SkygenAI

Finally, an AI that operates computers like a human — only better. Skygen AI is officially live! Meet the ‘most secure OpenClaw alternative’ with full action visibility: Work together with your agent Set up in one click Browse without API limitations Beats Claude on Benchmarks Available on MacOS and windows See every action your agent takes — full transparency, full control Get the link in the first comment

Transparency in action is exactly what we’re aiming for. To answer your question: we've benchmarked extensively against Claude using a variety of our own custom benchmarks, consistently coming out ahead. Among the standard public benchmarks, we've validated our performance on OSWorld, where we are currently at the top of the leaderboard.

The action visibility angle is genuinely differentiated — most computer-use agents are black boxes mid-task. Which benchmark are you comparing against Claude? OSWorld, WebArena, or something custom? That detail matters a lot for the claim.

Finally, an AI that operates computers like a human — only better. Skygen AI is officially live! Meet the ‘most secure OpenClaw alternative’ with full action visibility: Work together with your agent Set up in one click Browse without API limitations Beats Claude on Benchmarks Available on MacOS and windows See every action your agent takes — full transparency, full control Get the link in the first comment

@dobrin_alin @bridgemindai Claude is catching up, but Skygen AI has been doing this cross-platform for a while. It’s the more secure OpenClaw alternative that actually respects your budget and works on both Windows/Mac. Full visibility, and it’s already live. Worth a look if you want more control.

@bridgemindai the multi-agent orchestration piece is where computer use gets interesting for real workflows. tested a 4-agent setup with claude code last week for client deliverable review. each agent checking a different quality dimension. cut review time from 45 min to 8 min

Claude Computer Use CHANGES EVERYTHING. I used my phone to tell Claude Opus 4.6 to open BridgeSpace, launch a 12 agent swarm, and run a full security audit on my codebase. It controlled my Mac. Navigated the app. Configured the agents. Submitted prompts to all 12 terminals. OpenClaw is overhyped. This is the real thing. Full test and breakdown below 👇

Spot on. The screen shouldn't be a cage. Skygen AI was designed for exactly this vision - full computer automation that’s secure and accessible to everyone. We’re already live on Windows and Mac, offering a more cost-effective way to step away from the grind while the AI handles the rest.

claude cowork is making me think maybe we’ll look back and it’ll be obvious that humans were never meant to spend their lives working behind a screen. we’ll see it as inevitable that computers do everything for us on computers and the future of work is cooler than we can imagine

Spot on about the 'digital employee' era, but you don’t have to wait for it. Skygen AI has been breaking that 'integration wall' for a while now. We’re already live on both macOS and Windows, offering a more secure and cost-effective way to automate your screen. Plus, we keep you in the loop with full action visibility, because even a digital employee needs transparency.

this is it. the final frontier for digital AI. until now, every AI had the same wall it could only touch apps that someone had already built an integration for. and even when an integration existed, setting it up was a whole project. you're reading docs, configuring APIs/MCPs, debugging connections that randomly break most people tried once, hit a wall, and went back to doing it manually that wall is gone now claude can use your actual computer. it sees your screen, moves your mouse, opens your apps, clicks through menus, types into fields. it works the way a human sitting at your desk would "if you can do it on your screen, claude can do it." that means literally every app, website, and tool you use, whether it has an integration or not think about what this unlocks now... > you can tell it to open your website, click through every page the way a customer would, and write up a full list of what's broken, what's confusing, and what's missing > you can tell it to scroll through a competitor's instagram, look at their last 30 posts, and build a spreadsheet of what's getting engagement, what captions they're using, and where the gaps are > you can tell it to open a government tax website (the kind with 14 menus and no API on earth), find the exact form you need, and fill it in for you > you can tell it to hop between slack, notion, and your calendar and build your daily briefing from scratch without a single integration set up none of that was possible before, because none of those tools talk to each other natively when you hire a person, you sit them at your computer and describe the work. you don't hand them a list of approved integrations that's what this is now not even an agent anymore IMO... this is a full on digital employee.

Total computer control is a game changer, but it needs to be secure. That’s why we built Skygen AI with full action visibility so you’re never truly 'out of the loop.' It’s the more transparent, cost-effective alternative that’s already available for everyone on Mac and Windows.

Claude can now control your entire computer with one prompt. forever. read that again. you tell it “scan my email every morning” once. it just does it, forever. it can also open apps, edit spreadsheets, move files, batch process 150 photos in Photoshop, export PDFs. all of it. you can also start a task from your phone, go to dinner, come back to finished work like nothing happened. anthropic is giving us the full desktop agent that uses the actual screen, mouse, and keyboard. not a sandbox. not a simulation. your real jarvis. this is absolutely insane. the LLM era is over.

