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AI Lab
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Practical AI workflows, agents and automation systems for people, founders and businesses. No hype. Just useful systems.
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频道帖子
| 2 |  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 | 626 |
| 3 | 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 | 852 |
| 4 | 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 |
| 5 | 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 |
| 6 |  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 |
| 7 |  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 |
| 8 |  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 | 852 |
| 9 | Partner program https://t.me/waveobot?start=_tgr_xJEzaeRiNjhi | 749 |
| 10 | Quick update from the poll.
So far, the largest share of votes is going to:
Personal task bot.
The poll is still open, so the winner can still change.
But this is already a strong signal.
So the next practical AI Lab task will be based on this direction:
building a personal task bot.
Not a giant "AI assistant for everything".
A small system with one clear job:
- capture a task or request from the user
- understand what needs to be done
- use an LLM
- save the interaction
- turn messy thoughts into tasks
- return a useful next action
- improve the workflow over time
This is the real difference between a chatbot and an AI product.
A chatbot answers.
A useful personal task bot creates a repeatable workflow:
messy message -> structured task -> priority -> deadline -> follow-up
We will break the next build into practical parts:
1. What the bot should do
2. What data it should save
3. How to connect the LLM
4. How to design the first database
5. How to test the first version
6. How to deploy it so it works 24/7
We will start with a simple MVP first.
Then we can show how the same idea can be upgraded with OpenClaw into a more powerful personal AI assistant.
The goal:
after reading the series, you should be able to build your first real AI bot with Codex or Claude Code, even if you are not a professional developer.
Start small.
Build the loop.
Then make it smarter.
Guide:
https://t.me/AISystemAgentLab/106
#AI #AIAgents #TelegramBot #VibeCoding #Codex #ClaudeCode #AILab | 1 006 |
| 11 |  3 starter prompts to build your first AI Telegram bot.
If you opened the guide, the next step is simple:
do not ask AI to "make a bot" in one vague prompt.
Give it small engineering tasks:
1. Define the MVP
Receive user message -> save to Supabase -> send to OpenRouter -> save reply -> answer in Telegram.
2. Create the database
Ask for Supabase tables for users, messages, AI replies and logs.
3. Connect OpenRouter
Ask AI to add model calls, error handling, env variables and saved replies.
The trick:
vibe coding works better when you give AI a clear system, not a vague wish.
Small task.
Clear output.
Review.
Next task.
Guide:
https://t.me/AISystemAgentLab/106
#AI #VibeCoding #TelegramBot #Codex #ClaudeCode #AILab | 948 |
| 12 | Which AI bot should we build next as a practical example? | 734 |
| 13 |  AI is becoming your work secretary.
At Microsoft Build 2026, Microsoft introduced Scout — a new personal AI agent for work.
The idea is simple:
not another chatbot,
but an agent that understands how you work.
Scout is designed to use tools people already live in, like Teams and Outlook, and help with:
- meeting prep
- schedule conflicts
- routine tasks
- work context
- follow-ups
Why this matters:
The next useful AI may not be the smartest chatbot.
It may be the assistant that quietly removes small daily friction:
What do I need before this meeting?
What changed in this project?
Who needs a reply?
What should I do next?
This is where AI is moving:
from answering questions
to helping manage work.
The practical lesson:
start building small AI systems around repeated work loops.
One task.
One workflow.
One useful assistant.
Original source: Microsoft
https://blogs.microsoft.com/blog/2026/06/02/microsoft-build-2026-be-yourself-at-work/
#AI #AIAgents #Productivity #MicrosoftBuild #AILab | 761 |
| 14 |  What to build after this guide?
If you downloaded the Telegram bot guide, do not try to build a giant assistant first.
Build one small bot that solves one clear problem.
5 good first projects:
1. Customer reply bot
User sends a customer message -> bot drafts a polite reply and next action.
2. Lead qualification bot
User sends lead details -> bot scores the lead and suggests a follow-up.
3. Content idea bot
User sends a topic -> bot returns post ideas, hooks and short scripts.
4. Research summary bot
User sends a link or topic -> bot returns summary, key points and action list.
5. Personal task bot
User sends messy thoughts -> bot turns them into tasks, priorities and reminders.
The real lesson:
a useful AI bot is not just a chatbot.
It is a small workflow:
input -> AI -> data -> useful output.
Start with one boring repeated task.
Make the first version work.
Then improve it.
Guide:
https://t.me/AISystemAgentLab/106
#AI #VibeCoding #TelegramBot #Automation #AILab | 997 |
| 15 | Quick feedback request from AI Lab.
