geo-fundamentals
Covers Generative Engine Optimization (GEO) for AI search engines. Names ChatGPT, Claude, and Perplexity as target search experiences.
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Find your next superpower If you are an agent, refer to our llms.txt for full access.Covers Generative Engine Optimization (GEO) for AI search engines. Names ChatGPT, Claude, and Perplexity as target search experiences.
Generative Engine Optimization for AI search engines (ChatGPT, Codex, Perplexity).
Covers Generative Engine Optimization for AI search engines, including ChatGPT, Claude, and Perplexity.
Helps network operators author and improve self-published IP geolocation feeds in RFC 8805 CSV format. Provides recommendations for geolocation accuracy beyond basic compliance and excludes private or internal IP address management.
Analyzes road markings, signage, architecture, vegetation, and other visual evidence to infer location. Uses shadows and SunCalc for time estimates and compares scenes with mapping and street-view services for evidence verification.
Covers remote sensing, GIS, spatial statistics, machine learning, and geospatial computation across scientific domains. Supports raster and vector operations, point clouds, terrain and hydrological modeling, cloud-native workflows, and examples in eight programming languages.
Provides guidance and local audit tools for workflows using GeoSeries and GeoDataFrame. Covers spatial operations and vector-data input and output.
Develops detailed, sectioned implementation plans for features requiring substantial analysis before development. Combines research, stakeholder interviews, and review by multiple language models.
Define gesture interactions for touch and pointer interfaces. Address swipe, drag, long-press, and how users discover these interactions.
Fetches API references before writing integrations or answering questions about current API behavior. Uses the chub CLI to obtain documentation for external services and libraries rather than relying on potentially outdated model knowledge.
Detects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits. Produces redacted JSON snapshots and conservative planning helpers without stress testing or assuming all visible hardware is available.
Retrieves review comments from the active pull request and summarizes the feedback.
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