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Kanal postlari
Market Research Tracker in 30 seconds.
Most people do market research like this:
collect links -> ask AI -> forget everything.
A useful AI system works differently:
request -> sources -> competitor map -> insights -> decision.
The goal is not more information.
The goal is a better business decision.
Start simple:
1. Send a research topic to the bot.
2. Save sources in a database.
3. Let AI summarize each source.
4. Compare competitors in a table.
5. Generate a one-page decision brief.
That is the first useful version.
Full build plan:
https://t.me/AISystemAgentLab/131
| 2 | Market Research Tracker: build plan.
In the previous post, we broke down the idea.
Now let’s turn it into a real build plan.
Goal:
Build a Telegram-based AI system that helps a user research a market, collect sources, compare competitors and generate a short decision brief.
The system has 4 parts:
1. Telegram bot
2. Mini App
3. Database
4. AI backend
1. Telegram bot
The bot is the fast input and notification layer.
Basic commands:
/new_research
Start a new research task.
/add_source
Add a link, note, screenshot or competitor.
/status
Show active research tasks.
/brief
Generate the latest market brief.
/help
Show the workflow.
Example flow:
User:
“Research AI tools for small real estate agencies in the US.”
Bot:
“What do you want to compare: pricing, features, target customers, positioning or recent changes?”
User:
“Pricing, positioning and opportunities.”
Bot:
“Got it. Add 5-10 sources or open the Mini App to manage the research.”
2. Mini App
The Mini App is the visual workspace.
Minimum screens:
Screen 1: Research Tasks
Shows:
- active tasks
- market
- status
- number of sources
- last update
- open / archive buttons
Screen 2: Task Detail
Shows:
- research question
- target market
- customer segment
- competitors
- output goal
- progress
Screen 3: Sources
Shows:
- links
- notes
- source type
- AI summary
- confidence
- useful / not useful toggle
Screen 4: Competitor Table
Shows:
- company
- offer
- target audience
- pricing
- strengths
- weaknesses
- positioning
- recent signals
Screen 5: Final Brief
Shows:
- market summary
- competitor moves
- customer pains
- opportunities
- recommended next actions
- export / copy button
3. Database
Start with simple tables.
users
- telegram_user_id
- name
- username
- created_at
research_tasks
- id
- user_id
- title
- market
- customer_segment
- status
- output_goal
- created_at
- updated_at
sources
- id
- task_id
- url
- source_type
- raw_note
- ai_summary
- useful_signal
- created_at
competitors
- id
- task_id
- name
- website
- offer
- pricing
- strengths
- weaknesses
- positioning
insights
- id
- task_id
- insight_type
- summary
- evidence_source_id
- priority
reports
- id
- task_id
- brief_text
- generated_at
4. AI backend
The backend does the useful thinking.
Main AI jobs:
Clarify the request
Ask the user 2-3 questions before starting.
Summarize each source
Turn links and notes into structured summaries.
Extract market signals
Find pricing signals, customer pains, competitor moves and opportunities.
Build competitor table
Convert messy notes into comparable rows.
Generate final brief
Create a short decision-focused report.
Example AI prompt:
“You are a market research assistant.
Analyze this source for the research task below.
Extract only useful business signals.
Return:
1. summary
2. pricing signal
3. product signal
4. customer pain
5. competitor move
6. opportunity
7. confidence level
Source: {{source_text}}
Research task: {{task_goal}}”
5. Deployment
Simple stack:
Bot backend:
Node.js or Python
Database:
Supabase
LLM:
OpenRouter, OpenAI, Claude or another model provider
Mini App frontend:
React / Next.js / simple HTML
Hosting:
DigitalOcean, Render, Railway, Vercel or Netlify
Important security rule:
If you build a Telegram Mini App, validate Telegram initData on the backend before trusting the user identity.
First MVP:
Do not build everything at once.
Build this first:
1. /new_research command
2. Supabase tables
3. /add_source command
4. AI source summary
5. Simple competitor table
6. One-page final brief
Only after that add:
- automatic web monitoring
- weekly updates
- PDF export
- team access
- advanced dashboards
The practical lesson:
Do not start with a “big AI agent”.
Start with a repeatable workflow.
One clear input.
One database.
One AI summary step.
One useful report.
That is how a market research idea becomes a working AI system. | 289 |
| 3 | You asked for it in the comments.
After the last post about AI Mini Apps, several subscribers picked one idea:
Market Research Tracker.
So let’s break it down.
