AI Agents for Osint/Sigint
The goal was to bring down the cost at the context eng. level. We do it with Layout Memoization. Instead of dumping HTML into the context window, we have built a continual learning browser harness (read only for now). We have built an early prototype for you to try out, where you can: 1. Spins up a browser instance 2. Extract any structured or tabular data from anywhere on the open-web 3. And you can do all this at the cost of a vector search Would love to hear your thoughts on this. Thanks for taking the time to read it.
In plain words
AI Agents for Osint/Sigint is a web scraping tool that extracts structured and tabular data from dynamic websites while reducing processing costs. It uses layout memoization and a continual learning browser harness to minimize context window usage, allowing data extraction at vector search pricing levels rather than full large language model costs. The tool is designed for users who need efficient open-web data retrieval without traditional high expenses associated with browser automation and scraping approaches.
written from the facts on this page · September 2026
From the sources
Memoize page layouts so extraction cost falls as usage grows. Public beta is open.
75% of the web is dynamic. Basically database records presented in HTML. Built for humans. Fast-forward to 2026, and we're basically dumping anything and everything into the context windows; taking Nvidia to the moon. This consists of claws, browser/computer-use, and agentic crawlers, and scrapers. These have made it easier to retrieve data, but the cost has stayed the same. Underneath, these still rely on long-context LLMs. It's an work-in-progress implementation for a continual learning harness focusing on Browser-use. Scatter plot of correctness against average USD cost for Makra, Exa, and Firecrawl. Lower cost is toward thefrom makralabs.org
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