Saccade
Live semantic browser truth for AI agents
Details
- External ID
- 49516118
- Source
- HN
- Company
- —
- Product
- Saccade
- Website domain
- github.com
- Launched
- Aug. 31, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.12634408602150538
- Tags
- —
- Fetched at
- Sept. 10, 2026, 5:31 a.m.
- Updated at
- Sept. 10, 2026, 5:31 a.m.
Description
I build one this application is to resolve the most of the agent cannot handle the brother they extremely slow. So I come up with this idea, we install one of the extntion on chrome or edge to give continuously compile the tabs authorized by the user into semantically meaningful objects with stable identities, and push page changes as deltas to the local Node.js Broker. The Agent reads the full truth or delta of the specified tab via MCP and executes actions using object IDs bound to document. The preliminary result shows that it is very close to the performance of Playwright in terms of token use and speed.The only problem with this one is that at the very first time, it is going to send the full truth, and after that, for any page changes, it only sends the data. So, after the first read, the continuous operation of the page reaches the millisecond reaction loop.It can also upload filled forms, downloads, and all kinds of stuff. I want somebody to check and use it. If it is possible plz give me some feedback.
Enrichment
- Theme
- browser automation and scraping for AI
- Vertical
- Horizontal
- Function
- Agent / copilot
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- semantic browser context for ai agents
- Manually corrected
- False
Could you build this?
Partial A browser extension capturing DOM nodes is standard, but continuously compiling arbitrary web pages into stable, semantically meaningful object schemas for AI agents requires novel AST/DOM extraction algorithms.
What it would actually take: The system requires a Chrome extension with content scripts that monitor DOM mutation observers and shadow DOMs, piping trees into a specialized normalization engine. The hard challenge is designing resilient heuristics or lightweight local ML models to extract semantic intent (e.g., identifying interactive forms vs. decorative elements) and maintaining persistent cross-render element identifiers across dynamic SPAs. It requires deep browser engine knowledge and semantic extraction expertise.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 46 launches clear the similarity bar, closest 8 shown.
Attention rank: #44 of 47 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 298 days after the earliest competitor.
- Open-source browser for AI agents · hn · 2026-03-11 · 155 upvotes · similarity 0.46
- Tabstack Research · hn · 2026-02-04 · 10 upvotes · similarity 0.46
- Tabstack · hn · 2026-01-14 · 130 upvotes · similarity 0.45
- Tabbit AI · ph · 2026-09-03 · 153 upvotes · similarity 0.45
- I spent 3 months making desktop automation stop lying to AI agents · hn · 2026-08-15 · 6 upvotes · similarity 0.43
- ProofShot · hn · 2026-03-24 · 161 upvotes · similarity 0.42
- BrowserAct · ph · 2026-06-25 · 561 upvotes · similarity 0.40
- Usable Browser Agent · ph · 2026-09-08 · 2 upvotes · similarity 0.39
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a agent / copilot tool for Agriculture yet.