HuggingFace Statistics

Hugging Face, a trailblazer in AI and NLP, boasts 1M+ models, datasets, and apps, reaching a $4.5B valuation in 2023. Explore its journey and industry impact.

Hugging Face is a leader in innovation and collaboration in the rapidly evolving fields of artificial intelligence and Natural Language Processing (NLP), pushing the limits of language models and machine learning. As we delve into the intricate details of this industry titan, we reveal a community-driven platform that has emerged as a cornerstone in the NLP market. 

Hugging Face, which has over a million models, datasets, and apps, has attracted the attention of major tech companies such as Nvidia, Google, and Salesforce. In 2023, it completed a ground-breaking $235 million Series D investment round, which allowed it to reach a valuation of $4.5 billion. Beyond the numbers, Hugging Face's success stems from its dedication to open-source principles, which is demonstrated by the widespread use of its ground-breaking Transformers open-source framework. 

Hugging Face began as a chatbot in 2016 and has since grown into a thriving AI community. This thorough review covers the company's strategic decisions, including the launch of BLOOM, a 176 billion-parameter language model. This article delves into the nuances of Hugging Face's revenue trajectory, business model, and strategic partnerships with major industry players, positioning the company at the forefront of natural language processing innovation. 

Upon closer examination of its broad user base, active community involvement, and remarkable collection of models, datasets, and applications, Hugging Face is not just a platform—rather, it is a worldwide movement that is influencing the direction of NLP and artificial intelligence. 

Come along for the ride as we delve into this in-depth analysis, where Hugging Face is revealed not just as a technology supplier but also as a force behind the democratization of machine learning and the development of a community centered around the quest for excellence.

1. Key Points of Interest on Hugging Face

  1. Hugging Face is a well-known open-source software community and library for Natural Language Processing activities (Source).
  2. Hugging Face aspires to build the #1 community for machine learning (Source).
  3. Hugging Face, as of 2023, had raised a total of $395.2 million in funding, with its most recent Series D funding round bringing in $235 million and valuing the company at $4.5 billion (Source).
  4. Hugging Face has raised $396 million from 6 funding rounds, the first being in 2017 and the latest in 2023 (Source).
  5. Salesforce, Google, Nvidia, Amazon and others have all backed Hugging Face, which claims to have more than 1m models, datasets and apps on its platform (Source).
  6. Hugging Face generated less than $10 million in revenue in 2021 and is expected to have a revenue run rate of between $30 million to $50 million in 2023 (Source).
  7. Over 10,000 companies use Hugging Face's platform for machine learning and AI development (Source).
  8. Hugging Face, the AI startup backed by tens of millions in venture capital, has released an open source alternative to OpenAI’s viral AI-powered chabot, ChatGPT, dubbed HuggingChat (Source).
  9. Hugging Face released its first open-source large language model, BLOOM, in July 2022 (Source).
  10. Hugging Face Hub has 10,000+ pre-trained Transformers models for NLP, speech, and vision models (Source).
  11. Hugging Face has over 1,000 paying customers, including Intel, Pfizer, Bloomberg, and eBay (Source).
  12. Hugging Face directly competes with companies like, spaCy, AllenNLP, Genism, and
  13. Indirectly, Hugging Face competes with companies such as OpenAI, Cohere, and AI21 Labs (Source).
  14. The Hugging Face transformers have over 60,000 stars on GitHub (Source).
  15. Hugging Face's valuation is over 100 times its annualized revenue (Source).

2. Overview of the Natural Language Processing Market

What is the Market Size of the Global Natural Language Processing Market?

  • As artificial intelligence has become more prevalent, natural language processing has become increasingly important (Source).
  • The market size in the Natural Language Processing market is projected to reach US$29.19bn in 2024, and expected to show a compound annual growth rate of 13.79% from 2024 to 2030, resulting in a market volume of US$63.37bn by 2030.
Market Size Of the NLP Market From 2020 to 2023
  • The growth of the market is driven by the availability of a large volume of datasets, increased business interests in AI, advanced AI research, and frequent releases of more powerful language models with more model parameters (Source).
Factors responsible for growth in global natural language processing market
  • From 2020 to 2021, 60% of tech leaders increased their NLP budgets by at least 10%, with almost a fifth of them doubling it (Source).
Percentage Of Tech Leaders That Increased Their NLP Budgets From 2020 to 2021
  • From 2018 to 2021, the number of parameters making up notable NLP models increased from 340 million to 530 billion, demonstrating incredible growth (Source).
Growth of NLP models parameters from 2018 to 2021

What’s Hugging Face’s Contribution to the Natural Language Processing Market?

