You voted. Here is the first workflow.
In our poll, the strongest request was:
Messages -> action plan
So let’s turn that into something you can use today.
Most work does not start as a clean task.
It starts as:
- a messy client message
- a long team chat
- a voice note
- a random idea
- a customer complaint
- a founder brain dump
- a message with 5 hidden tasks inside
The mistake:
Using AI only to "summarize" it.
The better workflow:
`message -> meaning -> tasks -> owner -> deadline -> reply -> follow-up`
Try this prompt:
```text
Turn this message into a practical action plan.
Return:
1. One-sentence summary
2. Main request or problem
3. Action items
4. Suggested owner for each task
5. Suggested deadline or priority
6. Missing information
7. Risk or possible misunderstanding
8. Short reply draft
9. Follow-up reminder text
Keep it clear, practical and ready to use.
Message:
[paste the message here]
```
Why this works:
A summary helps you understand.
An action plan helps you move.
That is the difference between using AI as a chatbot and using AI as a work system.
Use it for:
- client requests
- sales leads
- support messages
- partner chats
- internal team updates
- crypto project updates
- meeting notes
Small upgrade:
After AI creates the action plan, ask:
```text
What is the one next action I should take in the next 10 minutes?
```
That question removes the biggest problem in knowledge work:
too much information, not enough movement.
Next, I can turn the winning poll topic into a visual workflow map.
Comment `MAP` if you want the visual version.
In our poll, the strongest request was:
Messages -> action plan
So let’s turn that into something you can use today.
Most work does not start as a clean task.
It starts as:
- a messy client message
- a long team chat
- a voice note
- a random idea
- a customer complaint
- a founder brain dump
- a message with 5 hidden tasks inside
The mistake:
Using AI only to "summarize" it.
The better workflow:
`message -> meaning -> tasks -> owner -> deadline -> reply -> follow-up`
Try this prompt:
```text
Turn this message into a practical action plan.
Return:
1. One-sentence summary
2. Main request or problem
3. Action items
4. Suggested owner for each task
5. Suggested deadline or priority
6. Missing information
7. Risk or possible misunderstanding
8. Short reply draft
9. Follow-up reminder text
Keep it clear, practical and ready to use.
Message:
[paste the message here]
```
Why this works:
A summary helps you understand.
An action plan helps you move.
That is the difference between using AI as a chatbot and using AI as a work system.
Use it for:
- client requests
- sales leads
- support messages
- partner chats
- internal team updates
- crypto project updates
- meeting notes
Small upgrade:
After AI creates the action plan, ask:
```text
What is the one next action I should take in the next 10 minutes?
```
That question removes the biggest problem in knowledge work:
too much information, not enough movement.
Next, I can turn the winning poll topic into a visual workflow map.
Comment `MAP` if you want the visual version.
👍3🔥2❤1👎1
Visual map: from messy message to action plan
This is the visual version of the workflow from the poll result.
Save it.
Use it when a message feels messy, emotional, long or unclear.
```text
MESSY MESSAGE
|
v
1. Meaning
What is the real request?
|
v
2. Context
What background matters?
|
v
3. Tasks
What needs to be done?
|
v
4. Owner
Who should do each task?
|
v
5. Deadline
What is urgent vs optional?
|
v
6. Reply
What should we say back?
|
v
7. Follow-up
What should not be forgotten?
```
Most people stop at step 1:
"summarize this"
That is useful, but weak.
The stronger prompt is:
```text
Turn this message into:
- real request
- useful context
- action items
- owner
- deadline
- reply draft
- follow-up reminder
```
Why this matters:
AI should not only make text shorter.
AI should make work clearer.
Use this for:
- client messages
- team chats
- founder notes
- customer complaints
- sales leads
- crypto project updates
- support tickets
The simple rule:
If a message contains work, do not just summarize it.
Extract the next action.
Comment `STACK` if you want the full AI workflow stack:
messages -> meetings -> replies -> research -> reports
This is the visual version of the workflow from the poll result.
Save it.
Use it when a message feels messy, emotional, long or unclear.
```text
MESSY MESSAGE
|
v
1. Meaning
What is the real request?
|
v
2. Context
What background matters?
|
v
3. Tasks
What needs to be done?
|
v
4. Owner
Who should do each task?
|
v
5. Deadline
What is urgent vs optional?
|
v
6. Reply
What should we say back?
|
v
7. Follow-up
What should not be forgotten?
```
Most people stop at step 1:
"summarize this"
That is useful, but weak.
The stronger prompt is:
```text
Turn this message into:
- real request
- useful context
- action items
- owner
- deadline
- reply draft
- follow-up reminder
```
Why this matters:
AI should not only make text shorter.
AI should make work clearer.
Use this for:
- client messages
- team chats
- founder notes
- customer complaints
- sales leads
- crypto project updates
- support tickets
The simple rule:
If a message contains work, do not just summarize it.
Extract the next action.
Comment `STACK` if you want the full AI workflow stack:
messages -> meetings -> replies -> research -> reports
🔥2
A small success story from the AI Lab audience
One of the subscribers recently built a website without being a programmer.
Not by learning frontend for months.
Not by hiring a team.