This is not “ask ChatGPT about competitors once”.
A real AI Market Research Tracker is a small system that helps you collect market signals, compare competitors, save sources, summarize insights and turn messy research into decisions.
What it should do:
1. Capture a research request
Example:
“Analyze AI tools for small real estate agencies in the US.”
The bot receives the request and asks 2-3 clarifying questions:
- target country
- customer segment
- competitors to include
- output format
- deadline
2. Save the request in a database
Every research task should be saved:
- who requested it
- topic
- market
- status
- sources
- AI summaries
- final report
This is where Supabase or another database becomes useful.
3. Collect sources
The system should collect and store links from:
- competitor websites
- pricing pages
- product updates
- reviews
- social posts
- YouTube videos
- newsletters
- public reports
Important: save the source link, not just the AI summary.
4. Turn sources into structured notes
For each source, AI should extract:
- what changed
- why it matters
- who it affects
- pricing signal
- product signal
- customer pain
- opportunity
This makes research reusable.
5. Compare competitors
The Mini App can show a simple table:
- company
- offer
- target audience
- pricing
- strengths
- weaknesses
- positioning
- recent changes
This is much more useful than a long AI paragraph.
6. Generate a short decision report
The final output should answer:
- what is happening in this market
- what competitors are doing
- what customers seem to need
- what opportunity exists
- what action should we take next
The goal is not “more information”.
The goal is a better decision.
Simple architecture:
Telegram Bot = receives research requests and sends updates.
Mini App = shows sources, competitor tables, summaries and reports.
Database = stores users, tasks, sources, notes and research history.
AI backend = summarizes, compares, extracts signals and drafts reports.
First MVP version:
Build only this:
1. User sends research topic to the bot.
2. Bot saves it to database.
3. User adds 5-10 source links.
4. AI summarizes each source.
5. Mini App shows a competitor table.
6. AI creates a one-page market brief.
That is enough for version one.
Later you can add:
- automatic web monitoring
- weekly competitor updates
- pricing change alerts
- trend detection
- PDF export
- team comments
- approval workflow
The key lesson:
Market research should not live in random chats and forgotten notes.
It should become a repeatable system:
request -> sources -> structured notes -> competitor map -> insight -> decision.
That is the kind of AI workflow worth building.
Next, we can break this into the actual build plan:
database tables,
Mini App screens,
bot commands,
AI prompts,
and deployment steps. | 326 |
| 4 | Most people think a Telegram bot is just a chat window.
But the real opportunity is bigger:
Telegram bot + Mini App + AI backend = a product inside Telegram.
Here are 5 practical AI Mini App ideas you can build next.
1. Personal Task Assistant
The bot receives messy thoughts, voice notes and quick tasks.
The Mini App turns them into a clean dashboard:
- tasks
- priorities
- deadlines
- reminders
- weekly summaries
Useful for founders, managers, freelancers and anyone who lives inside Telegram.
2. AI Content Dashboard
The bot collects links, ideas, screenshots and notes.
The Mini App shows:
- content ideas
- AI scores
- draft posts
- publishing status
- topic clusters
This is perfect for creators, media channels, agencies and personal brands.
3. Customer Replies Workspace
The bot receives customer questions from Telegram or another source.
The Mini App gives a simple approval screen:
- incoming message
- AI reply draft
- tone options
- risk notes
- approve / edit / reject
This can save hours for support, sales and small businesses.
4. Market Research Tracker
The bot collects research requests.
The Mini App organizes:
- competitors
- links
- summaries
- insights
- report drafts
- follow-up questions
Good for startups, marketers, investors and business owners.
5. AI Learning Coach
The bot answers quick questions.
The Mini App tracks:
- learning plan
- progress
- exercises
- saved explanations
- next steps
This can become a personal tutor for AI, coding, marketing, languages or business skills.
The pattern is always the same:
Bot = fast input and notifications.
Mini App = visual workspace.
AI backend = reasoning, memory and automation.
This is why Telegram is interesting.
You do not need to force users to install another app.
You can build the product where people already spend time.
The next step:
Pick one simple idea.
Build the bot first.
Add memory.
Then add the Mini App interface.
That is how a small Telegram bot becomes a real AI system.
Question:
Which one should we break down into a real build plan next? | 425 |
| 5 |  Most people think a Telegram bot is just a chat window.
But the real opportunity is bigger:
Telegram bot + Mini App + AI backend = a product inside Telegram.
