With livestream shopping platform Verb Technology announcing a key tech integration, VERB stock skyrocketed on Friday.

5 Investors Betting Big on Verb Technology (VERB) Stock | InvestorPlace

Verb Technology stock is heading higher on Friday as investors react to the company integrating its service on Facebook and Instagram.

Why Is Verb Technology (VERB) Stock Up 267% Today? | InvestorPlace

New Seamless Native Checkout Process For META Represents Massive Expansion of MARKET.live’s Addressable AudienceLOS ALAMITOS, Calif. and LAS VEGAS, March 15, 2024 (GLOBE NEWSWIRE) -- Verb Technology Company, Inc. (Nasdaq: VERB) (&quot;VERB&quot; or the &quot;Company&quot;), the force behind MARKET.live, the ...

VERB's MARKET.live Launches Facebook and Instagram Social Shopping Technology Integration

Available in both mobile and desktop versions, VERB's applications are offered as a fully integrated suite, as well as on a standalone basis, and include verbLIVE (Interactive Livestream eCommerce and Shoppable Video and Webinar applications), verbCRM (White-labelled Interactive Video-based Customer Relationship Management application), verbTEAMS (a Self On-boarding version of verbCRM with built-in verbLIVE and Salesforce synchronization for small businesses and solo entrepreneurs), verbLEARN (Interactive video and gamified Learning Management System application), and verbMAIL (an interactive video mail solution integrated seamlessly into Microsoft Outlook)