Not by waiting for the "perfect idea."
They used vibe coding.
The process was simple:
1. Describe the site in plain language
2. Ask AI to create the first version
3. Check what works and what looks wrong
4. Ask for small fixes
5. Repeat until the site becomes usable
That is the underrated part.
Vibe coding is not:
"AI, build me a perfect product."
It is:
"AI, build version one. Now let’s improve it step by step."
Why this matters:
Most people think building a website starts with code.
For beginners, it can start with clarity:
- what is the site for?
- who is it for?
- what should the first screen show?
- what action should the visitor take?
- what content is needed?
AI can turn those answers into a working first version.
You still need judgment.
You still need to review.
You still need to test.
But you no longer need to wait until you "become technical enough" to start.
Try this today:
```text
I want to build a simple website.
Ask me the questions you need first.
Then create a clear structure for:
1. homepage
2. sections
3. text
4. design direction
5. first version implementation plan
Keep it beginner-friendly.
```
The first goal is not a perfect website.
The first goal is to make your idea visible.
Once it is visible, you can improve it.
Example of a simple AI-built test site:
https://lively-gumption-a7beec.netlify.app
This is the point of a sandbox:
build fast, test the idea, improve the next version.
Comment `SITE` if you want a beginner checklist for building your first website with AI.
One of the subscribers recently built a website without being a programmer.
Not by learning frontend for months.
Not by hiring a team.
Not by waiting for the "perfect idea."
They used vibe coding.
The process was simple:
1. Describe the site in plain language
2. Ask AI to create the first version
3. Check what works and what looks wrong
4. Ask for small fixes
5. Repeat until the site becomes usable
That is the underrated part.
Vibe coding is not:
"AI, build me a perfect product."
It is:
"AI, build version one. Now let’s improve it step by step."
Why this matters:
Most people think building a website starts with code.
For beginners, it can start with clarity:
- what is the site for?
- who is it for?
- what should the first screen show?
- what action should the visitor take?
- what content is needed?
AI can turn those answers into a working first version.
You still need judgment.
You still need to review.
You still need to test.
But you no longer need to wait until you "become technical enough" to start.
Try this today:
```text
I want to build a simple website.
Ask me the questions you need first.
Then create a clear structure for:
1. homepage
2. sections
3. text
4. design direction
5. first version implementation plan
Keep it beginner-friendly.
```
The first goal is not a perfect website.
The first goal is to make your idea visible.
Once it is visible, you can improve it.
Example of a simple AI-built test site:
https://lively-gumption-a7beec.netlify.app
This is the point of a sandbox:
build fast, test the idea, improve the next version.
Comment `SITE` if you want a beginner checklist for building your first website with AI.
lively-gumption-a7beec.netlify.app
THAMESVISTA — Premium London Tours
Private guided tours and VIP experiences across London and Britain since 2010. Bespoke routes, expert guides, unforgettable moments.
🔥3👍2❤1🎉1
Welcome to everyone who recently joined AI Lab
This channel is about practical AI workflows, agents and automation systems.
Not hype.
Useful systems.
Today’s signal comes from Boris Cherny: creator of Claude Code at Anthropic, engineer, and author of Programming TypeScript.
In an interview with Sequoia Capital, he explained how he personally works with AI agents and where software development may be heading.
Source:
https://www.youtube.com/watch?v=SlGRN8jh2RI
5 unusual ideas from the interview
1. He says he no longer writes code by hand.
According to Boris, the model writes 100% of his code. He makes dozens of PRs per day, and once pushed the workflow toward around 150 PRs in one day.
2. Most of his work now happens from his phone.
Not from an IDE.
Not from a big developer setup.
From the Claude app, where he manages sessions with agents.
3. Hundreds of agents can work at the same time.
At night, he says, thousands of agents can run on deeper tasks.
This is no longer just "chatting with an assistant."
It looks more like managing a task factory.
4. Loops may become a core format of future work.
An agent does not just answer once.
It can run on a schedule:
- monitor PRs
- fix CI
- rebase branches
- investigate flaky tests
- collect feedback
- send summaries
5. The best accounting software might be built by an accountant, not an engineer.
If writing code becomes the easy part, the advantage moves to the person who understands the domain best.
For accounting software, that may be a strong accountant.
Not necessarily a programmer.
5 practical takeaways
1. Coding may become basic literacy.
Boris compares the shift to the printing press.
Reading and writing became widespread, but professional writers did not disappear.
Code may follow the same path.
More people will be able to build software, but strong developers will still matter because they understand systems, quality, architecture and product.
2. The biggest gap is not only the model.
It is the process.
Boris says Anthropic uses roughly the same models that users can access.
The difference is that the company is rebuilding work around agents, loops and automated task handoff.
3. SaaS will be harder to defend through customer inertia.
If agents can help move data, processes and integrations, products that survive only because "leaving is hard" become more vulnerable.
4. Startups may attack large companies faster.
Large companies need to change processes, retrain people and fight internal resistance.
New teams can build with AI inside from day one.
5. Product still matters.
People do not use a model in a vacuum.
They use a specific tool.
That is why details, workflow and daily usability still matter.
