ChatTree
AI Developer ToolsA collaborative AI chat app where engineering teams share code, ideas and curated resources live.
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A collaborative AI chat app where engineering teams share code, ideas and curated resources live.
Automates enterprise workflows end-to-end by connecting AI to CRM, ticketing and ERP systems.
ChainGPT is a blockchain AI platform offering various tools and services.
Supports interpretability across modalities, minimal modification needed.
Botsheets generates Google Slides directly from your data, with secure storage on Drive.
Bethge Lab is an AI research group focusing on lifelong learning.
Applies AI and a proprietary knowledge graph to speed up biopharma drug discovery.
AutoKT automates documentation writing and updating; it can be costly for small teams.
Generate and fix SQL queries in plain English.
Identifies unseen website risks and new threats to your data.
Capture more leads and close deals faster.
Manual updates are a thing of the past.
For AI engineers and researchers, Semiring bridges precise specifications to executable systems.
Too simple for complex models.
Magic Dash AI helps automate project management tasks.
GitHub keeps you ahead with AI-powered development tools and secure workflows. It excels in automation, security, and collaboration but can be pricey for small teams.
Frederick AI agents build and run applications.
AI for everything you build.
AI-powered agents manage Salesforce implementation.
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.