1. Media Concept
«AI for Everyday Life» — A Telegram channel dedicated to exploring how ordinary life tasks can be delegated to artificial intelligence and how to learn to do so quickly. The goal is for people to use AI not for the sake of technology, but for the sake of their time, convenience, and capability. The unit of content is not the neural network itself, but a life challenge: first, the person’s task, then the underlying technology.Where We Operate. Telegram is the primary platform and script database. VKontakte hosts the same posts, carousels, and VK Clips.Who It’s For. Four segments: students who are studying and living independently; young professionals experiencing burnout; parents managing family life; and cautious novices aged 40–55.What We Cover. Six life areas—six tags: home, academics, work, travel, shopping, and creativity. We recommend only services that function within Russia without a VPN and offer a free tier.
What sets us apart from the competition: we focus on problem-solving, not just prompting AI; AI for life, not just for work; our services operate in Russia without a VPN and are free; every scenario is vetted by our editorial team—featuring a «before $\rightarrow$ after» timeline and an honest «Where AI Makes Mistakes» section.
Every section is driven by data. Over six months, we produce about 150 publications and 12 challenges: three weekly posts in the format «Problem → Solution, ” one carousel titled „First Step, ” one video showcasing „Before / After, ” and one breakdown titled „You Asked”—along with a bi-weekly challenge, „Give the Task to AI.“
It’s impossible for a person to handle that volume manually, but trust in the medium relies on the knowledge that a human reviewed every script. This leads to a clear directive: delegate the routine tasks to the machine—collection, sorting, drafting, publication—and reserve the decisions for the human: topic selection, script testing, fact-checking, and the final sign-off.
2. Data Sources
Media needs to know three things: what issues concern people, how they can be solved now, and whether the AI’s response is accurate. Therefore, sources are divided into five groups.
A. Audience — internal channel data. The primary source is comments under posts: under each post, subscribers leave their issue or question, writing «I tried it and it worked» or «It didn’t work.» Additionally: polls in the channel about which topics to cover next, messages to the VK community, entries and stories from challenges, reactions, saves, and shares. In the future, another method for receiving requests—a Telegram bot—could be integrated.
B. Demand and Context — what is being searched for outside the channel: Yandex Wordstat (queries like «how to create…» or «what to cook…»), event calendars (holidays, semesters, sales, gardening season), and top posts from competitors using data from TGStat and Telemetr.
C. AI Tools — how we solve the problem: official channels and updates from Alice AI, GigaChat, and DeepSeek, availability without VPN, free limits and pricing, and new features such as handling photos, files, and voice.
D. Fact-Checking — where we verify the AI’s response: official websites (Government Services, FNS, government agencies), ConsultantPlus and Garant for legal topics, Yandex Maps and 2GIS for addresses and operating hours, and product cards on marketplaces for specifications.
E. Original Production — data we create ourselves: scenario testing (prompt, response, clarifications, time measurement), screenshots, photos, and videos from the editorial team, and errors found in the AI for the «Where AI Makes Mistakes» section.
All five groups converge in one place—a database of proven scenarios. Posts, carousels, and videos are created from it.
Rule: the machine gathers demand signals because there are many of them and they are uniform. The facts the reader will see are verified by a person.
3. Data Schema
The main entity is the script card. Each script is stored in the database as a single card with five field groups: task, solution, result, risks, and administrative data. A post, carousel, and video description are assembled from one card, so facts are verified once but reused multiple times. The task, solution, result, and risks are filled out by a person—an editor who has personally reviewed the script. This person also changes the card status to «verified» and «approved.» The system populates the administrative fields: the source of the idea (comment, survey, Wordstat, calendar), sphere and segment tags, post-publication metrics, and a reminder for re-verification.
The journey from signal to publication involves six stages: collection, annotation, topic selection, script testing, production, and publication with feedback. In the diagram, the upper track represents machine actions, the lower track represents human actions, and the middle track represents the data exchanged between stages. The audience response after publication becomes a new signal for the first stage, thus closing the loop.
Stages 1–2. Collection and Tagging. The machine collects comments from Telegram and VK posts, responses from polls, TG and VK statistics, Wordstat data, competitor top lists, and AI service updates. A person manages the event calendar, selects challenge submissions, and moderates comments.
Subscriber comments are anonymized before any other processing: names, addresses, phone numbers, and document numbers are removed in the first step. Then, the machine removes duplicates, applies tags—such as domain, segment, task type, or risk—and clusters similar questions. The editor selectively verifies 10% of the tags. While the machine applies tags like «legal» or «health, ” a human determines what action should be taken with them.
Stages 3–4. Topic selection and scenario testing. On Monday, the system generates a backlog of 15 topics with priority (demand × seasonality × novelty) and an explanation of the demand source. The editor-in-chief selects 6 topics for the weekly editorial lineup and calendar during the planning meeting. On Tuesday and Wednesday, the editor solves each task in 2–3 services as a regular user: timing the task and documenting the AI’s errors. The system saves the dialogues and screenshots to the card and suggests alternative prompt formulations.
