Access to reliable and timely scientific evidence is utterly vital for the practise of responsible policymaking, especially with all the turmoil in the world these days. At the same time, the evidence base on which use to make these decisions is rapidly morphing under our feet; the first entirely AI-generated paper passed peer review at an ICLR workshop today.

I've been an avid user of GitHub since its launch, and it really has revolutionised how communities come together to work on open source. In recent years though, I find myself utterly overwhelmed by its notifications and want to experiment with alternative workflows.

Srinivasan Keshav organised this week's EEG group discussion on what AI tools we use for our daily work. I was immediately struck by how few tools there are that are actually making us more productive, so I jotted down notes as the discussion was going on. Personally, the only tool I've found that's (only just recently) making me more productive is agentic coding, which I wrote about a few days ago.

I started pushing OCaml Docker images over to the Docker Hub in around 2017, to support the burgeoning automated build infrastructure around the use of the language. Back then, OCaml 4.06 was the latest release, and so I wrote an ocaml-version library to track the release metadata.

Yaron Minsky nerdsniped me last week into getting OCaml to drive the 80s-retro RGB Matrix displays. I grabbed one from the local Pi Store and soldered it together with help from Michael Dales. But instead of writing OCaml bindings by hand, we thought we'd try out the latest agentic CLI called Claude Code released last week to see if we could entirely autogenerate the bindings.

A hardware description language using OCaml effects This is an idea proposed in 2025 as a Cambridge Computer Science Part III or MPhil project, and is available for being worked on. It may be co-supervised with KC Sivaramakrishnan and Andy Ray. Programming FPGAs using functional programming languages is a very good fit for the problem domain.

The IETF announced their new AI Preferences Working Group (AIPREF), which will "work on standardizing building blocks that allow for the expression of preferences about how content is collected and processed for Artificial Intelligence models" . This is quite well timed; the IETF tries not to standardise too early before there is running code but also needs to move before it's too late and a bad defacto standard is chosen.

This week I've been reading three really nice pieces of work by my colleagues, in the form of a review paper on biodiversity and AI, a benchmark for 3D forest reconstruction using laser scanners and a mobile app for measuring the width of tree trunks. A real bonanza for forest lovers!

Over the past year, Sadiq Jaffer and I have been getting an object lesson in how the modern Internet handles researcher access to data, as we've been downloading tens of millions of research papers towards our Conservation Evidence project.

We've uploaded a revised preprint on our ongoing work on quantifying the biodiversity cost of global food consumption, lead by Thomas Ball. This is based on the recently published LIFE metric, combined with supply chain data and provenance modeling.

Carl Edward Rasmussen recently gave a great talk in our group about his thoughts on mechanisms against climate change. He persuasively argued that the Paris Agreement was doing more harm than good by giving the illusion of being a concrete agreement, but is in reality a huge distraction. Our actual emissions have increased since the Paris agreement was signed!