AI Multiprompt Extension
AI Developer ToolsSends one prompt to several AI chatbots at once, then lines up the answers for comparison.
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Tick Compare on any two to four cards to put them side by side.
Sends one prompt to several AI chatbots at once, then lines up the answers for comparison.
For patent litigators, not for general legal work.
AI-enhanced catalog images. Richer than ever.
A newsroom CMS built for college papers, with writing tools, scheduling and reader analytics.
Optimizes HR processes with expert consulting and AI, but not for automation beginners.
Simplifies RAG and ML apps with Postgres and GPUs, but requires a VPC for more than one node.
GeoAI for environmental monitoring and decision-making.
Otica AI agent for routine tasks.
Control your AI, layer by layer. Each layer delivers value on its own.
Trace and evaluate LLMs with OpenTelemetry, manage prompts, and compare models.
A blockchain network for publishing and running decentralized AI apps, backed by its own OFN token.
Puts a chat interface for your local Ollama models, from Llama to Mistral, right in the browser.
Optimizes engineering team with data.
What The Diff helps you make pull requests accessible for everyone, but it requires full access to your code.
Weights & Biases helps track ML experiments, but requires manual setup for large teams.
Creates engaging stories for modern businesses.
Automate community management with AI, get automatic rule enforcement in seconds.
Compare over 47 vector databases.
Compare multiple AI models side-by-side.
Slot Depo 5K enables players to start playing slots with as little as $5, making it a convenient choice for those on a tight budget.
Sends event attendance data.
Real-time analytics without ETL, but requires technical setup and expertise.
Turns dense TC39 spec language into plain-English explanations with one click.
Fast tensor library for TypeScript and JavaScript.
Deploys autonomous AI agents that resolve support tickets and unify enterprise knowledge sources.
Nexonauts offers four local tools for PDF editing, screen recording, LaTeX typesetting and docs compilation. Nothing uploads to a server first.
An AI tutor that walks you through any LeetCode problem step by step.
Turns a blog post's list of places into an embeddable, interactive map.
LocalAI runs AI models offline with zero setup.
A community hub for Llama models with docs, GPU compute access, and a forum.
Open-source, self-hosted AI gateway.
Lightning AI is a PyTorch framework for building and training models.
LM Studio lets you run LLMs offline on your laptop. Supports various models.
LLM Pricing Comparison tool lacks comprehensive coverage.
Best for AI enthusiasts and developers, not suitable for beginners.
A Chrome Q&A assistant that searches your private knowledge base or the open web.
Build and manage complex AI agent teams without coding.
AI research and implementation.
CodeLogician extends LLM capabilities for software analysis.
It suits developers who want to simplify Git but may miss more granular control.
Keep docs accurate, fix issues proactively.
For prompt engineering and iterative coding, not for complex projects.
GPTBots.ai builds AI agents with an intuitive interface.
Lets enterprises build no-code AI agents for support, sales leads and internal search.
Use pre-trained models for serverless Node.js apps.
Not suitable for basic file handling alone, Echobase AI excels in custom-trained AI Agents.
Build intelligent documentation with AI.
Trains AI to write code, secure environments required.
Three worth starting with
Terms to know
Developer tools in this group help build with AI rather than only write code: model hubs like HuggingFace, agent frameworks, browser add-ons and learning aids such as Marble LeetCode Tutor Chrome Extension. Engineers and technical founders use them to prototype AI features and test models. The deciding question is hosting: some tools run in the cloud, others let you run models on your own hardware.
Code assistants help you write code in your editor. Developer tools are the wider set for building AI products: model libraries, agent frameworks, testing utilities and deployment platforms. A team might use one of each at the same time.
Not always. Many offer hosted models and simple APIs, so ordinary application developers can add AI features without training anything. Fine-tuning or running models yourself does require more background in data and infrastructure.
Hosted models are quicker to start with and need no hardware, but your data goes to a third party. Running a model yourself keeps data in-house and gives control over versions, at the cost of setup and maintenance.