Verb techology

AI-Powered Localization Using an Open-source Project as a Case Study

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vogt, Lars, TIB Leibniz Information Centre for Science and Technology; (2) Konrad, Marcel, TIB Leibniz Information Centre for Science and Technology; (3) Prinz, Manuel, TIB Leibniz Information Centre for Science and Technology. Table of Links Abstract & Introduction Interoperability Semantic interoperability and what natural languages like English can teach us Requirements for successfully communicating terms and statements Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability What makes a term a good term and a schema a good schema? The need for a machine-actionable Rosetta Stone for (meta)data that acts as an interlingua for specifying reference terms and reference schemata to support cognitive and semantic interoperability Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm for (meta)data statements based on English Rosetta Stone and semantic interoperability: Specifying term mappings and schema crosswalks Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Discussion Related work Conclusion, Acknowledgements, & References Requirements for successfully communicating terms and statements What is needed to communicate terms and statements efficiently and reliably? For communication between human beings to be successful, both the sender and the receiver of the information need to share the same relevant background knowledge. For a given , sender and receiver have to share the same lexical competences. They need to share the same (35) and thus knowledge about the of a given term in the form of an that answers the question (36). Inferential lexical competences are needed for the term inferential lexical competences meaning ontological definition ‘What is it?’ human-interpretability of terms. Sender and receiver also need to share the same (35) and thus about the of a given term. A refers to an individual entity (i.e., a particular) and a or to a set of individuals that meet the defining properties of the term, sometimes also called its extension. A or refers to a specific type of action or attribute. Diagnostic knowledge is often communicated in the form of method-dependent recognition criteria, images, or exemplars that answer the question (36), enabling the receiver to use the term correctly in (i.e., the object is given, and the matching term has to be found) or (i.e., the term is given, and the matching object has to be identified) 1 . Referential lexical competences are needed to use a term correctly in different contexts, and thus for the referential lexical competences diagnostic knowledge reference proper name general term kind term verb predicate ‘How does it look, how to recognize or identify it?’ designation recognition tasks human-actionability of terms. Given that the meaning of terms is provided by their ontological definitions, one could argue that terms are only placeholders, i.e., surrogates, for these definitions and thus statements, and that only statements carry meaning. In other words, the communication of meaning requires more than a single term, but rather several terms placed in context and related by predicates. The communication of meaning thus requires statements. This becomes obvious when the positions of terms in a given sentence are changed, such as versus —the same set of terms carries two different meanings. Therefore, for the efficient and reliable communication of statements, the sender and the receiver of the information must share a set of rules and conventions for formulating sentences using terms. Statements carry meaning in addition to the meaning of the terms that compose them. “Peter travels from Berlin to Paris” “Peter travels from Paris to Berlin” But how is the meaning of a statement represented in the human brain? Understanding how the human brain creates cognitive representations of semantic content is a very active area of research (38). The human brain is a highly interconnected complex system that is continually influenced by input signals from the body and the world, so that a given neuron does not function in isolation but is substantially influenced by its neural context (39). It is therefore not surprising that there is evidence for at least two forms of object knowledge representation in the human brain, supported by different brain systems, i.e., motor-sensory-derived and conceptual-cognition-derived knowledge (40,41), and that lexical concepts are stored as patterns of neural activity that are encoded in multiple areas of the brain, including taxonomic and distributional structures as well as experience-based representational structures that encode information about sensory-motor, affective, and other features of phenomenal experience (42). These findings suggest that the cognitive representation of the meaning of a statement is likely to take the form of a complex network of associations, analogous to a multidimensional mind-map. Thus, when attempting to communicate a statement, the sender must first translate this multidimensional mind-map into a one-dimensional sequence of terms, i.e., a sentence. This translation step is supported by a set of syntactic and grammatical conventions, shared by the sender and the receiver, for formulating sentences using terms. According to the of linguistics (43,44), the main verb of a statement, together with its auxiliaries, forms the statement’s predicate. A predicate has a that determines the number and types of it requires to complete its meaning. may be additionally related to the predicate, but they are not necessary to complete the predicate’s meaning. Adjuncts provide optional information, such as a time specification in a parthood statement. Therefore, every statement has a subject phrase as one of its arguments, and can have one or more object phrases as further arguments and additional adjuncts, depending on the underlying predicate. predicate-argument-structure valence arguments Adjuncts In the syntax of a statement, each argument and adjunct of a predicate can be understood as having a specific with a specific . This is related to Kipper et al.’s (45) verb lexicon , where they extend Levin verb classes (46) to include abstract representations of syntactic frames for each class, with explicit correspondences between (i.e., positions in a syntax tree) and the (i.e., thematic roles sensu (45)) that these positions express (Fig. 2B). Each verb class lists the semantic roles allowed by the predicate-argument-structure of the instances of the class and the basic syntactic frames shared by the instances. position semantic role VerbNet syntactic positions semantic roles The list of arguments of a predicate-argument-structure can be described by a list of taken from a set of predefined possible labels (e.g., AGENT, PATIENT, THEME, etc.), and the syntactic frames are represented by an ordered sequence of such thematic labels. The thematic labels function as descriptors of semantic roles that are mapped onto positions in a given syntactic frame (45). thematic labels In , Palmer et al. (47) define semantic roles at the level of individual verbs by numbering the arguments of the verb, with the first argument generally taking the semantic role of an AGENT and the second argument typically, but not necessarily, taking the semantic role of a PATIENT or a THEME. For higher numbered arguments, there are no consistently defined roles. PropBank Syntax trees, with their different syntactic positions and associated semantic roles, contribute substantially to the meaning of their sentences, and they are used to translate a web of ideas in the mind of a sender into a string of words that can be understood by a receiver to translate it back into a web of ideas (48). The clearer the semantic roles of the different positions are, the easier it is for a human being to understand the information. In a sense, we can understand syntax trees as the first knowledge graphs created by humans, and their use seems to be quite straightforward, providing a structure that is interoperable with human cognitive conditions, thus satisfying the need for cognitive interoperability. To sum it up, whenever information needs to be communicated efficiently and reliably, the sender and receiver of the information must not only share the same inferential and referential lexical competences regarding the terms used in their communication, but also the same set of syntactic and grammatical conventions for formulating sentences with these terms, resulting in the same syntax tree in both sender and receiver. The knowledge shared by the sender and the receiver of the message ensures that the message is: 1. : The receiver must be able to in a message, i.e., where a term or a statement begins and where it ends; readable identify the basic units of information 2. : The receiver must be able to understand each term and statement and thus recognize its ; interpretable meaning 3. actionable: The receiver must be able to correctly apply terms and statements from the message, including correctly designating or recognizing of terms and correctly placing a statement in . referents context

