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.

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.

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

Most AI tools still live in a browser tab.

Hermes Agent is a different idea:

an AI assistant that lives closer to your workflow.

It can work through Telegram, CLI or desktop, remember context, use tools, run scheduled tasks, create skills and connect to external services.

That is the interesting part.

Not “one more chatbot.”

More like a small personal operator:

- you send a task
- it checks context
- uses tools
- remembers useful details
- can run again on schedule
- connects to Telegram and other channels

For AI Lab, this is the direction:

AI systems that do work, not just answer questions.

Where to start:

1. Install Hermes Agent
2. Choose your model/provider
3. Add tools and memory
4. Connect a gateway like Telegram
5. Give it one simple recurring job

Docs:
https://hermes-agent.nousresearch.com/docs/

This is worth watching.

The next wave of AI will not be “better prompts.”

It will be personal systems that know your work and keep moving when you are away.

Anthropic just released Claude Fable 5.

This is not just "a smarter chatbot."

Fable 5 is built for longer, harder work:

- coding
- research
- vision tasks
- document analysis
- multi-step agent workflows

The key signal:

AI is moving from "answer my question" to "help me execute a real workflow."

Practical use cases:

- migrate a codebase
- investigate a complex bug
- analyze many documents
- turn screenshots into app code
- prepare a research brief
- run agentic coding tasks

There is also a safety twist.

Anthropic says sensitive cyber, bio/chem and model-distillation requests can fall back to Claude Opus 4.8. Claude Mythos 5 also launched, but access is restricted.

My take:

The best way to use stronger models is not random prompting.

Brief them like operators:

goal -> context -> files -> constraints -> tools -> output -> review

That is where models like Fable 5 start to matter.

Source:
https://www.anthropic.com/news/claude-fable-5-mythos-5

Let’s do something practical.

AI Lab Workflow Clinic is open.

Write one task from your work or business that you would like to automate or improve with AI.

Examples:

- reply to customer messages faster
- research competitors
- create content every week
- summarize meetings
- track crypto/news signals
- build a Telegram bot
- prepare weekly reports
- analyze documents
- turn ideas into code

I will pick several real examples from the comments and turn them into simple AI workflow maps:

task -> tools -> data -> AI step -> human approval -> final result

No theory.

Real use cases from this channel.

If you do not know what to write, start with:

“I spend too much time on...”

or

“I want AI to help me with...”

Drop your task in the comments.

Most AI agents fail before the first line of code.

Not because the model is weak.

Because the system is unclear.

Use this 6-block blueprint:

1. Input
What starts the agent?
Message, email, file, alert, schedule, comment.

2. Context
What should the agent know?
User profile, history, documents, rules, memory.

3. Tools
What can the agent do?
Search, read files, call APIs, write to database, send messages.

4. AI Step
What should the model decide or create?
Classify, compare, summarize, draft, plan, extract, reason.

5. Human Approval
Where should a person review?
Before posting, spending money, changing data or making risky decisions.

6. Result
What is the useful output?
Sent reply, report, task list, saved record, alert, decision brief.

Formula:

input -> context -> tools -> AI step -> approval -> result

If you cannot describe your agent with these 6 blocks, do not start coding yet.

First make the system clear.

Most people use Claude like a chat box.

That is the smallest use case.

Better way:

use Claude as a workbench for thinking, building and shipping.

20 practical ways:

1. Ask better questions - turn a vague idea into a clear prompt, checklist or plan.

2. Think through hard decisions - compare options, risks, trade-offs and next steps.

3. Summarize long information - compress articles, meetings, PDFs or reports into useful notes.

4. Reason through options - ask Claude to explain why one path is stronger than another.

5. Build project plans - turn a goal into milestones, tasks, owners and deadlines.

6. Write and review code - create small features, find bugs, improve structure and explain logic.

7. Analyze data - paste tables, numbers or exports and ask for patterns, insights and anomalies.

8. Create slide structure - transform raw notes into a presentation outline with key messages.

9. Draft UI mockups - describe an app screen and get layout ideas, sections and user flows.

10. Browse and summarize sources - use it to research a topic and extract only what matters.

11. Automate computer tasks - create scripts, workflows and repeatable instructions.

12. Delegate remote work - write clear tasks for freelancers, assistants or team members.

13. Search faster - ask for search queries, angles, keywords and comparison criteria.

14. Compare tools - evaluate software by price, use case, limits, integrations and risks.

15. Investigate topics deeply - build a research map instead of collecting random links.

16. Keep projects organized - turn messy notes into docs, roadmaps and decision logs.

17. Automate repeated tasks - create templates for emails, reports, replies and weekly updates.

18. Connect with tools - plan how AI should work with Telegram, Google Sheets, Notion, CRM or APIs.

19. Create artifacts - generate drafts, tables, guides, checklists, briefs and technical specs.

20. Refine content - improve posts, ads, scripts, landing pages and messages without losing meaning.

The shift is simple:

Do not ask Claude for one answer.