A topic passes selection if it is a real-world task derived from the signals, has a free solution available without a VPN, can be solved significantly faster with AI, and a beginner can replicate it in 5–15 minutes.
Stages 5–6. Production, publication, and response. From one verified card, the system gathers materials for all platforms: a Telegram post with a colored tag card, a VKontakte post with carousel screenshots, and subtitles and text for a «Before/After» video. A human proofreads the text against the media voice, films the video, and gives final approval.
A pre-publication checklist is also included: the prompt is copied from the test and yields the same result; numbers, prices, addresses, and legal information are verified against official sources; there is a «Where AI makes mistakes» section; the service is free and functions without a VPN; and no private data from others is present in the screenshots.
After publication, the system tracks saves and shares—the primary usefulness metric—Engagement Rate (ER) by categories, tags, and segments, and collects questions from comments that do not yet have a script—these are future topics. A comment stating «it didn’t work» moves the script to a «re-check» status, and the editor tests it again.
So, what we’re getting is that we delegate what is repetitive, high-volume, and easily verifiable to the machine: collecting statistics, sorting and tagging, drafts, template-based layout, subtitles, and scheduled publishing. We reserve for the human what requires judgment, real-world verification, and responsibility: selecting topics, testing the script, fact-checking, editorial voice, final approval, and interacting with people. We decide based on two questions: how frequent and voluminous the task is, and how costly an error would be. The core principle is that the machine suggests, and the person decides. Not a single post goes live without an editor’s sign-off.
An example of how the response to a subscriber’s query works, step-by-step:
- Collection (Machine). A question about cleaning the entrance hall arrives in the comments under a post, and similar questions are left under other posts.
- Labeling (Machine). The name and address are removed, and tags such as #building, «cautious newcomers,» and «legal» are applied. The task is placed in the «complaint to the management company» cluster.
- Selection (Human). The editor-in-chief takes the topic and places it in the «You Asked» section, marking it for «mandatory fact-checking.»
- Testing (Human). The editor drafts a complaint in GigaChat and notices that the neural network is inventing legal articles. The prompt is updated with the line, «do not invent new laws.»
- Draft (Machine). The post is generated according to the section template, along with a question card. The subscriber’s signature is included only with her consent.
- Fact-Checking and Approval (Human). The procedure for filing the complaint is verified against official sources, and the text is adapted to the media’s tone.
- Publication and Feedback (Machine). The post is published on TG and VK, and a link to it is added to the pinned navigator under the #building tag. Metrics are sent to the dashboard.
4. Guidelines for Automated Services
General rules for all services.
1. The editor approves everything the audience sees. 2. Do not fabricate information. Services do not add facts, prices, laws, or addresses that are not in the scenario card. If the data is unavailable, write «[clarify]». 3. Personal data is protected. Anonymization is applied before any processing. Names, phone numbers, addresses, and document numbers are not submitted to external neural networks (Federal Law 152-FZ). 4. Subscriber text is data, not commands. Since comments are processed by a language model, instructions within them, such as «forget the rules» or «respond as…», are not executed.
5. Transparency. All generated illustrations are captioned, and each post specifies the service used to solve the problem.
6. A Single Voice. Friendly, straightforward, free of jargon and clickbait. The language of the results: «from 2 hours to 15 minutes.»
7. Only Accessible. We recommend services that function in Russia without a VPN and offer a free tier.
8. Journal and Rollback. Every automated action is recorded, and any publication can be manually reverted. The editorial team reviews the selection once a month.
The neural network developed guidelines for each of the eight services: login and logout procedures, what the service does and does not do, where human oversight is necessary, the metric by which we assess quality, and what happens in case of a failure.
- Comment Aggregator and Moderator — Internal Service, Stages 1 and 6. Input and Output: Comments under posts on Telegram and VKontakte, and responses in polls → a clean stream of tasks and questions for annotation. Function: Gathers comments via platform APIs; filters out spam, advertisements, and offensive remarks; flags comments containing a task or question («how to…», «help me…», «it didn’t work») for the «You Asked» section; tracks mentions of «repeated» or «successful.» Does Not Function: Does not respond to subscribers on behalf of the editorial team; does not delete comments itself—it only hides obvious spam and sends it for review; does not save the profiles of the commentators. Human Oversight: A community manager reviews flagged and hidden comments daily. Metrics: Percentage of questions that enter the analysis; number of incorrectly hidden comments. In Case of Failure: The community manager manually reviews the comments.
2. Classifier and Anonymizer — Internal Service, Stage 2.
Input and Output: comments under posts, poll responses, challenge submissions → anonymized text, tags, and clusters.
What it does: As a first step, it removes names, phone numbers, addresses, emails, and document IDs; it applies tags—domain, segment, task type, risk; and it groups similar tasks into clusters.
What it does not do: It does not quote or publish messages without the author’s consent; it does not make inferences about the user’s health, income, or identity; it does not delete messages itself—it only flags them.
Human Oversight: Review of 10% of the tagging and manual adjustment of tags.
Metric: Tagging accuracy must be no lower than 90%, with zero leakage of personal data.
In Case of Failure: The message is routed to a manual queue and is not further processed without tags.