  • Hugging Face is a company and an open-source platform that focuses on Natural Language Processing (NLP) technologies and machine learning models (Source).
  • Hugging Face has gained significant popularity in the NLP community due to its contributions in the form of pre-trained models, libraries, tools, and resources that make it easier for researchers and developers to work with NLP tasks (Source).
  • Hugging Face brings NLP to the mainstream through its open-source framework Transformers that has over 1 million installations (Source).
  • Today, Transformers is the most widely adopted software library for machine learning models to deal with NLP applications, and has 63.3k stars and 14.9k forks on GitHub (Source).
Hugging Faces Transformer Model is Widely Adopted Library for Developers
  • Already-trained neural networks for comprehending natural language can be downloaded from hugging face’s website (Source).
  • The number and size of language models exploded in 2018 after Google open-sourced BERT with 340 million model parameters. In 2019, Open-AI's GPT-2 debuted with 1.5 billion parameters, Nvidia’s Megatron-LM with 8.3 billion parameters, and Google’s T5 with 11 billion parameters. Microsoft introduced Turing-NLG with 17.2 billion parameters in early 2020 (Source).
  • Open-AI then released GPT-3 in June 2020 with 175 billion parameters and was considered at the time to be the largest language model ever made. However, Nvidia and Microsoft broke the record in 2021 when they unveiled Megatron-Turing NLG with 530 billion parameters. Hugging Face joined the fray in July 2022 when it released BLOOM with 176 billion parameters (Source).
Number Of Parameters Of Projects Similar to Hugging Face

What’s the Market Size of the MLOps Market?

  • The MLOps market is expected to reach $6.2 billion in 2028, up from $612 million in 2021. Several well-funded startups exist in the space, notably Weight and Biases, DataRobot, Comet, and Dataiku (Source).
Market Size of the MLOps market in billion USD

3. Unveiling Hugging Face: A Comprehensive Overview

What is the Definition of Hugging Face?

  • Hugging Face is a company and an AI community that provides access to free open-source tools for developing machine learning and AI apps (Source).
  • Hugging Face also hosts open-source datasets and libraries and serves as a way for teams to collaborate, including a repository, similar to GitHub (Source).
  • Hugging Face is now the fastest growing community & most used platform for machine learning (Source).
  • Hugging Face aspires to build the #1 community for machine learning.
  • Hugging Face is fast becoming the go-to place for open-source AI, with AI experts and enthusiasts referring to it as the open-source alternative to ChatGPT and the "GitHub of AI." (Source).

When was Hugging Face Established?

Was Hugging Face initially a Machine Learning Platform?

  • Before becoming a platform for AI, Hugging Face was first introduced as a chatbot in 2016 to entertain and provide emotional support for teenagers  (Source).
  • The Hugging Face brand came from the hugging face emoji to look caring and friendly for the teens trying to chat with it (Source).

What’s Hugging Face’s Business Model?

  • Hugging Face is fortunate enough to have investors who provide the funds to invest in the long-term rather than short-term monetization (Source).
  • Hugging Face employs the open core business model. The company offers some features for free and other features to paying customers (Source).

What Products have Hugging Face Created?