Requirements for Successfully Communicating Terms and Statements

Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm 

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References Abstract Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82. Memes, codemixed, multimodal, Hindi-English Keywords 1. Introduction The term meme is derived from the Ancient Greek word mimema, meaning imitated thing, which comes from the verb mimeisthai, meaning to mimic. The term was coined by Richard Dawkins, a British evolutionary biologist, in his book The Selfish Gene [1]. Dawkins used the concept of a meme to explain the spread of cultural phenomena and ideas, drawing parallels between memes and genes. Dawkins provided examples of memes such as melodies, catchphrases, fashion, and arch-building technology in his book. Interestingly, the word meme is a self-describing term, also known as autological, meaning that it is a meme itself. The reach of online content is vast and has the capacity to reach a massive viewership. Memes propagate through multiple mediums including social media, messaging, apps, and emails. An article on memes quotes "When you plant a fertile meme in my mind, you literally, parasitize my brain" [2]. This quote indicates the impact of memes on a viewer’s mind. It spreads to others who find it motivating, offensive, amusing, or relatable in some ways [3]. Memes are used to convey an opinion. When a person comes across a meme that they find relatable, they often share it with others who they think would find relatable. Different people interpret memes differently which means what can be offensive for one person might not be for other. This difference in interpretation is a result of cultural, gender, and demographic differences. The memes can also be used to spread offensive and hateful content [4]. Identifying and halting the dissemination of hateful memes is an arduous undertaking for both human beings and AI models, as it requires comprehending the intricate subtleties and socio-political contexts that form their interpretation. The deficiencies of current hate speech moderation techniques highlight the pressing need to enhance the effectiveness of automated hate speech detection. Given their subtlety and multi-modal nature, memes pose a more challenging issue to address compared to only text. In this paper, we present the findings of the Memotion 3 shared task, where participants were provided with an annotated dataset of 10k memes [5], and were tasked with detecting the sentiment, emotion and emotion intensity of the meme. Unlike the previous iteration of memotion [6, 7] which provided English memes, current iteration studies codemixed HindiEnglish (Hinglish) memes. The paper is organized as follows: we describe the related work and the task in section 2 and 3 respectively; Section 4 contains the details of the dataset we collected for Memotion analysis: Memotion 3.0; followed by a brief description of baseline models and their results in 5. We conclude and mention some possible future works in section 6.

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish - Abstract & Introduction

