Weavel AI
AI Developer ToolsCreates engaging stories for modern businesses.
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Tick Compare on any two to four cards to put them side by side.
Creates engaging stories for modern businesses.
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.
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.
Launching soon, generates excitement for your business.
Set up your bot in a couple of minutes.
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.
Chathero is limited to medium-sized businesses and lacks enterprise-level complexity.
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.
Build AI systems easily.
Generate and fix SQL queries in plain English.
Identifies unseen website risks and new threats to your data.
Manual updates are a thing of the past.
Improve data security with Tet Drošība’s enhanced V Fitter.
For AI engineers and researchers, Semiring bridges precise specifications to executable systems.
Too simple for complex models.
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.