AI use cases
2981 ways people actually use AI, compiled from the videos in this
library. Each names the job and why it is worth doing, and links to the
moment in the source video.
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Control terminals, files, processes, and code execution
— Allow AI assistants to operate a desktop and perform data analysis.
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Generate, audit, redesign, or study HTML and CSS designs
— Apply anti-AI-slop design rules and self-critique gates.
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Share conversations, documents, code, skills, wikis, and code graphs with agents
— Provide reusable team memory while controlling ownership, versions, and visibility.
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Run continuous workflow agents
— Automate workflows and respond to outside events.
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Simulate thousands of agents with personalities and memory
— Rehearse possible futures and produce prediction reports.
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Build auditable knowledge graphs and trace agent decisions
— Give agents grounded, provenance-backed, queryable context.
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Generate a 3D animation video from text and references
— Produce a continuous story video with controlled characters, locations, visuals, and audio.
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Create a complete 3D animation story prompt
— Expand the selected story title into a prompt containing the story, character details, and scene-by-scene visuals.
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Generate creative 3D animation story ideas
— Create an initial story concept when the user does not already have one.
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Research
— Mole decomposes questions, searches sources, verifies quotations, checks contradictions, and returns citations within a chosen model-call budget.
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Persistent coding context
— MCP Memory stores structured project memories in readable files and provides local search and checkpoint information.
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Code review
— OSS PR Reviewer asks OpenAI for findings from pull request metadata and patches, then validates and filters the results.
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Agent safety
— Agent Safe Pipeline separates agent intent submission from policy decisions and credential use.
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Human-agent collaboration
— Human Review packages edits and comments into a batch for an agent to process.
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Agent execution
— DeepSeek Harness provides a plug-in-based agent harness with a local web UI.
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Generating agent skills
— Book to Skill converts technical books and documentation into compact skills, chapter notes, glossaries, patterns, and reference rules.
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Simplifying generated text
— Claudish to English sends Claude Code responses to a local model and rewrites the displayed output into plainer language.
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Edit office documents and files
— GenOffice provides a shared AI panel and tool-calling agent for documents, spreadsheets, slides, and PDFs.
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Restyle video
— Finger Frame AI uses a Google Gemini video model to transform clips while preserving motion alignment.
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Create interactive system documentation
— Archify generates grounded, searchable diagrams from codebases or system descriptions.
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Generate intelligence summaries and visualizations
— World Monitor synthesizes news, geopolitics, and infrastructure signals into AI-written briefs and maps.
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Coordinate multiple AI agents
— Omnigent lets agents collaborate, review one another, switch harnesses, and share or fork sessions.
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Generate coding-agent plans and execute terminal coding workflows
— DeepSeek-Reasonix is a terminal coding agent, while Embabel uses dynamic planning to mix LLM calls with code.
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Provide agents with an executable computer environment
— Cloudflare Computer lets agents access a virtual file system and run code through configurable backends.
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Deepfake detection
— Identify synthetic faces during live video calls before an interaction proceeds.
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AI spend intelligence
— Unify AI-tool seats, usage, and spend into a governed view for finance, IT, and leadership.
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B2B data enrichment
— Search, enrich, verify, and organize contacts into CRM-ready lists.
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AI-assisted communications
— Handle calls, email, scheduling, and related tasks under user-defined rules.
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Voice dictation
— Convert speech into polished, formatted text across applications.
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Advertising automation
— Research markets, create and audit Google Ads campaigns, and implement human-approved optimizations.
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CRM administration
— Generate pipeline work, meeting preparation, follow-ups, and answers grounded in customer conversations.
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Exercise tracking and form feedback
— Count repetitions and provide real-time training corrections from an iPhone camera.
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Semantic search
— Find relevant past AI coding conversations by meaning rather than keywords.
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Parallel coding
— Coordinate several coding agents in one repository while preventing file conflicts.
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Break the implementation plan into ordered tasks
— Give the coding agent small, concrete units of work that can be reviewed individually.
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Create a technical implementation plan
— Define the intended implementation approach and limit improvised architecture and extra scope.
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Convert feature requirements into user stories and acceptance criteria
— Make the requested behavior testable and establish a definition of done.
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Generate and implement application code from structured project documentation
— Use the constitution, feature specification, implementation plan, and task list to guide the coding agent and reduce guesswork.
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Write detailed product descriptions
— Draft pros-and-cons content and pricing information for a long description field.
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Research and populate directory data
— Produce information about email marketing tools for the generated directory.
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Generate an application
— Create an initial directory structure and generated records from a natural-language prompt.
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Support website creation
— Hostinger offered an AI image generator, AI SEO assistant, AI logo maker, and other AI tools.
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Generate written content
— Hostinger's AI writer generated text about online side-hustle ideas from a text description.
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Create a website
— Hostinger's website builder used AI to generate a headline, button, and initial website content.
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Expanding services for postcard advertisers
— AI tools and AI agents were mentioned as possible services to offer alongside websites and marketing.
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Researching unfamiliar local markets
— ChatGPT or Claude can provide facts and local context about a city being prospected.
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Supplier communication
— Generate supplier inquiries, ask common questions, and automatically reply when a manufacturer responds.
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Product comparison
— Analyze similar listings and identify possible reasons one product sold more than another.
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Tech-pack generation
— Create production specifications, measurements, materials, colors, and construction details without engineering, CAD, or 3D-modeling skills.
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Image-based product search
— Find products that visually resemble a described product.
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Prompt generation
— Improve a product-sourcing prompt when the initial prompt is unclear.
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Product sourcing
— Search for products and suppliers across countries using natural-language descriptions.
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Supporting search and content discovery
— The speaker says directories are well suited to AI-driven search because AI systems favor structured information.
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Creating directory content and listings
— ChatGPT is described as making human-like, customizable content creation faster and enabling content to be produced at scale.
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Recording soccer games
— Pixellot uses AI to track and record games so parents can watch normally and obtain a better recording.
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Add safety checks to automated workflows.
— Provide guardrails for AI-agent actions.
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