Tag Archives: getting started

Welcome to Ghost

Post Syndicated from Ghost original https://yovko.net/welcome/

A few things you should know

  1. Ghost is designed for ambitious, professional publishers who want to actively build a business around their content. That’s who it works best for.
  2. The entire platform can be modified and customised to suit your needs. It’s very powerful, but does require some knowledge of code. Ghost is not necessarily a good platform for beginners or people who just want a simple personal blog.
  3. It’s possible to work with all your favourite tools and apps with hundreds of integrations to speed up your workflows, connect email lists, build communities and much more.

Behind the scenes

Welcome to Ghost

Ghost is made by an independent non-profit organisation called the Ghost Foundation. We are 100% self funded by revenue from our Ghost(Pro) service, and every penny we make is re-invested into funding further development of free, open source technology for modern publishing.

The version of Ghost you are looking at right now would not have been made possible without generous contributions from the open source community.

Next up, the editor

The main thing you’ll want to read about next is probably: the Ghost editor. This is where the good stuff happens.

By the way, once you’re done reading, you can simply delete the default Ghost user from your team to remove all of these introductory posts!

Writing posts with Ghost ✍️

Post Syndicated from Ghost original https://yovko.net/the-editor/

Just start writing

Writing posts with Ghost ✍️

Ghost has a powerful visual editor with familiar formatting options, as well as the ability to add dynamic content.

Select your text to add formatting such as headers or to create links. Or use Markdown shortcuts to do the work for you – if that’s your thing.

Writing posts with Ghost ✍️

Rich editing at your fingertips

The editor can also handle rich media objects, called cards, which can be organised and re-ordered using drag and drop.

You can insert a card either by clicking the  +  button, or typing  /  on a new line to search for a particular card. This allows you to efficiently insert images, markdown, html, embeds and more.

For example:

  • Insert a video from YouTube directly by pasting the URL
  • Create unique content like buttons or forms using the HTML card
  • Need to share some code? Embed code blocks directly
<header class="site-header outer">
    <div class="inner">
        {{> "site-nav"}}
    </div>
</header>

It’s also possible to share links from across the web in a visual way using bookmark cards that automatically render information from a websites meta data. Paste any URL to try it out:

Ghost: The #1 open source headless Node.js CMS
The world’s most popular modern open source publishing platform. A headless Node.js CMS used by Apple, Sky News, Tinder and thousands more. MIT licensed, with 30k+ stars on Github.
Writing posts with Ghost ✍️

Working with images in posts

You can add images to your posts in many ways:

  • Upload from your computer
  • Click and drag an image into the browser
  • Paste directly into the editor from your clipboard
  • Insert using a URL

Image sizes

Once inserted you can blend images beautifully into your content at different sizes and add captions and alt tags wherever needed.

Writing posts with Ghost ✍️

Image galleries

Tell visual stories using the gallery card to add up to 9 images that will display as a responsive image gallery:

Image optimisation

Ghost will automatically resize and optimise your images with lossless compression. Your posts will be fully optimised for the web without any extra effort on your part.

Next: Publishing Options

Once your post is looking good, you’ll want to use the publishing options to ensure it gets distributed in the right places, with custom meta data, feature images and more.

Publishing options

Post Syndicated from Ghost original https://yovko.net/publishing-options/

Distribute your content

Publishing options

Access the post settings menu by clicking the settings icon in the top right hand corner of the editor and discover everything you need to get your content ready for publishing. This is where you can edit things like tags, post URL, publish date and custom meta data.

Feature images, URL & excerpts

Insert your post feature image from the very top of the post settings menu. Consider resizing or optimising your image first to ensure it’s an appropriate size. Below this, you can set your post URL, publish date and add a custom excerpt.

Tags & authors

You can easily add multiple tags and authors to any post to filter and organise the relationships between your content in Ghost.