  • Hugging Face is an AI startup and community that offers free tools for creating machine learning and AI applications (Source).
  • Hugging Face released a series of products and services in 2020, such as Autotrain, Inference API, and on-premises and private cloud hosting options focused on serving enterprise customers (Source).
  • In April 2023, Hugging Face released HuggingChat, their open-source generative AI, as a ChatGPT competitor (Source).
  • One of Hugging Face’s recently completed projects in July 2022, is a 176 billion parameter large language model called BLOOM, which is available to anyone who agrees to abide by their Responsible AI license (Source).
  • BLOOM was downloaded over 40,000 times as of August 2022 (Source).
Bloom Was downloaded over 40000 times as of August 2022
  • The unveiling of BLOOM shows a clear intent from Hugging Face to pursue creating its own large language models (Source).
  • BLOOM’s model has a similar architecture to OpenAI’s widely popular GPT-3, and was trained on 46 spoken languages and 13 programming languages (Source).
  • Hugging Face also provides autoML solutions enabling organizations to build AI models without code (Source).
  • Hugging Face Hub is a platform by Hugging Face, that serves as a centralized web service for hosting Git-based code repositories, web applications, and discussions for projects (Source).
  • Hugging Face Hub enables users to load, share, and collaborate on models, datasets, and machine learning applications (Source).
  • To normalize NLP and make models accessible to all, Hugging Face created an NLP library that provides various resources like datasets, transformers, and tokenizers, etc (Source).
  • On releasing NLP libraries called Transformers and a wide variety of tools, Hugging Face instantly became very popular among big tech companies (Source).
  • Hugging Face’s other open-source endeavors include managing an AI research initiative known as BigScience, involving 900 researchers from around the world.
  • BigScience is currently being trained on a dataset that includes 300 billion words written in 46 languages (Source).
  • Some of Hugging Face’s models like BERT and DistilBERT have over 100,000 weekly downloads (Source).
  • StarCoder, Hugging Face's AI coding assistant, was trained on 80 programming languages and has 15 billion parameters (Source).
  • AWS collaborated with Hugging Face to create Hugging Face AWS Deep Learning Containers, which provide data scientists and ML developers a fully managed experience for building, training, and deploying state-of-the-art NLP models on Amazon SageMaker (Source).

What’s responsible for the Success of the Hugging Face Platform?

  • The success of Hugging Face's platform largely depends on the continued contributions of its community (Source).
  • At the heart of Hugging Face’s success lies the Hugging Face Hub, a groundbreaking platform that boasts an astonishing repository of resources (Source).

How many Models and Datasets does Hugging Face host?

  • Hugging Face is the most-used open platform for AI builders (Source).
  • The platform allows users to freely share their models and datasets, fostering a spirit of collaboration and innovation within the AI community (Source).
  • Hugging Face offers a repository of pre-trained NLP models, including transformers like BERT, GPT-2, GPT-3, and more. These models can be fine-tuned on specific tasks, saving researchers and developers a considerable amount of time and resources (Source).
  • Hugging Face hosts over 300k models, 250k datasets, and 250k spaces, making it the most extensive collection of models and datasets (Source).
Total Hosts Number Of Hugging Face Models, Datasets And Spaces
  • Hugging Face hosts over 130 architectures (Source).
  • The Hugging Face Hub is a platform with over 350k models, 75k datasets, and 150k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together (Source).
  • Hugging Face Hub is home to over 75,000 datasets in more than 100 languages that can be used for a broad range of tasks across NLP, Computer Vision, and Audio (Source).
  • Hugging Face now hosts more than 50,000 organizations and over a million repositories (Source).
  • For text categorization and sequence labeling, Hugging Face provides more than 6000 open data sets that span a variety of use cases and industries (Source).
  • These open data sets include code generation, news, weather forecasts, conversational assistants, and customer service (Source).
  • Hugging Face also hosts some of the most popular and widely used machine-learning models that are already trained on large existing data sets, like Google's BERT and OpenAI's GPT-2 (Source).
  • Hugging Face also provides a Metrics library, which is used for evaluating a model’s predictions (Source).

How many Employees does Hugging Face have?

Total Number Of Hugging Face Employees From 2021 to 2023

Is Hugging Face Free?

  • Many of Hugging Face services are available for free, pro and enterprise levels (Source).
  • Hugging Face offers a premium platform called Hugging Face Pro for $25/month (Source).
  • In 2021,Hugging Face started offering paid plans and services, and they are currently already used by over 1,000 companies (Source).
Total Number of Hugging Face Paid Customer
  • Hugging Face only began offering paid features in 2021 and ended that year with about $10 million in revenue (Source).

Who Uploads the Largest Models to Hugging Face?

