Top 10 GitHub-starred skill stacks for coding agents
Coding agents are getting a new layer: skills.
Not just prompts.
Not just MCP tools.
Skills are reusable instruction packs that teach agents how to do specific work better: plan, test, audit, browse, design, deploy, document, and work with real APIs.
GitHub stars checked on May 27, 2026. Filter: repositories that publish or power skills / agent workflows for coding agents.
1. Superpowers — 208k stars
https://github.com/obra/superpowers
A full software development methodology for coding agents: specs, plans, TDD, subagents, reviews, and longer autonomous work. Useful if you want Claude Code, Codex, Cursor, Gemini CLI, or OpenCode to behave more like an engineering process.
2. Anthropic Skills — 141k stars
https://github.com/anthropics/skills
Anthropic's reference repository for Agent Skills. Good for understanding the standard structure: SKILL.md, scripts, resources, and progressive loading. Useful for creating reusable skills for documents, analysis, automation, testing, or company workflows.
3. wshobson/agents — 36k stars
https://github.com/wshobson/agents
A large multi-harness marketplace: plugins, agents, skills, commands, and orchestrators for Claude Code, Codex CLI, Cursor, OpenCode, Gemini CLI, and Copilot. Useful for ready-made expert agents across architecture, security, infra, data, ML, docs, SEO, and full-stack work.
4. Agent Browser — 34k stars
https://github.com/vercel-labs/agent-browser
A browser automation CLI built for AI agents. Important because coding tasks now require checking real pages, clicking flows, testing UI states, and validating that the app actually works instead of only reading code.
5. Vercel Agent Skills — 27k stars
https://github.com/vercel-labs/agent-skills
A practical skill collection for web development: React / Next.js best practices, UI review, Vercel optimization, performance, cost, reliability, caching, and deployment workflows. Strong for SaaS, frontend, and production web apps.
6. OpenAI Skills — 20.5k stars
https://github.com/openai/skills
The Codex skills catalog. Useful for learning how skills are packaged for Codex and how to install curated or experimental skills. Key reference if you want repeatable workflows inside Codex instead of one-off prompts.
7. skills CLI — 20.2k stars
https://github.com/vercel-labs/skills
The open ecosystem CLI for installing agent skills across many coding agents. Supports Codex, Claude Code, Cursor, OpenCode, and more: one skill source, multiple agent environments.
8. Lark CLI + Agent Skills — 12.8k stars
https://github.com/larksuite/cli
A business-operations skill stack for agents. Connects agents to Lark / Feishu domains like Messenger, Docs, Sheets, Calendar, Mail, Tasks, and Meetings. Useful for turning agents into workplace operators, not just code writers.
9. Google Stitch Skills — 5.7k stars
https://github.com/google-labs-code/stitch-skills
Design and build skills for Google's Stitch ecosystem, compatible with Codex, Gemini CLI, Claude Code, Cursor, and others. Useful when the agent needs to move from idea to UI/design artifacts.
10. Trail of Bits Skills — 5.4k stars
https://github.com/trailofbits/skills
Security-focused skills from Trail of Bits. Useful for code auditing, smart contract security, vulnerability research, testing, and safer development workflows.
The takeaway:
The next productivity jump is not just a better model.
It is a better operating system around the model.
Skills turn an AI coding agent from:
"answer this prompt"
into:
"follow this proven workflow every time."
That is where agentic development is going.
#agentskills #codingagents #claudecode #codex #aiworkflow
Coding agents are getting a new layer: skills.
Not just prompts.
Not just MCP tools.
Skills are reusable instruction packs that teach agents how to do specific work better: plan, test, audit, browse, design, deploy, document, and work with real APIs.
GitHub stars checked on May 27, 2026. Filter: repositories that publish or power skills / agent workflows for coding agents.
1. Superpowers — 208k stars
https://github.com/obra/superpowers
A full software development methodology for coding agents: specs, plans, TDD, subagents, reviews, and longer autonomous work. Useful if you want Claude Code, Codex, Cursor, Gemini CLI, or OpenCode to behave more like an engineering process.
2. Anthropic Skills — 141k stars
https://github.com/anthropics/skills
Anthropic's reference repository for Agent Skills. Good for understanding the standard structure: SKILL.md, scripts, resources, and progressive loading. Useful for creating reusable skills for documents, analysis, automation, testing, or company workflows.
3. wshobson/agents — 36k stars
https://github.com/wshobson/agents
A large multi-harness marketplace: plugins, agents, skills, commands, and orchestrators for Claude Code, Codex CLI, Cursor, OpenCode, Gemini CLI, and Copilot. Useful for ready-made expert agents across architecture, security, infra, data, ML, docs, SEO, and full-stack work.
4. Agent Browser — 34k stars
https://github.com/vercel-labs/agent-browser
A browser automation CLI built for AI agents. Important because coding tasks now require checking real pages, clicking flows, testing UI states, and validating that the app actually works instead of only reading code.
5. Vercel Agent Skills — 27k stars
https://github.com/vercel-labs/agent-skills
A practical skill collection for web development: React / Next.js best practices, UI review, Vercel optimization, performance, cost, reliability, caching, and deployment workflows. Strong for SaaS, frontend, and production web apps.
6. OpenAI Skills — 20.5k stars
https://github.com/openai/skills
The Codex skills catalog. Useful for learning how skills are packaged for Codex and how to install curated or experimental skills. Key reference if you want repeatable workflows inside Codex instead of one-off prompts.
7. skills CLI — 20.2k stars
https://github.com/vercel-labs/skills
The open ecosystem CLI for installing agent skills across many coding agents. Supports Codex, Claude Code, Cursor, OpenCode, and more: one skill source, multiple agent environments.
