Mixpanel Reviews

1. Deepak Kumar

   | Technical Reviewer at A/B Testing | over 3 years ago
   Mixpanel helps track user behavior and measure success for your application

   I recommend Mixpanel to anyone who needs to do tracking of user behavior in web applications in order to measure against goals.

   🏁 Competitors: VWO, Optimizely, Convertize.io, Picreel

   👍 Pros:    Easy to use|Powerful analytics|Tracks every user actions

   👎 Cons:    Dashboard is complex|Pricing is too high|Hard to implement own data

Hugging Face Reviews

Mixpanel mentions (22)

• A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

  Mixpanel — 100,000 monthly tracked users, unlimited data history and seats, US or EU data residency. - Source: dev.to / 4 months ago

• What do I do after ProductHunt?

  You can test sales copy with Mixpanel to see where your traffic is coming from so you can see which content (Informative or promotional) generates the most engagement. Source: 7 months ago

• Essential Product Metrics Every Startup Founder Should Know

  Custom Data Collection As you grow you can consider a bit more precise configurations and data analytics approachs. Implement custom data collection through or event tracking for example via Mixpanel. This allows you to capture specific metrics tailored to your product's unique needs. - Source: dev.to / 12 months ago

• What issues / painpoints have you experienced with current Analytics Platforms? Mixpanel, Plausible etc.

  I am looking into whether you have experienced any pain points or issues not solved by current analytics platforms. By current solutions I'm talking plausible.io, mixpanel.com, usermaven.com and alike, providing features such as: total visitors, page views, online users, custom events tracking, top sources. Source: over 1 year ago

• Ask HN: What’s your startup’s analytics setup? (2023)

  Ten years ago, there was a popular Ask HN: "what's your startup’s analytics setup?” [0]. Like the poster then (malandrew), I have the same problem in 2023: Selecting analytics solutions for the startup I'm working at has proven to be more daunting than I imagined. There are many services to choose from and it's not immediately obvious how you should choose from the many offerings to get not only complete analytics... - Source: Hacker News / over 1 year ago

Hugging Face mentions (256)

• Understanding GPT: How To Implement a Simple GPT Model with PyTorch

  These libraries are fundamental for building and training our GPT model. PyTorch is a deep learning framework that provides flexibility and speed, while the Transformers library by Hugging Face offers pre-trained models and tokenizers, including GPT-2. - Source: dev.to / 2 days ago

• Building a Simple Chatbot using GPT model - part 2

  Hugging Face is a company and community platform making AI accessible through open-source tools, libraries, and models. It is most notable for its transformers Python library, built for natural language processing applications. This library provides developers a way to integrate ML models hosted on Hugging Face into their projects and build comprehensive ML pipelines. - Source: dev.to / 11 days ago

• Add Vector Search to Your Site

  We will use the OpenAI embeddings API to convert the content of the blog posts into vector embeddings. You will need to sign up for an API key on the OpenAI website to use the API. You will need to provide your credit card information as there is a cost associated with using the API. You can review the pricing on the OpenAI website. There are alternatives to generate embeddings. Hugging Face provides... - Source: dev.to / 9 days ago

• How to run any LLM model with Hugging-Face 🤗

  Hugging-face 🤗 is a repository to host all the LLM models available in the world. https://huggingface.co/. - Source: dev.to / 17 days ago

• Chat with your Github Repo using llama_index and chainlit

  HuggingFaceEmbeddings is a function that we use for converting our documents to vector which is called embedding, you can use any embedding model from huggingface, it will load the model on your local computer and create embeddings(you can use external api/service to create embeddings), then we just pass this to context and create index and store them into folder so we can reuse them and don't need to recalculate it. - Source: dev.to / about 2 months ago