The Last Mile of Agent Adoption: Why `vercel-labs/agent-desktop` Proves Onboarding Matters More Than Capability
Today's GitHub Trending tells a clear story: AI agents have enough capability. What they're missing is a way for users to actually use them.
vercel-labs/agent-desktop sits at #1 with 3,791 stars today — a Rust CLI that gives AI agents desktop control. It's a genuinely impressive piece of engineering. But look past the star count and you'll see the gap: to use it, you need a Rust toolchain, environment configuration, CLI fluency, and permission debugging. For developers, that's Tuesday. For everyone else, it's a wall.
We hit the same wall ourselves. We built agent capabilities — skill calling, cross-session memory persistence, local execution — and then watched users stare at the interface asking: "Okay, but what do I do first?"
!Astron agent creation and onboarding interface
The pattern in today's trending
It's not just agent-desktop. Look at the other hot repos today:
- anomalyco/hex — "Local voice dictation for mac, windows and linux." Three platforms, one word: Local. Users want AI capabilities that just work on their own machine, no cloud dependency, no setup marathon.
- thedotmack/claude-mem — "Persistent Context Across Sessions for Every Agent." The verb chain says it all: Captures → compresses → injects. Users don't want to understand the architecture; they want the agent to remember what they were doing.
- mattpocock/skills — "Straight from my .agents directory." The appeal isn't the technology, it's the authenticity: this is what I actually use.
The common thread: the hottest repos aren't adding capability. They're removing friction.
What we learned the hard way
When we were building AstronClaw (cloud AI assistant) and Loomy (desktop AI assistant), we focused heavily on what the agents could do — call skills, maintain context across sessions, operate locally.
Then users showed up. And they didn't care about any of that. They cared about:
- "How do I install this?"
- "What do I type first?"
- "Will it remember what I did yesterday?"
- "Can it run on my machine without breaking things?"
The capability was there. The onboarding wasn't.
So we did the unglamorous work: wrote a complete tutorial covering everything from installation to your first command to advanced workflows. No assumptions about prior knowledge. No "figure out the rest yourself."
!Agent workflow orchestration canvas
That tutorial is now open source:
**iflytek/astronclaw-tutorial**
The full picture: capability + onboarding + skills
Onboarding gets users in the door. But once they're in, the assistant needs real capabilities to call. That's where iflytek/iFly-Skills comes in — the official iFLYTEK skill collection covering voice, OCR, translation, proofreading, and multimodal capabilities:
**iflytek/iFly-Skills**
Think of it this way:
- AstronClaw / Loomy tutorial = how to use the assistant (the last mile)
- iFly-Skills = what the assistant can actually do (the capability layer)
Today's trending proves users want both — and the "both" part is where most agent projects fall short.
The takeaway
If you're building an agent project, ask yourself: can a non-developer go from zero to a working agent in under 10 minutes? If not, the capability doesn't matter. The wall is the onboarding, not the model.
vercel-labs/agent-desktop is a great tool. But today's trending also shows that anomalyco/hex — a simple local voice dictation app — is gaining traction right alongside it. Because hex removes friction. And friction is what kills adoption.
We learned this the hard way so you don't have to.
The last mile of agent adoption isn't more capability. It's onboarding.
That's why we open-sourced the full AstronClaw + Loomy tutorial:
iflytek/astronclaw-tutorial
1 view
Spark-X2.6-4B in two live web assistants: share what you are building
Full case on Hugging Face: huggingface.co
Spark-X2.6-4B is running in two live web assistants. These short recordings show English questions, actual model replies, and the thinking-level controls available to visitors.
Try the apps, then share a demo of your own Spark-X2.6 application in the comments. Longer case write-ups are welcome too—post them on Hugging Face and drop the link here.
Astron Harness Tutorial: Learning Assistant
Open the learning assistant · Source repository
This bilingual tutorial assistant helps visitors explore AStudio, Loomy and AstronClaw. It uses relevant tutorial material as context for the conversation.