Here are 5 practical AI Mini App ideas you can build next:
1. Personal Task Assistant
2. AI Content Dashboard
3. Customer Replies Workspace
4. Market Research Tracker
5. AI Learning Coach
Full breakdown below. | 360 |
| 6 | Telegram bots can be more than chat.
In our previous guide, we built a simple AI Telegram bot:
user message -> bot -> LLM -> database -> reply
That is a good first step.
But there is a next level:
Telegram bot + Mini App.
A Mini App is a web application that opens inside Telegram. The bot remains the entry point, but instead of forcing the user to type everything in chat, you can give them a real interface: forms, dashboards, buttons, lists, cards, filters, settings and workflows.
Think of it like this:
Bot = conversation layer.
Mini App = product interface.
Backend = memory, AI, database and automation.
Why this matters
Chat is great for quick commands:
- ask a question
- send a task
- upload a file
- get a short reply
But chat becomes messy when the user needs to:
- manage many tasks
- review AI outputs
- edit structured data
- approve drafts
- compare options
- track progress
- configure preferences
- see analytics
That is where Mini Apps become useful.
Example:
A personal task bot can receive messages in chat.
But the Mini App can show:
- all tasks
- priorities
- deadlines
- status
- AI summaries
- reminders
- memory settings
- weekly reports
The bot becomes the notification and command layer.
The Mini App becomes the workspace.
What can you build?
1. AI task manager
User sends messy thoughts to the bot. Mini App shows clean tasks, priorities and progress.
2. Content curation assistant
User saves links from Telegram, Instagram, X or YouTube. Mini App shows candidates, AI scores, topics and repost drafts.
3. Customer reply dashboard
Bot receives customer messages. Mini App shows reply drafts, tone options and approval buttons.
4. Market research workspace
Bot collects research requests. Mini App displays sources, summaries, competitors, insights and report drafts.
5. AI business assistant
Bot handles quick input. Mini App manages workflows: leads, reports, meetings, follow-ups and documents.
Basic architecture
Telegram Bot:
- receives messages
- sends notifications
- opens the Mini App button
- delivers AI replies
Mini App frontend:
- React / Next.js / simple HTML
- runs inside Telegram WebView
- uses Telegram WebApp SDK
- sends user actions to your backend
Backend:
- validates Telegram initData
- stores users and data in Supabase
- calls OpenRouter / OpenAI / Claude / other LLMs
- manages tasks, memory and workflows
- returns structured data to the Mini App
Database:
- users
- messages
- tasks
- AI runs
- user preferences
- workflow history
Deployment:
- bot backend on DigitalOcean, Render, Railway or similar
- Mini App frontend on Vercel, Netlify or same server
- database in Supabase
Important security rule:
Do not trust Mini App user data directly.
Telegram provides initData to the Mini App. Your backend must validate it before using the Telegram user identity.
The simple product flow:
1. User opens bot.
2. Bot sends a button: Open Mini App.
3. Mini App opens inside Telegram.
4. Frontend sends Telegram initData to backend.
5. Backend validates it.
6. Backend loads the user profile and data.
7. User works with tasks, drafts, reports or AI workflows.
8. Bot sends notifications or follow-ups back in chat.
Why this is powerful:
You do not need to force users to install a separate app.
Telegram becomes:
- login layer
- notification layer
- chat layer
- app container
- AI assistant interface
The practical lesson:
Start with a simple bot.
Add memory.
Then add a Mini App when chat is no longer enough.
That is how a Telegram AI bot becomes a real AI product.
Sources:
https://core.telegram.org/bots/webapps
https://core.telegram.org/bots
https://core.telegram.org/bots/features | 431 |
| 7 |  Telegram bots can be more than chat.
The next level is:
Bot + Mini App = AI product.
A bot gives you conversation, commands and notifications.
A Mini App gives users a real interface: forms, dashboards, tasks, approvals, settings and workflow screens.
This is how a simple AI Telegram bot becomes a practical product for work, business and personal automation.
Full breakdown below. | 409 |
| 8 |  Kimi Claw may be the easiest way to try an OpenClaw-style AI assistant.
OpenClaw is powerful, but for beginners it can feel heavy: installation, server, model setup, Telegram connection, skills, logs and maintenance.
Kimi Claw removes most of this friction.
The idea:
OpenClaw = flexible self-hosted agent gateway.
Kimi Claw = easier cloud path to the same type of assistant.
