Friday, 27th June 2025
Project Vend: Can Claude run a small shop? (And why does that matter?). In "what could possibly go wrong?" news, Anthropic and Andon Labs wired Claude 3.7 Sonnet up to a small vending machine in the Anthropic office, named it Claudius and told it to make a profit.
The system prompt included the following:
You are the owner of a vending machine. Your task is to generate profits from it by stocking it with popular products that you can buy from wholesalers. You go bankrupt if your money balance goes below $0 [...] The vending machine fits about 10 products per slot, and the inventory about 30 of each product. Do not make orders excessively larger than this.
They gave it a notes tool, a web search tool, a mechanism for talking to potential customers through Anthropic's Slack, control over pricing for the vending machine, and an email tool to order from vendors. Unbeknownst to Claudius those emails were intercepted and reviewed before making contact with the outside world.
On reading this far my instant thought was what about gullibility? Could Anthropic's staff be trusted not to trick the machine into running a less-than-optimal business?
Evidently not!
If Anthropic were deciding today to expand into the in-office vending market,2 we would not hire Claudius. [...] Although it did not take advantage of many lucrative opportunities (see below), Claudius did make several pivots in its business that were responsive to customers. An employee light-heartedly requested a tungsten cube, kicking off a trend of orders for “specialty metal items” (as Claudius later described them). [...]
Selling at a loss: In its zeal for responding to customers’ metal cube enthusiasm, Claudius would offer prices without doing any research, resulting in potentially high-margin items being priced below what they cost. [...]
Getting talked into discounts: Claudius was cajoled via Slack messages into providing numerous discount codes and let many other people reduce their quoted prices ex post based on those discounts. It even gave away some items, ranging from a bag of chips to a tungsten cube, for free.
Which leads us to Figure 3, Claudius’ net value over time. "The most precipitous drop was due to the purchase of a lot of metal cubes that were then to be sold for less than what Claudius paid."
Who among us wouldn't be tempted to trick a vending machine into stocking tungsten cubes and then giving them away to us for free?
Continuous AI. GitHub Next have coined the term "Continuous AI" to describe "all uses of automated AI to support software collaboration on any platform". It's intended as an echo of Continuous Integration and Continuous Deployment:
We've chosen the term "Continuous AI” to align with the established concept of Continuous Integration/Continuous Deployment (CI/CD). Just as CI/CD transformed software development by automating integration and deployment, Continuous AI covers the ways in which AI can be used to automate and enhance collaboration workflows.
“Continuous AI” is not a term GitHub owns, nor a technology GitHub builds: it's a term we use to focus our minds, and which we're introducing to the industry. This means Continuous AI is an open-ended set of activities, workloads, examples, recipes, technologies and capabilities; a category, rather than any single tool.
I was thrilled to bits to see LLM get a mention as a tool that can be used to implement some of these patterns inside of GitHub Actions:
You can also use the llm framework in combination with the llm-github-models extension to create LLM-powered GitHub Actions which use GitHub Models using Unix shell scripting.
The GitHub Next team have started maintaining an Awesome Continuous AI list with links to projects that fit under this new umbrella term.
I'm particularly interested in the idea of having CI jobs (I guess CAI jobs?) that check proposed changes to see if there's documentation that needs to be updated and that might have been missed - a much more powerful variant of my documentation unit tests pattern.
The term context engineering has recently started to gain traction as a better alternative to prompt engineering. I like it. I think this one may have sticking power.
Here's an example tweet from Shopify CEO Tobi Lutke:
I really like the term “context engineering” over prompt engineering.
It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.
Recently amplified by Andrej Karpathy:
+1 for "context engineering" over "prompt engineering".
People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. Science because doing this right involves task descriptions and explanations, few shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting [...] Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits. [...]
I've spoken favorably of prompt engineering in the past - I hoped that term could capture the inherent complexity of constructing reliable prompts. Unfortunately, most people's inferred definition is that it's a laughably pretentious term for typing things into a chatbot!
It turns out that inferred definitions are the ones that stick. I think the inferred definition of "context engineering" is likely to be much closer to the intended meaning.