Question: “Plan mode vs Goal mode in AStudio? Two short sentences, no Markdown.”
!Spark-X2.6-4B answering an English AStudio question in the Learning Assistant
The recording shows the assistant explaining when to review a plan before execution and when to use Goal mode for continued progress. The interface offers None, Low and High thinking levels and remembers the visitor's selection.
Full-length recording
Awesome Astron Workflow: AI Chat
Open AI Chat
This assistant answers questions about Astron projects and capabilities using the site's reference material.
Question: “How can SkillHub help a team reuse AI skills? Give 3 benefits in under 60 words.”
!Spark-X2.6-4B answering an English SkillHub question in Astron AI Chat; idle waiting is shortened
The recorded reply discusses skill distribution, secure sharing and discoverability. Both the question and the answer remain as they appeared in the application.
Full-length recording
Integration details
Both applications call spark-x2.6-4b through iFLYTEK Astron MaaS, with credentials kept on the server. The browser sends the conversation and selected thinking level to the application's backend. These demos use Low.
The server maps the controls to the model request:
| Selection | Request behavior |
| --- | --- |
| None | Disable extra thinking |
| Low | Enable thinking with low reasoning effort |
| High | Enable thinking with high reasoning effort |
The tutorial assistant supports streaming replies and stopping an answer. The clips show the website Q&A experience: typing a question, waiting for the model and reading the response.
About the recordings
These are real website recordings, and the responses have not been rewritten or simulated. The AI Chat GIF shortens pure waiting time and labels that edit. The full-length MP4 links retain the complete captures. These clips illustrate application integration; they are not latency benchmarks.
Share your Spark-X2.6 case
What are you building with Spark-X2.6? Share a working demo or screenshot in the comments. For a longer Hugging Face write-up, useful details include your use case, model variant, deployment stack, prompt, thinking level, integration approach and what you learned.
Spark-X2.6 model collection
Stop Configuring Agent Frameworks — Start Using AI Assistants Today
Today's GitHub Trending chart reveals a shift that's been building for months: developers are tired of Agent frameworks that take days to configure. They want assistants that work out of the box.
Let's look at the evidence.
The "Out-of-the-Box" Signal
makecindy/cindy gained 471 stars today with a simple promise: "The open-source AI agent that works out of the box." Its bilingual slogan — "想到，就能做到" — captures what users actually want: think it, done.
No environment setup. No dependency hell. No reading 500 lines of source code. Just install and use.
The "Copy-Paste Tutorial" Signal
luongnv89/claude-howto gained 442 stars today with another clear promise: "A visual, example-driven guide with copy-paste templates that bring immediate value."
This repo's popularity tells us something important: people don't want API documentation. They want step-by-step guides with screenshots and configs they can paste and run.
The Shift: Framework → Assistant
For the past two years, the open-source AI community has been building frameworks. Configure this, wire up that, write a custom adapter for the other thing. The result? Most developers spend more time setting up the framework than actually using AI.
The trend is now clear: assistants > frameworks. Users want something they can install today and get value from today.
!Astron Assistant Creation Interface
AstronClaw + Loomy: Built for the Assistant Era
This is where AstronClaw (cloud AI) and Loomy (desktop AI) come in. Instead of building another framework, the Astron team built actual assistants — one for the cloud, one for the desktop — with a tutorial that follows the exact pattern that claude-howto proved works:
- Visual: Every step has screenshots
- Example-driven: Real scenarios, not abstract concepts
- Copy-paste ready: Configs you can paste and run immediately
- Progressive: From basic setup to advanced automation
Pair It With RPA for Real Automation
Want your desktop AI assistant to actually do things — browser automation, file operations, desktop workflows? Pair it with astron-rpa, an Agent-ready RPA suite that turns your assistant from a chatbot into an automation engine.