8. Lark CLI + Agent Skills — 12.8k stars
https://github.com/larksuite/cli
A business-operations skill stack for agents. Connects agents to Lark / Feishu domains like Messenger, Docs, Sheets, Calendar, Mail, Tasks, and Meetings. Useful for turning agents into workplace operators, not just code writers.
9. Google Stitch Skills — 5.7k stars
https://github.com/google-labs-code/stitch-skills
Design and build skills for Google's Stitch ecosystem, compatible with Codex, Gemini CLI, Claude Code, Cursor, and others. Useful when the agent needs to move from idea to UI/design artifacts.
10. Trail of Bits Skills — 5.4k stars
https://github.com/trailofbits/skills
Security-focused skills from Trail of Bits. Useful for code auditing, smart contract security, vulnerability research, testing, and safer development workflows.
The takeaway:
The next productivity jump is not just a better model.
It is a better operating system around the model.
Skills turn an AI coding agent from:
"answer this prompt"
into:
"follow this proven workflow every time."
That is where agentic development is going.
#agentskills #codingagents #claudecode #codex #aiworkflow
GitHub
GitHub - obra/superpowers: An agentic skills framework & software development methodology that works.
An agentic skills framework & software development methodology that works. - obra/superpowers
1🔥3
Practical AI workflow: weekly reports.
Stop spending Friday afternoon turning scattered updates into a report.
Use AI as a reporting system:
`updates -> progress -> blockers -> decisions -> next actions -> report`
The full step-by-step guide is below.
#AI #AIWorkflow #WeeklyReports #AILab
Stop spending Friday afternoon turning scattered updates into a report.
Use AI as a reporting system:
`updates -> progress -> blockers -> decisions -> next actions -> report`
The full step-by-step guide is below.
#AI #AIWorkflow #WeeklyReports #AILab
1🔥2
You voted for practical AI workflows.
Next topic: weekly reports.
Most weekly reports are painful because the information is scattered:
- chats
- tasks
- meetings
- emails
- notes
- customer updates
- spreadsheet numbers
So people either skip it or spend too much time writing a vague summary.
A better AI workflow:
`raw updates -> progress summary -> blockers -> decisions -> next actions -> report`
The goal: make the week easier to understand.
1. Collect raw updates
Do not start by asking AI to "write a weekly report."
Start by collecting messy inputs:
- completed tasks
- open tasks
- blocked tasks
- meeting notes
- messages
- customer feedback
- decisions made
Simple folder:
```text
weekly-report/
raw-updates.md
tasks.md
meeting-notes.md
metrics.md
report.md
```
Then paste rough notes into the files. Messy input is fine.
2. Ask AI to extract signal
Prompt:
```text
Read the raw weekly updates.
Extract:
1. completed work
2. work still in progress
3. blockers
4. risks
5. important decisions
6. next actions
7. missing information
Do not write the final report yet.
Only structure the facts.
```
This prevents generic reports. AI organizes the week before writing.
3. Create a report structure
A useful weekly report usually has five sections:
```text
1. Executive summary
2. Completed this week
3. Blockers and risks
4. Decisions needed
5. Priorities for next week
```
For personal work, add:
```text
What I learned
What I should improve next week
```
For business/team work, add:
```text
Customer impact
Revenue / delivery impact
Owner for each next action
```
4. Generate the first draft
Prompt:
```text
Using the structured weekly facts, write a clear weekly report.
Audience: [manager / client / team / myself]
Rules:
- short paragraphs
- no vague phrases
- separate facts from opinions
- highlight blockers clearly
- include next actions with owners
- keep it useful, not decorative
```
AI should not invent progress. If something is missing, it should say so.
5. Make three versions
One report can become several formats.
Prompt:
```text
Turn this weekly report into three versions:
1. short executive summary
2. team update
3. client-friendly version
Keep the facts the same.
Change only tone, length and emphasis.
```
Different readers need different levels of detail.
6. Add a risk check
Before sending, ask AI to review the report:
```text
Review this report.
Flag:
1. unclear claims
2. missing owners
3. missing deadlines
4. weak next actions
5. risks that are hidden or softened
6. anything that sounds like empty corporate language
Then improve the report.
```
This turns the report into a management tool.
7. Save reusable templates
After a few weeks, create a template:
```text
weekly-report-template.md
Sections:
- summary
- completed
- blockers
- decisions needed
- next week priorities
- metrics
- notes
```
Then each week you only add updates.
The agent does the structure. You do the judgment.
8. Simple tool stack
For individuals:
- ChatGPT / Claude
- Google Docs / Notion
- Telegram or email notes
- Google Sheets
For teams:
- Slack / Telegram / email exports
- Jira / Trello / Linear / Asana
- Google Sheets / Notion
- Claude Code / Codex working with files
- human review
Practical agent prompt:
```text
Read the weekly-report/ folder.
Use raw-updates.md, tasks.md, meeting-notes.md and metrics.md.
Create:
1. a structured fact summary
2. a weekly report for the team
3. a short executive summary
4. a list of blockers and decisions needed
5. next actions with owners
Save the final report to report.md.
Do not invent missing facts.
```
Practical takeaway:
A good weekly report is not:
`write what happened this week`
It is:
`collect updates -> extract signal -> show progress -> expose blockers -> define next actions`
That is how AI saves time without turning reporting into noise.
Sources:
OpenAI prompt engineering: https://platform.openai.com/docs/guides/prompt-engineering
Claude Code: https://docs.anthropic.com/en/docs/claude-code/overview
Next topic: weekly reports.