Give it a real workflow:

goal -> context -> files -> constraints -> output -> review

That is where AI becomes useful.

The best AI tools of 2026 are not just a list.

They are a stack.

Everyone is collecting AI tools.

But the smarter move is to build an AI stack.

Not 33 random apps.

6 working layers:

1. General assistants
Claude, ChatGPT, Perplexity
For thinking, drafting, planning and checking ideas.

2. Research & writing
Gemini, NotebookLM, Grammarly, Zotero
For sources, notes, summaries, citations and cleaner text.

3. Dev & no-code
Cursor, Lovable, Replit, Base44, Emergent
For prototypes, MVPs, apps and internal tools.

4. Content creation
HeyGen, Gamma, Descript, Opus Clip, Beeniv, Synthesia
For videos, slides, clips, scripts and tutorials.

5. Automation
n8n, Zapier, Apollo, Clay, Apify, Lindy, Figma
For workflows, data enrichment, scraping and repeat tasks.

6. Visual & audio
ElevenLabs, Higgsfield, Kling, Runway, Midjourney, Artlist, Veo 3, Suno
For voice, music, images and cinematic video.

Rule:

start with the workflow,
then choose the tool.

AI is moving from apps into the operating system.

Apple's new Siri AI is not just another chatbot update. It is a signal: the next big AI interface will live inside your device.

WWDC coverage says Siri AI is being redesigned to understand personal context, work across apps, read the screen and connect with photos, messages and Safari.

Why it matters:

1. AI becomes invisible
You will not always open a separate AI app. The assistant will appear inside your phone, browser and inbox.

2. Context becomes the real power
The best assistant is not the one that only answers. It understands your files, messages and tasks.

3. Automation goes mainstream
When AI understands context and triggers actions, normal users start using agent-like workflows without calling them agents.

Practical takeaway:

Do not ask only "which AI tool should I use?"

Ask: what parts of my work should an assistant understand and automate?

Source: https://www.theverge.com/tech/942416/apple-siri-ai-update-wwdc

Do not just learn AI.

Build AI capabilities.

The 10 practical skills worth mastering in 2026.

Full breakdown below.

Most people are trying to learn "AI".

That is too vague.

In 2026, the useful question is:

which AI skills can actually make you faster, more valuable and harder to replace?

Here is the practical AI Lab list.

1. Prompt engineering
Not magic words. Clear thinking.
Use it to turn messy ideas into structured outputs, checklists, plans and decisions.
Tools: ChatGPT, Claude, Gemini, Perplexity, Poe.

2. AI workflow automation
This is where AI starts saving real time.
Connect apps, trigger actions, summarize data, route tasks and remove repetitive work.
Tools: Make, Zapier, n8n, Pipedream, Power Automate.

3. AI video generation
Short videos are becoming a business skill.
Use AI to create explainers, ads, product demos, reels and educational clips.
Tools: Runway, Pika, Synthesia, HeyGen, CapCut AI.

4. AI image generation
Visuals are no longer only for designers.
Use it for thumbnails, post covers, ad creatives, product concepts and moodboards.
Tools: Midjourney, DALL-E, Leonardo AI, Ideogram, Stable Diffusion.

5. AI content writing
The skill is not "let AI write".
The skill is giving direction, structure, audience, tone and a clear output format.
Tools: ChatGPT, Jasper, Copy.ai, Writesonic, Notion AI.

6. AI presentation creation
Useful for founders, consultants, managers and creators.
Turn rough notes into story, structure, slides and pitch logic.
Tools: Gamma, Tome, Beautiful.ai, Canva AI, SlidesAI.

7. AI chatbot building
Every business has repeat questions.
A chatbot can handle support, onboarding, lead qualification and internal knowledge.
Tools: Botpress, ManyChat, Voiceflow, Landbot, Tidio AI.

8. AI audio and voice generation
Voice is becoming part of content production.
Use it for voiceovers, podcasts, tutorials, ads and multilingual content.
Tools: ElevenLabs, Murf AI, PlayHT, Descript, Adobe Podcast.

9. AI research and summarization
This may be the most underrated skill.
Use AI to read faster, compare sources, extract signals and turn information into decisions.
Tools: Perplexity, ChatGPT, Humata, Scholarcy, Elicit.

10. AI resume and career optimization
AI can help package your work better.
Not by lying, but by turning your experience into clear positioning, CVs, cover letters and interview prep.
Tools: ChatGPT, Kickresume, Teal, Rezi, LinkedIn AI Tools.

The real lesson:

Do not collect AI tools.

Build AI capabilities.

One useful path:

research -> writing -> visuals -> automation -> chatbot -> video

That sequence can turn one person into a small content, research and automation team.

Start with one skill.
Build one workflow.
Then stack the next one.