The big idea
The future may not be:
`AI writes code`
It may be:
`human goal -> agent work -> loop -> review -> approved result`
This is exactly why AI Lab focuses on workflows and systems.
Comment `AGENTS` if you want a beginner-friendly breakdown of how to start using agents in your own work.
This channel is about practical AI workflows, agents and automation systems.
Not hype.
Useful systems.
Today’s signal comes from Boris Cherny: creator of Claude Code at Anthropic, engineer, and author of Programming TypeScript.
In an interview with Sequoia Capital, he explained how he personally works with AI agents and where software development may be heading.
Source:
https://www.youtube.com/watch?v=SlGRN8jh2RI
5 unusual ideas from the interview
1. He says he no longer writes code by hand.
According to Boris, the model writes 100% of his code. He makes dozens of PRs per day, and once pushed the workflow toward around 150 PRs in one day.
2. Most of his work now happens from his phone.
Not from an IDE.
Not from a big developer setup.
From the Claude app, where he manages sessions with agents.
3. Hundreds of agents can work at the same time.
At night, he says, thousands of agents can run on deeper tasks.
This is no longer just "chatting with an assistant."
It looks more like managing a task factory.
4. Loops may become a core format of future work.
An agent does not just answer once.
It can run on a schedule:
- monitor PRs
- fix CI
- rebase branches
- investigate flaky tests
- collect feedback
- send summaries
5. The best accounting software might be built by an accountant, not an engineer.
If writing code becomes the easy part, the advantage moves to the person who understands the domain best.
For accounting software, that may be a strong accountant.
Not necessarily a programmer.
5 practical takeaways
1. Coding may become basic literacy.
Boris compares the shift to the printing press.
Reading and writing became widespread, but professional writers did not disappear.
Code may follow the same path.
More people will be able to build software, but strong developers will still matter because they understand systems, quality, architecture and product.
2. The biggest gap is not only the model.
It is the process.
Boris says Anthropic uses roughly the same models that users can access.
The difference is that the company is rebuilding work around agents, loops and automated task handoff.
3. SaaS will be harder to defend through customer inertia.
If agents can help move data, processes and integrations, products that survive only because "leaving is hard" become more vulnerable.
4. Startups may attack large companies faster.
Large companies need to change processes, retrain people and fight internal resistance.
New teams can build with AI inside from day one.
5. Product still matters.
People do not use a model in a vacuum.
They use a specific tool.
That is why details, workflow and daily usability still matter.
The big idea
The future may not be:
`AI writes code`
It may be:
`human goal -> agent work -> loop -> review -> approved result`
This is exactly why AI Lab focuses on workflows and systems.
Comment `AGENTS` if you want a beginner-friendly breakdown of how to start using agents in your own work.
YouTube
Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next
Boris Cherny, creator of Claude Code at Anthropic, joins Sequoia partner Lauren Reeder at AI Ascent 2026 to talk about where coding goes from here. He explains why he hasn't written a line of code in 2026, why he now ships dozens of PRs a day from his phone…
🔥4❤2👍1
Claude Code workflow cheatsheet
Useful find from Reddit.
The main lesson:
Claude Code works better when you stop treating it like a chatbot and start treating it like a workflow system.
What matters most:
- project memory with `CLAUDE.md`
- clear file structure
- small focused tasks
- plan before implementation
- hooks for repeatable checks
- permissions and safety rules
- frequent commits
- one feature per session
For beginners, the simple pattern is:
`context -> plan -> small change -> review -> test -> commit`
Save the image if you use Claude Code or want to start vibe coding more seriously.
Source:
https://www.reddit.com/r/AskVibecoders/comments/1t0tt7w/claude_code_workflow_cheatsheet/
Comment `CLAUDE` if you want a beginner setup guide for Claude Code.
Useful find from Reddit.
The main lesson:
Claude Code works better when you stop treating it like a chatbot and start treating it like a workflow system.
What matters most:
- project memory with `CLAUDE.md`
- clear file structure
- small focused tasks
- plan before implementation
- hooks for repeatable checks
- permissions and safety rules
- frequent commits
- one feature per session
For beginners, the simple pattern is:
`context -> plan -> small change -> review -> test -> commit`
Save the image if you use Claude Code or want to start vibe coding more seriously.
Source:
https://www.reddit.com/r/AskVibecoders/comments/1t0tt7w/claude_code_workflow_cheatsheet/
Comment `CLAUDE` if you want a beginner setup guide for Claude Code.
🔥4❤3
OpenClaw is an important signal for AI agents
OpenClaw is not just another AI chat interface.
It points to a bigger shift:
AI agents are moving from "answer my question" to "operate my workflow."
What makes it interesting:
1. It is open-source.
The project is built around the idea of a personal AI assistant that can run with its own workspace, memory files and skills.
In the GitHub docs, OpenClaw uses a workspace like:
`~/.openclaw/workspace`
with files such as:
- `AGENTS.md`
- `SOUL.md`
- `TOOLS.md`
- skills inside `skills/<skill>/SKILL.md`
That is not just chat history.
That is agent infrastructure.
2. It can control tools.
OpenClaw docs describe a managed browser profile that is separate from your personal browser.