Most weekly reports are painful because the information is scattered:
- chats
- tasks
- meetings
- emails
- notes
- customer updates
- spreadsheet numbers
So people either skip it or spend too much time writing a vague summary.
A better AI workflow:
`raw updates -> progress summary -> blockers -> decisions -> next actions -> report`
The goal: make the week easier to understand.
1. Collect raw updates
Do not start by asking AI to "write a weekly report."
Start by collecting messy inputs:
- completed tasks
- open tasks
- blocked tasks
- meeting notes
- messages
- customer feedback
- decisions made
Simple folder:
```text
weekly-report/
raw-updates.md
tasks.md
meeting-notes.md
metrics.md
report.md
```
Then paste rough notes into the files. Messy input is fine.
2. Ask AI to extract signal
Prompt:
```text
Read the raw weekly updates.
Extract:
1. completed work
2. work still in progress
3. blockers
4. risks
5. important decisions
6. next actions
7. missing information
Do not write the final report yet.
Only structure the facts.
```
This prevents generic reports. AI organizes the week before writing.
3. Create a report structure
A useful weekly report usually has five sections:
```text
1. Executive summary
2. Completed this week
3. Blockers and risks
4. Decisions needed
5. Priorities for next week
```
For personal work, add:
```text
What I learned
What I should improve next week
```
For business/team work, add:
```text
Customer impact
Revenue / delivery impact
Owner for each next action
```
4. Generate the first draft
Prompt:
```text
Using the structured weekly facts, write a clear weekly report.
Audience: [manager / client / team / myself]
Rules:
- short paragraphs
- no vague phrases
- separate facts from opinions
- highlight blockers clearly
- include next actions with owners
- keep it useful, not decorative
```
AI should not invent progress. If something is missing, it should say so.
5. Make three versions
One report can become several formats.
Prompt:
```text
Turn this weekly report into three versions:
1. short executive summary
2. team update
3. client-friendly version
Keep the facts the same.
Change only tone, length and emphasis.
```
Different readers need different levels of detail.
6. Add a risk check
Before sending, ask AI to review the report:
```text
Review this report.
Flag:
1. unclear claims
2. missing owners
3. missing deadlines
4. weak next actions
5. risks that are hidden or softened
6. anything that sounds like empty corporate language
Then improve the report.
```
This turns the report into a management tool.
7. Save reusable templates
After a few weeks, create a template:
```text
weekly-report-template.md
Sections:
- summary
- completed
- blockers
- decisions needed
- next week priorities
- metrics
- notes
```
Then each week you only add updates.
The agent does the structure. You do the judgment.
8. Simple tool stack
For individuals:
- ChatGPT / Claude
- Google Docs / Notion
- Telegram or email notes
- Google Sheets
For teams:
- Slack / Telegram / email exports
- Jira / Trello / Linear / Asana
- Google Sheets / Notion
- Claude Code / Codex working with files
- human review
Practical agent prompt:
```text
Read the weekly-report/ folder.
Use raw-updates.md, tasks.md, meeting-notes.md and metrics.md.
Create:
1. a structured fact summary
2. a weekly report for the team
3. a short executive summary
4. a list of blockers and decisions needed
5. next actions with owners
Save the final report to report.md.
Do not invent missing facts.
```
Practical takeaway:
A good weekly report is not:
`write what happened this week`
It is:
`collect updates -> extract signal -> show progress -> expose blockers -> define next actions`
That is how AI saves time without turning reporting into noise.
Sources:
OpenAI prompt engineering: https://platform.openai.com/docs/guides/prompt-engineering
Claude Code: https://docs.anthropic.com/en/docs/claude-code/overview
Openai
Prompt engineering | OpenAI API
Learn strategies and tactics for better results using large language models in the OpenAI API.
2👍3🔥2❤1
Open Design AI: the missing design layer for AI builders
Most people use AI to write text or code.
But the next workflow is bigger:
idea -> design -> page -> deck -> video -> launch assets
Open Design AI is an open-source, local-first design system for AI agents. It helps tools like Claude Code, Codex, Cursor and Gemini CLI create visual assets, not just code.
Why it matters:
AI can already help with logic and implementation. But the visual layer is still a bottleneck: layout, hierarchy, colors, pitch decks, landing pages, mockups and campaign assets.
This is where design becomes part of the AI workflow.
Full breakdown below.
Source: https://open-design.ai/ru/
#AI #Design #AIAgents #OpenSource #VibeCoding
Most people use AI to write text or code.
But the next workflow is bigger:
idea -> design -> page -> deck -> video -> launch assets
Open Design AI is an open-source, local-first design system for AI agents. It helps tools like Claude Code, Codex, Cursor and Gemini CLI create visual assets, not just code.
Why it matters:
AI can already help with logic and implementation. But the visual layer is still a bottleneck: layout, hierarchy, colors, pitch decks, landing pages, mockups and campaign assets.
This is where design becomes part of the AI workflow.
Full breakdown below.
Source: https://open-design.ai/ru/
#AI #Design #AIAgents #OpenSource #VibeCoding
👍3❤1🔥1
Open Design AI: the missing design layer for AI builders
Most people use AI to write text or code.
But the next big workflow is different:
idea -> design -> page -> deck -> video -> launch assets
Open Design AI is an open-source, local-first design system for AI agents. In simple words: it helps tools like Claude Code, Codex, Cursor, Gemini CLI and other coding agents create visual assets, not just code.