  • The largest contributor to Hugging Face in terms of uploaded models is the "Other" category with 129,157 models (Source).
  • The contributions from universities, non-profits, and companies are fairly evenly distributed, with 5,271, 6,546, and 6,031 models, respectively. The community group follows closely with 4,040 models (Source).
  • The only significant outlier is the "classroom" category with 148 models (Source).
Total Number of models contributed to hugging face based on category
  • IBM has contributed over 200 open models and datasets on Hugging Face, including the recent release of the Geospatial Foundation Model in partnership with NASA (Source).
IBM has contributed over 200 open models and datasets to Hugging Face
  • The majority of Open-source Large Language Models on Hugging Face are contributed by entities categorized as "Other," with a total of 100 uploads (Source).
  • Following closely, “Companies” rank second, contributing 63 LLMs with an average parameter count of 10 billion. The “Community” category follows with 39 uploads, and “Non-Profit” organizations contribute 24 (Source).
  • In contrast, “Universities” have the lowest contribution, uploading only 3 LLMs, which also happen to be the smallest on average (Source).
Total Number of Open Source LLMs Contributed to Hugging Face Based on Organization

How many Stars does Hugging Face have on GitHub?

  • Hugging Face's tool Transformers has 61,300 stars on GitHub, which is often a measure of success for developer tools (Source).
Hugging Faces Transformers has 61000 stars measure of success for developer tools
  • For comparison, PyTorch, Meta's popular machine-learning framework, has 55,500 stars, and Google's TensorFlow has 164,000 stars. Snowflake's Streamlit has 18,700 stars to Gradio's 6,000 (Source).
Hugging Faces Stars Number on Github in Comparison to that of other projects

How Effective is the Hugging Face AI Detector in terms of Accuracy?

  • Hugging Face’s fake news detector model is up to 95% accurate in detecting misinformation and disinformation online (Source).
95 percent of accuracy level of Hugging faces fake news detector model

Is Hugging Face Open for Public Review?

  • Hugging Face is one of the seven A.I. companies that have agreed to open up their A.I. systems for public review as part of a joint effort with the Biden administration to ensure safe and responsible innovation in A.I (Source).

4. Hugging Face User Demographics

How many people use Hugging Face?

  • Over 10,000 organizations use Hugging Face products to create AI-powered tools for their businesses, as of June 2022 (Source).
  • As of 2022, Hugging Face had over 1.2 million registered users. This makes it the largest AI community online (Source).
Total Number of Hugging Face Registered Users as of 2022

How many Paying Customers does Hugging Face have?

  • Hugging Face’s paying customers are primarily large enterprises seeking expert support, additional security, auto train features, private cloud, SaaS, and on-premise model hosting (Source).
  • As of June 2022, Hugging Face had over 1,000 paying customers, including Intel, Qualcomm, Pfizer, Bloomberg, and eBay (Source).

How many People visit Hugging Face?

  • The traffic to Hugging Face's website has been steady, receiving almost 25 million visitors in both April and May, 2023. That's an increase of 25% over the 20 million visits in March (Source).
  • In September 2023, Hugging Face's website had 27.1 million visits, 34.25 million visits in October, and 35.79 million in November (Source).
Total number of visits in Millions to Hugging Faces website from april to november 2023
  • Users spend an average of 4.1 minutes on the site, which is only slightly less than the average of 4.53 minutes that visitors spend on (Source).
Comparison of Average time spent per visit on Hugging Face and Open AI in Minutes
  • Hugging Face visitors view an average of 4.5 pages per visit (Source).
Hugging Face Visitors View an Average of 4.5 pages per visit
  • As of November 2023, Hugging Face's website received 35.79 million visits, surpassing its competitors: 2.11M, 2.83M, 13.65M, 24.11M, 3.98M, 2.16M, and 10.79M (Source).
Number Of Visits in Millions to Hugging Faces Website compared to it's competitors
  • Hugging Face's website boasts an authority score of 65, surpassing its competitors such as with 64, with 60, with 52, with 49, with 47, with 45, and with 39 (Source).
Hugging Face Website Authority Score compared to its competitors
  • Hugging Face's website boasts a 53.88% bounce rate, outperforming direct competitors like 62.03%, 58.03%, 55.27%, 55.53%, 69.46%, 68.85%, and 57.1% (Source).
Hugging Faces Website Bounce Rate Compared to its Competitors

Where does Hugging Face get its Traffic from?

