Pipe17 Solutions for Providers If your company provides third-party logistics, warehouse management, drop shipping, SaaS applications or other fulfillment services for merchants and brands, then Pipe17 has a solution for you. Land More Deals Pipe17 solutions seamlessly connect ERPs, giving ...

Solutions for Providers - Pipe17

See a product demo, reach out to find out how to be a partner or get in contact with us for general questions about your ecommerce operations.

Contact the Pipe17 Company - Pipe17

Pipe17 offers a career working on the future of ecommerce operations, solving challenging problems with fun, hardworking people in a relaxed environment.

Pipe17 Careers, Jobs and Job Roles - Pipe17

Pipe17 is a better way to connect and integrate your carts, 3PLs, ERPs or marketplaces with an ecommerce application that helps you manage your operation.

Pipe17 - About Pipe17 Team, Company Leadership

Take back control of your ecommerce operations with Pipe17. Get every order to the right channel with bespoke routing that&#x27;s fast and simple to connect and manage.

Pipe17 Pricing - Software and Integration Costs - Pipe17

Turbocharge Your eCommerce Business Unify and automate all ecommerce order flows easily, rapidly, and cost-effectively. Confronting the OrderOps Problem Missed sales, out-of-stocks, overstocks, expedites, late deliveries, dissatisfied customers—they drain profits and plague every ecommerce ...

Why Pipe17 - Pipe17

Pipe17 is the fast, easy way to make omnichannel order flows touchless, cost efficient, and effortlessly scalable.

Fully Automated Omnichannel Order Operations - Pipe17

Pipe17 solves ecommerce fragmentation, integrating selling channels to your ERPs, 3PLs and everything else. No code and no IT required.

Ecommerce ERP Integration Platform - How it Works - Pipe17

Get fast affordable ecommerce platform integration to 3PLs, ERPs, shopping carts, Marketplaces and more with added visiblity and automation

Ecommerce Platform Integrations for ERP, 3PLs and more - Pipe17

The prevailing view in the e-commerce industry, the co-founders explained, is that to fix a problem e-commerce players should purchase another application. Pipe17 thinks that most e-commerce companies probably have enough tooling, and that they instead need to get their existing tooling to ...

Pipe17 closes $8M to connect a range of e-commerce tools without ...

Pipe17

pipe17

- HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ ” award, featuring the most promising and innovative companies in the Seattle startup ecosystem. Seattle, WA - May 23,2023 Startups of the Year The list, compiled by the HackerNoon team, showcases that are making significant contributions to the Seattle economy and driving innovation in their respective industries. 200+ startups https://www.youtube.com/watch?v=ZqIW54yMsNc&t=2s&embedable=true Nominations are open. You can nominate your company as the . startup of the year in Seattle here The for Startups of the Year 2023 include: nominated Startups in Seattle Xealth Diagrid Twelve Labs XetData Coda Derisk pyq Logixboard Otto Robotics Protect AI Augment AI Proxi XetData Bungee Tech InsightStudios Pipe17 Coffee Meets Bagel Symbl.ai DefenseStorm Ozette Karat Kevala Moment Veeve Observa Cakework Forum3 Hal9 MotherDuck Read SBL Spiral “We are thrilled to recognize the top startups in Seattle and celebrate their achievements,” said . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO is a community-driven voting platform that recognizes startups that are changing technology and the world for the better. Starting May 8th, 2023, the internet can nominate and vote for the best ventures in their city. The voting period will remain open until December 31, 2023, and winners will be announced in January 2024 after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 In its , a total of 44,427 startups were nominated in 16 different regions, and the community cast 234,344 votes during a 6-month long campaign in favor of budding companies that were growing and trending across every major city in the world. The received perks from sponsors and a free from HackerNoon. previous iteration 1,382 winners tech company news page As a proponent of the open data movement, HackerNoon releases the unique social data gathered from Startups of the Year on GitHub as “ ” for valuable insights. /where-startups-trend Apart from validation from the world's greenest tech publication, all nominees relevant to their domain, region, and funding. They will also receive an and a free version of their very own on HackerNoon. These pages include business overviews (such as head count, founding year, bio, social links, and domain ranking) coupled with dynamic updates every time the company is mentioned in HackerNoon stories or relevant headlines around the web. get free interviews amazing discount package designed for their startup Tech Company News Page People can , and vote once per day per award for who they think should be Startup of the Year all the way up to December 31st. until June 8th nominate their favorite startups In addition to the award itself, winners receive exclusive interviews, an exclusive NFT from HackerNoon, and a .Tech Domain for life. About HackerNoon HackerNoon is a technology publication that aims to empower developers, entrepreneurs, and tech enthusiasts to share their knowledge and expertise with a global audience. With a readership of over 4M+ monthly visitors, HackerNoon provides a platform for startups and innovators to showcase their work and connect with a vibrant community of like-minded individuals. Did you know that companies that actively engage in blogging experience a whopping 67% increase in leads? Take advantage of HackerNoon's ' ' to kickstart your own company's blogging journey. Startups Package Startups Of The Year 2023 is sponsored by . .Tech Domains For more information about the “Startups of the Year 2023” list and the nominated companies, please visit our . FAQ page Nominations are open. You can nominate your company as the . startup of the year in Seattle here