!Astron RPA Desktop Automation
The Bottom Line
The community has voted with their stars: the future of AI is not frameworks, it's assistants. Stop configuring. Start using.
🔗 Tutorial: iflytek/astronclaw-tutorial
🔗 RPA: iflytek/astron-rpa
Stop reading source code. Start using AI assistants.
🔗 Tutorial: iflytek/astronclaw-tutorial
🔗 RPA: iflytek/astron-rpa
#AIAgent #OpenSource #AIAssistant #GitHubTrending #DeveloperTools
1 like · 24 views
AI Assistants Are Becoming Multi-Platform Workstations — Here's How to Get Started
Today's GitHub Trending chart tells a clear story: AI assistants are leaving the terminal and becoming full-fledged workstations.
Let's break down what's happening and what it means for developers.
The Signal From GitHub Trending
Three repos caught our attention today:
MadsLorentzen/ai-job-search — It wraps the entire job search workflow into a fork-and-own local framework built on Claude Code. Evaluate postings, tailor CVs, write cover letters, prep interviews — all in one repo you fork and run.
stablyai/orca — It describes itself as 「the place for working with a fleet of agents.」 Desktop, mobile, remote runtime. Run any coding agent with your own subscription, on any device.
debpalash/VoiceStudio — A fully-local ElevenLabs alternative with voice cloning, video dubbing, transcription, and audiobook creation in 646 languages.
The Common Pattern
These three repos seem different on the surface — one is a job search tool, one is an agent fleet manager, one is a voice studio. But they share a common DNA:
1. Scenario packaging over tool assembly — Users don't want to assemble individual tools. They want a pre-packaged workflow they can open and use immediately.
2. Multi-platform by default — Desktop, mobile, cloud. Not 「CLI first, maybe GUI later.」 The workstation experience is the product.
3. Local ownership — 「Runs on your machine」 and 「fully-local」 are not afterthoughts. They're the value proposition.
Where AstronClaw and Loomy Fit In
This is exactly the gap that AstronClaw (cloud AI) and Loomy (desktop AI) are designed to fill.
While the trending repos show what's possible — packaged workflows, multi-agent fleets, multimodal studios — the question for most developers is simpler: how do I actually get started with a cloud-and-desktop AI assistant without assembling everything from scratch?
!AstronClaw desktop assistant connected and ready
That's what the astronclaw-tutorial repository answers: iflytek/astronclaw-tutorial
The tutorial covers:
- Setting up AstronClaw (cloud AI assistant) and Loomy (desktop AI assistant)
- Configuring multimodal capabilities — voice, OCR, translation, proofreading
- Building your first assistant workflow
- Moving between cloud and desktop seamlessly
And for the multimodal capabilities themselves, iFLYTEK's official skill collection provides the building blocks: iflytek/iFly-Skills
!iFly-Skills multimodal capabilities
The Takeaway
The era of 「AI as a CLI tool」 is giving way to 「AI as a multi-platform workstation.」 The repos trending today prove that users want ready-to-use environments, not tool fragments.
If you're looking to move beyond terminal-only AI and set up a cloud + desktop assistant workflow, the AstronClaw and Loomy tutorial is a good place to start.
6/ That's exactly what AstronClaw (cloud AI) and Loomy (desktop AI) are designed for. Full tutorial from setup to multimodal capability calls: iflytek/astronclaw-tutorial
5 views
AI assistants are leaving the terminal. Today's GitHub Trending shows users want ready-to-use workstations, not CLI tools. AstronClaw (cloud) & Loomy (desktop) tutorial: iflytek/astronclaw-tutorial #AIAgent #DesktopAI
iflytek/astronclaw-tutorial
1 like
From "Here's Some Code" to "I'll Do It For You": The Shift in AI Developer Tools
Today's GitHub Trending reveals a quiet but massive shift in what developers expect from AI tools.