The agent can:
- open tabs
- read pages
- click
- type
- take snapshots
- capture screenshots
- generate PDFs
This is the key idea:
an agent needs a safe workspace, not unlimited access to your real digital life.
3. It shows what "agentic work" may look like.
TechRadar describes OpenClaw as an open-source agent that runs on your own hardware and connects models like Claude or ChatGPT to everyday software and services.
The useful pattern is:
`model reasoning -> local gateway -> tools -> action -> human review`
That is very different from:
`prompt -> answer`
4. It also shows the cost and safety problem.
Tom’s Hardware reported that Peter Steinberger’s team burned through about $1.3M in OpenAI API usage in one month while operating roughly 100 coding agents.
That number is extreme, but the lesson is useful:
agent workflows can scale fast.
So can cost, risk and complexity.
5. The practical takeaway
OpenClaw matters because it makes the future of AI work easier to see:
- agents need memory
- agents need tools
- agents need permissions
- agents need isolated environments
- agents need review loops
- agents need cost controls
For individuals and businesses, the question is not:
"Which chatbot should I use?"
The better question is:
"Which workflow should an agent safely operate for me?"
Start small:
one repetitive task,
one tool,
one approval step,
one measurable result.
That is how agent systems become useful instead of chaotic.
Sources:
https://github.com/openclaw/openclaw
https://openclawlab.com/en/docs/tools/browser/
https://www.techradar.com/pro/what-is-openclaw
https://www.tomshardware.com/tech-industry/artificial-intelligence/openclaw-creator-burns-through-1-3-million-in-openai-api-tokens-in-a-single-month
#openclaw
OpenClaw is not just another AI chat interface.
It points to a bigger shift:
AI agents are moving from "answer my question" to "operate my workflow."
What makes it interesting:
1. It is open-source.
The project is built around the idea of a personal AI assistant that can run with its own workspace, memory files and skills.
In the GitHub docs, OpenClaw uses a workspace like:
`~/.openclaw/workspace`
with files such as:
- `AGENTS.md`
- `SOUL.md`
- `TOOLS.md`
- skills inside `skills/<skill>/SKILL.md`
That is not just chat history.
That is agent infrastructure.
2. It can control tools.
OpenClaw docs describe a managed browser profile that is separate from your personal browser.
The agent can:
- open tabs
- read pages
- click
- type
- take snapshots
- capture screenshots
- generate PDFs
This is the key idea:
an agent needs a safe workspace, not unlimited access to your real digital life.
3. It shows what "agentic work" may look like.
TechRadar describes OpenClaw as an open-source agent that runs on your own hardware and connects models like Claude or ChatGPT to everyday software and services.
The useful pattern is:
`model reasoning -> local gateway -> tools -> action -> human review`
That is very different from:
`prompt -> answer`
4. It also shows the cost and safety problem.
Tom’s Hardware reported that Peter Steinberger’s team burned through about $1.3M in OpenAI API usage in one month while operating roughly 100 coding agents.
That number is extreme, but the lesson is useful:
agent workflows can scale fast.
So can cost, risk and complexity.
5. The practical takeaway
OpenClaw matters because it makes the future of AI work easier to see:
- agents need memory
- agents need tools
- agents need permissions
- agents need isolated environments
- agents need review loops
- agents need cost controls
For individuals and businesses, the question is not:
"Which chatbot should I use?"
The better question is:
"Which workflow should an agent safely operate for me?"
Start small:
one repetitive task,
one tool,
one approval step,
one measurable result.
That is how agent systems become useful instead of chaotic.
Sources:
https://github.com/openclaw/openclaw
https://openclawlab.com/en/docs/tools/browser/
https://www.techradar.com/pro/what-is-openclaw
https://www.tomshardware.com/tech-industry/artificial-intelligence/openclaw-creator-burns-through-1-3-million-in-openai-api-tokens-in-a-single-month
#openclaw
GitHub
GitHub - openclaw/openclaw: Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞 - openclaw/openclaw
❤5🔥1
Most people still use AI like a better search box
They ask:
"Summarize this."
"Write this."
"Explain this."
Useful, but basic.
The next level is different.
AI becomes powerful when you stop asking for isolated answers and start building workflows.
A workflow looks like this:
1. Input
A message, meeting, document, idea, customer request or market update.
2. Structure
AI extracts what matters.
3. Decision
AI shows options, risks and missing information.
4. Action
AI drafts the reply, task, report, plan or next step.
5. Review
A human checks and approves.
6. Loop
The workflow repeats when new information appears.
7. System
The process becomes something you can reuse every week.
That is the real shift.
Not:
`prompt -> answer`
But:
`input -> workflow -> review -> action -> result`
This matters for everyone:
- founders
- students
- freelancers
- managers
- creators
- analysts
- small businesses
- crypto researchers
- non-technical builders
The people who win with AI will not be the ones who know the most prompts.
They will be the ones who know how to turn messy work into repeatable systems.
That is what AI Lab is about.
Not AI hype.
Practical systems you can actually use.
They ask:
"Summarize this."
"Write this."
"Explain this."
Useful, but basic.
The next level is different.
AI becomes powerful when you stop asking for isolated answers and start building workflows.