Structured data & SEO

There’s no need to hard code your meta data. In fact, Ghost will generate default meta data automatically using the content in your post.

Alternatively, you can override this by adding a custom meta title and description, as well as unique information for social media sharing cards on Facebook and Twitter.

It’s also possible to set custom canonicals, which is useful for guest posts or curated lists of external links.

Ghost will automatically implement structured data for your publication using JSON-LD to further optimise your content.

{
    "@context": "https://schema.org",
    "@type": "Article",
    "publisher": {
        "@type": "Organization",
        "name": "Publishing options",
        "logo": "https://static.ghost.org/ghost-logo.svg"
    },
    "author": {
        "@type": "Person",
        "name": "Ghost",
        "url": "http://demo.ghost.io/author/ghost/",
        "sameAs": []
    },
    "headline": "Publishing options",
    "url": "http://demo.ghost.io/publishing-options",
    "datePublished": "2018-08-08T11:44:00.000Z",
    "dateModified": "2018-08-09T12:06:21.000Z",
    "keywords": "Getting Started",
    "description": "The Ghost editor has everything you need to fully optimise your content. This is where you can add tags and authors, feature a post, or turn a post into a page.",
    }
}
    

You can test that the structured data schema on your site is working as it should using Google’s structured data tool.

Code injection

This tool allows you to inject code on a per post or page basis, or across your entire site. This means you can modify CSS, add unique tracking codes, or add other scripts to the head or foot of your publication without making edits to your theme files.

To add code site-wide, use the code injection tool in the main admin menu. This is useful for adding a Google Analytics tracking code, or to start tracking with any other analytics tool.

To add code to a post or page, use the code injection tool within the post settings menu. This is useful if you want to add art direction, scripts or styles that are only applicable to one post or page.

Next: Admin settings

Now you understand how to create and optimise content, let’s explore some admin settings so you can invite your team and start collaborating.

Managing admin settings

Post Syndicated from Ghost original https://yovko.net/admin-settings/

Make your site private

Managing admin settings

If you’ve got a publication that you don’t want the world to see yet because it’s not ready to launch, you can hide your Ghost site behind a basic shared pass-phrase.

You can toggle this preference on at the bottom of Ghost’s General Settings:

Managing admin settings

Ghost will give you a short, randomly generated pass-phrase which you can share with anyone who needs access to the site while you’re working on it. While this setting is enabled, all search engine optimisation features will be switched off to help keep your site under the radar.

Do remember though, this is not secure authentication. You shouldn’t rely on this feature for protecting important private data. It’s just a simple, shared pass-phrase for some very basic privacy.

Invite your team

Ghost has a number of different user roles for your team:

Contributors
This is the base user level in Ghost. Contributors can create and edit their own draft posts, but they are unable to edit drafts of others or publish posts. Contributors are untrusted users with the most basic access to your publication.

Authors
Authors are the 2nd user level in Ghost. Authors can write, edit  and publish their own posts. Authors are trusted users. If you don’t trust users to be allowed to publish their own posts, they should be set as Contributors.

Editors
Editors are the 3rd user level in Ghost. Editors can do everything that an Author can do, but they can also edit and publish the posts of others – as well as their own. Editors can also invite new Contributors & Authors to the site.

Administrators
The top user level in Ghost is Administrator. Again, administrators can do everything that Authors and Editors can do, but they can also edit all site settings and data, not just content. Additionally, administrators have full access to invite, manage or remove any other user of the site.

The Owner
There is only ever one owner of a Ghost site. The owner is a special user which has all the same permissions as an Administrator, but with two exceptions: The Owner can never be deleted. And in some circumstances the owner will have access to additional special settings if applicable. For example: billing details, if using Ghost(Pro).

It’s a good idea to ask all of your users to fill out their user profiles, including bio and social links. These will populate rich structured data for posts and generally create more opportunities for themes to fully populate their design.