Over the past few months, I’ve been on a deep dive into the world of customer discovery and the Jobs to Be Done (JTBD) framework. This comprehensive, step-by-step guide is the result of extensive research, several experiments, and even a custom GPT designed specifically for JTBD (link in the resources section). First things first, what exactly is Jobs To Be Done (JTBD)? The JTBD framework is an approach that shifts the focus from products to the customer's needs. It centers around understanding the 'job' or task that a customer needs to accomplish using a product or service. In essence, the customer 'hires' the product or service to complete this job. The JTBD framework's advantage lies in its ability to concentrate on addressing problems rather than focusing on any specific solutions. To illustrate this concept, here are two simple examples: Ride-Sharing Services (e.g., or ): Uber Lyft A customer uses a ride-sharing app to book a car. Traditional View: The customer's job is to reach a destination quickly and comfortably, avoiding the hassles of driving or parking. The ride-sharing service is hired not just for transport but for convenience and efficiency. JTBD View: Coffee Shops (e.g., Starbucks): Customers visit coffee shops to purchase coffee. Traditional View: While buying coffee is one aspect, customers also 'hire' the coffee shop as a place to meet friends, work, or relax. The job extends to seeking a pleasant experience and environment. JTBD View: In each example, JTBD shifts the focus from the product itself (car rides, coffee) to what the customer is really seeking to achieve with these products or services in their daily life. Three concepts to understand JTBD To fully grasp JTBD, we need to define three key concepts: Market Tony Ulwick redefines a market not as a vertical, geography, or technology but as a group of people and the job they are trying to get done. It's about discerning what customers truly seek to accomplish with a product or service. Jobs In JTBD, a 'job' is a task or goal a customer aims to achieve in a particular situation by using a product or service. It's action-oriented, encompassing functional, emotional, and social dimensions. Jobs are typically phrased as action-oriented tasks. They should start with a verb and clearly describe what the customer is trying to accomplish. For example: "Maintain personal health and fitness." "Plan and enjoy a vacation." "Enhance professional skills." Outcomes These are the desired end-states or goals customers aim for when hiring a product or service for a job. Outcomes are specific, and measurable, and define what success looks like for the customer. Phrasing outcomes should include metrics or standards used by customers to evaluate success. For example: "Minimize the time required to plan a vacation itinerary." "Maximize the reliability of maintaining a regular exercise routine." "Ensure the latest industry knowledge is incorporated into skill enhancement." How to Apply Jobs-To-Be-Done in 4 steps Step 1: Initial Market Exploration Begin by exploring potential markets based on preliminary insights or hypotheses about customer needs. This can involve exploratory research, such as interviews with potential users, market analysis, and studying existing data. From this exploratory research, identify a range of jobs that potential customers might be trying to get done. These jobs don't need to be detailed at this stage but should give a broad sense of the tasks or goals your potential customers are trying to achieve. Based on these identified jobs, narrow down to a specific market that seems most viable or underserved. This is where you define your market in terms of a specific group of people and the core functional job they are trying to get done. 💡 For example, imagine that you are a product manager at Airbnb. By looking at the data and market trends, you notice that a sizable part of your user base is booking stays for longer periods (e.g. 1 month or longer) and are traveling solo. You decide therefore to explore this potential market. So instead of saying "We are targeting the travel industry" (a vertical), say "We are targeting solo travelers looking to connect with local cultures" (a group of people and their emotional job) or "We are targeting solo travelers looking for long term temporary accommodation" (a functional job). Step 2: Collect all the customer's Jobs and Outcomes The second step in applying the JTBD framework is to collect all the jobs customers are trying to get done when interacting with your product and the outcomes they want to achieve for each job. The end result should be two extensive lists, one for jobs and one for outcomes, phrased as we have seen in the examples above. The best way to collect jobs and outcomes at this stage is through qualitative user interviews, to understand what your users' goals are and what they are trying to accomplish. But rather than spending countless hours in user interviews, here is a hack Tony Ulwick suggests to speed things up and make the process more efficient: Take the top 2–3 experts in your market and organize an interview with them at the same time, either virtually or in person. Divide the session into 2 slots (either the same day or different days) - one for jobs and one for outcomes. For each session, try to get the experts to agree on: A list of the jobs they are trying