What it can help you produce:
- landing pages and website sections
- pitch decks and presentations
- posters, banners and social media visuals
- infographics and diagrams
- product mockups and UI concepts
- video-style visual compositions
Why this matters
AI builders often have the same bottleneck:
The logic is ready.
The idea is clear.
The code can be generated.
But the visual layer still takes time: layout, hierarchy, colors, export formats, brand consistency, presentation quality.
Open Design AI tries to turn that design layer into a reusable system that an agent can operate.
The interesting part is not just "generate a design".
The interesting part is:
- local-first workflow
- open-source project
- bring-your-own-key setup
- reusable skills and design systems
- exports for real work, not just pretty previews
- compatibility with multiple AI coding agents
Who is it useful for?
Founders:
Turn an idea into a landing page, pitch deck or product visual faster.
Developers:
Add a design layer to agentic coding workflows without becoming a full-time designer.
Marketers:
Create campaign visuals, ads, banners and content concepts with more structure.
Product managers:
Convert product ideas into mockups, diagrams and presentation material.
Small teams:
Move from "we need a designer for every visual task" to "we can prototype 80% of it ourselves".
The bigger idea
AI is not only changing coding.
It is changing the whole production chain:
research -> strategy -> copy -> design -> implementation -> publishing
Tools like Open Design AI show where things are going: not one chatbot, but a stack of specialized agent skills that can produce real business assets.
For people and companies, this is the practical question:
What part of your work can become a repeatable AI workflow?
Source: https://open-design.ai/ru/
GitHub: https://github.com/nexu-io/open-design
#AI #Design #AIAgents #OpenSource #VibeCoding
Most people use AI to write text or code.
But the next big workflow is different:
idea -> design -> page -> deck -> video -> launch assets
Open Design AI is an open-source, local-first design system for AI agents. In simple words: it helps tools like Claude Code, Codex, Cursor, Gemini CLI and other coding agents create visual assets, not just code.
What it can help you produce:
- landing pages and website sections
- pitch decks and presentations
- posters, banners and social media visuals
- infographics and diagrams
- product mockups and UI concepts
- video-style visual compositions
Why this matters
AI builders often have the same bottleneck:
The logic is ready.
The idea is clear.
The code can be generated.
But the visual layer still takes time: layout, hierarchy, colors, export formats, brand consistency, presentation quality.
Open Design AI tries to turn that design layer into a reusable system that an agent can operate.
The interesting part is not just "generate a design".
The interesting part is:
- local-first workflow
- open-source project
- bring-your-own-key setup
- reusable skills and design systems
- exports for real work, not just pretty previews
- compatibility with multiple AI coding agents
Who is it useful for?
Founders:
Turn an idea into a landing page, pitch deck or product visual faster.
Developers:
Add a design layer to agentic coding workflows without becoming a full-time designer.
Marketers:
Create campaign visuals, ads, banners and content concepts with more structure.
Product managers:
Convert product ideas into mockups, diagrams and presentation material.
Small teams:
Move from "we need a designer for every visual task" to "we can prototype 80% of it ourselves".
The bigger idea
AI is not only changing coding.
It is changing the whole production chain:
research -> strategy -> copy -> design -> implementation -> publishing
Tools like Open Design AI show where things are going: not one chatbot, but a stack of specialized agent skills that can produce real business assets.
For people and companies, this is the practical question:
What part of your work can become a repeatable AI workflow?
Source: https://open-design.ai/ru/
GitHub: https://github.com/nexu-io/open-design
#AI #Design #AIAgents #OpenSource #VibeCoding
Open Design
Open Design — официальная open-source альтернатива Claude Design
Open Design — официальная open-source и local-first альтернатива Claude Design. Создавайте презентации, лендинги, дашборды и бренд-системы через Claude Code, Codex, Cursor, Gemini, OpenCode или Qwen на базе 132 skills и 150 DESIGN.md-систем.
🔥3
Want to see AI workflows in motion?
We are adding more visual AI content on Instagram.
Telegram is where we publish practical guides, step-by-step workflows and useful AI systems for work and business.
Instagram will be the visual layer:
- short video explainers
- AI tool demos
- workflow breakdowns
- before/after examples
- content, research and automation ideas
- quick reels you can save and reuse later
Some ideas are easier to understand when you see them moving.
A workflow that looks complex in text becomes obvious in a 20-second video:
idea -> prompt -> tool -> output -> business use case
Telegram = deep practical guides.
Instagram = fast visual examples.
Follow AI Lab on Instagram:
https://www.instagram.com/aisystemagentlab/
Together, they become your AI workflow lab.
#AI #AIAgents #AIWorkflow #VibeCoding
We are adding more visual AI content on Instagram.
Telegram is where we publish practical guides, step-by-step workflows and useful AI systems for work and business.
Instagram will be the visual layer:
- short video explainers
- AI tool demos
- workflow breakdowns
- before/after examples
- content, research and automation ideas
- quick reels you can save and reuse later
Some ideas are easier to understand when you see them moving.
A workflow that looks complex in text becomes obvious in a 20-second video:
idea -> prompt -> tool -> output -> business use case
Telegram = deep practical guides.
Instagram = fast visual examples.
Follow AI Lab on Instagram:
https://www.instagram.com/aisystemagentlab/
Together, they become your AI workflow lab.
#AI #AIAgents #AIWorkflow #VibeCoding
1👍4❤2🔥2
AI agents just crossed a serious line: money.
Robinhood announced Agentic Trading and an Agentic Credit Card.
In simple words: users can connect third-party AI agents to Robinhood via MCP servers, give them access to a separate funded account, and let them analyze portfolios, suggest strategies and place stock trades.
