent of AI-assisted coding has made it almost trivially easy to spin up custom software.

As our use of LLMs has changed from conversational chatbots and into integral decision-making components of complex systems, our inference approach must also evolve.

Ever been asked to deploy a machine learning model that outputs a "vector" or an "embedding"?

No, this is not another hot take about how AI is going to replace software engineers.

Whenever people ask me about my philosophy for building software, top of my list is always to say that I am a proponent of modular design.

The last few years have seen an explosion of innovation within the technology sector, driven by the advent of widely-accessible, commodity-priced Large Language Models (LLMs) such as OpenAI’s GPT family or Anthropic’s Claude models.

Many job postings include a required skill for candidates to display “comfort with ambiguity”, a definition that itself can seem ambiguous alongside harder skills such as experience managing people, knowledge of particular technologies and so on.

Any technological endeavour will operate under a series of constraints.

The most successful organisations have leaders with clear divisions in their responsibilities.

In Newtonian mechanics, the degree of acceleration is proportional to the force and inversely proportional to the mass:

This article is the second in a series applying this framework to thinking about how software engineering teams operate and deliver.

A recent paper, published in Nature(https://www.nature.com/articles/s41586-024-07566-y), explores a critical issue facing generative AI models, termed "model collapse," which occurs when models are trained on data generated by earlier models, rather than…

This article defines a few terms, borrowed wholly from Newtonian mechanics, to define a framework for thinking about how software engineering teams operate.