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Policy2026-07-22 · source-backed
The tool scans posts, notes, replies, and comments to estimate whether text was machine-written, making Substack the first major newsletter platform to surface detection scores natively to readers rather than using them for internal enforcement (The Verge). The problem is straightforward: detection accuracy on well-edited hybrid human/AI writing is poor, and Substack is putting a probabilistic number in front of readers as if it were a verdict. Every writer using AI for research or editing is about to be graded by a classifier that can't distinguish that from generation. Watch this collide with Codeberg's proposed ban, both attempting enforcement without a reliable detector.
Each link below shares sources, entities, or timing with this story.
PR #1253 would extend the ToU to prohibit "LLM-extrusions," making Codeberg the first major code forge attempting a blanket policy ban rather than leaving it to per-project maintainer discretion. The HN thread (41 points, 58 comments) mostly argued about enforceability, which...
The July 29 technical report claims AUROC 0.9916 with a 0.0041% false positive rate and 0.3396% false negative rate, plus better out-of-distribution generalization and adversarial robustness than Pangram 3, with fine-grained discrimination of edits and mixed AI-human co-writin...
TechCrunch, July 30: flagged posts get reduced algorithmic reach the way "not interested" works today. Simultaneously LinkedIn is retiring "enhance your post," the feature that rewrote user text, replacing it with a proofreader that fixes grammar and spelling without touching...
Following Codeberg's move to amend its terms prohibiting LLM-generated content, two critical posts landed: an "I regret migrating to Codeberg" account at 522 points and 526 comments, and Armin Ronacher's "Codeberg Divides" at 128 points and 186 comments. Comment counts matchin...
The Verge reports roughly 93 employees, with CEO Jack Conte framing it in an internal memo first reported by 404 Media. A creator-economy platform is a different data point than big-tech restructuring, because Patreon isn't cutting to fund a capex bet. Watch whether mid-size S...
At early deployment maturity, task-level error detection may be infeasible, so monitor architecture integrity first (arXiv). This matches what I see. The agents that fail in ugly ways aren't getting individual answers wrong, they're stuck in loops, calling tools in bad orders,...
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