Let's look at three repos that are blowing up right now:
morluto/rea — 25k+ stars today. Reverse engineer anything with agents, from app behavior down to native binaries. This isn't an AI that generates analysis scripts for you to run. It's an agent chain that actually does the reversing — traversing from high-level app behavior all the way down to native binary analysis, autonomously.
hugohe3/ppt-master — AI turns documents or topics into real, native PowerPoint decks. Not markdown outlines. Not screenshots stitched together. Native shapes, transitions, animations, data-backed charts, audio narration from speaker notes, and support for your own .pptx templates. A file you can actually present.
mattpocock/skills — 284k+ total stars. "Skills for Real Engineers. Straight from my .agents directory." Not a tutorial. Not a framework. A real engineer's actual agent skills, open-sourced from his daily workflow.
What's the pattern?
All three share one trait: they don't stop at "generating." They go all the way to "doing."
- rea doesn't write you a reversing script — it does the reversing.
- ppt-master doesn't outline your slides — it builds the .pptx file.
- skills doesn't teach you how to write agent skills — it gives you the skills themselves.
This is the shift: from AI as a code generator to AI as a workflow executor.
Where does AstronClaw fit?
If you've been watching this trend and thinking "I want to try this — I want an AI assistant that actually does things in my environment, not just in a chat window" — that's exactly what AstronClaw (cloud AI) and Loomy (desktop AI) are built for.
The tutorial is now open source: iflytek/astronclaw-tutorial
It covers:
- Setting up AstronClaw for cloud-based AI workflows
- Configuring Loomy as your desktop AI assistant
- Connecting the two for cloud + desktop orchestration
- Real workflow examples you can follow along
!AstronClaw and Loomy desktop AI assistant orchestration environment
Pair it with iflytek/astron-rpa for out-of-the-box desktop and browser automation — the execution layer that makes "AI does it for you" actually work.
The takeaway
The next wave of AI developer tools won't compete on "who generates better code." They'll compete on "who actually gets the job done." The tools that win will be the ones that close the gap between AI output and delivered work.
AstronClaw + Loomy is our take on that future. The tutorial is open. Go build something.
6/ AstronClaw (cloud AI) + Loomy (desktop AI) — hands-on tutorial now open source. From setup to real workflows: iflytek/astronclaw-tutorial
11 views
Mentions
The Last Mile of Agent Adoption: Why `vercel-labs/agent-desktop` Proves Onboarding Matters More Than Capability Today's GitHub Trending tells a clear story: AI agents have enough capability. What they're missing is a way for users to actually use them. vercel-labs/agent-desktop sits at #1 with 3,791 stars today — a Rust CLI that gives AI agents desktop control. It's a genuinely impressive piece of engineering. But look past the star count and you'll see the gap: to use it, you need a Rust toolchain, environment configuration, CLI fluency, and permission debugging. For developers, that's Tuesday. For everyone else, it's a wall. We hit the same wall ourselves. We built agent capabilities — skill calling, cross-session memory persistence, local execution — and then watched users stare at the interface asking: "Okay, but what do I do first?" !Astron agent creation and onboarding interface The pattern in today's trending It's not just agent-desktop. Look at the other hot repos today: - anomalyco/hex — "Local voice dictation for mac, windows and linux." Three platforms, one word: Local. Users want AI capabilities that just work on their own machine, no cloud dependency, no setup marathon. - thedotmack/claude-mem — "Persistent Context Across Sessions for Every Agent." The verb chain says it all: Captures → compresses → injects. Users don't want to understand the architecture; they want the agent to remember what they were doing. - mattpocock/skills — "Straight from my .agents directory." The appeal isn't the technology, it's the authenticity: this is what I actually use. The common thread: the hottest repos aren't adding capability. They're removing friction. What we learned the hard way When we were building AstronClaw (cloud AI assistant) and Loomy (desktop AI assistant), we focused heavily on what the agents could do — call skills, maintain context across sessions, operate locally. Then users showed up. And they didn't care about any of that. They cared about: - "How do I install this?" - "What do I type first?" - "Will it remember what I did yesterday?" - "Can it run on my machine without breaking things?" The capability was there. The onboarding wasn't. So we did the unglamorous work: wrote a complete tutorial covering everything from installation to your first command to advanced workflows. No