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AI Automation Society

335.2k members • Free

54 contributions to AI Automation Society
Hermes vs Openclaw AI
📌 Which One to Pick? Choose OpenClaw if you want: ✔ Wide platform coverage and plugins ✔ A mature community with lots of ready-made skills ✔ Multi-agent orchestration across services. Choose Hermes Agent if you want: ✔ Agents that learn and build skills automatically ✔ Deep task performance and repeatable workflows ✔ A system that gets stronger over time Many builders even run both together OpenClaw for broad orchestration and Hermes for deep task mastery. The image shows OpenClaw acting as a central orchestration layer that coordinates multiple systems and agents, while delegating complex, hands-on execution to Hermes, which performs deep tasks using its rich toolset and returns results back to the orchestrator.
Hermes vs Openclaw AI
0 likes • 4m
@Ryan Johnson 🙏🏻 thanks, keep building.
0 likes • 2m
@Nigel Vargas 🙏🏻 One of the best agentic design. Keep building.
Read my post on LinkedIn about 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈
https://www.linkedin.com/posts/mroqa_enterpriseai-multimodalai-audittrails-share-7451331453730127872-gv-x?utm_source=share&utm_medium=member_android&rcm=ACoAABGbeqoBJh-LBvkIh0cMQL-ZxpC_yWvK_6U
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Read my post on LinkedIn about 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈
Automation Is Finally Closing the Design ↔ Code Loop
Automation has transformed infrastructure, testing, and deployment. Design and UI handoff? That was still mostly manual. Until now. The automation of one of the last human bottlenecks in software delivery: the gap between design tools and code. 🧱 The Old World: Manual, Fragile, Slow Think about how UI work usually flows: Designers create in Figma Developers manually recreate layouts in code Changes trigger rework Drift accumulates silently 🧱 This process depends on: Visual interpretation Human memory Perfect communication (which never exists) It’s high-effort, low-reliability work—exactly the kind automation should eliminate. ⚙️ Design → Code: Automating UI Translation Now imagine this instead: A developer provides a Figma link. An AI agent automatically: Queries the design structure Extracts layout, hierarchy, and constraints Produces structured, production-ready UI code This isn’t “export HTML.” It’s semantic automation: Frames → layout containers Spacing rules → constraints Text styles → tokens Components → reusable code 💡 Result: UI implementation becomes a deterministic process, not a manual craft step. 🔄 Code → Design: Automating Feedback Automation doesn’t stop at generation. When code evolves, the agent: Captures real UI behavior from the DOM Infers structure and layout intent Automatically reconstructs an editable Figma design This means: No more manual design updates No stale mockups No design/code divergence Design documentation stays in sync automatically. 🔁 The Real Win: Closed-Loop Automation This is the breakthrough: > Design and code now form an automated feedback loop. Changes in design → propagate to code Changes in code → propagate back to design Humans focus on decisions, not translation This mirrors what CI/CD did for deployment: Less ceremony Fewer errors Faster iteration 🚀 Why Automation People Should Care If you care about: Reducing lead time Eliminating non-value work Increasing system reliability Scaling teams without scaling coordination cost
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Automation Is Finally Closing the Design ↔ Code Loop
🔥Hot take: AI will replace most graphic designers within 3 years.
Not all of them. But the ones doing templated work, basic ads, and social content? That market is already shifting fast. The designers who survive will be the ones who understand how to direct AI, not compete with it. Agree or disagree? Where do you think the line gets drawn? 👇
🔥Hot take: AI will replace most graphic designers within 3 years.
0 likes • 6d
I think AI now is a fundamental part of the graphics pipeline. Why? AI has moved past being a simple "performance booster". It focuses on Neural Rendering, where AI essentially allowing for visuals that would otherwise require impossible amounts of raw hardware power. Neural Texture Compression (NTC) Back of the days, high-resolution textures eat up a massive amount of Video RAM (VRAM). AI now compresses these textures far beyond what traditional math could achieve without losing visual clarity. Neural Reconstruction While early AI upscaling just made small images look big, current versions use Transformer-based AI models to actually fix broken pixels and eliminate "shimmering" in complex geometry. AI Denoising (Ray Reconstruction) Ray tracing (realistic light) is naturally "noisy" or grainy. AI now paints over this noise by understanding what a scene should look like.
Attained Level :-> 9
hey , I'm finally attained level 9 !! thanks for this much support to all of you and so happy to be here . I learned a lot of things from here and also geted my great team member from here !! And as well going to keep it up and share more and as well learn more !! Appreciate you all guys !! 🎉I want all of your suggestions to improve more in work??
1 like • 10d
Congratulations! All the best. Please do focus on what you love to do everyday, every meeting and every task.
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Mohammed Roqa
5
200points to level up
@mohammed-roqa-7379
Technologist, Creator and Handsome

Active 1m ago
Joined Jan 20, 2026
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