Next: Organising content

Find out how to organise your content with sensible tags and authors, or for more advanced configurations, how to create custom content structures using dynamic routing.

Organising your content

Post Syndicated from Ghost original https://yovko.net/organising-content/

Sensible tagging

Organising your content

You can think of tags like Gmail labels. By tagging posts with one or more keyword, you can organise articles into buckets of related content.

When you create content for your publication you can assign tags to help differentiate between categories of content.

For example you may tag some content with News and other content with Podcast, which would create two distinct categories of content listed on /tag/news/ and /tag/podcast/, respectively.

If you tag a post with both News and Podcast – then it appears in both sections. Tag archives are like dedicated home-pages for each category of content that you have. They have their own pages, their own RSS feeds, and can support their own cover images and meta data.

The primary tag

Inside the Ghost editor, you can drag and drop tags into a specific order. The first tag in the list is always given the most importance, and some themes will only display the primary tag (the first tag in the list) by default.

News, Technology, Startup

So you can add the most important tag which you want to show up in your theme, but also add related tags which are less important.

Private tags

Sometimes you may want to assign a post a specific tag, but you don’t necessarily want that tag appearing in the theme or creating an archive page. In Ghost, hashtags are private and can be used for special styling.

For example, if you sometimes publish posts with video content – you might want your theme to adapt and get rid of the sidebar for these posts, to give more space for an embedded video to fill the screen. In this case, you could use private tags to tell your theme what to do.

News, #video

Here, the theme would assign the post publicly displayed tags of News – but it would also keep a private record of the post being tagged with #video. In your theme, you could then look for private tags conditionally and give them special formatting.

You can find documentation for theme development techniques like this and many more over on Ghost’s extensive theme docs.

Dynamic routing

Dynamic routing gives you the ultimate freedom to build a custom publication to suit your needs. Routes are rules that map URL patterns to your content and templates.

You may not want content tagged with News to exist on: example.com/tag/news. Instead, you want it to exist on example.com/news .

In this case you can use dynamic routes to create customised collections of content on your site. It’s also possible to use multiple templates in your theme to render each content type differently.

There are lots of use cases for dynamic routing with Ghost, here are a few common examples:

  • Setting a custom home page with its own template
  • Having separate content hubs for blog and podcast, that render differently, and have custom RSS feeds to support two types of content
  • Creating a founders column as a unique view, by filtering content created by specific authors
  • Including dates in permalinks for your posts
  • Setting posts to have a URL relative to their primary tag like example.com/europe/story-title/

Dynamic routing can be configured in Ghost using YAML files. Read our dynamic routing documentation for further details.

Next: Apps & Integrations

Work with all your favourite apps and tools using our integrations, or create your own custom integrations with webhooks.

Apps & integrations

Post Syndicated from Ghost original https://yovko.net/apps-integrations/

Work with your existing tools

Apps & integrations

It’s possible to connect your Ghost site to hundreds of the most popular apps and tools using integrations that take no more than a few minutes to setup.

Whether you need to automate workflows, connect your email list, build a community or embed products from your ecommerce store, our integrations library has got it all covered with hundreds of tutorials.

Apps & integrations

Zapier

On top of this, you can connect your Ghost site to more than 1,000 external services using the official integration with Zapier.

Zapier sets up automations with Triggers and Actions, which allows you to create and customise a wide range of connected applications.

Example: When someone new subscribes to a newsletter on a Ghost site (Trigger) then the contact information is automatically pushed into MailChimp (Action).

Here are the most popular Ghost<>Zapier automation templates:


Custom integrations

At the heart of Ghost sits a robust JSON API – designed to create, manage and retrieve content with ease.

It’s possible to create custom Ghost integrations with dedicated API keys and webhooks from the Integrations page within Ghost Admin.

Apps & integrations

Beyond that, the API allows you to build entirely custom publishing apps. You can send content from your favourite desktop editor, build a custom interface for handling editorial workflow or use Ghost as a full headless CMS with a custom front-end.