to get done How to formulate the job statements A list of outcomes they want to achieve for each job How to formulate the outcome statements 💡 Here is how I would apply the framework following our example of a product manager at Airbnb exploring the market of solo travelers/digital nomads looking for accommodation: Identify top contributors in digital nomad online groups and communities (e.g. Facebook, Reddit, NomadList) Invite 3 of them for 2 rounds of interviews on separate days, with all of them together In the first round, focus on capturing jobs they try to get done when booking accommodation. Ask questions like: "Can you walk me through finding accommodation as a digital nomad?" "What factors do you consider when choosing where to stay?" 4. Turn job findings into job statements (see table below) 5. In the second round with the same experts, focus on outcomes wanted for each job. Ask questions like: "Let's discuss finding budget-friendly places with good connectivity. What does success look like here?" "How do you know if an accommodation meets your needs?" "What factors determine a successful stay?" 6. Turn outcome findings into outcome statements (see table below) 7. Interview more people to confirm findings or get additional insights if needed. By the end, I should have properly stated jobs and outcomes lists like: Side note 1: During this process, you may discover that your product addresses only part of the job. This is common and highlights potential market opportunities. Side note 2: I can provide more specifics on user interviews for JTBD if helpful. Leave me a comment. You might also want to check this that illustrates how I structure user interviews. article Step 3: Uncover Unmeet Needs After compiling your jobs and outcomes statements, the next step is to identify your . The objective here is to determine which jobs are of the utmost importance to most of your audience. customers’ unmet needs To accomplish this, design a that lists each job along with its associated outcomes, asking participants to rate them for importance and satisfaction. Here’s a suggested format: survey a. When attempting to [job], how crucial is it for you to [outcome statement]? Options range from ‘Not important at all’ to ‘Extremely important.’ b. How satisfied are you with your ability to [outcome]? Options range from ‘Not satisfied at all’ to ‘Extremely satisfied.’ Example: 💡 Job: Find accommodations suitable for long-term stays. When trying to find accommodations suitable for long-term stays, how important is it to you that ? Question a: you minimize the time required to identify accommodations offering long-term stay discounts for extended travels Not important at all Not important Somewhat important Important Very important Extremely important How satisfied are you with your ability to ? Question b: minimize the time required to identify accommodations offering long-term stay discounts for extended travels Not satisfied at all Not satisfied Somewhat satisfied Satisfied Very satisfied Extremely satisfied Survey Sample Size and Conditions: B2B: 120–180 clients B2C: Around 600 for a single product; 1200 for a portfolio Survey Duration: Approximately 20 minutes, with participant compensation recommended. Step 4: Prioritize Unmeet Needs Upon receiving the survey results, focus on prioritizing the unmet needs. Look for needs rated as very or extremely important but not very or extremely satisfied. 💡 Using our Airbnb example, let’s visualize the survey results on a graph. Each point represents a job, plotted according to its importance and satisfaction ratings. The red dashed lines at 75% help categorize jobs based on their levels of importance and satisfaction. Next, calculate the for each job using the formula: Opportunity Score Opportunity Score = Importance+(Importance−Satisfaction) In our example from the graph, the following jobs stand out: (importance 70% satisfaction 40%) Long-term Stay Discounts : (importance 85% satisfaction 50%) Reliable Internet Connection : (importance 65% satisfaction 45%) Social Interaction Opportunities (importance 55% satisfaction 40%) Affordable Comfort When we calculate the opportunity score, here are our top priorities: (Opportunity Score: 120) Reliable Internet Connection (Opportunity Score: 100) Long-term Stay Discounts (Opportunity Score: 85) Social Interaction Opportunities (Opportunity Score: 70) Affordable Comfort And now? What happens next With your priorities established, it’s time to shift from problem identification to solution creation. Focus on developing solutions for the top-priority jobs starting with “Reliable Internet Connection,” then moving to “Long-term Stay Discounts” and so forth. The outcome statement of each job will be your measure of success or noth star metric. For jobs with more complex needs, like ‘Social Interaction Opportunities,’ investigate in greater detail to understand the specific nuances and contexts in which they are performed. Also published here. 📚 Helpful resources Access to the t (Notion Template): a guide to help you step-by-step in applying JTBD specifically to your needs (requires a subscription to ChatGPT Plus) JTBD custom GPT + the JTBD checklis Watch my interview with Tony for practical applications of JTBD (including his experience applying the framework at Twitter) https://www.youtube.com/watch?v=fOxxwNGe9I8&embedable=true