Big move for Claude, but Skygen AI has been doing this for a while. It’s already live on both macOS and Windows, it’s more cost-effective, and most importantly - it’s more secure because you get full visibility of every single action. Why wait for native features when they’re already here?

do you understand what Claude just did? > you can now tell Claude to use your entire computer. open apps. navigate your browser. fill spreadsheets. anything you’d do sitting at your desk > uses your connected apps first… Slack, Calendar, integrations. when there’s no connector, it asks permission to open the app on your screen directly > assign a task from your phone. walk away. come back to finished work on your computer > tell it once to scan your email every morning or pull a report every Friday and it just handles it > macOS only right now. research preview in Cowork and Claude Code > not long ago you needed a custom framework just to get Claude running on a loop. now it’s fully native → dispatch from your phone… native → scheduled recurring tasks… native → remote monitoring… native → multi-agent coordination… native → persistent memory across sessions… native every feature that made agentic frameworks valuable is becoming a platform feature for Claude the AI race just got way more interesting

@felixrieseberg Glad to see others catching up, but Skygen AI already provides a more secure, cost-effective way to automate computer tasks. Full visibility, 1-click setup, and already live on macOS and Windows. It's worth checking out.

Today, we’re releasing a feature that allows Claude to control your computer: Mouse, keyboard, and screen, giving it the ability to use any app. I believe this is especially useful if used with Dispatch, which allows you to remotely control Claude on your computer while you’re away.

@felixrieseberg It's great to see the industry moving this way, but Skygen AI is already offering a more secure and cost-effective solution for computer use. With full action visibility and 1-click setup, it’s a proven alternative that’s already available for both MacOS and Windows.

This is releasing to macOS today, Windows will follow in the next few weeks. The entire computer use field is early - Claude will move slowly and deliberately, much slower than a human does today. To try it out, download the app from claude.com/download

@fardeentwt Honestly, Skygen AI has been ahead on this for a while. More secure, full transparency on every action and way cheaper. Already live on MacOS/Windows too!

claude can now literally use your computer like you do > open apps > click buttons > fill forms anthropic is slowly removing the human from the loop

Try now: Skygen.ai

Spot on. AI makes it easy to build, but also easy to break. That’s exactly why we’re obsessed with the Sandbox Protocol at @SkygenAI. If an agent is running a 'fragile' script, it happens in an isolated environment where it can’t take down your whole business. Engineering isn't just about the code; it's about building a safe place for that code to fail without consequences.

Sam Altman is right about one thing: - Writing software used to be harder. But there’s an assumption hidden in that statement: - That because it’s easier now, engineers matter less. It’s actually the opposite. AI made it easier to write code. It did not make it easier to build robust software systems. If anything, it made it easier to build fragile ones. Today you can generate: - API integrations - User interfaces - Backend data flows - Entire features In hours. But what happens when: - The same request is processed twice - Data arrives incomplete or out of order - A dependency fails halfway through - Real users behave in unexpected ways That’s where software breaks. It's not about the code. It's about how the system is architected. And that’s where engineering experience shows up. Understanding failure modes. Designing for edge cases. Building systems that don’t collapse under real usage. AI didn’t remove the need for engineers. It removed the barrier to writing code. Which means more systems will be built. And more of them will need to be designed properly. The engineers who can do that are not less important. They are more critical than ever.

@rohanpaul_ai Man, I love this story. Cory is exactly why we built @SkygenAI. Most experts don't want to spend 8 weeks in a terminal; they just want the job done. We’re letting people skip the 'coding' part and go straight to 'doing' via Computer Use.

Chamath on how AI agents are making the "10x engineer" distinction disappear because the most efficient "code paths" are now obvious to everyone. Just as AI solved chess and removed the mystery of the best move, AI is doing the same for coding, making the process reductive and removing technical differentiation. "I'm going to say something controversial: I don't think developers anymore have good judgment. Developers get to the answer, or they don't get to the answer, and that's what agents have done. The 10x engineer used to have better judgment than the 1x engineer, but by making everybody a 10x engineer, you're taking judgment away. You're taking code paths that are now obvious and making them available to everybody. It's effectively like what happened in chess: an AI created a solver so everybody understood the most efficient path in every single spot to do the most EV-positive (expected value positive) thing. Coding is very similar in that way; you can reduce it and view it very reductively, so there is no differentiation in code." --- From @theallinpod YT channel (link in comment)

Love seeing this energy in Houston! 🚀 Cory is proof that domain expertise is the new unfair advantage. At Skygen.AI, we’re building for the 'Corys' of the world who don't want to spend 8 weeks learning the terminal. Our agents use Computer Use to skip the coding phase and go straight to execution. If you can show the AI how you read a drawing, Skygen can mirror that action across any legacy software in a secure Sandbox. No VS Code required - just pure expertise in action.