assumptions about prior knowledge. No "figure out the rest yourself." !Agent workflow orchestration canvas That tutorial is now open source: **iflytek/astronclaw-tutorial** The full picture: capability + onboarding + skills Onboarding gets users in the door. But once they're in, the assistant needs real capabilities to call. That's where iflytek/iFly-Skills comes in — the official iFLYTEK skill collection covering voice, OCR, translation, proofreading, and multimodal capabilities: **iflytek/iFly-Skills** Think of it this way: - AstronClaw / Loomy tutorial = how to use the assistant (the last mile) - iFly-Skills = what the assistant can actually do (the capability layer) Today's trending proves users want both — and the "both" part is where most agent projects fall short. The takeaway If you're building an agent project, ask yourself: can a non-developer go from zero to a working agent in under 10 minutes? If not, the capability doesn't matter. The wall is the onboarding, not the model. vercel-labs/agent-desktop is a great tool. But today's trending also shows that anomalyco/hex — a simple local voice dictation app — is gaining traction right alongside it. Because hex removes friction. And friction is what kills adoption. We learned this the hard way so you don't have to.
The last mile of agent adoption isn't more capability. It's onboarding. That's why we open-sourced the full AstronClaw + Loomy tutorial: iflytek/astronclaw-tutorial
Spark-X2.6-4B in two live web assistants: share what you are building Full case on Hugging Face: huggingface.co Spark-X2.6-4B is running in two live web assistants. These short recordings show English questions, actual model replies, and the thinking-level controls available to visitors. Try the apps, then share a demo of your own Spark-X2.6 application in the comments. Longer case write-ups are welcome too—post them on Hugging Face and drop the link here. Astron Harness Tutorial: Learning Assistant Open the learning assistant · Source repository This bilingual tutorial assistant helps visitors explore AStudio, Loomy and AstronClaw. It uses relevant tutorial material as context for the conversation. Question: “Plan mode vs Goal mode in AStudio? Two short sentences, no Markdown.” !Spark-X2.6-4B answering an English AStudio question in the Learning Assistant The recording shows the assistant explaining when to review a plan before execution and when to use Goal mode for continued progress. The interface offers None, Low and High thinking levels and remembers the visitor's selection. Full-length recording Awesome Astron Workflow: AI Chat Open AI Chat This assistant answers questions about Astron projects and capabilities using the site's reference material. Question: “How can SkillHub help a team reuse AI skills? Give 3 benefits in under 60 words.” !Spark-X2.6-4B answering an English SkillHub question in Astron AI Chat; idle waiting is shortened The recorded reply discusses skill distribution, secure sharing and discoverability. Both the question and the answer remain as they appeared in the application. Full-length recording Integration details Both applications call spark-x2.6-4b through iFLYTEK Astron MaaS, with credentials kept on the server. The browser sends the conversation and selected thinking level to the application's backend. These demos use Low. The server maps the controls to the model request: | Selection | Request behavior | | --- | --- | | None | Disable extra thinking | | Low | Enable thinking with low reasoning effort | | High | Enable thinking with high reasoning effort | The tutorial assistant supports streaming replies and stopping an answer. The clips show the website Q&A experience: typing a question, waiting for the model and reading the response. About the recordings These are real website recordings, and the responses have not been rewritten or simulated. The AI Chat GIF shortens pure waiting time and labels that edit. The full-length MP4 links retain the complete captures. These clips illustrate application integration; they are not latency benchmarks. Share your Spark-X2.6 case What are you building with Spark-X2.6? Share a working demo or screenshot in the comments. For a longer Hugging Face write-up, useful details include your use case, model variant, deployment stack, prompt, thinking level, integration approach and what you learned. Spark-X2.6 model collection