Claude is moving into enterprise systems.

Anthropic announced a multi-year alliance with DXC Technology to bring Claude into banks, airlines, insurers, manufacturing and public-sector work.

DXC says it built OASIS, an AI-native orchestration platform, with Claude generating over 95% of the code reviewed by engineers. They claim 10x faster development and 50+ customers.

Why it matters:

1. AI is entering regulated workflows
Banks, insurance and aviation need security, auditability and review, not chatbots.

2. Coding agents are becoming modernization tools
Enterprise software has old systems, messy integrations and high maintenance cost. AI agents can help rebuild that layer.

3. The real product is workflow orchestration
Not "ask Claude a question", but:

context -> tools -> code -> review -> deployment -> monitoring

Takeaway:
business AI needs security, approvals, logs, integrations and measurable outcomes.

Source:
https://www.anthropic.com/news/dxc-anthropic-alliance

You do not need a $2,000 course to get better at Claude.

You need a few practical workflows.

Here are 10 free ways to master Claude faster:

1. Claude for files
Give Claude real files, not vague questions.

2. Claude as a coworker
Use one folder as shared working context.

3. Claude Projects
Create one project per recurring task.

4. Voice in a file
Save your tone, rules, examples and preferences.

5. Claude Skills
Turn repeat work into reusable commands.

6. Obsidian + Claude
Use your notes as a second brain.

7. Claude Code
Build from goals, screenshots, files and constraints.

8. Slides with Gamma
Shape the story first, slides second.

9. Save your tokens
Plan, reuse files and edit instead of restarting.

10. Claude Certified
Start with free official learning resources.

The real lesson:

Claude is not just a chatbot.

It becomes useful when you give it files, memory, workflow, examples, constraints and a clear output.

Start with one recurring task this week.

The AI tool stack is changing fast.

Not because old tools suddenly became useless.

Because every regular work task is getting an AI-first layer.

Important: old tools are not dead.

But the workflow around them is changing.

The real shift:

2025 was about using tools.

2026 is about building workflows.

The people and companies who win will not be the ones who blindly switch to every new app.

They will be the ones who connect the right AI tools into repeatable systems:

research -> draft -> edit -> publish -> reply -> report

Do not ask:
"Which tool is best?"

Ask:
"Which part of my work should become an AI workflow first?"

Start there.

AI access can disappear overnight.

Anthropic has reportedly cut off access to its Fable 5 and Mythos 5 models for users outside the U.S. after a government directive.

The practical lesson is bigger than one company:

Do not build your whole workflow around one model, one provider, or one account.

What to do:

1. Keep 2-3 model options ready
2. Use routers like OpenRouter, LiteLLM or your own API layer
3. Save prompts, docs and workflows outside one chat app
4. Design your system so the LLM can be replaced
5. Learn the skill, not just the tool

Models change. Access rules change.

Your AI workflow should keep working anyway.

Sources:
The Verge, Business Insider

Most people use AI tools randomly.

The result: scattered prompts, unfinished drafts, no system.

Here is a simple AI Marketing Stack for 2026:

1. AI Strategy
Claude, ChatGPT, Gemini - angles, offers, positioning, campaign logic.

2. Research & Insights
Perplexity, NotebookLM, Grammarly - market signals, competitors, customer pain points, source-backed ideas.

3. Productivity
Notion, Motion, Granola, Wispr - meetings, tasks, summaries, decisions.

4. Build & Deploy
Replit, Cursor, Lovable, Bolt - landing pages, prototypes, quick experiments.

5. Content Creation
Gamma, Descript, HeyGen, Synthesia, Opus Clip - decks, videos, avatars, clips.

6. Visuals & Media
Midjourney, Runway, Kling, Veo, ElevenLabs - images, video scenes, voiceovers, ads.

7. Marketing Automation
Make, n8n, Zapier, Clay, Apollo - leads, CRM, outreach, enrichment, reports.

Do not collect tools.
Build a marketing machine.

OpenAI is now facing a multistate probe over possible user harm linked to ChatGPT.

This is bigger than one company.

It is a signal for everyone building AI bots, agents and assistants:

AI safety is no longer a “nice extra”.
It is becoming part of the product.

If your AI system talks to real users, it needs:

1. Clear boundaries
What the bot can and cannot help with.

2. Refusal logic
When the model must stop instead of “being helpful”.

3. Human handoff
When a real person should review or step in.

4. Logs and audit trail
So you can understand what happened later.

5. Privacy rules
What data is stored, where, and for how long.

6. Safer defaults for sensitive users
Especially around health, minors, finance and crisis situations.

The next wave of AI products will not win only because they are powerful.

They will win because they are useful, controlled and trusted.

Source: AP News
https://apnews.com/article/openai-chatgpt-subpoena-attorneys-general-probe-a95894407773307fae8ae3ce9742b586