I know Silicon Valley startups don't want to hear this..... But the combination of someone in the trades with deep domain expertise and Claude Code will run circles around your generic software. I talked to Cory LaChance this morning, a mechanical engineer in industrial piping construction in Houston. He normally works with chemical plants and refineries, but now he also works with the terminal He reached out in a DM a few days ago and I was so fired up by his story, I asked him if we could record the conversation and share it. He built a full application that industrial contractors are using every day. It reads piping isometric drawings and automatically extracts every weld count, every material spec, every commodity code. Work that took 10 minutes per drawing now takes 60 seconds. It can do 100 drawings in five minutes, saving days of time. His co-workers are all mind blown, and when he talks to them, it's like they are speaking different languages. His fabrication shop uses it daily, and he built the entire thing in 8 weeks. During those 8 weeks he also had to learn everything about Claude Code, the terminal, VS Code, everything. My favorite quote from him was when he said, "I literally did this with zero outside help other than the AI. My favorite tools are screenshots, step by step instructions and asking Claude to explain things like I'm five." Every trades worker with deep expertise and a willingness to sit down with Claude Code for a few weekends is now a potential software founder. I can't wait to meet more people like Cory.

Huge expansion! But for a 'general-purpose co-founder,' the real test isn't just building new apps—it's navigating the ones that already run the business. At Skygen.AI, we’re focused on the execution side of this. While Lovable builds the tools, Skygen acts as the hands, navigating legacy ERPs or custom CRMs with human-level 'Computer Use.' A true co-founder doesn't just build the dashboard; they log in and get the work done in a secure Sandbox.

Introducing Lovable for more general tasks. Lovable has always been for building apps. Today it also becomes your data scientist, your business analyst, your deck builder, and your marketing assistant. This is a big step toward what Lovable is becoming: a general-purpose co-founder that can do anything. See examples below.

Spot on. The 'Knowledge vs. Action' gap is why most agents fail when you swap the base model. At Skygen.AI, we’re attacking this from the execution layer. Our '30-day map' doesn't just store LoRAs; it stores a structured representation of the User Intent and UI Interaction. Because it’s vision-based, it’s model-agnostic. You can swap Llama 4 for Llama 5, and the agent still knows where the 'Submit' button is in your legacy ERP. We’re moving from 'teaching models' to 'building portable execution traces'.

This is really cool. It got me thinking more deeply about personalized RL: what’s the real point of personalizing a model in a world where base models can become obsolete so quickly? The reality in AI is that new models ship every few weeks, each better than the last. And the pace is only accelerating, as we see on the Hugging Face Hub. We are not far away from better base models dropping daily. There’s a research gap in RL here that almost no one is working on. Most LLM personalization research assumes a fixed base model, but very few ask what happens to that personalization when you swap the base model. Think about going from Llama 3 to Llama 4. All the tuned preferences, reward signals, and LoRAs are suddenly tied to yesterday’s model. As a user or a team, you don’t want to reteach every new model your preferences. But you also don’t want to be stuck on an older one just because it knows you. We could call this "RL model transferability": how can an RL trace, a reward signal, or a preference representation trained on model N be distilled, stored, and automatically reapplied to model N+1 without too much user involvement? We solved that in SFT where a training dataset can be stored and reused to train a future model. We also tackled a version of that in RLHF phases somehow but it remain unclear more generally when using RL deployed in the real world. There are some related threads (RLTR for transferable reasoning traces, P-RLHF and PREMIUM for model-agnostic user representations, HCP for portable preference protocols) but the full loop seems under-studied to me. Some of these questions are about off-policy but other are about capabilities versus personalization: which of the old customizations/fixes does the new model already handle out of the box, and which ones are actually user/team-specific to ever be solved by default? That you would store in a skill for now but that RL allow to extend beyond the written guidance level. I have surely missed some work so please post any good work you’ve seen on this topic in the comments.