What it can do:
- run as a 24/7 cloud AI assistant
- keep memory and preferences
- connect to Telegram as a bot
- run scheduled tasks
- use ClawHub skills
- process files and generate outputs
- use web search and workflows
Simple rule:
Use Kimi Claw if you want to test the idea fast.
Use OpenClaw if you want to fully own the system.
We are moving from bots that answer messages to assistants that remember context, use tools and run workflows.
Sources:
https://www.kimi.com/help/kimi-claw/overview
https://www.kimi.com/help/kimi-claw/telegram-bot | 548 |
| 9 |  Matn yo'q... | 628 |
| 10 |  AI agents are becoming the new work interface.
This is why we spent the last posts talking about Telegram bots, personal assistants and OpenClaw.
The trend is clear:
Big tech is no longer building only chatbots.
They are building agent systems.
GitHub is moving Copilot toward an agent-native work experience. Microsoft is pushing agent infrastructure: multi-agent workflows, memory, tool connections, observability and governance.
Why does this matter?
Because the useful AI product of the future will not be:
Ask AI one question.
It will be:
Give AI a workflow and let it help you run it.
Examples:
- Telegram AI bot with memory
- personal task assistant
- customer reply assistant
- weekly report generator
- coding agent connected to tools
Most people are still learning prompts.
Builders are already learning workflows.
Sources:
https://github.blog/news-insights/product-news/github-copilot-app-the-agent-native-desktop-experience/
https://azure.microsoft.com/en-us/products/ai-foundry/ | 697 |
| 11 | The OpenClaw guide is ready.
Yesterday we talked about moving from a simple AI bot to a real personal assistant.
Now here is the full practical guide:
OpenClaw Practical Guide
Inside:
- what OpenClaw is and why it matters
- how it works as an AI agent gateway
- when to use it instead of a simple Telegram bot
- how to install OpenClaw
- how to connect it to Telegram
- how to use it as a personal AI assistant
- how to set up memory, sessions and safety limits
- common mistakes and first workflow ideas
The main idea is simple:
A chatbot answers.
An AI assistant remembers, routes tasks, uses tools and helps you operate workflows.
This is the next step after building your first Telegram AI bot.
Download the PDF, save it and use it as a practical setup map.
AI Lab will continue turning these tools into simple workflows for real work, business and personal productivity. | 753 |
| 12 |  Matn yo'q... | 1 184 |
| 13 |  Next practical track in AI Lab:
OpenClaw.
We started with a simple Personal Task Bot:
message -> database -> LLM -> structured task -> memory
But that is only the first layer.
The next question is bigger:
how do you turn a bot into an always-available AI assistant that can work through Telegram, use tools, keep sessions and operate workflows?
That is why I am preparing a practical OpenClaw guide.
What we will cover:
1. What OpenClaw actually is
2. When it is useful and when it is overkill
3. How to install it step by step
4. How to connect Telegram
5. How workspace, memory, sessions and skills work
6. How to build the first useful workflow
7. How to add safety limits and avoid chaos
The full guide is already in preparation and will be published soon.
If this topic is useful, support the post with a reaction.
Sources:
https://github.com/openclaw/openclaw
#AI #AIAgents #OpenClaw #openclaw #TelegramBot #VibeCoding #AILab | 1 374 |
| 14 | Continuation of the Personal Task Bot series.
In the previous post, we defined what we are building:
a simple Telegram bot that turns messy thoughts into structured tasks.
But if we want this bot to be genuinely useful, it needs memory.
Not vague "AI remembers everything" magic.
Real system memory:
- users
- messages
- tasks
- preferences
- recent context
- memory summary
- privacy commands
I prepared a practical PDF guide:
Build a Personal Task Bot with Memory
Inside:
1. The exact bot architecture
2. The first prompt for Codex / Claude Code
3. Supabase database schema
4. OpenRouter LLM flow
5. How bot memory actually works
6. How to save user preferences and open tasks
7. How to test the bot locally
8. How to deploy it to DigitalOcean
This is the difference between a simple chatbot and a useful AI system.
Download the guide below.
#AI #Codex #ClaudeCode #TelegramBot #Supabase #OpenRouter #VibeCoding #AILab | 1 453 |
| 15 | Key skills for the next generation of AI professionals
Here’s the boring truth: “AI” isn’t one skill. It’s a stack. If you’re missing one layer, you’ll ship demos instead of systems.
Claim: you need to be great at models.
Counter-claim: if you can’t build the workflow around the model, you’re not useful.
What you should actually get good at:
- Problem framing: turn “make it smart” into one job, one input, one output, one success metric.