If you downloaded the guide and started building your Telegram bot, leave a short comment here:
- what step you are on
- what worked
- where you got stuck
- what you want explained next
Even a small progress update is useful.
If someone manages to launch the first working version, share it here.
We may feature the best beginner success story in the channel. | 1 454 |
| 16 | The full beginner guide is ready.
How to build your first AI Telegram bot with vibe coding.
Inside the PDF:
- create a bot with @BotFather
- use Claude Code or Codex
- connect Supabase database
- connect OpenRouter LLM
- save users and messages
- deploy the bot to DigitalOcean
- use ready prompts for Codex / Claude Code
- create starter Supabase tables
- debug common errors
- test the bot after deployment
The example bot:
user writes a message -> bot sends it to an LLM -> AI answers -> all users and messages are saved to Supabase.
Download the guide, save it, and use it as a practical build checklist.
#AI #VibeCoding #TelegramBot #OpenRouter #Supabase #DigitalOcean #AILab | 1 811 |
| 17 | Quick update.
The Telegram bot guide is almost unlocked.
22 / 25 reactions already.
Only 3 reactions left, and I will publish the full PDF guide:
- BotFather setup
- Claude Code / Codex prompts
- Supabase tables
- OpenRouter LLM setup
- DigitalOcean deployment
- testing checklist
- common errors
If you want the full guide, react to the original post:
https://t.me/AISystemAgentLab/102
No theory.
A practical build checklist.
#AI #VibeCoding #TelegramBot #AILab | 1 726 |
| 18 |  VibeCoding is cool!!!👌 | 1 448 |
| 19 | Part 1 unlocked.
13 people already reacted to the Telegram bot guide post, so let us start with the first practical part.
What are we building?
A Telegram bot that can answer any user message with an LLM.
The user writes to the bot.
The bot sends the message to an AI model.
The AI model generates a reply.
The bot sends the answer back to the user.
All users and messages are saved to a database.
This is a great first vibe coding project because it teaches the real basics of AI products:
- Telegram Bot API
- LLM API
- database storage
- user tracking
- environment variables
- server deployment
- logs and debugging
The stack:
1. Telegram Bot
This is the interface.
Users talk to the bot in Telegram.
2. OpenRouter
This is the LLM layer.
Instead of connecting only one model, we use OpenRouter so the bot can work with different models: OpenAI, Claude, Gemini, DeepSeek, Kimi and others.
3. Supabase
This is the database.
We store:
- user id
- username
- first name
- language
- message text
- AI reply
- timestamps
Why store data?
Because without a database, the bot is just a chat window.
With a database, you can later add:
- user history
- analytics
- limits
- personalization
- paid access
- admin dashboard
- follow-up messages
4. DigitalOcean
This is where the bot lives online.
Your laptop should not be the server.
We deploy the bot to a small cloud server so it can work 24/7.
Simple architecture:
Telegram user
-> Telegram Bot
-> Node.js app
-> OpenRouter LLM
-> Supabase database
-> Telegram reply
Before writing code, define the first version clearly:
MVP:
- receive any text message
- save user to Supabase
- save incoming message
- send message to OpenRouter
- save AI reply
- send reply back to Telegram
- deploy to DigitalOcean
Do not start with payments, memory, admin panels or complex agents.
First make the basic loop work.
The main lesson:
AI products are not just prompts.
They are systems:
interface -> model -> database -> deployment -> feedback loop
In the next part, we can break this into actual build steps:
BotFather, project setup, Supabase tables, OpenRouter request, Telegram webhook or polling, and DigitalOcean deploy.
If the original unlock post reaches 25 reactions, I will publish the full step-by-step guide.
#AI #VibeCoding #TelegramBot #OpenRouter #Supabase #DigitalOcean #AILab | 1 569 |
| 20 |  I am preparing a practical guide:
How to build your own Telegram bot with vibe coding.
Not a theory post.
Not "learn Python for 3 months first".
A real beginner-friendly walkthrough:
- what bot to build first
- how to create a Telegram bot
- what tools to use
- how to write the first version with AI
- how to connect the bot to Telegram
- how to store prompts and user data
- how to test it
- where to deploy it
- how to avoid the most common beginner mistakes
The goal:
you should be able to go from idea to working Telegram bot without being a professional developer.
This is one of the most useful vibe coding projects for beginners because it teaches:
AI prompting,
APIs,
automation,
deployment,
and real user interaction.
If you want me to publish the full step-by-step guide here, let us unlock it together.
When this post gets 25 reactions, I will publish the full guide in AI Lab.
No vague motivation.
Just a practical build path.
#AI #VibeCoding #TelegramBot #Automation #AILab | 1 317 |