A workflow looks like this:
1. Input
A message, meeting, document, idea, customer request or market update.
2. Structure
AI extracts what matters.
3. Decision
AI shows options, risks and missing information.
4. Action
AI drafts the reply, task, report, plan or next step.
5. Review
A human checks and approves.
6. Loop
The workflow repeats when new information appears.
7. System
The process becomes something you can reuse every week.
That is the real shift.
Not:
`prompt -> answer`
But:
`input -> workflow -> review -> action -> result`
This matters for everyone:
- founders
- students
- freelancers
- managers
- creators
- analysts
- small businesses
- crypto researchers
- non-technical builders
The people who win with AI will not be the ones who know the most prompts.
They will be the ones who know how to turn messy work into repeatable systems.
That is what AI Lab is about.
Not AI hype.
Practical systems you can actually use.
🔥3❤1
AI is moving from chat to daily work
Practical signal from Google I/O 2026:
Gemini is becoming more proactive with Daily Brief, Gemini Spark and deeper Gmail / Calendar / Docs integration.
The shift:
`ask -> answer`
becomes:
`inbox + calendar + docs -> AI brief -> suggested actions -> human approval`
Why it matters:
- less context switching
- faster morning planning
- urgent emails surfaced earlier
- drafts and follow-ups prepared
- scattered information turned into next actions
The everyday value of AI is not only smarter answers.
It is better prepared work.
Sources:
https://blog.google/innovation-and-ai/products/gemini-app/next-evolution-gemini-app/
https://techcrunch.com/2026/05/19/google-introduces-gemini-spark-a-24-7-agentic-assistant-with-gmail-integration/
Practical signal from Google I/O 2026:
Gemini is becoming more proactive with Daily Brief, Gemini Spark and deeper Gmail / Calendar / Docs integration.
The shift:
`ask -> answer`
becomes:
`inbox + calendar + docs -> AI brief -> suggested actions -> human approval`
Why it matters:
- less context switching
- faster morning planning
- urgent emails surfaced earlier
- drafts and follow-ups prepared
- scattered information turned into next actions
The everyday value of AI is not only smarter answers.
It is better prepared work.
Sources:
https://blog.google/innovation-and-ai/products/gemini-app/next-evolution-gemini-app/
https://techcrunch.com/2026/05/19/google-introduces-gemini-spark-a-24-7-agentic-assistant-with-gmail-integration/
🔥3👍1
Build your own AI Daily Brief
You do not need to wait for every AI assistant feature to roll out.
You can build a simple version today.
Every morning, collect:
- today’s meetings
- unread important emails
- open tasks
- yesterday’s unfinished work
- one current project or goal
Then ask AI:
```text
Create my daily brief.
Return:
1. top 3 priorities
2. urgent risks
3. messages I should send
4. decisions needed
5. tasks to move today
6. one first action for the next 10 minutes
```
The point is not a perfect assistant.
The point is a better morning workflow:
`scattered context -> clear priorities -> next actions`
That is how AI becomes useful in daily work.
You do not need to wait for every AI assistant feature to roll out.
You can build a simple version today.
Every morning, collect:
- today’s meetings
- unread important emails
- open tasks
- yesterday’s unfinished work
- one current project or goal
Then ask AI:
```text
Create my daily brief.
Return:
1. top 3 priorities
2. urgent risks
3. messages I should send
4. decisions needed
5. tasks to move today
6. one first action for the next 10 minutes
```
The point is not a perfect assistant.
The point is a better morning workflow:
`scattered context -> clear priorities -> next actions`
That is how AI becomes useful in daily work.
🔥4❤2🙏1
The biggest mistake with AI assistants
Most people give AI isolated tasks:
`summarize this`
`write this`
`explain this`
That is useful, but limited.
The better move is to give AI a repeatable workflow.
Example:
Bad:
`summarize this email`
Better:
`extract the request -> detect urgency -> draft reply -> create follow-up -> wait for approval`
This is the difference between using AI as a text tool and using AI as a work system.
If you repeat a task every week, it should not stay as a random prompt.
Turn it into a workflow:
`input -> structure -> decision -> action -> review -> repeat`
That is where everyday AI becomes actually useful.
Most people give AI isolated tasks:
`summarize this`
`write this`
`explain this`
That is useful, but limited.
The better move is to give AI a repeatable workflow.
Example:
Bad:
`summarize this email`
Better:
`extract the request -> detect urgency -> draft reply -> create follow-up -> wait for approval`
This is the difference between using AI as a text tool and using AI as a work system.
If you repeat a task every week, it should not stay as a random prompt.
Turn it into a workflow:
`input -> structure -> decision -> action -> review -> repeat`
That is where everyday AI becomes actually useful.
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Diagrams should be part of your AI workflow
Most people use AI coding agents only for code.
But there is another underrated use case:
turn messy ideas into clear diagrams.
I found a useful skill for this:
`Agents365-ai/drawio-skill`
It lets agents like Claude Code, Codex, Cursor, Copilot and OpenClaw generate draw.io diagrams from natural language.
