Since the launch of the Cisco Spark for Developers program in December 2015, we have seen an explosion of developers building innovative and unique solutions on top of Cisco Spark. Many of our customers use the Cisco Spark Depot to discover turnkey integrations and bots to enhance their Spark experience. At the same time, we have seen huge demand for bots built custom to the needs of the organization and deployed in environments controlled by that organization. In fact, approximately 50 percent of all new chatbots used on Cisco Spark today are developed and hosted directly by our customers. The reason for this surge in custom bots is primarily because the line of business applications that our enterprise customers have deployed are often heavily customized from a standard offering. This could be something as simple as adding new custom field mappings but in many cases, this involves bespoke API changes. In addition to customization, certain customers choose to host their chatbots in their preferred cloud (public or private), or even on premise. We want the process of building custom bots on Cisco Spark to be as frictionless as possible, so today we are announcing a new initiative called Cisco Spark Starter Kits. Cisco has worked with the Cisco Spark Ambassador community to provide Open Source Starter Kits for some of the most popular lines of business applications, all of which can be freely customized and deployed in your preferred cloud. The aim of these Starter Kits is to provide Spark developers like you access to the source code of well-written Bots/Integrations that work with the standard configuration of popular third-party applications. You can then take the source code and customize it to suit your purposes. All Starter Kits are licensed under the MIT License, so they are as permissible as possible, allowing you to make changes and use them however you wish. Each Starter Kit Listing consists of a GitHub repository link to the source code. The language, bot framework, and storage technologies are also outlined in each listing. To make it easy for you to get up and running, each Starter Kit has a pre-composed Dockerfile and a Deploy to Heroku button, enabling you to deploy a Starter Kit in less than 10 minutes. Initially there are 12 Starter Kits available, these range from popular enterprise applications like Service Now and Jira to Polling and HR Onboarding use cases. CONTRIBUTING: As a member of the Cisco Spark for Developers community, we encourage you to get involved with Open Source. If you develop an enhancement for any of the Starter Kits which you believe would benefit the community, and you want to share it, we'd love to see you submit a pull request. Alternatively, if you want to develop something new and want to list it as a Starter Kit, you can review the contributing guidelines here. A WORD ON SUPPORT: Open Source Starter Kits are not a Cisco product, but a collection of awesome projects which have been built by your peers, namely, the Spark Developer community. They are provided 'as is', so if you need support or have a feature request, you can raise it in the issues section of the respective GitHub repository. We would ask that you don't raise issues or feature requests with the Cisco Spark for Developers support team, as they will direct you to the relevant repository. Go forth and prosper! - Jonathan Field, Business Development Manager
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We’re very excited to announce one of the newest integrations with Cisco Spark: API.AI. With this integration, developers can use Spark and API.AI to configure their Spark bot with customized, nuanced, human language that enables users to interact more easily and effectively with their bot. With natural language understanding, API.AI lets developers create simple, fluid communications that increase efficiency and improve self-service. Let’s take a look at how this works. Say you want to configure your Spark bot to help your users manage their schedules and set reminders. There are lots of ways a person could ask a bot to set a reminder. Using API.AI, you can create a category defining your request type. You might call this request type “Reminders.” You can then use API.AI to establish some of the different phrases and language that might be used when a user makes a request for a schedule reminder. For example, a user might type: Remind me to call Evan at 3 p.m. Set a meeting with Evan at 3 p.m. Appointment with Evan at 3 p.m. Set a reminder for me to call Evan at 3 p.m. After you’ve filled out the possible ways a person might request to set a reminder, you can program a specific action so the bot knows what to do when it encounters this request. For instance, any of the above requests may prompt the bot to send a reply to the user that says, “I have set an automatic reminder for this request. Is there anything else I can help you with?” One of the key benefits of the API.AI integration with Spark is that it creates an outstanding, easy user experience. Users can interact with the Spark bot intuitively without having to memorize things like slash commands, and developers can have the bot up and running with just one click!
Using Api.ai with Cisco Spark from Cisco Spark for Developers on Vimeo.
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