Results for “chatgpt”

Optimize content to be cited by AI search systems and LLMs, including AI Overviews, ChatGPT, Perplexity, Claude, and Gemini, through structure, authority, and presence improvements.

Optimize for AI recommendations so ChatGPT, Claude, and Perplexity suggest your brand first.

Analyze and optimize content for visibility in AI Overviews, ChatGPT, Perplexity, and other AI search systems by evaluating citability, structure, authority, and technical accessibility.

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow through the openai-docs skill or an official OpenAI documentation connector before generating code.

Optimizes content for AI search engines like Google AI Overviews, ChatGPT, and Perplexity by analyzing citability, structure, brand mentions, and technical accessibility, then generating a GEO analysis report.

Audit a site for answer-engine / GEO citation readiness (ChatGPT, Perplexity, AI Overviews), then a phased plan. Use when "AEO", "GEO", "show up in ChatGPT/Perplexity", "AI search visibility", "llms.txt", or "am I blocking AI crawlers". Classic SEO meta/sitemap → enhance-web-seo.

Use after the user has authorized GPT Pro help; manage a prompt-plus-sources workspace, route through ChatGPT Projects when available, and audit returned reasoning.

Optimizes content for AI-powered search engines like Perplexity, ChatGPT Search, and Google AI Overviews by restructuring pages, implementing schema, and building topical authority clusters to increase citations in zero-click answers.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).

Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Build production-quality Model Context Protocol (MCP) servers in C#/.NET using the official 1.x NuGet packages, covering transports, tools, prompts, resources, and common pitfalls.

Train and experiment with a minimal GPT implementation in ~300 lines of PyTorch, from character-level Shakespeare to GPT-2 scale.

Optimize content for AI Overviews, ChatGPT, Perplexity, and other AI search systems. Use when improving GEO, AI citations, llms.txt readiness, crawler accessibility, and passage-level citability.

Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.

Generate GPT API request payloads. Use when building chat completions, embeddings, fine-tuning data, or estimating API costs.

Create MCP servers using the C# SDK and .NET project templates, covering scaffolding, tool/prompt/resource implementation, and transport configuration for stdio and HTTP.

Publish and deploy C# MCP servers to NuGet, Docker, Azure, and the MCP Registry.

Migrate .NET projects from per-project package versioning to NuGet Central Package Management (CPM) using Directory.Packages.props, with baseline build capture, version conflict resolution, and post-conversion validation.

Build MCP servers for Copilot Chat with interactive widgets using React and Fluent UI, supporting both OpenAI Apps SDK and MCP Apps Extension standards.

Launches Chrome with an unpacked extension, opens its UI (sidepanel, popup, or options page), and enables interaction via CDP commands for testing and automation.

Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver via Python, C/C++, or CLI interfaces.