- Transcription Pearl
A Python-based GUI application for transcribing and processing images containing historical handwritten text using Large Language Models (LLMs) via API services (OpenAI, Google, and Anthropic APIs). Designed for academic and research purposes.
- Tesserae V6
Tesserae is an intertextual analysis tool for discovering textual parallels in classical literature. It helps scholars find passages where one author may have been influenced by, alluding to, or directly quoting another text. Founded in 2008 by Neil Coffee (Department of Classics) and J. P. Koenig (Department of Linguistics) at the University at Buffalo, SUNY, this is Version 6, a modern reimplementation of the V3 algorithm.
Source: Tesserae V6
- Welcome to Recogito
Work on texts and images. Identify and mark named entities. Use your data in other tools or connect to other data on the Web. Without the need to learn code.
Source: Welcome to Recogito
- Writing notes by hand does seem to help learning, but a new experiment shows the real advantage is smaller and stranger than the internet says – Silicon Canals
More words were associated with better quiz performance in both the original study and the replication. Verbatim overlap was associated with worse performance, although that relationship was less robust in the replication. This makes the simple story awkward in a useful way: recording more can help, copying without processing can hurt, and a keyboard makes both easier.
- Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming | Towards Data Science
The scale and level of mathematical sophistication differ enormously from my own weekend project, but the underlying workflow is remarkably similar: generate many candidate approaches, eliminate most of them through exact computation and criticism, formalise what can be formalised, and reserve human judgement for novelty, correctness and significance. The important observation is not that either system solved a famous open problem. It is that mathematical experimentation itself is becoming dramatically cheaper, while proof, understanding, novelty and independent review remain scarce.
Source: Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming | Towards Data Science
- How Claude’s text watermarking works Anthropic
There are limitations to the effectiveness of watermarking. Using our key, one can only answer the question “What is the likelihood this was partly written by Claude?” It doesn’t confirm whether the text was human-written, and it can’t tell whether the text was written by a different AI (even if that other AI uses watermarking, it would have a different key; it might also use a different watermarking method altogether). Detecting a watermark also doesn’t work well on small samples, where there are fewer word choices and thus less information to go on. As a passage increases in length, confidence about Claude’s involvement increases too.