Documenting what you’re willing to support (and not)

The idea is that I’ll be able to point at it and go “that, right there, see, I’m not being capricious or picking on you in particular… this represents a common problem which has existed since well before you showed up”. The items are short and sweet and have unique identifiers so it’s possible to point at one and say “do it like that”.

Let Your Kid Climb That Tree – The Atlantic

Childhood might be a particularly costly time to not move, because this is when developing brains prune unused potential. “One extreme view” of this neurological dwindling “would be that you start out wired up for every possible contingency,” the Harvard neuroscientist Jeff Lichtman said in an article in Proceedings of the National Academy of Sciences—but as you age, unused connections in the brain get permanently disconnected through a process known as synaptic pruning, leaving you with “a narrower nervous system.”

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity – METR

When developers are allowed to use AI tools, they take 19% longer to complete issues—a significant slowdown that goes against developer beliefs and expert forecasts. This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%.

The lethal trifecta for AI agents: private data, untrusted content, and external communication

The lethal trifecta of capabilities is:

Access to your private data—one of the most common purposes of tools in the first place!

Exposure to untrusted content—any mechanism by which text (or images) controlled by a malicious attacker could become available to your LLM

The ability to externally communicate in a way that could be used to steal your data (I often call this “exfiltration” but I’m not confident that term is widely understood.)

AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County

Despite the Supreme Court holding such racial covenants unenforceable in 1948, they persist in property records across the United States. Many jurisdictions have moved to identify and strike these provisions, including California, which mandated in 2021 that all counties implement such a process. Yet the scale can be overwhelming, with Santa Clara County (SCC) alone having over 24 million property deed documents, making purely manual review infeasible. We present a novel approach to addressing this pressing issue, developed through a partnership with the SCC Clerk-Recorder’s Office. First, we leverage an open large language model, fine-tuned to detect racial covenants with high precision and recall. We estimate that this system reduces manual efforts by 86,500 person hours and costs less than 2% of the cost for a comparable off-the-shelf closed model. Second, we illustrate the County’s integration of this model into responsible operational practice, including legal review and the creation of a historical registry, and release our model to assist the hundreds of jurisdictions engaged in similar efforts. Finally, our results reveal distinct periods of utilization of racial covenants, sharp geographic clustering, and the disproportionate role of a small number of developers in maintaining housing discrimination.