The same idea applies to spending: an agent can use a dedicated virtual card with limits and optional manual approvals.
Why this matters:
AI is moving from "give me advice" to "take action inside real systems".
The practical lesson:
Never give an agent unlimited power.
Good agent design needs separate accounts, limits, logs, approval steps and an instant kill switch.
This is not financial advice.
It is a signal: agentic workflows are entering finance, commerce and everyday operations.
Sources:
https://robinhood.com/us/en/newsroom/robinhood-is-now-open-to-agents/
https://techcrunch.com/2026/05/27/robinhood-now-lets-your-ai-agents-trade-stocks/
#AI #AIAgents #FinTech #MCP
Robinhood announced Agentic Trading and an Agentic Credit Card.
In simple words: users can connect third-party AI agents to Robinhood via MCP servers, give them access to a separate funded account, and let them analyze portfolios, suggest strategies and place stock trades.
The same idea applies to spending: an agent can use a dedicated virtual card with limits and optional manual approvals.
Why this matters:
AI is moving from "give me advice" to "take action inside real systems".
The practical lesson:
Never give an agent unlimited power.
Good agent design needs separate accounts, limits, logs, approval steps and an instant kill switch.
This is not financial advice.
It is a signal: agentic workflows are entering finance, commerce and everyday operations.
Sources:
https://robinhood.com/us/en/newsroom/robinhood-is-now-open-to-agents/
https://techcrunch.com/2026/05/27/robinhood-now-lets-your-ai-agents-trade-stocks/
#AI #AIAgents #FinTech #MCP
🔥5❤3
Where should you start learning AI?
Not with a $2,000 course from a random guru.
Start with 5 free official paths from the people building the AI era:
1. Anthropic Academy
Claude, prompting, workflows, skills and Claude Code from Anthropic.
https://www.anthropic.com/learn/claude-for-work
2. OpenAI Academy
AI fundamentals, ChatGPT, Codex, AI at work and building with AI.
https://openai.com/academy/
3. Google Skills
Generative AI basics, cloud labs and practical Google AI learning paths.
https://www.cloudskillsboost.google/course_templates/536
4. Microsoft GenAI
A beginner-friendly path for understanding GenAI and building simple AI apps.
https://microsoft.github.io/generative-ai-for-beginners/
5. DeepLearning.AI
AI strategy, prompt engineering and real business use cases.
https://www.deeplearning.ai/alpha/courses/ai-for-everyone/
Best start:
pick one course, finish one lesson, then apply AI to one real task today.
#AI #AILearning #ChatGPT #Claude #Codex #AItools
Not with a $2,000 course from a random guru.
Start with 5 free official paths from the people building the AI era:
1. Anthropic Academy
Claude, prompting, workflows, skills and Claude Code from Anthropic.
https://www.anthropic.com/learn/claude-for-work
2. OpenAI Academy
AI fundamentals, ChatGPT, Codex, AI at work and building with AI.
https://openai.com/academy/
3. Google Skills
Generative AI basics, cloud labs and practical Google AI learning paths.
https://www.cloudskillsboost.google/course_templates/536
4. Microsoft GenAI
A beginner-friendly path for understanding GenAI and building simple AI apps.
https://microsoft.github.io/generative-ai-for-beginners/
5. DeepLearning.AI
AI strategy, prompt engineering and real business use cases.
https://www.deeplearning.ai/alpha/courses/ai-for-everyone/
Best start:
pick one course, finish one lesson, then apply AI to one real task today.
#AI #AILearning #ChatGPT #Claude #Codex #AItools
2🔥4🙏1
Claude Opus 4.8 is out.
But the bigger signal is not just the model itself.
The AI race is becoming a release rhythm race.
Anthropic says Opus 4.8 improves agentic coding, long workflows, reliability, effort control and Claude Code Dynamic Workflows. Fast Mode is positioned as faster and cheaper for coding tasks.
Why it matters:
1. Frontier AI is moving from rare big launches to constant iteration.
2. Coding agents are becoming the main battlefield.
3. The winner may be the lab that ships useful upgrades fastest.
4. For users and businesses, model choice is becoming a moving target.
Practical takeaway:
do not build your workflow around one model forever.
Build a system where you can test, switch and combine models as they improve.
Source: Anthropic
https://www.anthropic.com/news/claude-opus-4-8
#AI #Claude #Anthropic #AIAgents #ClaudeCode
But the bigger signal is not just the model itself.
The AI race is becoming a release rhythm race.
Anthropic says Opus 4.8 improves agentic coding, long workflows, reliability, effort control and Claude Code Dynamic Workflows. Fast Mode is positioned as faster and cheaper for coding tasks.
Why it matters:
1. Frontier AI is moving from rare big launches to constant iteration.
2. Coding agents are becoming the main battlefield.
3. The winner may be the lab that ships useful upgrades fastest.
4. For users and businesses, model choice is becoming a moving target.
Practical takeaway:
do not build your workflow around one model forever.
Build a system where you can test, switch and combine models as they improve.
Source: Anthropic
https://www.anthropic.com/news/claude-opus-4-8
#AI #Claude #Anthropic #AIAgents #ClaudeCode
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Vibe coding is not “AI magically builds everything”.
It is a new way to build software:
you describe the result in natural language, an AI coding agent creates the first version, and you guide it through testing, fixes and improvements.
Why it matters:
1. Non-technical people can turn ideas into prototypes.
2. Businesses can test tools, landing pages and automations faster.
3. Developers move from typing code to designing systems and reviewing changes.
The value is not “no code”.
The value is faster experiments.
