Show HN: AI Agents for Osint/Sigint — The State of AI Web Scraping in 2026
75% of the web is dynamic content (database records rendered as HTML), posing ongoing challenges for AI agents RAG (2024) and long-context LLMs (2025) were first-generation solutions, but 2026 approaches still rely on dumping content into context windows, driving up costs linearly Three critical limitations of current LLM-based scraping: cost scaling linearly per page, hallucinations, and context pollution/prompt injection Makra introduces a memoization-driven knowledge retrieval engine that red
Analysis
TL;DR
- 75% of the web is dynamic content (database records rendered as HTML), posing ongoing challenges for AI agents
- RAG (2024) and long-context LLMs (2025) were first-generation solutions, but 2026 approaches still rely on dumping content into context windows, driving up costs linearly
- Three critical limitations of current LLM-based scraping: cost scaling linearly per page, hallucinations, and context pollution/prompt injection
- Makra introduces a memoization-driven knowledge retrieval engine that reduces cost to a vector query by caching page layouts
- The in-house browser harness achieves structured/tabular data extraction at approximately 1/10th the cost of existing solutions (Makra, Exa, Firecrawl)
Why It Matters
As AI agents increasingly depend on real-time web data, the economic and reliability bottlenecks of LLM-based scraping are becoming critical blockers to scaling. This article highlights a paradigm shift from context-window-heavy approaches toward memoization-based retrieval, offering a path to cost-effective, hallucination-resistant data extraction that could redefine how practitioners build data pipelines for agentic systems.
Technical Details
- Problem framing: 75% of the web is dynamic—database records rendered as HTML for human browsers, not machines—requiring agents to parse and extract structured data from unstructured DOMs
- Evolution of solutions: RAG (2024) → long-context LLMs (2025) → 2026's "dump everything into context windows" approach, which scales poorly in cost
- Makra's memoization engine: Caches and reuses page layout extractions so subsequent requests become cheap vector queries rather than full LLM calls, theoretically eliminating hallucinations by extracting data like a traditional scraper
- Browser harness capabilities: Spins up browser instances with proxy support, extracts structured and tabular data read-only, and reduces per-page cost by ~10x compared to Makra, Exa, and Firecrawl
- Evaluation: Scatter plot comparing correctness vs. average USD cost across 400 pages from 20 sites, demonstrating Makra's superior cost-correctness tradeoff
Industry Insight
- The memoization paradigm could become a foundational layer for agentic infrastructure, shifting the industry away from per-request LLM calls toward cached, query-driven retrieval models
- Practitioners should evaluate whether their current scraping pipelines are vulnerable to linear cost scaling and context pollution, especially for large-scale data collection
- The 1/10th cost reduction positions memoization-based tools as competitive advantages for any application requiring high-volume, real-time web data extraction
Disclaimer: The above content is generated by AI and is for reference only.