You can ask an agent to create:
- architecture diagrams
- flowcharts
- ER diagrams
- UML class diagrams
- sequence diagrams
- ML / deep learning diagrams
The interesting part:
the skill can generate `.drawio` XML, export to PNG / SVG / PDF / JPG, and run a self-check loop for overlaps, clipped labels and messy edges.
That turns diagrams into a real workflow artifact:
`idea -> diagram -> review -> improve -> build`
Useful for founders, vibe coders, teams, consultants and businesses mapping automation workflows.
Source:
https://github.com/Agents365-ai/drawio-skill
Most people use AI coding agents only for code.
But there is another underrated use case:
turn messy ideas into clear diagrams.
I found a useful skill for this:
`Agents365-ai/drawio-skill`
It lets agents like Claude Code, Codex, Cursor, Copilot and OpenClaw generate draw.io diagrams from natural language.
You can ask an agent to create:
- architecture diagrams
- flowcharts
- ER diagrams
- UML class diagrams
- sequence diagrams
- ML / deep learning diagrams
The interesting part:
the skill can generate `.drawio` XML, export to PNG / SVG / PDF / JPG, and run a self-check loop for overlaps, clipped labels and messy edges.
That turns diagrams into a real workflow artifact:
`idea -> diagram -> review -> improve -> build`
Useful for founders, vibe coders, teams, consultants and businesses mapping automation workflows.
Source:
https://github.com/Agents365-ai/drawio-skill
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Gemini Presentation vs Claude Design
AI presentation tools are splitting into two different paths.
This is not only about “who makes better slides.”
The better question is:
`What kind of work artifact do you need?`
Quick comparison:
1. Core idea
Gemini:
turn a topic, document or research brief into a Google Slides deck.
Claude Design:
turn an idea into a visual design artifact: deck, prototype, interactive page or branded presentation.
Best for:
Gemini = fast office deck.
Claude = stronger visual story.
2. Workflow
Gemini:
prompt or upload source -> Gemini Canvas -> export to Google Slides -> team edits.
Claude:
prompt -> design canvas -> iterate with chat / comments -> export or hand off.
Best for:
Gemini = teams already working in Google Workspace.
Claude = founders, builders and teams shaping a product narrative.
3. Input
Gemini:
documents, Drive files, notes, reports, briefs, class materials.
Claude:
prompts, screenshots, brand direction, product ideas, code/context, design requirements.
Best for:
Gemini = “make slides from this material.”
Claude = “help me design the story and visual structure.”
4. Output
Gemini:
Google Slides deck.
Claude:
HTML presentation, PPTX, PDF, zip, Canva export or Claude Code handoff.
Best for:
Gemini = editable business slides.
Claude = reusable visual asset or interactive demo.
5. Strongest use case
Gemini:
reports -> slides
sales briefs -> pitch drafts
lesson notes -> structured decks
research -> summary presentation
Claude:
startup pitch
product story
interactive proposal
executive narrative
visual prototype
6. Practical choice
Use Gemini when:
- you need speed
- your team lives in Google Slides
- the source material already exists
- the deck is mostly for internal work
Use Claude Design when:
- the visual story matters
- you need more than static slides
- you want to explore layout and narrative
- the deck should feel like a product artifact
Simple rule:
Gemini is better for turning documents into presentations.
Claude Design is better for turning ideas into designed experiences.
Sources:
https://workspaceupdates.googleblog.com/2025/10/generate-presentations-in-gemini-app.html
https://support.google.com/docs/answer/14355071
https://support.claude.com/en/articles/14604416-get-started-with-claude-design
https://claude.com/resources/tutorials/using-claude-design-for-presentations-and-slide-decks
AI presentation tools are splitting into two different paths.
This is not only about “who makes better slides.”
The better question is:
`What kind of work artifact do you need?`
Quick comparison:
1. Core idea
Gemini:
turn a topic, document or research brief into a Google Slides deck.
Claude Design:
turn an idea into a visual design artifact: deck, prototype, interactive page or branded presentation.
Best for:
Gemini = fast office deck.
Claude = stronger visual story.
2. Workflow
Gemini:
prompt or upload source -> Gemini Canvas -> export to Google Slides -> team edits.
Claude:
prompt -> design canvas -> iterate with chat / comments -> export or hand off.
Best for:
Gemini = teams already working in Google Workspace.
Claude = founders, builders and teams shaping a product narrative.
3. Input
Gemini:
documents, Drive files, notes, reports, briefs, class materials.
Claude:
prompts, screenshots, brand direction, product ideas, code/context, design requirements.
Best for:
Gemini = “make slides from this material.”
Claude = “help me design the story and visual structure.”
4. Output
Gemini:
Google Slides deck.
Claude:
HTML presentation, PPTX, PDF, zip, Canva export or Claude Code handoff.
Best for:
Gemini = editable business slides.
Claude = reusable visual asset or interactive demo.
5. Strongest use case
Gemini:
reports -> slides
sales briefs -> pitch drafts
lesson notes -> structured decks
research -> summary presentation
Claude:
startup pitch
product story
interactive proposal
executive narrative
visual prototype
6. Practical choice
Use Gemini when:
- you need speed
- your team lives in Google Slides
- the source material already exists
- the deck is mostly for internal work
Use Claude Design when:
- the visual story matters
- you need more than static slides
- you want to explore layout and narrative
- the deck should feel like a product artifact
Simple rule:
Gemini is better for turning documents into presentations.