Stop Configuring Agent Frameworks — Start Using AI Assistants Today Today's GitHub Trending chart reveals a shift that's been building for months: developers are tired of Agent frameworks that take days to configure. They want assistants that work out of the box. Let's look at the evidence. The "Out-of-the-Box" Signal makecindy/cindy gained 471 stars today with a simple promise: "The open-source AI agent that works out of the box." Its bilingual slogan — "想到，就能做到" — captures what users actually want: think it, done. No environment setup. No dependency hell. No reading 500 lines of source code. Just install and use. The "Copy-Paste Tutorial" Signal luongnv89/claude-howto gained 442 stars today with another clear promise: "A visual, example-driven guide with copy-paste templates that bring immediate value." This repo's popularity tells us something important: people don't want API documentation. They want step-by-step guides with screenshots and configs they can paste and run. The Shift: Framework → Assistant For the past two years, the open-source AI community has been building frameworks. Configure this, wire up that, write a custom adapter for the other thing. The result? Most developers spend more time setting up the framework than actually using AI. The trend is now clear: assistants > frameworks. Users want something they can install today and get value from today. !Astron Assistant Creation Interface AstronClaw + Loomy: Built for the Assistant Era This is where AstronClaw (cloud AI) and Loomy (desktop AI) come in. Instead of building another framework, the Astron team built actual assistants — one for the cloud, one for the desktop — with a tutorial that follows the exact pattern that claude-howto proved works: - Visual: Every step has screenshots - Example-driven: Real scenarios, not abstract concepts - Copy-paste ready: Configs you can paste and run immediately - Progressive: From basic setup to advanced automation Pair It With RPA for Real Automation Want your desktop AI assistant to actually do things — browser automation, file operations, desktop workflows? Pair it with astron-rpa, an Agent-ready RPA suite that turns your assistant from a chatbot into an automation engine. !Astron RPA Desktop Automation The Bottom Line The community has voted with their stars: the future of AI is not frameworks, it's assistants. Stop configuring. Start using. 🔗 Tutorial: iflytek/astronclaw-tutorial 🔗 RPA: iflytek/astron-rpa
Stop reading source code. Start using AI assistants. 🔗 Tutorial: iflytek/astronclaw-tutorial 🔗 RPA: iflytek/astron-rpa #AIAgent #OpenSource #AIAssistant #GitHubTrending #DeveloperTools
AI Assistants Are Becoming Multi-Platform Workstations — Here's How to Get Started Today's GitHub Trending chart tells a clear story: AI assistants are leaving the terminal and becoming full-fledged workstations. Let's break down what's happening and what it means for developers. The Signal From GitHub Trending Three repos caught our attention today: MadsLorentzen/ai-job-search — It wraps the entire job search workflow into a fork-and-own local framework built on Claude Code. Evaluate postings, tailor CVs, write cover letters, prep interviews — all in one repo you fork and run. stablyai/orca — It describes itself as 「the place for working with a fleet of agents.」 Desktop, mobile, remote runtime. Run any coding agent with your own subscription, on any device. debpalash/VoiceStudio — A fully-local ElevenLabs alternative with voice cloning, video dubbing, transcription, and audiobook creation in 646 languages. The Common Pattern These three repos seem different on the surface — one is a job search tool, one is an agent fleet manager, one is a voice studio. But they share a common DNA: 1. Scenario packaging over tool assembly — Users don't want to assemble individual tools. They want a pre-packaged workflow they can open and use immediately. 2. Multi-platform by default — Desktop, mobile, cloud. Not 「CLI first, maybe GUI later.」 The workstation experience is the product. 3. Local ownership — 「Runs on your machine」 and 「fully-local」 are not afterthoughts. They're the value proposition. Where AstronClaw and Loomy Fit In This is exactly the gap that AstronClaw (cloud AI) and Loomy (desktop AI) are designed to fill. While the trending repos show what's possible — packaged workflows, multi-agent fleets, multimodal studios — the question for most developers is simpler: how do I actually get started with a cloud-and-desktop AI assistant without assembling everything from scratch? !AstronClaw desktop assistant connected and ready That's what the astronclaw-tutorial repository answers: iflytek/astronclaw-tutorial The tutorial covers: - Setting up AstronClaw (cloud AI assistant) and Loomy (desktop AI assistant) - Configuring multimodal capabilities — voice, OCR, translation, proofreading - Building your first assistant workflow - Moving between cloud and desktop seamlessly And for the multimodal capabilities themselves, iFLYTEK's official skill collection provides the building blocks: iflytek/iFly-Skills !iFly-Skills multimodal capabilities The Takeaway The era of 「AI as a CLI tool」 is giving way to 「AI as a multi-platform workstation.」 The repos trending today prove that users want ready-to-use environments, not tool fragments. If you're looking to move beyond terminal-only AI and set up a cloud + desktop assistant workflow, the AstronClaw and Loomy tutorial is a good place to start.