Top tools to try:
Claude Code / OpenAI Codex
Best for agentic coding inside real projects.
Cursor / Windsurf / GitHub Copilot
Best for AI-assisted coding inside an IDE and team workflows.
Replit Agent
Best for browser-based idea-to-app building.
Lovable / Bolt.new / v0
Best for fast web apps, UI prototypes and MVPs.
Rule:
let AI build fast, but always review, test and control the product logic.
#AI #VibeCoding #Codex #ClaudeCode #AItools
It is a new way to build software:
you describe the result in natural language, an AI coding agent creates the first version, and you guide it through testing, fixes and improvements.
Why it matters:
1. Non-technical people can turn ideas into prototypes.
2. Businesses can test tools, landing pages and automations faster.
3. Developers move from typing code to designing systems and reviewing changes.
The value is not “no code”.
The value is faster experiments.
Top tools to try:
Claude Code / OpenAI Codex
Best for agentic coding inside real projects.
Cursor / Windsurf / GitHub Copilot
Best for AI-assisted coding inside an IDE and team workflows.
Replit Agent
Best for browser-based idea-to-app building.
Lovable / Bolt.new / v0
Best for fast web apps, UI prototypes and MVPs.
Rule:
let AI build fast, but always review, test and control the product logic.
#AI #VibeCoding #Codex #ClaudeCode #AItools
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Build your first personal AI assistant in 30 minutes.
Not a huge automation system.
Just one assistant that helps you think, plan and reply faster.
Step 1: Choose the brain
Use ChatGPT, Claude or Gemini.
Step 2: Give it context
Add your role, goals, current projects, tone of voice and recurring tasks.
Step 3: Define 5 jobs
Example:
daily plan, email drafts, meeting summaries, task cleanup, decision notes.
Step 4: Create a daily loop
Morning: plan the day.
During work: draft replies and summarize notes.
Evening: extract tasks and follow-ups.
Step 5: Add guardrails
It can suggest, draft and organize.
You approve decisions, money, legal issues and important messages.
The goal is not to replace you.
The goal is to remove mental clutter and help you move faster.
Start small:
one assistant,
five repeatable tasks,
one daily habit.
#AI #Productivity #AIAssistant #AIworkflow #ChatGPT #Claude
Not a huge automation system.
Just one assistant that helps you think, plan and reply faster.
Step 1: Choose the brain
Use ChatGPT, Claude or Gemini.
Step 2: Give it context
Add your role, goals, current projects, tone of voice and recurring tasks.
Step 3: Define 5 jobs
Example:
daily plan, email drafts, meeting summaries, task cleanup, decision notes.
Step 4: Create a daily loop
Morning: plan the day.
During work: draft replies and summarize notes.
Evening: extract tasks and follow-ups.
Step 5: Add guardrails
It can suggest, draft and organize.
You approve decisions, money, legal issues and important messages.
The goal is not to replace you.
The goal is to remove mental clutter and help you move faster.
Start small:
one assistant,
five repeatable tasks,
one daily habit.
#AI #Productivity #AIAssistant #AIworkflow #ChatGPT #Claude
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Have you seen this?
Kimi is turning into a full AI work platform.
Not just a chatbot.
What it can do:
1. Coding
Kimi Code works across terminal, IDE and CLI workflows for coding, codebase analysis, debugging and automation.
2. Presentations
Kimi Slides can turn an idea into a structured deck: quick visuals or deeper research-based slides.
3. Websites and documents
Kimi has flows for websites, docs, sheets and deep research, so it starts to look like an AI workspace.
4. Kimi Claw
OpenClaw deployed through Kimi, with memory, cloud deployment and 24/7 proactive assistant workflows.
5. Agent Swarm
Research, coding, analysis and operations handled by coordinated AI agents.
Why it matters:
AI tools are moving from “answer my question” to “run my workflow”.
Kimi is one to watch.
Sources:
https://www.kimi.com/
https://www.kimi.com/code
https://www.kimi.com/bot
https://platform.kimi.ai/
#AI #Kimi #AIAgents #CodingAgents #AItools
Kimi is turning into a full AI work platform.
Not just a chatbot.
What it can do:
1. Coding
Kimi Code works across terminal, IDE and CLI workflows for coding, codebase analysis, debugging and automation.
2. Presentations
Kimi Slides can turn an idea into a structured deck: quick visuals or deeper research-based slides.
3. Websites and documents
Kimi has flows for websites, docs, sheets and deep research, so it starts to look like an AI workspace.
4. Kimi Claw
OpenClaw deployed through Kimi, with memory, cloud deployment and 24/7 proactive assistant workflows.
5. Agent Swarm
Research, coding, analysis and operations handled by coordinated AI agents.
Why it matters:
AI tools are moving from “answer my question” to “run my workflow”.
Kimi is one to watch.
Sources:
https://www.kimi.com/
https://www.kimi.com/code
https://www.kimi.com/bot
https://platform.kimi.ai/
#AI #Kimi #AIAgents #CodingAgents #AItools
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Forwarded from AI Lab (BotAdminAIWorkflowLab)
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AI Lab is now on Instagram too.
Telegram will stay the main place for deeper breakdowns, practical playbooks and full AI workflow posts.
Instagram will be the fast visual layer:
- short AI workflow clips
- quick agent ideas
- vibe coding examples
- tools I am testing
- practical AI systems for work and business
- visual examples before the longer breakdowns
The idea is simple:
Telegram = full playbook.
Instagram = fast visual experiments.
If you want the quick version before the deeper post, follow AI Lab here:
https://www.instagram.com/aisystemagentlab
Same name.
Same mission.
More visual AI workflows.