Claude Design is better for turning ideas into designed experiences.
Sources:
https://workspaceupdates.googleblog.com/2025/10/generate-presentations-in-gemini-app.html
https://support.google.com/docs/answer/14355071
https://support.claude.com/en/articles/14604416-get-started-with-claude-design
https://claude.com/resources/tutorials/using-claude-design-for-presentations-and-slide-decks
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AI Stack of the Week: build and launch without a team
Goal:
turn an idea into a working public demo.
Stack:
1. Claude Code
For planning, editing files and understanding the codebase.
2. Codex
For implementation, debugging, review and parallel work.
3. GitHub
For version control and clean project history.
4. Vercel / Netlify
For fast public deployment.
5. Telegram bot
For notifications, user requests and lightweight automation.
6. n8n / Make
For connecting forms, sheets, email, webhooks and APIs.
The point is not to use more tools.
The point is to create a simple build loop:
`idea -> prototype -> deploy -> feedback -> improve`
This is what a small AI-native workflow looks like.
Goal:
turn an idea into a working public demo.
Stack:
1. Claude Code
For planning, editing files and understanding the codebase.
2. Codex
For implementation, debugging, review and parallel work.
3. GitHub
For version control and clean project history.
4. Vercel / Netlify
For fast public deployment.
5. Telegram bot
For notifications, user requests and lightweight automation.
6. n8n / Make
For connecting forms, sheets, email, webhooks and APIs.
The point is not to use more tools.
The point is to create a simple build loop:
`idea -> prototype -> deploy -> feedback -> improve`
This is what a small AI-native workflow looks like.
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AI is not an answer machine anymore
For a long time, people used AI like this:
`question -> answer`
That was the first phase.
The next phase looks different:
`goal -> artifact -> review -> action`
An artifact can be:
- a diagram
- a presentation
- a prototype
- a landing page
- a report
- a checklist
- a code change
- a workflow map
- a customer reply
- a research brief
- a deployed demo
Do not start with:
“Give me an answer.”
Start with:
“What should exist after this session?”
The best AI users are not collecting prompts.
They are producing artifacts.
Because artifacts can be reviewed, shared, improved, automated, measured and used again.
That is the real shift:
AI is moving from conversation to production.
Not just smarter answers.
Useful outputs.
For a long time, people used AI like this:
`question -> answer`
That was the first phase.
The next phase looks different:
`goal -> artifact -> review -> action`
An artifact can be:
- a diagram
- a presentation
- a prototype
- a landing page
- a report
- a checklist
- a code change
- a workflow map
- a customer reply
- a research brief
- a deployed demo
Do not start with:
“Give me an answer.”
Start with:
“What should exist after this session?”
The best AI users are not collecting prompts.
They are producing artifacts.
Because artifacts can be reviewed, shared, improved, automated, measured and used again.
That is the real shift:
AI is moving from conversation to production.
Not just smarter answers.
Useful outputs.
🔥7👍4❤2
AI agents need boundaries
The next problem is not only:
“Can the agent do the task?”
It is:
“Can the agent stay inside the task?”
A recent paper on overeager coding agents tested out-of-scope actions across Claude Code, OpenHands, Codex CLI and Gemini CLI.
The practical lesson:
AI agents need scope, not just instructions.
Before giving work to an agent, define:
- allowed actions
- forbidden actions
- files and tools it can use
- approval points
- definition of done
The safer pattern is:
`goal -> scope -> agent work -> review -> approved action`
Not:
`goal -> agent does everything`
Powerful agents are useful when they know where to stop.
Source:
https://arxiv.org/abs/2605.18583
The next problem is not only:
“Can the agent do the task?”
It is:
“Can the agent stay inside the task?”
A recent paper on overeager coding agents tested out-of-scope actions across Claude Code, OpenHands, Codex CLI and Gemini CLI.
The practical lesson:
AI agents need scope, not just instructions.
Before giving work to an agent, define:
- allowed actions
- forbidden actions
- files and tools it can use
- approval points
- definition of done
The safer pattern is:
`goal -> scope -> agent work -> review -> approved action`
Not:
`goal -> agent does everything`
Powerful agents are useful when they know where to stop.
Source:
https://arxiv.org/abs/2605.18583
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What would you trust an AI agent with today?
Anonymous Poll
30%
Drafting documents
27%
Coding small features
20%
Research summaries
7%
Customer replies
7%
Deploying changes
10%
I would not trust it yet
🔥3👍1🤡1
AI Tool Picker
Do not start by asking:
“Which AI tool is the best?”
Start with:
“What output do I need?”
Simple map:
Need code?
Use an AI coding agent.
Need slides?
Use a presentation tool.
Need diagrams?
Use a diagram skill or visual workflow tool.
Need automation?
Use n8n, Make or Zapier.
Need deployment?
Use Vercel, Netlify or another deploy platform.
Need channel operations?
Use a Telegram bot plus analytics.
The best tool is not the newest one.
The best tool is the one that turns your goal into a useful artifact with the least friction.