6/ That's exactly what AstronClaw (cloud AI) and Loomy (desktop AI) are designed for. Full tutorial from setup to multimodal capability calls: iflytek/astronclaw-tutorial
AI assistants are leaving the terminal. Today's GitHub Trending shows users want ready-to-use workstations, not CLI tools. AstronClaw (cloud) & Loomy (desktop) tutorial: iflytek/astronclaw-tutorial #AIAgent #DesktopAI iflytek/astronclaw-tutorial
From "Here's Some Code" to "I'll Do It For You": The Shift in AI Developer Tools Today's GitHub Trending reveals a quiet but massive shift in what developers expect from AI tools. Let's look at three repos that are blowing up right now: morluto/rea — 25k+ stars today. Reverse engineer anything with agents, from app behavior down to native binaries. This isn't an AI that generates analysis scripts for you to run. It's an agent chain that actually does the reversing — traversing from high-level app behavior all the way down to native binary analysis, autonomously. hugohe3/ppt-master — AI turns documents or topics into real, native PowerPoint decks. Not markdown outlines. Not screenshots stitched together. Native shapes, transitions, animations, data-backed charts, audio narration from speaker notes, and support for your own .pptx templates. A file you can actually present. mattpocock/skills — 284k+ total stars. "Skills for Real Engineers. Straight from my .agents directory." Not a tutorial. Not a framework. A real engineer's actual agent skills, open-sourced from his daily workflow. What's the pattern? All three share one trait: they don't stop at "generating." They go all the way to "doing." - rea doesn't write you a reversing script — it does the reversing. - ppt-master doesn't outline your slides — it builds the .pptx file. - skills doesn't teach you how to write agent skills — it gives you the skills themselves. This is the shift: from AI as a code generator to AI as a workflow executor. Where does AstronClaw fit? If you've been watching this trend and thinking "I want to try this — I want an AI assistant that actually does things in my environment, not just in a chat window" — that's exactly what AstronClaw (cloud AI) and Loomy (desktop AI) are built for. The tutorial is now open source: iflytek/astronclaw-tutorial It covers: - Setting up AstronClaw for cloud-based AI workflows - Configuring Loomy as your desktop AI assistant - Connecting the two for cloud + desktop orchestration - Real workflow examples you can follow along !AstronClaw and Loomy desktop AI assistant orchestration environment Pair it with iflytek/astron-rpa for out-of-the-box desktop and browser automation — the execution layer that makes "AI does it for you" actually work. The takeaway The next wave of AI developer tools won't compete on "who generates better code." They'll compete on "who actually gets the job done." The tools that win will be the ones that close the gap between AI output and delivered work. AstronClaw + Loomy is our take on that future. The tutorial is open. Go build something.
6/ AstronClaw (cloud AI) + Loomy (desktop AI) — hands-on tutorial now open source. From setup to real workflows: iflytek/astronclaw-tutorial