@june_thalia_michael@literatur.social#EroticMusings 41: Are you self-taught, or have you been trained or mentored in your craft? Would you like more?
Trained in writing patterns by wolfing down absurd amounts of books starting at age nine.
Honed by writing novel-length stuff by hand during school out of sheer boredom (even though my wrist hurt badly).
Polished by receiving Bachelor and Master degrees related …
@thomasfuchs@hachyderm.ioOr maybe sometimes you stop writing a reply and instead you're starting to make a post of your own about a topic, etc.
This is actually a nice demonstration of how the writing process itself helps you think things through.
@rainerzufall_le@mastodon.social"Bitcoin is crashing hard, reaching historic lows of well below the $70,000 mark. At the time of writing, the token is hovering just above $63,000, levels we haven’t seen since October 2024."
"According to Coindesk, the average cost to mine one Bitcoin is currently around $87,000 — far higher than its current going rate, making it an extremely unprofitable proposition."
🥳🥳🥳
@Mediagazer@mstdn.socialThe Atlantic hires David Brooks as a staff writer and host of a new weekly video podcast, starting in February, after 22 years as an NYT opinion columnist (The Atlantic)
https://www.theatlantic.com/press-releases/2026/01/atlantic-hires-davi…
@arXiv_csCL_bot@mastoxiv.pageEarlySciRev: A Dataset of Early-Stage Scientific Revisions Extracted from LaTeX Writing Traces
L\'eane Jourdan, Julien Aubert-B\'educhaud, Yannis Chupin, Marah Baccari, Florian Boudin
https://arxiv.org/abs/2603.28515 https://arxiv.org/pdf/2603.28515 https://arxiv.org/html/2603.28515
arXiv:2603.28515v1 Announce Type: new
Abstract: Scientific writing is an iterative process that generates rich revision traces, yet publicly available resources typically expose only final or near-final versions of papers. This limits empirical study of revision behaviour and evaluation of large language models (LLMs) for scientific writing. We introduce EarlySciRev, a dataset of early-stage scientific text revisions automatically extracted from arXiv LaTeX source files. Our key observation is that commented-out text in LaTeX often preserves discarded or alternative formulations written by the authors themselves. By aligning commented segments with nearby final text, we extract paragraph-level candidate revision pairs and apply LLM-based filtering to retain genuine revisions. Starting from 1.28M candidate pairs, our pipeline yields 578k validated revision pairs, grounded in authentic early drafting traces. We additionally provide a human-annotated benchmark for revision detection. EarlySciRev complements existing resources focused on late-stage revisions or synthetic rewrites and supports research on scientific writing dynamics, revision modelling, and LLM-assisted editing.
toXiv_bot_toot
