We used their DC proxies and Residential proxies. Resi proxies were having quite low success rate. We had to use resi solution from other proxy providers. Unblocker didn't work well either also it was way too expensive.
Based on our record, Jupyter should be more popular than Bright Data. It has been mentiond 216 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.
Exactly that. It's an arms race between companies that offer a large number of residential IPs as proxies and companies that run unauthenticated web services trying not to die from denial of service. https://brightdata.com/. - Source: Hacker News / 15 days ago
Reddit Recap is an application that scrapes subreddits using BrightData and generates concise summaries every two hours. These summaries are then converted into audio briefings, all accessible through a beautiful web app, allowing users to effortlessly stay informed about their favorite communities. - Source: dev.to / 5 months ago
Make sure to sign up on BrightData. Also complete the steps for the initial setup for Proxies & Scraping Infrastructure and Web Scraping API. Please make a note on the WSS Browser Credential, Webscraper Api Token. - Source: dev.to / 5 months ago
So my goal here is creating a web scraper and web searcher using bright and gemini openai compatible model to make cursor composer more smarter with functionality like web search and web scrape. - Source: dev.to / 5 months ago
Paid proxies: services like Bright Data or ScraperAPI provide reliable proxies with better performance and support, but you have to pay. - Source: dev.to / 6 months ago
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
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