  • At 58.11%, YouTube is the largest source of social traffic for Hugging Face. That's almost 6 in 10 visits. OpenAI follows a similar trend, with YouTube also sending the most social traffic of 55.47% (Source).
Percentage Of traffic from youtube to hugging face
  • Visitors to Hugging Face's website are made up of 75.25% male and 24.75% female (Source).
Gender Percentage of Hugging Face Visitors
  • Hugging Face's user base is predominantly aged between 25 and 34, constituting 36.87%, followed by 18-24-year-olds at 28.26%. The 35-44 age group accounts for 17.9%, while users aged 45 and above make up the remaining 16.97% (Source).
  • Collectively, users aged 18–44 constitute 83.03% of the platform's traffic (Source).
Age groups percentage of hugging face users

5. Money Matters: Hugging Face’s Revenue, Valuation and Funding

What is Hugging Face Revenue in 2023?

  • Forbes reported that Hugging Face brought in $10 million in revenue in 2021, $15 million in 2022, and now has an annualized revenue of approximately $50 million, as at 2023 (Source).
Revenue of Hugging Face in Millions from 2021 to 2023

What is Hugging Face’s Valuation?

  • Hugging Face's most recent valuation is $4.5 billion, based on raising $235 million in their Series D funding round in 2023. That's more than double their previous valuation of $2 billion in 2022 (Source).
Hugging faces Valuation in Billion Usd from 2022 to 2023
  • Hugging Face's valuation is reportedly over 100 times its annualized revenue, highlighting the significant investor interest in AI and ML platforms (Source).
Hugging Faces Valuation Over 100 times its annual revenue

How much has Hugging Face raised from Funding?

  • Hugging Face raised $15M in a Series A funding led by Lux Capital, with participation from A.Capital, Betaworks, Richard Socher (chief scientist at Salesforce), Greg Brockman (cofounder & CTO OpenAI), Kevin Durant and other angels in December, 2019 (Source).
  • In March 2021, Hugging Face successfully raised $40 million in a Series B funding round, with participation from Lux Capital, A.Capital, Betaworks, Alex Wang, Kevin Durant, Addition VC and many more (Source).
  • Hugging Face went ahead to raise $100M in a Series C funding round, with major participants being, AIX Ventures, Lux Capital, A.Capital, Betaworks, Alex Wang, Kevin Durant, Addition VC, Olivier Pomel, Coatue Management and many more (Source).
  • In its latest Series D funding round in August 2023, Hugging Face raised $235 million, leading to a valuation of $4.5 billion. Notable participants in this funding round included Salesforce, Google, Amazon, Nvidia, AMD, Intel, IBM, and Qualcomm (Source).
  • Hugging Face generated a revenue of $15 million in 2022 (Source).
Hugging Face generated a revenue of $15 million in 2022
  • As of 2023, Hugging Face had raised a total of $395.2 million, placing it among the better-funded AI startups in the space. However, it is still behind some companies such as “OpenAI“ with $11.3 billion funding, “Anthropic“ with $1.6 billion funding, “Inflection AI“ with $1.5 billion funding, “Cohere“ with $435 million funding, and “Adept“ with $415 million funding (Source).
  • Hugging Face is well capitalized. In May 2022, after its $100 million Series C round, the company also had roughly $40 million in the bank from previous rounds, bringing its cash reserves to $140 million (Source).
  • Investors believe that Hugging Face could be a $50-100 billion company if it ever goes public (Source).
  • Hugging Face’s first funding round was on Oct 01, 2016, with an undisclosed fee from The Chernin Group (Source).
  • Hugging Face’s latest funding round was a Series D round on Aug 22, 2023, for $235M (Source).
  • Hugging Face has 21 institutional investors including Lux Capital, Salesforce, and SV Angel (Source).
  • Lee Fixel and 11 others are Angel Investors in Hugging Face (Source).
  • In 2017, Hugging Face was part of the Voicecamp startup accelerator hosted by Betaworks in New York City (Source).
  • Hugging Face has been attracting attention, first thanks to their open-source transformers library, and then due to their business and fundraising, such as their $40 million raise in March of 2021, following $15 million in late 2019 (Source).
  • Hugging Face has 35 investors including Google and AMD (Source).
  • Hugging Face is the latest in a series of AI tooling startups to have raised funding at unicorn valuations over the past year. Dataiku Inc., which helps enterprise build and manage AI applications, last August raised $400 million from investors at a $4.6 billion valuation. AI development specialist DataRobot Inc. is worth $6.3 billion thanks to a $300 million funding round that it announced last June, 2021 (Source).
  • Hugging Face has raised a total of $396 million since being founded in 2016 (Source).
Hugging Face Raised 396 million usd from funding since 2016
Hugging Face Totally Raised 396 million usd from funding

6. A Closer Look at Hugging Face and its Industry Counterparts

Who are Hugging Face Competitors?

