OmniParser ‘tokenizes’ UI screenshots from pixel spaces into structured elements in the screenshot that are interpretable by LLMs. This enables the LLMs to do retrieval based next action prediction given a set of parsed interactable elements.
OmniParser V2 is introducing an innovative approach to UI interaction with LLMs. Launched by Chris Messina (known for inventing the hashtag), it's already showing strong performance at #3 for the day and #27 for the week with 258 upvotes.
What's technically impressive is their novel approach to making UIs "readable" by LLMs:
Screenshots are converted into tokenized elements
UI elements are structured in a way LLMs can understand
This enables predictive next-action capabilities
The fact that it's free and available on GitHub suggests a commitment to open development and community involvement. This could be particularly valuable for:
AI developers working on UI automation
Teams building AI assistants that need to interact with interfaces
Researchers exploring human-computer interaction
Being their first launch under OmniParser V2, they're likely building on lessons learned from previous iterations. The combination of User Experience, AI, and GitHub tags positions this as a developer-friendly tool that could significantly impact how AI interfaces with computer systems.
This could be a foundational tool for creating more sophisticated AI agents that can naturally interact with computer interfaces.
Report
hi, Congratulation friend,iwish you sucesso and a Very good product,i Hope It Will sono be inmay linguagem tô mais It easier
OmniParser V2 is redefining how LLMs interact with UIs, bringing a groundbreaking approach to interface understanding. Spearheaded by Chris Messina (the mind behind the hashtag), it’s already making waves—ranking #3 for the day and #27 for the week with 258 upvotes.
What’s particularly impressive is their innovative method of making UIs "readable" for LLMs:
✅ Screenshots are transformed into structured, tokenized elements ✅ UI components are formatted for seamless comprehension by LLMs ✅ This unlocks predictive next-action capabilities
The fact that it’s free and available on GitHub underscores a strong commitment to open development and community-driven innovation. This has massive potential for:
🔹 AI developers advancing UI automation 🔹 Teams building AI-powered assistants for interactive workflows 🔹 Researchers exploring next-gen human-computer interaction
As the first launch under OmniParser V2, it’s clear they’re refining their approach based on past iterations. With its focus on AI, UX, and open-source collaboration, this could be a foundational tool for creating AI agents that interact naturally with digital interfaces. Looking forward to seeing how this evolves! 🚀
Raycast
Microsoft Research has unveiled their own Computer Use model trained on a ton of labeled screenshots.
The v2 achieves a 60% improvement in latency compared to V1 (avg latency: 0.6s/frame on A100, 0.8s on single 4090).
DiffSense
Really cool! Hopefully it will be ported to more languages soon!
Shram
Congrats on the launch and lots of wins to the team :)
KIVA (By Wellows)
Chance AI: Curiosity Lens
OmniParser V2 is introducing an innovative approach to UI interaction with LLMs. Launched by Chris Messina (known for inventing the hashtag), it's already showing strong performance at #3 for the day and #27 for the week with 258 upvotes.
What's technically impressive is their novel approach to making UIs "readable" by LLMs:
Screenshots are converted into tokenized elements
UI elements are structured in a way LLMs can understand
This enables predictive next-action capabilities
The fact that it's free and available on GitHub suggests a commitment to open development and community involvement. This could be particularly valuable for:
AI developers working on UI automation
Teams building AI assistants that need to interact with interfaces
Researchers exploring human-computer interaction
Being their first launch under OmniParser V2, they're likely building on lessons learned from previous iterations. The combination of User Experience, AI, and GitHub tags positions this as a developer-friendly tool that could significantly impact how AI interfaces with computer systems.
This could be a foundational tool for creating more sophisticated AI agents that can naturally interact with computer interfaces.
Chance AI: Curiosity Lens
OmniParser V2 is redefining how LLMs interact with UIs, bringing a groundbreaking approach to interface understanding. Spearheaded by Chris Messina (the mind behind the hashtag), it’s already making waves—ranking #3 for the day and #27 for the week with 258 upvotes.
What’s particularly impressive is their innovative method of making UIs "readable" for LLMs:
✅ Screenshots are transformed into structured, tokenized elements
✅ UI components are formatted for seamless comprehension by LLMs
✅ This unlocks predictive next-action capabilities
The fact that it’s free and available on GitHub underscores a strong commitment to open development and community-driven innovation. This has massive potential for:
🔹 AI developers advancing UI automation
🔹 Teams building AI-powered assistants for interactive workflows
🔹 Researchers exploring next-gen human-computer interaction
As the first launch under OmniParser V2, it’s clear they’re refining their approach based on past iterations. With its focus on AI, UX, and open-source collaboration, this could be a foundational tool for creating AI agents that interact naturally with digital interfaces. Looking forward to seeing how this evolves! 🚀