explainx / blog
Substack launched an AI-writing detector powered by Pangram, scoring posts and comments as human, AI-assisted, or AI-generated. How it works.
Jul 23, 2026
Peter Yang released /no-ai-slop on July 22, 2026 — an open-source skill for Claude and similar tools that targets over 20 repetitive AI writing patterns (fake-profound closers, binary contrasts, throat-clearing openers) while explaining every edit and preserving your voice. It hit nearly 1,000 GitHub stars within a day. Here's how the skill works and how to install it.
Jul 17, 2026
A Hacker News hit (~162 pts) revives classical ML for AI text detection: TF-IDF features, LinearSVC, seven binary classifiers with majority voting. lyc8503 trains on human web fiction plus LLM-regenerated twins — ~85% sentence accuracy, ~70% on unseen Claude Sonnet 4.6 and GPT 5.2. This guide explains the method, web demo, bypass limits, and responsible use.
Jul 23, 2026
Simon Willison's "SVG of a pelican riding a bicycle" prompt has become AI's most famous informal benchmark, and the obvious suspicion is that labs quietly train on it. Dylan Castillo tested the hypothesis directly — generating 1,008 SVGs across 8 animals x 6 vehicles x 7 models and running a difficulty-adjusted regression. The result: no statistically significant pelican-specific or bicycle-specific boost at any lab.
TL;DR: Substack launched an AI detection feature on July 21, 2026, integrating with Pangram to scan posts, notes, replies, and comments over 100 words, showing an estimated split between human-written, AI-assisted, and AI-generated content. It's live on web and iOS, with Android coming. CEO Chris Best framed the goal as stopping Substack from becoming "like LinkedIn" — flooded with low-quality AI content. Writers are split: some cheer it as validation for real effort, others worry about false positives on genuinely human writing.
@Substack announced the feature on X:
"Today, Substack is launching an AI detection feature, via an integration with @pangram. Going forward, you'll be able to scan posts, replies, and comments on the Substack app to see an estimate of how much of it was written by a human, or with AI assistance."
Pangram is an AI-text-detection company; Substack's integration runs its detection model over content published on the platform. The scan applies to posts, notes, replies, and comments over 100 words, covering content published since July 21, 2026 — it does not retroactively score the platform's entire back catalog. The feature is live on web and iOS, with Android support coming per the announcement.
Substack CEO Chris Best (@chrisbest) introduced the reasoning: the platform wants to avoid becoming a venue flooded with "AI slop" — the same term driving Peter Yang's /no-ai-slop skill release the same week, a coincidence of timing that put both stories in front of the same audience simultaneously.
| Question | Answer |
|---|---|
| What launched? | AI detection on posts, notes, replies, comments |
| Powered by? | Pangram, an AI-text-detection company |
| Launch date? | July 21, 2026 |
| Minimum content length? | 100 words |
| Coverage window? | Content published since July 21, 2026 |
| Platforms? | Web and iOS now; Android coming |
| Output? | Estimated human / AI-assisted / AI-generated percentage split |
| Can writers opt out? | Yes — can add AI usage statements or opt out of display |
| Retroactive? | No — doesn't score pre-July 21 content |
Best's framing directly names the fear driving this feature: that Substack could become like LinkedIn, a platform widely mocked for volumes of generic, AI-flavored "thought leadership" content that reads as formulaic regardless of topic. Substack's business model depends on readers trusting that a subscription buys access to a specific writer's actual thinking — if a meaningful share of content on the platform is AI-generated without disclosure, that trust proposition weakens for every writer on the platform, not just the ones producing low-effort content.
This is a different motivation than, say, a search engine trying to demote AI spam for ranking-quality reasons. Substack's stakes are more direct: writers pay to be there, readers pay to read specific writers, and the entire value chain depends on perceived authenticity of authorship.
The most substantive pushback on X targeted detection accuracy, not the feature's intent. @AdamRy_n wrote:
"i'm 1000% for AI slop to be removed from the world. also, when i run my old tweets from 2021/2022 into these programs they come back ~80% AI. The world has people who wrote a certain way and the LLMs learned how to write from those people. And now their way of writing can be [flagged]"
This is a structural problem with AI-text detection generally, not specific to Pangram: LLMs were trained on human writing, including specific writers' distinctive styles. A writer whose natural voice already features rhythms or patterns that overlap with common AI outputs — repetitive sentence structures, certain transitional phrases, particular punctuation habits — risks being misclassified, especially on older writing predating the current AI-slop aesthetic entirely. The claim of ~80% AI on 2021-2022 tweets (written before ChatGPT's public release in most cases) is a pointed illustration: text that could not possibly have been AI-generated at time of writing still triggered a high AI-likelihood score.
Substack's mitigation is disclosure, not certainty: writers can add an AI usage statement or opt out of the display. That shifts the burden toward writers proactively explaining their process rather than the platform claiming definitive detection accuracy — a more defensible design than presenting Pangram's score as ground truth.
Within hours of the launch, at least one account was already positioning a business around defeating AI detectors:
"i'm launching a new startup. we're training a custom model whose sole purpose is to defeat ai writing detectors. our guarantee is simple… you write with ai, submit it anywhere, & every detector will confidently certify it as 100% human. we'll always have flat pricing & charge [...]" — @signulll
Whether that's a genuine venture pitch or commentary-as-satire, it names the predictable dynamic: any new content-classification system creates a market incentive to build a generator or post-processor specifically engineered to defeat that classifier. This mirrors the long-running arms race in spam filtering and plagiarism detection — no detector stays effective indefinitely once evasion becomes commercially valuable, and Pangram will likely need continuous retraining to keep pace with adversarial writing tools built specifically against it.
This is precisely where Peter Yang's /no-ai-slop skill sits in an interesting middle ground: it's explicitly framed as a style cleanup tool, not a detector-evasion tool, but the practical effect of removing AI "tells" from text is adjacent to what a detector-defeat service would also want to do. The difference is stated intent and workflow (manual draft + AI edit pass, per Yang) rather than anything technically distinguishable in the output.
Not everyone treats detection as obviously good. Shreyas Doshi posted a widely shared flowchart-style take:
"It really is this simple: Is it useful? → Yes → Then why does it matter that AI wrote it? Take what's useful, ignore the rest."
Doshi's framing pushes back on origin-based judgment entirely — arguing readers should evaluate content on its merits rather than its production method. He clarified in a follow-up that this isn't a blanket endorsement of AI writing, just a rejection of treating AI-origin as automatically disqualifying.
This is a genuine philosophical fork in how platforms could handle AI content: Substack's approach (surface the estimate, let readers and writers factor it in) versus Doshi's implied approach (evaluate output quality, treat provenance as secondary). Substack's choice to show a percentage rather than block or penalize AI-flagged content actually leaves room for both approaches to coexist — readers who care about provenance can filter on it; readers who only care about quality can ignore the badge.
If you write on Substack:
/no-ai-slop skill targets exactly this, though it addresses readability, not guaranteed detector outcomes.If you're building on other content platforms:
Primary sources: @Substack on X · @chrisbest on X · @pangram on X
Feature details reflect Substack's July 21, 2026 announcement and X reaction as of publication. Detection accuracy figures cited are user-reported, not independently benchmarked — verify current behavior directly on the platform.