Startups of The Year 2023: 200+ Startups Nominated in Seattle

This is a derived work from the research paper Game Engine Learning from Video . The credit goes to Matthew Guzdial, Boyang Li, Mark O. Riedl from Georgia Institute of Technology. In the following video Mario is played by an artificial intelligent agent, which uses the process of neural evolution to play the game like a master player. Original Mega Man game play video on the left. Cloned game engine on the right. Image from Georgia Tech Overview The system scans each frame to find out the list of objects present in them, later they run an algorithm across adjacent frames to see how objects change between frames. Finally an engine search algorithm is run when the change detected is more than a set threshold. Parsing Frames The system needs two inputs, a sprite pallet(set of characters and objects in a game ) and a gameplay video. Using these inputs and OpenCV(Computer Vision Library) we can understand the number of sprites and their spatial positions in respective frames. Given sequence of frames of sprites and their locations. An algorithm is run to match each sprites to its closest neighbour in the next frame. If the count of sprites doesn’t match , a blank sprite is created to match the remaining. This happens in some cases , ex: when Mario jumps over an enemy and destroys it. Finally parsing is completed when these sprite representations are converted to a list of facts. Each of these fact types require a pre-written function to derive it from a given input frame. Here are the list of fact types. Animation This is a collection of all the sprite images according to their original filename, width an height. Ex: If an image of “mario1.png” with height size [26,26] was found at the position 0,0 , the animation fact would be {mario1,26,26} Spatial This is the sprite’s filename with the x,y coordinates in the frame. RelationshipX This is the relationships between pair of sprites in the x-dimension. Ex: (mario1, mario1’s closest edge to pipe1, 3px, pipe1, pipe1’s closest edge to mario1). This allows the system to learn collision rules like Mario’s velocity changes to 0, when it hits a pipe) RelationshipY The same fact mentioned above in the y-dimension like when Mario hits a brick. VelocityX This records sprite’s velocity in x-dimension , it compares the previous frame and the next frame. Ex: If Mario is at [0,0] in frame 1 and [10,0] in frame 2 , then the fact would be VelocityX: {mario,10} VelocityY The same fact mentioned above in the y-dimension. CameraX This stores how far the camera goes in a level. Engine Learning The engine learning approach tries to make a game engine which can predict the changes observed in the parsed frames. A game engine is a set of rules with each rule having IF and THEN . Ex: IF Mario collides with a pipe THEN change the velocity-x to 0. This approach consists of frame scan algorithm and engine search algorithm. The frame scan algorithm scans through the parsed frames and begin to search for rules that justifies the difference between predicted frame and actual frame. If a game engine is found which reduces the difference to an extend, then another scan is made to make sure the new engine can accurately predict prior frames. Frame Scan Algorithm engine = new Engine()currentFrame = frames [0] while i=1 to frameSize do Check if this engine predicts within the threshold frameDist = Distance(engine, currentFrame, i + 1)if frameDist < threshold thencurrentFrame = Predict(engine, currentFrame, i + 1)continue Update engine and start parse over engine = EngineSearch(engine, currentFrame, i + 1)i=1currentFrame = frames[0] The frame scan algorithm takes a set of parsed frames and a threshold as input and outputs a game engine. The distance function gives a pixel by pixel distance between the actual frame and the predicted frame . If the distance is less than set threshold, the predict function returns the closest frame to the actual frame. Engine Scan Algorithm closed = []open = PriorityQueue()open.push(1,engine) while open is not empty do node = open.pop() if node[0]<threshold thenreturn node [1] engine = nodeclosed.add(engine) for Neighbor n of engine doif n in closed thencontinuedistance = Distance(engine, currentFrame, goalFrame)open.push(distance + engine.rules.length, n) The engine scan algorithm makes a search to find a set of rules that creates the predicted frame within some threshold to the actual frame. This is done by generating neighbours for a given engine. Neighbours are generated in the following ways Adding rules This requires picking up a pair of facts of same kind, one from the current frame and the other from the goal frame. Ex: Current frame has a velocityX fact of {mario1,5} and the goal frame has a fact of {mario1,0}. These pair of fact represents the change that rule handles. Here Mario’s velocity dropped. The other facts in the current frame make the initial condition for this rule. Though these will contain conditions that are not required for the change to happen. For Ex: Mario’s velocity dropped because his spatial distance between the pipe was too close. This might also include the condition of a cloud present above him. These set of initial conditions is minimised by modifying rule’s condition facts. Modifying rule’s condition facts The set of condition for a given rule is minimised by taking common conditions between an existing rule’s conditions and current set of conditions. If this neighbour reduces the pixel distance of predicted and the goal frame , then this is likely to be chosen than another neighbour who adds a new rule. This result in giving preference for smaller and generic engines. Modifying a rule to cover an additional sprite This works like the above one expect the changes it can make can be extended. For Ex: When Mario jumps on an enemy it vanishes or technically it goes from Animation Fact with values to one without. So one single rule can handle multiple cases. Modifying a rule into a control rule This changes the rule from being handled normally to a control rule. These are the cases of rules that player makes a decision upon such as the inputs that control the character (left, right, jump). Here is an example of the output of a learned game engine. Follow Hackernoon and me ( ) for more stories. I am also writing a book to raise awareness on the Blue Whale Challenge, which has claimed lives of many teenagers in several countries. It is intended to help parents understand the threat of the dark web and to take actions to ensure safety of their children. The book is available for pre-order on Amazon. The title will be released on 20th of this month. Febin John James Fight The Blue Whale

Stories, News & Mentions about PIPE17

Pipe17’s Stories on HackerNoon

Pipe17's News & Mentions