Choose by output.
Then build the workflow around it.
Do not start by asking:
“Which AI tool is the best?”
Start with:
“What output do I need?”
Simple map:
Need code?
Use an AI coding agent.
Need slides?
Use a presentation tool.
Need diagrams?
Use a diagram skill or visual workflow tool.
Need automation?
Use n8n, Make or Zapier.
Need deployment?
Use Vercel, Netlify or another deploy platform.
Need channel operations?
Use a Telegram bot plus analytics.
The best tool is not the newest one.
The best tool is the one that turns your goal into a useful artifact with the least friction.
Choose by output.
Then build the workflow around it.
❤5❤🔥1👍1
AI agents are becoming infrastructure
Google is moving agents closer to a managed cloud workflow:
agents with tools, memory, configs, deployment and observability.
The important shift:
AI agents are no longer just chat sessions.
They are becoming systems you can define, run, monitor and improve.
That changes the question for builders and businesses.
Not:
“Which chatbot should I use?”
But:
“Which repeatable process should an agent operate safely?”
A useful agent setup needs:
- clear goal
- tool access
- memory / context
- permissions
- deployment path
- logs and review
- human approval points
The future of AI work looks less like one prompt and more like managed infrastructure for useful tasks.
Source:
https://developers.googleblog.com/en/agents-adk-google-ai-studio-gemini-api/
Google is moving agents closer to a managed cloud workflow:
agents with tools, memory, configs, deployment and observability.
The important shift:
AI agents are no longer just chat sessions.
They are becoming systems you can define, run, monitor and improve.
That changes the question for builders and businesses.
Not:
“Which chatbot should I use?”
But:
“Which repeatable process should an agent operate safely?”
A useful agent setup needs:
- clear goal
- tool access
- memory / context
- permissions
- deployment path
- logs and review
- human approval points
The future of AI work looks less like one prompt and more like managed infrastructure for useful tasks.
Source:
https://developers.googleblog.com/en/agents-adk-google-ai-studio-gemini-api/
🔥5❤1👍1
Customer support is becoming an AI agent network
Zendesk is pushing a clear signal:
support is moving from simple tickets to agentic service workflows.
The old model:
`customer asks -> human searches -> human replies`
The new model:
`customer asks -> AI agent finds context -> takes action -> escalates when needed`
Why this matters:
Customer support is one of the most practical places for AI agents.
Agents can help with:
- finding customer context
- checking orders or account data
- drafting replies
- routing requests
- preparing refunds or actions
- escalating risky cases to humans
The real value is not “faster chat.”
It is a support workflow where AI handles the repeatable work and humans review the important decisions.
For businesses, this is the pattern to watch:
`request -> context -> action -> review -> resolution`
Source:
https://www.zendesk.co.uk/newsroom/articles/relate-2026/
Zendesk is pushing a clear signal:
support is moving from simple tickets to agentic service workflows.
The old model:
`customer asks -> human searches -> human replies`
The new model:
`customer asks -> AI agent finds context -> takes action -> escalates when needed`
Why this matters:
Customer support is one of the most practical places for AI agents.
Agents can help with:
- finding customer context
- checking orders or account data
- drafting replies
- routing requests
- preparing refunds or actions
- escalating risky cases to humans
The real value is not “faster chat.”
It is a support workflow where AI handles the repeatable work and humans review the important decisions.
For businesses, this is the pattern to watch:
`request -> context -> action -> review -> resolution`
Source:
https://www.zendesk.co.uk/newsroom/articles/relate-2026/
🔥7❤3
Coding agents are moving into enterprise workflows
OpenAI says Codex was named a Leader in the 2026 Gartner Magic Quadrant for Enterprise AI Coding Agents.
The signal is bigger than one ranking:
AI coding is moving from side projects into real delivery systems.
For companies, the useful pattern is not:
`prompt -> code`
It is:
`issue -> agent work -> PR -> tests -> security review -> human approval -> deploy`
That is where coding agents become practical.
They can help with:
- small features
- bug fixes
- test generation
- code review
- refactoring
- documentation
- investigation work
But the enterprise version needs more than speed.
It needs permissions, audit trails, CI checks, security review and clear approval points.
The takeaway:
The future developer workflow is not “AI writes everything.”
It is human teams operating AI agents inside controlled delivery pipelines.
Source:
https://openai.com/index/gartner-2026-agentic-coding-leader/
OpenAI says Codex was named a Leader in the 2026 Gartner Magic Quadrant for Enterprise AI Coding Agents.
The signal is bigger than one ranking:
AI coding is moving from side projects into real delivery systems.
For companies, the useful pattern is not:
`prompt -> code`
It is:
`issue -> agent work -> PR -> tests -> security review -> human approval -> deploy`
That is where coding agents become practical.
They can help with:
- small features
- bug fixes
- test generation
- code review
- refactoring
- documentation
- investigation work
But the enterprise version needs more than speed.
It needs permissions, audit trails, CI checks, security review and clear approval points.
The takeaway:
The future developer workflow is not “AI writes everything.”
It is human teams operating AI agents inside controlled delivery pipelines.
Source:
https://openai.com/index/gartner-2026-agentic-coding-leader/
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