cagz

Exactly. As a software developer, AI has given me more time to tackle higher-order problems. The actual architecture, optimisation, and user experience.

Mathematicians could:

- Continue working on philosophical aspects of the solution and not bothered by whether the solution is known or not. It is not a movie that gets spoiled when the ending is known.

- Or better, demand from AI labs, how they would like to see AI helping them on philosophical aspects.

The article starts interestingly; I like the analogy between cold reading and LLM responses (well, the ones we were getting three years ago). Then it goes on to generalise to the tune of "This new era of tech seems to be built on superstition and pseudoscience" which I think was a conclusion better left to the reader.

We are used to articles, blogs that stayed relevant for years. It is hardly the case with AI, the speed of change is so high that an observation can become stale in a year. This makes it very important for articles to state that the ideas are based on the technology in year X, rather than generalising them as universal truths.

The title feels clickbaity. Direct API, CLI and MCP all have their uses.

Direct access (Curl/own small function): This requires agent to have full understanding of the API spec. Yes, context can be protected using progressive disclosure, but this essentially means agent needing to understand the API again and again, before every use. Also, a typical API spec may or may not be agent-friendly. If there are nuances when calling an endpoint, where do we put these? Into OAS description? Works, but clunky.

CLI: It works beautifully, especially when a 3rd party CLI already exists for a complex backend. Assumes a well documented, agent friendly CLI, most CLIs are designed for human or CI/CD consumption. Talking about MCP taking up too much context, think about agent starting with my_cli --help, and going down through the switches and parameters one at a time to figure out how the CLI should be called. Less of a problem when calling a well know CLI (e.g. aws), but anything more niche (or custom) requires multiple turns to compose the final CLI command.

MCP: Has its issues, but offers an agent-native solution. Everything agent needs to know about a tool becomes available at once. In an enterprise environment MCPs can be served through an MCP Gateway, providing governance and permission management, this is quite contrast against running a CLI that requires agent to have execute permissions in its shell.

I wish this were a document that clearly described how mathematicians expect AI to behave, rather than how it is misaligned. I can kind of decipher the expectation from the declaration, but it still sounds a bit like "it would be better if AI didn't meddle with mathematics", instead of "we want AI to approach problems like this and that way".

Yes, quite a few uses in prod, main use case being abstracting API access for agents. In our case these are mostly in-house MCP servers, purpose built for the given agent.

Why MCP instead of CLI or agent accessing API directly?

Direct access (Curl/own small function): This requires agent to have full understanding of the API spec. Yes, context can be protected using progressive disclosure, but this essentially means agent needing to understand the API again and again, before every use in that context. Also, a typical API spec may or may not be agent-friendly. If there are nuances when calling an endpoint, where do we put these? Into OAS description? Works, but clunky.

CLI: It works beautifully, especially when a 3rd party CLI already exists for a complex backend. Assumes a well documented, agent friendly CLI, most CLIs are designed for human or CI/CD consumption. Talking about MCP taking up too much context, think about agent starting with my_cli --help, and going down through the switches and parameters one at a time to figure out how the CLI should be called. Less of a problem when calling a well know CLI (e.g. aws), but anything more niche (or custom) requires multiple turns to compose the final CLI command.

MCP: Has its issues, but offers an agent-native solution. Everything agent needs to know about a tool becomes available at once. In an enterprise environment MCPs can be served through an MCP Gateway, providing governance and permission management, this is quite contrast against running a CLI that requires agent to have execute permissions in its shell.

I must mention that we also utilise lazy-loading of MCPs. In use cases where 10s of tools needs to be loaded, only the most common ones are pre-loaded, then agent kernel connects the others as an when needed, and release them after a timeout (in case of long running sessions). This keeps the context lean.

It is BTP, not London Underground, they are not the same thing.

Outages happen. What concerned me most was the time taken for the full resolution (almost 12 hours). I am hoping that root cause analysis promised will look into this as well and how it can be improved.

Is the 1/10+ reduction in memory usage applicable to larger models, i.e. would be possible to run a 200Gb model, by using 20Gb of memory?

I couldn't help noticing on the screenshot that user having direct access to a production database from their laptop :)

My workflow is: - Write the test first, confirm it fails - Write the minimum code to make that test pass - Confirm it is green