  • Hugging Face directly competes with companies like, spaCy, AllenNLP, Genism, and (Source).
  • Indirectly, Hugging Face competes with companies such as OpenAI, Cohere, and AI21 Labs. Any company building and training large language models represents competition (Source).

How does Hugging Face’s Revenue Compare to that of its Competitors?

  • While Hugging Face has attracted a significant developer interest, its revenue lags behind some other companies (Source).
  • On GitHub, Hugging Face has accumulated "stars" at a faster pace than other projects like Confluent, Databricks, and MongoDB, but they all have far higher revenues of $388 million, $800 million, and $874 million respectively, compared to $50 million for Hugging Face (Source).
Hugging Faces revenue in Million USD compared to its competitors as at 2023

How is Hugging Face different from its Competitors?

  • Unlike many other companies in the AI and ML space, Hugging Face provides a platform where developers can freely share code, models, and datasets (Source). It led to broad adoption both by NLP researchers and practitioners (Source).
  • Hugging Face's funding rounds have seen participation from major tech companies such as Salesforce, Google, Amazon, Nvidia, AMD, Intel, IBM, and Qualcomm. This implies strong backing from industry giants, which is not the case for a lot of AI and ML startups (Source).
  • Hugging Face's valuation is reportedly over 100 times its annualized revenue, reflecting the significant investor interest in AI and ML platforms. This is a stark contrast to many traditional businesses where valuation is often a multiple of the revenue. For example, Hugging Face's revenue run rate is between $30 million to $50 million, while its valuation stands at $4.5 billion (Source).
  • Hugging Face is edging closer and closer to their goal of democratizing machine learning, and making bleeding-edge AI accessible to even companies and teams without the resources to build them from scratch (Source).

How’s Hugging Face different from

  • offers an automated machine learning (autoML) platform for use cases in financial services, healthcare, insurance, manufacturing, marketing, retail, and telecommunications (Source).
  • Over 18,000 organizations use The company’s most popular product offerings serve R and Python developers in the corporate sector (Source).
  • The main differentiating characteristic between and Hugging Face is their business models (Source).
  • Hugging Face is community oriented and offers a two-sided platform for data scientists and engineers to build off each other’s work by sharing their models., on the other hand, sells a product focused on serving corporations instead of a general open-source community of scientists and engineers (Source).
  • Hugging Face clearly has a much more active and passionate community compared to (Source).


Leading the charge in the NLP revolution, Hugging Face is accelerating innovation to make cutting-edge natural language processing accessible to everyone. They have become the driving force behind ground-breaking improvements in models, libraries, datasets, and benchmarks, with an unrelenting dedication to open-source ideals.

Hugging Face offers a comprehensive ecosystem that is tailored to meet the needs of a wide range of users, including researchers who are pushing the boundaries of what is possible, experienced developers creating state-of-the-art applications, and inquisitive students exploring the complexities of artificial intelligence. Their abundance of freely available technology empowers people everywhere, opening up a world of opportunities only constrained by one's creativity.

Hugging Face offers the keys to unleashing the potential of natural language comprehension in a world where knowledge is power, creating a community that thrives on cooperation, discovery, and the common pursuit of excellence. Their purpose is not merely to make strong technology accessible; it is also a catalyst for transformation, making sure that the next wave of innovation boosts the aspirations of researchers, developers, and learners alike and is not limited to the elite. With Hugging Face, a global community is able to shape the future of NLP, and the impact is immense and the adventure limitless.

Jonathan Gillham

Founder / CEO of I have been involved in the SEO and Content Marketing world for over a decade. My career started with a portfolio of content sites, recently I sold 2 content marketing agencies and I am the Co-Founder of, the leading place to buy and sell content websites. Through these experiences I understand what web publishers need when it comes to verifying content is original. I am not For or Against AI content, I think it has a place in everyones content strategy. However, I believe you as the publisher should be the one making the decision on when to use AI content. Our Originality checking tool has been built with serious web publishers in mind!

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