This document was adapted from Style Directives in Jack Bradshaw’s Monorepo. Effective documentation is the cornerstone of sustainable development; however, the inevitability of team growth makes it hard to write documentation as team that isn’t fragmented and inconsistent; therefore, some degree of coordination between contributors is necessary. Even solo engineers, who once had a free pass, must now contend with the inconsistency of AI-generated documentation. I myself use AI to generate some of the documentation in my repository, […]

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Thinking Machines Lab has moved its Tinker training API into general availability and added 3 major capabilities, support for the Kimi K2 Thinking reasoning model, OpenAI compatible sampling, and image input through Qwen3-VL vision language models. For AI engineers, this turns Tinker into a practical way to fine tune frontier models without building distributed training infrastructure. What Tinker Actually Does? Tinker is a training API that focuses on large language model fine tuning and hides the heavy lifting […]

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Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that maximizes an entropy function while respecting prior information represented as potential functions in miscellaneous forms of constraints and/or penalties. We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry. The proposed approach leads to a family of discriminative semi-supervised […]

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Converting bytes into readable strings in Python is an effective way to work with raw bytes fetched from files, databases, or APIs. You can do this in just three steps using the bytes.decode() method. This guide lets you convert byte data into clean text, giving you a result similar to what’s shown in the following example: Python >>> binary_data = bytes([100, 195, 169, 106, 195, 160, 32, 118, 117]) >>> binary_data.decode(encoding=”utf-8″) ‘déjà vu’ By interpreting the bytes according […]

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Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The most popular performance models are networks of queues. However, no current methods exist for parameter estimation or inference in networks of queues with missing data. In this paper, we present a novel viewpoint that combines queueing networks and graphical models, allowing Markov chain Monte Carlo to be applied. We demonstrate the effectiveness of […]

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This intriguing paper kept me thinking long enough for me to I decide it’s time to resurrect my blogging (I started writing this during ICLR review period, and realised it might be a good idea to wait until that’s concluded) Sang Michael Xie, Aditi Raghunathan, Percy Liang and Tengyu Ma (2021) An Explanation of In-context Learning as Implicit Bayesian Inference I liked this paper because it relates to one of my favourite concepts and ideas: exchangeability. And it […]

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The Bateman-Horn conjecture is a conjecture on prime values in polynomials. We prove it by Golomb’s method.

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