Telegram will stay the main place for deeper breakdowns, practical playbooks and full AI workflow posts.
Instagram will be the fast visual layer:
- short AI workflow clips
- quick agent ideas
- vibe coding examples
- tools I am testing
- practical AI systems for work and business
- visual examples before the longer breakdowns
The idea is simple:
Telegram = full playbook.
Instagram = fast visual experiments.
If you want the quick version before the deeper post, follow AI Lab here:
https://www.instagram.com/aisystemagentlab
Same name.
Same mission.
More visual AI workflows.
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AI Agent vs Chatbot: what is the real difference?
A chatbot answers.
An AI agent works toward an outcome.
Chatbot:
- waits for your prompt
- gives one reply
- needs you to bring the context
- usually stops after the answer
AI Agent:
- starts from a goal
- uses tools
- remembers context
- takes steps
- checks results
- asks for approval on risky actions
Simple example:
Chatbot:
"Write a reply to this customer."
Agent:
"Read the customer message, check order status, draft the reply, flag refund risk, save the note, and ask me before sending."
That is the real shift.
Not smarter chat.
A workflow with memory, tools, actions and human review.
For people: planning, research, emails, notes and follow-ups.
For business: sales, customer replies, reports, operations and internal workflows.
The future is not just asking AI better questions.
It is building AI systems that can help finish real work.
#AI #AIAgents #Chatbots #AIWorkflow #AILab
A chatbot answers.
An AI agent works toward an outcome.
Chatbot:
- waits for your prompt
- gives one reply
- needs you to bring the context
- usually stops after the answer
AI Agent:
- starts from a goal
- uses tools
- remembers context
- takes steps
- checks results
- asks for approval on risky actions
Simple example:
Chatbot:
"Write a reply to this customer."
Agent:
"Read the customer message, check order status, draft the reply, flag refund risk, save the note, and ask me before sending."
That is the real shift.
Not smarter chat.
A workflow with memory, tools, actions and human review.
For people: planning, research, emails, notes and follow-ups.
For business: sales, customer replies, reports, operations and internal workflows.
The future is not just asking AI better questions.
It is building AI systems that can help finish real work.
#AI #AIAgents #Chatbots #AIWorkflow #AILab
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AI video is getting weird, funny and useful very fast.
This clip is a reminder: video is no longer only cameras, studios and editors.
Tools to watch:
Kling - cinematic image/text-to-video
https://klingai.com
Veo - Google video model
https://deepmind.google/models/veo/
Wan - open video models from Alibaba
https://wan.video
Higgsfield - stylish social video effects
https://higgsfield.ai
Runway - pro video generation/editing
https://runwayml.com
HeyGen - avatars and explainers
https://www.heygen.com
Sora - OpenAI video generation line
https://openai.com/sora/
Hedra - character/video storytelling
https://www.hedra.com
Hailuo - fast AI video generator
https://hailuoai.video
Seedance - ByteDance video model
https://seed.bytedance.com/en/seedance
We are testing AI video too.
More examples here:
Instagram: https://www.instagram.com/aisystemagentlab
YouTube: https://www.youtube.com/@AI_Lab-h6n
TikTok: https://www.tiktok.com/@ai.lab636
#AI #AIVideo #VideoAI #AILab
This clip is a reminder: video is no longer only cameras, studios and editors.
Tools to watch:
Kling - cinematic image/text-to-video
https://klingai.com
Veo - Google video model
https://deepmind.google/models/veo/
Wan - open video models from Alibaba
https://wan.video
Higgsfield - stylish social video effects
https://higgsfield.ai
Runway - pro video generation/editing
https://runwayml.com
HeyGen - avatars and explainers
https://www.heygen.com
Sora - OpenAI video generation line
https://openai.com/sora/
Hedra - character/video storytelling
https://www.hedra.com
Hailuo - fast AI video generator
https://hailuoai.video
Seedance - ByteDance video model
https://seed.bytedance.com/en/seedance
We are testing AI video too.
More examples here:
Instagram: https://www.instagram.com/aisystemagentlab
YouTube: https://www.youtube.com/@AI_Lab-h6n
TikTok: https://www.tiktok.com/@ai.lab636
#AI #AIVideo #VideoAI #AILab
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MCP in plain English.
MCP means Model Context Protocol.
It is a standard way for AI apps and agents to connect to tools, files, APIs and databases.
Think of it like an adapter layer:
AI app -> MCP client -> MCP server -> tools/data
Why it matters:
- one integration can work across many AI apps
- agents can use real tools, not just answer in chat
- context can come from files, docs, repos, tickets or databases
- workflows become reusable across platforms
Example:
Without MCP:
"Summarize this project" means pasting context manually.
With MCP:
the agent can read the repo, check issues, inspect docs, draft a summary and ask before risky action.
Important:
MCP is not magic security.
If you connect powerful tools, use trusted servers, least privilege, logs and human approval.
Read more:
https://modelcontextprotocol.io
https://docs.anthropic.com/en/docs/mcp
https://openai.github.io/openai-agents-js/guides/mcp/
https://github.com/modelcontextprotocol/servers
#AI #MCP #AIAgents #AIWorkflow #AILab
MCP means Model Context Protocol.
It is a standard way for AI apps and agents to connect to tools, files, APIs and databases.
Think of it like an adapter layer:
AI app -> MCP client -> MCP server -> tools/data
Why it matters:
- one integration can work across many AI apps
- agents can use real tools, not just answer in chat
- context can come from files, docs, repos, tickets or databases
- workflows become reusable across platforms
Example:
Without MCP:
"Summarize this project" means pasting context manually.