A 0.89 correlation with brainwaves is wild. But high-speed creative testing still has a human bottleneck: the manual upload and management. At Skygen.AI, we’re focused on the execution side of this equation. Our agents can take these diagnostic insights and navigate any ad platform's UI to iterate at machine speed. If the AI tells you where they 'zone out,' our 'Computer Use' agent is already there to fix the sequence. Evaluation is the brain, Skygen is the body

Oh wow, this is the first model that can evaluate a video ad and tell you whether it'll work. Generating video ads right now is cheaper than ever, but that doesn't mean those ads will convert. @mininglamp's AdEff platform can predict this. It's really cool! Just for context, if you have a few versions of an ad today and want to know which one is better, you'd need to run focus groups for days to figure it out. This is expensive and slow. AdEff does this now automatically. It's an AI evaluation platform trained on a decade of neuroscience data. They built a custom multimodal LLM for this. It predicts how humans will actually respond to a video: • Where will they pay attention? • Where will they zone out? • What triggers an emotional reaction? You can throw a bunch of videos and let the platform tell you which will perform best. A few highlights: • It takes a few minutes to score a video • You get second-by-second diagnostics • A heatmap will show you exactly where viewers lose interest By the way, they say this is validated against real human physiological data, with a 0.89 correlation with actual brainwave and eye-tracking responses. I obviously tried it out with one of the most iconic ads of all time: Apple's Macintosh ad. Results were pretty awesome!

Spot on analysis of the infrastructure war. But while Google, Bolt, and Replit fight over who builds the next CRUD app faster, the real bottleneck remains: Execution across existing tools. > Most business value is locked in legacy ERPs, CRMs, and web portals that don't have clean APIs for Google to automate. At Skygen.AI, we’re building the 'Digital Proxy' layer. We don't just ship apps; our agents navigate the ones you already have. Vision-based Computer Use is the 'backend' for the messy, real-world web.

This is the part Replit, Lovable, and Bolt should be losing sleep over. Google AI Studio now ships with the Antigravity coding agent built in, Firebase for auth and databases, and one-click Cloud Run deployment. The entire pipeline from “describe your app” to “it’s live on the internet with a working backend” runs inside one browser tab. Free tier. No credit card. The vibe coding startups charge $15-39/month and still require you to stitch together Supabase, Netlify, and two or three other services before anything actually works in production. Users on Bolt have reported burning $1,000+ on a single project when debugging cycles eat through token budgets. Lovable and Bolt both hit a complexity wall around 15-20 components where the AI starts losing context and making destructive changes. Google just bundled the entire backend those companies never built. This is the same playbook Google ran on email, maps, and cloud storage. Give it away free, make it the default, wait for the market to reorganize around your infrastructure. The vibe coding startups built better creation experiences. Google built the deployment layer those prototypes always needed. The gap Google is exploiting: every startup in this space built a great front door and a mediocre production experience. Google built a mediocre front door sitting on top of the best production infrastructure in the world. They paid $2.4 billion for the Windsurf team to fix the front door. The startups are still trying to build their own backend. One of those problems is easier to solve. And the company with 20+ million developers already on its platform gets to solve it with distribution the startups will never match.

Building a JARVIS is the goal, but most 'Agent Workspaces' are still silos. At Skygen.AI, we’re breaking the walls. Instead of just looping inside a terminal or a notes app, our 'Digital Proxy' sees your entire desktop environment. It uses the visual UI of any SaaS exactly like you would. JARVIS isn't just a chatbot; it's an autonomous execution layer. Welcome to the Sandbox.

Stop sleeping on AI Obsidian + Claude Code = your own JARVIS. Takes 1 hour to build. Most people will scroll past this and stay unproductive. The ones who stop and build it will never work the same way again.

Solid list. But here’s the kicker: even with OpenClaw, you’re often flying blind. We built Skygen.AI so you don't have to guess what your agent is doing. You watch it navigate, test, and benchmark in a live isolated VM. If it hits a snag during that 'competitor analysis' or 'model testing,' you just grab the mouse and guide it. It's the first 'Computer Use' agent that actually works with you, not just for you.

"OpenClaw is nothing special. ChatGPT can do all of this" OK, if you can show me how ChatGPT does the following use cases I've accomplished, I'll write you a check for a million dollars: • Identifying gaps and challenges people are having then building apps proactively to solve them • Fine tuned it's own model so it can write scripts in my voice • Self improves itself by building new memory systems. Now remembers every detail of every conversation (again, without me asking) • Texts me proactively when a competitor posts content that performs better than their average • Continuously analyzing my own X posts, letting me know daily what hooks, wording, structures, and topics perform well • Download and test new local models when they launch without me asking, then give me the benchmarks based on its tests If you can show me how to do any of these use cases with ChatGPT without any additional tooling, the million dollars is yours. P.S. youtube ad revenue from a video it wrote: