"AI Slop": Telegram Bot Developer Figured Out Why the AI Label Irritates the Audience

A developer created a Telegram bot that uses a neural network for automatic classification of articles and videos from the "read later" queue.

What the bot does

The bot solves a concrete problem: an endless "read later" folder in a browser that nobody gets around to for weeks. A user throws in a link — the bot takes over:

• automatically determines the category of the material
• estimates the time to read an article or watch a video
• reminds again and again until the user deals with the content
• helps complete online courses and books instead of abandoning them halfway

Three months of evening development, real utility — and one unfortunate word in the description.

Why the top comment got +14 votes

The author was starting each text about the bot with phrases like "runs links through AI" or "AI inside" — for gravitas. The top comment under the latest article got more votes than all the rest of the thread discussion combined. It showed a grimace with the caption "my face when I read about another AI product."

"My face when I read about another AI product" — top comment in the

thread, +14 votes.

The grimace looked familiar to the developer himself: that's exactly how he looks at every "AI-powered" landing page. When positions switched, it became clear what exactly annoys the technical audience — not the bot, but how it's presented. The developer went to figure out why two words irritate the technical community so much. He figured it out — and discovered that he himself liberally scattered "AI" in places he hadn't thought to look before.

Where AI-fatigue comes from

"AI-fatigue" is not audience caprice but a reaction to a persistent pattern repeated too often. Several reasons feeding it:

• Label inflation: over the last two years "AI-powered" has appeared on apps, cameras, vacuum cleaners and coffee machines — regardless of whether an actual model works inside
• No specifics: the phrase "uses AI" doesn't explain what the model does or why it's needed in this particular product
• Broken trust: too many "AI revolutions" never happened, and now the audience sees the label as a warning rather than an advantage
• Platform shapes expectations: Habr is not Product Hunt. Here readers ask "how exactly?" and "why?" and don't accept marketing claims on faith

All of this works as a filter: when a reader sees "AI-powered", the first thought is "what exactly?". If there's no answer in the next sentence — trust falls rather than rises.

The developer's story is noteworthy because he wasn't trying to sell hot air. The bot actually works, solves the problem. The issue turned out to be purely in wording — but that's exactly what determined how the audience perceived the entire project.

What this means

If a language model inside a product solves a concrete task, it's worth showing this through the result: "classifies from twelve categories" works better than "runs on AI". When "AI inside" is the first argument — that's a signal there are no other arguments. The technical audience on Habr feels this — and responds with a grimace with fourteen votes.

What problem does the

Telegram bot with a neural network solve? Helps automatically organize a "read later" folder — the bot determines the material category, estimates reading time for an article or video watch time and reminds the user.

How does a neural network help organize content in the bot?

Automatically determines the material category and estimates reading time.

Why might AI in such tools irritate users?

According to the developer, the bot's description matches typical descriptions of annoying "AI products".