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Scotoma-2 is an updated fine-tune of Gemma-4-31B-it that reduces repetitive writing tics via targeted preference training while keeping the base model's intelligence. It is not uncensored but aims to produce cleaner, less annoying prose for roleplay.
Writers are deliberately altering their prose to avoid AI-like patterns, such as avoiding em dashes and tripartite lists, as part of a growing 'anti-AI literary counterculture' to signal human authorship.
Johnny Heo shares a tip for improving user-facing explanations by adding a style rule to agent or Claude.md files to write clearly without jargon, and setting personality to none in .codex/config.toml.
Fenng has updated the Chinese Tech Doc Style project, a writing skill focused on Chinese tech documentation, product copy, and UI text. It's approaching 400 Stars.
Jerry Liu shares that creating a skill to reflect his writing style and hillclimbing based on real samples solves 70% of the style problem, but notes that articulating clear insights remains a post-training challenge.
This blog post explores using reinforcement learning to train a small model to detect writing styles of different LLMs, providing code and traces.
A user describes how they've developed an intuition for detecting ChatGPT's writing style, noting patterns that persist even after editing, and confirms this using the Lynote AI detector.
A Twitter user shares a voice-dna.md file that configures Claude to adopt the user's personal writing style by setting rules, banned phrases, and requiring writing samples to pattern-match against.
The author shares a personal experience of being accused of using AI-generated writing because of their frequent use of emdashes, reflecting on the broader issue of AI detection and the stigma around certain writing styles.
The author investigates how LLMs are influencing word usage in coding and everyday language, finding that words favored by LLMs show increased frequency in both coding sessions and Google Trends, raising concerns about humans adopting LLM writing styles.