- Data hygiene: logging, schemas, basic analytics. If you don’t save inputs/outputs, you can’t improve anything.
- Evaluation: simple test sets, edge cases, regression checks. Stop trusting “seems fine.”
- Tool use + integration: APIs, webhooks, queues, databases. Real work is glue code.
- Prompting as interface design: you’re writing specs for a stochastic engine. Structure > clever wording.
- Security + privacy basics: secrets, access control, data minimization. You will leak things otherwise.
- Reliability: retries, timeouts, fallbacks, rate limits. Your bot will break at 3am.
- Deployment and ops: env vars, logs, monitoring. “Works on my laptop” is not a product.
- Human-in-the-loop thinking: when the model is uncertain, route to a person or ask a better question.
If you want a simple way to practice: build one small bot that does one repeated task end-to-end (interface → model → database → deploy). Then iterate.
Which layer are you weakest at right now? #AI #AIAgents #Automation #VibeCoding #AILab | 3 |
| 16 | Q: You want to learn AI workflows without drowning in hype - what should you actually use?
A: Start with these, in this order:
1) This channel’s archive (seriously)
Read the pinned post + the bot-building series: interface → model → database → deployment. That loop is the whole game.
2) Telegram Bot API docs
If you can’t read basic API docs, you’ll stay stuck in “prompting” forever.
3) Supabase docs (tables, auth, logs)
Workflows need state. Learn the minimum database moves: users, messages, timestamps.
4) OpenRouter docs
One clean LLM layer so you can swap models without rewriting your app.
5) One IDE workflow: Claude Code or Codex
Use it like a junior engineer: small tasks, clear outputs, review, commit. No “build me an app” prompts.
A practical way to study:
Pick one boring loop you repeat (tasks, customer replies, lead scoring). Build the tiniest bot that does it end-to-end. Then add one upgrade at a time: memory, limits, analytics.
If you’re learning AI workflows right now: what are you building, and where are you stuck?
#AI #AIWorkflows #Automation #TelegramBot #VibeCoding #Supabase #OpenRouter #AILab | 1 |
| 17 |  You don’t need “AI transformation”. You need fewer repeated clicks.
Old way: you copy/paste the same stuff all day and call it “work”.
New way: you build tiny automations that run every time, without asking.
Why: consistency beats motivation.
Practical tips you can apply this week:
- Start from a log: write down every repeat action for 2 days. Automate the top 3.
- One trigger, one outcome: “new Telegram message → save → summarize → reply”. No extra features.
- Store everything: if it isn’t saved (Supabase/DB), you can’t improve it later.
- Make errors visible: log failures + send yourself a ping. Silent automations rot.
- Ship the dumb version: rules first, LLM second. Add the model only where it actually helps.
What workflow are you sick of repeating right now?
#AI #Automation #Workflows #AIAgents #TelegramBot #AILab | 1 |
| 18 |  What skills will actually matter for AI work in the next few years?
You don’t need “AI vibes”. You need boring, repeatable skills that ship.
You should get good at writing clear problem specs and test cases, because most “AI failures” are just unclear requirements.
You should learn data basics: what to store, why to store it, and how to track users, prompts, inputs, outputs, and errors.
You should be able to wire systems: interface → model → database → deployment → logs. If you can’t deploy it, it doesn’t exist.
You should practice evaluation: define what “good” means, measure it, and iterate.
And yes, you should learn to work with models pragmatically (prompting, tool calls, limits), not worship them.
Which one are you weakest at right now?
#AI #AIAgents #Automation #VibeCoding #AILab | 11 |
| 19 |  Personal Task Bot: what exactly are we building?
Not a giant AI assistant.
Not another chatbot that answers everything.
We start with one useful loop:
messy thought -> clear task -> priority -> next action -> follow-up
The MVP:
1. Capture a task
2. Understand the intent
3. Structure the output
4. Save the task
5. Return a useful next action
No calendar sync yet.
No complex agents yet.
No full automation yet.
First we build the loop.
Then we can upgrade it with reminders, daily planning, weekly reviews and eventually OpenClaw.
AI becomes useful when it turns messy input into a structured action.
That is what we will build next.
Guide:
https://t.me/AISystemAgentLab/106
#AI #AIAgents #TelegramBot #VibeCoding #AIWorkflow #AILab | 1 346 |
| 20 | Partner program https://t.me/waveobot?start=_tgr_xJEzaeRiNjhi | 1 145 |