With MCP:
the agent can read the repo, check issues, inspect docs, draft a summary and ask before risky action.
Important:
MCP is not magic security.
If you connect powerful tools, use trusted servers, least privilege, logs and human approval.
Read more:
https://modelcontextprotocol.io
https://docs.anthropic.com/en/docs/mcp
https://openai.github.io/openai-agents-js/guides/mcp/
https://github.com/modelcontextprotocol/servers
#AI #MCP #AIAgents #AIWorkflow #AILab
1🔥5❤2
OpenAI just launched Rosalind Biodefense.
This is not another chatbot feature.
It is a signal that AI is moving into specialized domains.
It gives vetted developers and public-health partners access to GPT-Rosalind, OpenAI's frontier model for life sciences.
The goal:
build defensive tools for public health, pandemic preparedness and bio threat response.
Why it matters:
The next AI wave may not be one assistant for everything.
It may be domain AI:
- AI for medicine
- AI for law
- AI for finance
- AI for cybersecurity
- AI for operations
Each field needs its own data, tools, safeguards and approval.
The lesson for companies:
do not just "add AI".
Build a controlled workflow:
expert context -> specialized model -> tools/data -> human review -> output
Useful AI looks less like a chat window and more like a trusted expert system.
Sources:
https://openai.com/index/strengthening-societal-resilience-with-rosalind-biodefense/
#AI #OpenAI #GPT #AIAgents #AIWorkflow #AILab
This is not another chatbot feature.
It is a signal that AI is moving into specialized domains.
It gives vetted developers and public-health partners access to GPT-Rosalind, OpenAI's frontier model for life sciences.
The goal:
build defensive tools for public health, pandemic preparedness and bio threat response.
Why it matters:
The next AI wave may not be one assistant for everything.
It may be domain AI:
- AI for medicine
- AI for law
- AI for finance
- AI for cybersecurity
- AI for operations
Each field needs its own data, tools, safeguards and approval.
The lesson for companies:
do not just "add AI".
Build a controlled workflow:
expert context -> specialized model -> tools/data -> human review -> output
Useful AI looks less like a chat window and more like a trusted expert system.
Sources:
https://openai.com/index/strengthening-societal-resilience-with-rosalind-biodefense/
#AI #OpenAI #GPT #AIAgents #AIWorkflow #AILab
❤5🔥5👍2👏1🆒1
RAG in plain English.
RAG means Retrieval-Augmented Generation.
It makes AI answer using your real knowledge base, not only model memory.
Simple idea:
question -> retrieve sources -> add context -> answer with evidence
Why it matters:
- fewer hallucinations
- answers can use fresh or private data
- works with docs, PDFs, tickets, wikis, repos and policies
- easier to verify with source links
Where to use it:
- internal knowledge assistants
- customer support
- legal or policy search
- product docs
- sales enablement
- research over documents
Basic workflow:
documents -> chunks -> embeddings -> vector database -> retrieval -> context -> answer
Important:
RAG is not truth by default.
Bad docs or weak retrieval still create bad answers.
Start small:
one use case, trusted documents, source links, human review.
Read more:
https://docs.aws.amazon.com/prescriptive-guidance/latest/retrieval-augmented-generation-options/what-is-rag.html
#AI #RAG #AIAgents #AIWorkflow #AILab
RAG means Retrieval-Augmented Generation.
It makes AI answer using your real knowledge base, not only model memory.
Simple idea:
question -> retrieve sources -> add context -> answer with evidence
Why it matters:
- fewer hallucinations
- answers can use fresh or private data
- works with docs, PDFs, tickets, wikis, repos and policies
- easier to verify with source links
Where to use it:
- internal knowledge assistants
- customer support
- legal or policy search
- product docs
- sales enablement
- research over documents
Basic workflow:
documents -> chunks -> embeddings -> vector database -> retrieval -> context -> answer
Important:
RAG is not truth by default.
Bad docs or weak retrieval still create bad answers.
Start small:
one use case, trusted documents, source links, human review.
Read more:
https://docs.aws.amazon.com/prescriptive-guidance/latest/retrieval-augmented-generation-options/what-is-rag.html
#AI #RAG #AIAgents #AIWorkflow #AILab
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One small AI system that saves 5 hours a week.
Most people use AI like Google:
ask one question, get one answer, close the chat.
The real leverage starts when you turn one repetitive task into a tiny system.
Example:
incoming messages -> summary -> reply drafts -> tasks -> follow-up reminders
You can build this with ChatGPT, Claude, Gemini, Codex, Notion, Google Sheets, Make, Zapier or n8n.
Start with one workflow:
1. Collect messy input.
2. Give AI your context and rules.
3. Ask for a structured output.
4. Review before sending.
5. Repeat every day.
Do not automate your whole life.
Automate one annoying loop first.
That is how AI becomes a worker, not just a chat window.
#AI #Productivity #AIWorkflow #Automation #AILab
Most people use AI like Google:
ask one question, get one answer, close the chat.
The real leverage starts when you turn one repetitive task into a tiny system.
Example:
incoming messages -> summary -> reply drafts -> tasks -> follow-up reminders
You can build this with ChatGPT, Claude, Gemini, Codex, Notion, Google Sheets, Make, Zapier or n8n.
Start with one workflow:
1. Collect messy input.
2. Give AI your context and rules.
3. Ask for a structured output.
4. Review before sending.
5. Repeat every day.
Do not automate your whole life.
Automate one annoying loop first.
That is how AI becomes a worker, not just a chat window.
#AI #Productivity #AIWorkflow #Automation #AILab
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