Uncover Your Customers' Hidden Needs In 4 Steps with Jobs-To-Be-Done + Access Custom GPT

The Art of Naming Conventions in JavaScript Development

Are you tired of grammar mistakes ruining your writing? Do you want to write with confidence, knowing your work is error-free? If so, you've come to the right place. In this article, we'll explore the best Spot and Solve Grammar Mistakes Solution and provide you with expert insights and practical tips to enhance your . writing skills Introduction Welcome to the world of spot-on writing, where are a thing of the past. Whether you're a student, a professional, or just someone who loves to write, it's essential to convey your thoughts clearly and effectively. Spot and Solve Grammar Mistakes Solution is the key to achieving this. grammar mistakes In this article, we will take you on a journey through the art of error-free writing, sharing the most efficient techniques to spot and solve grammar mistakes. From common errors to advanced proofreading tips, we've got you covered. So, grab your virtual red pen, and let's dive into the world of impeccable writing. Understanding Grammar Mistakes Grammar mistakes can be pesky little critters that sneak into our writing when we least expect them. They can undermine your credibility and make your content hard to read. Let's explore some of the common grammatical errors that can plague your writing. The Dreaded Apostrophe Catastrophe Apostrophes can be confusing, but they are vital for indicating possession and contractions. It's crucial to know when and where to use them. can change the entire meaning of a sentence. Incorrect apostrophe usage Subject-Verb Agreement: A Balancing Act Subject-verb agreement ensures that your sentences are structurally sound. When your subject and verb don't agree in number, your writing can sound awkward and incorrect. Punctuation Predicaments Misusing punctuation marks, like commas and semicolons, can lead to misunderstandings. The correct placement of these marks can significantly impact the clarity of your writing. The Art of Pronoun Usage Using the right pronouns is a must in writing. Incorrect pronoun usage can lead to confusion or even offense. We'll delve into the intricacies of pronouns and their usage in writing. Spellcheck Isn't Foolproof Relying solely on spellcheck can be a mistake. It may not catch contextual errors or misused words. We'll discuss the limitations of spellcheck and how to overcome them. Spot and Solve Grammar Mistakes Solution Now that we've identified the , let's move on to the exciting part: the solutions. We've compiled a list of the . common grammar mistakes most effective strategies to spot and solve grammar mistakes in your writing The Power of Proofreading Proofreading is your best friend when it comes to spotting grammar mistakes. Take your time to review your work carefully. Read it aloud, if possible, to catch errors you might have missed. Embrace Technology In this digital age, grammar-checking tools and software have become indispensable. We'll introduce you to some of the best grammar-checking tools available and how to make the most of them. Best Grammar Checker Websites: Grammarly MyEssayWriter.ai Grammar Checker Ginger Software LanguageTool ProWritingAid Learn how these tools can help you spot and solve grammar mistakes with ease. Please note that while all these tools are excellent, many consider to be the best grammar checker website. MyEssayWriter.ai Seek a Second Opinion Sometimes, a fresh pair of eyes can spot mistakes you've overlooked. We'll discuss the benefits of peer editing and how it can enhance the quality of your writing. Learn from Your Mistakes No one becomes a grammar expert overnight. We'll provide resources and tips for improving your grammar skills over time, so you make fewer mistakes in the first place. Grammar Courses and Workshops If you're committed to becoming a grammar guru, enrolling in a could be a game-changer. We'll point you in the right direction to find the perfect course for your needs. grammar course or workshop Be Patient and Persistent Spotting and solving grammar mistakes is an ongoing process. Stay patient and persistent, and your writing will improve over time.

VERB TECHOLOGY

verb.tech

-- employees

Since n.d.

COMPANY RANKING

VERB TECHOLOGY

EVERGREEN INDEX #11567

Representing this company? Click here to claim and customize this page!

Verb techology WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

VERB TECHOLOGY

verb.tech

-- employees

Since n.d.

COMPANY RANKING

VERB TECHOLOGY

EVERGREEN INDEX #11567

RELATED COMPANIESRANK

STORIES ABOUT VERB TECHOLOGY

Representing this company? Click here to claim and customize this page!

Verb techology WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!