
Supabase
Firebase
AppWrite
Next.js
Vercel
PocketBase.io
Hasura
Railway
TFlearn
Keras
Clarifai
DeepPy
Microsoft Cognitive Toolkit (Formerly CNTK)
Merlin
Knet
Swift Brain
Supabase
TFlearnBased on our record, Supabase seems to be a lot more popular than TFlearn. While we know about 553 links to Supabase, we've tracked only 2 mentions of TFlearn. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Supabase is an open-source backend platform built around managed PostgreSQL. You get a database, auto-generated REST APIs (via PostgREST), Auth, file Storage, Realtime subscriptions, and Edge Functions - with a dashboard and SQL editor on top. - Source: dev.to / about 1 month ago
If youโre starting fresh, go to Supabase and create a new project. Once your project is ready, copy the project URL and publishable (anon) key from the project settings. - Source: dev.to / about 1 month ago
So I had to discover that and fix that, and start leaning on our database (Supabase is what Lovable uses by default). - Source: dev.to / about 1 month ago
Verdict: start with Supabase on day one. Free tier carries you through launch. Upgrade to Pro when you legitimately outgrow it. - Source: dev.to / about 1 month ago
The stack: Python/Flask, PostgreSQL (via Supabase), Tailwind CSS, plain JavaScript, Render for deployment, Cloudflare for DNS, and Anthropic's Claude Haiku as the primary LLM with Google Gemini as a fallback, orchestrated through LiteLLM. Authentication is OTP email-based. Payments are handled through Stripe. The whole thing is WCAG 2.1 AA accessible and PWA-friendly. - Source: dev.to / about 2 months ago
TFLearn โ Deep learning library featuring a higher-level API for TensorFlow. - Source: dev.to / almost 4 years ago
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBIโs, and walkโs are all taken into account and passed through layers. Thereโs no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called... - Source: dev.to / over 5 years ago
Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
AppWrite - Appwrite provides web and mobile developers with a set of easy-to-use and integrate REST APIs to manage their core backend needs.
Clarifai - The World's AI
Next.js - A small framework for server-rendered universal JavaScript apps
DeepPy - DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.