ml 673

The ML category is a broad collection of 674 tools that apply machine learning across industries and functions, from healthcare documentation and legal research to user research, code generation, and content creation. It captures AI applications that do not fit cleanly into a single vertical.

TNY.dev

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API-first link shortener built for AI agents

EasySplitter

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AI vocal isolation and removal tool

CXCortex

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Multi-channel customer service agent platform

Kortical

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End-to-end AI and machine learning consulting

Aster Lab

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AI video summarization and key information extraction

Wizardly

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Interactive product onboarding tutorials

aiToggler

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Compare AI models by performance and cost

Codeaid

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AI-powered developer hiring and skills matching

WEVO

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AI user research through behavioral simulation

Roleplayr

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Have conversations with any online image

Outfit

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Plan trips with personalized itineraries

Draftsmith

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AI writing refinement for clarity and style

LabelGPT

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Fast AI-powered image annotation

Diglot

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Bilingual writing editor for ESL writers

Travelandz

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AI travel planning and booking

Gpt-tab

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Access ChatGPT quickly from Mac with a draggable window

GPT4 Vision Chatbot

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Use ChatGPT in multiple languages

Cheeta

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Check grammar, style, tone, and plagiarism in your writing

EasyChat AI

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Fast, responsive ChatGPT app for Windows

GPT Researcher

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AI research assistant that gathers and summarizes sources

Thinkbuddy

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Access 15+ AI models in one platform

DapperGPT

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Unified interface for multiple AI models

Echo Now AI

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Extract insights and summarize documents with AI

Desksense

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Chrome extension with AI chat and image generation

Because this category covers so many domains, browsing by sub-use case is more efficient than scrolling the full list. Tools like Hippo Scribe and SopCreator serve very specific professional workflows, while others like User Evaluation or Userpersona target product and UX teams. The quality bar across the category is uneven: some tools are mature products with enterprise customers, while others are early-stage experiments. When evaluating any tool in this space, look for evidence of actual accuracy and reliability in your specific domain, since ML performance varies dramatically across tasks. Integration depth and data handling are often the deciding factors for business use. Pricing models are diverse, from usage-based API billing to flat-rate SaaS subscriptions. Open-source alternatives exist for many of the underlying tasks, so for teams with technical resources, comparing commercial tools against self-hosted options is worth the effort.