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Hugging Face
ReplyStackReplyStack's answer:
ReplyStack was born from a simple frustration: existing review management tools either cost hundreds of dollars per month or only work with a handful of platforms. After seeing local businesses struggle to keep up with reviews scattered across Google, TripAdvisor, Booking, and Facebook, I built a solution that works everywhere โ by combining a lightweight Chrome extension with a centralized dashboard. No complex integrations, no enterprise pricing, just a practical tool that helps SMEs protect their online reputation.
ReplyStack's answer:
Backend: Laravel 12 (PHP 8.3+) with Sanctum authentication Frontend Dashboard: React + TypeScript + Vite + Tailwind CSS Browser Extension: Plasmo framework (Chrome & Firefox) AI: Claude API by Anthropic for response generation Database: MySQL 8 with Redis caching Infrastructure: Railway (hosting), Cloudflare (DNS/email)
ReplyStack's answer:
ReplyStack is currently in early launch phase. We're onboarding our first customers from the restaurant and beauty salon industries in France.
ReplyStack's answer:
ReplyStack is the only review management tool that combines a Chrome extension with a SaaS dashboard. This hybrid approach lets businesses respond to reviews on platforms that don't offer APIs โ like TripAdvisor, Booking.com, and Yelp โ something competitors simply cannot do. Instead of expensive API integrations or risky scraping, our extension works directly in the browser where business owners already manage their reviews. It's legal, reliable, and works everywhere.
ReplyStack's answer:
ReplyStack's answer:
Small and medium businesses that depend on online reviews but lack the time or budget for enterprise reputation management tools. This includes restaurant owners, hotel managers, e-commerce sellers, beauty salon operators, healthcare providers, and auto repair shops. Our typical user manages 1-5 locations, receives reviews on multiple platforms, and wants to respond professionally without spending hours each week on it.
Based on our record, Hugging Face seems to be more popular. It has been mentiond 329 times since March 2021. 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.
Download from Hugging Face with a single command. Models come in different quantization levels (compression trade-offs). A 4-bit quantized version is roughly 4x smaller than the full-precision version, with minor quality loss. For most team use cases, the quantized versions are the practical choice because they fit in less GPU memory. - Source: dev.to / 5 days ago
There are a couple of options. One good way to find inference providers for open models is through hugging face (https://huggingface.co). You can select a model and see which inference providers serve it. You can even access it through hugging face. If you just wanted to test a model or have super light work you can get some free access to alot of open source models through nvidia (https://build.nvidia.com). There... - Source: Hacker News / 9 days ago
Both the base and instruct variants of Aether-7B-5Attn, plus a live interactive demo, are publicly available on Hugging Face. Search for VIDRAFT or Aether-7B-5Attn on huggingface.co to find the model cards and repository. - Source: dev.to / 19 days ago
Hugging Face hosts thousands of open models for NLP, vision, and other tasks. The Inference API (via Inference Providers) lets you call those models over HTTP. The @huggingface/inference package from huggingface.js is the Node.js client. - Source: dev.to / 2 months ago
Right now, I don't. If model foo is deleted from HuggingFace but its compare rows are still in the DB, those compare pages will still be served at build time. They'll have the old data until the model's row in models.json is removed โ which only happens if the model falls out of the top-500 in the nightly fetch. It's a known gap. For now, the risk is low; popular models don't disappear. A more robust system would... - Source: dev.to / 3 months ago
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