The Ghost API is thoroughly documented and straightforward to work with for developers of almost any level.

Final step: Themes

Alright, on to the last post in our welcome-series! If you’re curious about creating your own Ghost theme from scratch, find out how that works.

Creating a custom theme

Post Syndicated from Ghost original https://yovko.net/themes/

Ghost themes

Creating a custom theme

Ghost comes with a default theme called Casper, which is designed to be a clean, readable publication layout and can be easily adapted for most purposes.

If you need something a little more customised, it’s entirely possible to build on top of existing open source themes, or to build your own from scratch. Rather than giving you a few basic settings which act as a poor proxy for code, we just let you write code.

Marketplace

There are a huge range of both free and premium pre-built themes which you can download from the Ghost Theme Marketplace:

Creating a custom theme
Anyone can write a completely custom Ghost theme with some solid knowledge of HTML and CSS

Theme development

Ghost themes are written with a templating language called handlebars, which has a set of dynamic helpers to insert your data into template files. For example: {{author.name}} outputs the name of the current author.

The best way to learn how to write your own Ghost theme is to have a look at the source code for Casper, which is heavily commented and should give you a sense of how everything fits together.

  • default.hbs is the main template file, all contexts will load inside this file unless specifically told to use a different template.
  • post.hbs is the file used in the context of viewing a post.
  • index.hbs is the file u
  • cused in the context of viewing the home page.
  • and so on

We’ve got full and extensive theme documentation which outlines every template file, context and helper that you can use. You can also get started with our useful starter theme, which includes the most common foundations and components required to build your own theme.

If you want to chat with other people making Ghost themes to get any advice or help, there’s also a themes section on our public Ghost forum.

How to set up and use your brand-new Raspberry Pi

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/how-to-set-up-and-use-your-brand-new-raspberry-pi/

If you’re reading this, it’s probably because you bagged yourself a brand-new Raspberry Pi for Christmas, and you’re wondering what you should do next.

Well, look no further, for we’re here to show you the ropes. So, sit back, pull on a pair of those nice, warm socks that you found in your stocking, top up your eggnog, and let’s get started.

Do I need an operating system?

Unless your Raspberry Pi came in a kit with a preloaded SD card, you’ll need to download an operating system. Find a microSD card (you may have one lurking in an old phone) and click here to download the latest version of Raspbian, our dedicated Raspberry Pi operating system.

To get Raspbian onto the microSD card, use free online software such as Etcher. Here’s a video from The MagPi magazine to show you how to do it.

Use Etcher to install operating systems onto an SD card

Lucy Hattersley shows you how to install Raspberry Pi operating systems such as Raspbian onto an SD card, using the excellent Etcher. For more tutorials, check out The MagPi at http://magpi.cc ! Don’t want to miss an issue? Subscribe, and get every issue delivered straight to your door.

Turn it on!

Here, this video should help:

How to set up your Raspberry Pi || Getting started with #RaspberryPi

Learn #howto set up your Raspberry Pi for the first time, from plugging in peripherals to setting up #Raspbian.

Insert your microSD card into your Raspberry Pi. The microSD card slot should be fairly easy to find, and you need to make sure that you insert it with the contact side facing the board. If you feel like you’re having to force it in, you have it the wrong way round.

Next, plug your HDMI cable into the Raspberry Pi and your chosen HDMI display. This could be a computer monitor or your home television.

If you’re using a Raspberry Pi Zero or Raspberry Pi Zero W, you’ll need a mini HDMI to HDMI cable or adapter.

If you’re using a Raspberry Pi 4, you’ll need a micro HDMI to HDMI cable or adapter.

Raspberry Pi official keyboard

Next, plug in any peripherals that you want to use, such as a mouse or keyboard.

Lastly, plug your power cable into your Raspberry Pi. This is any standard micro USB cable (if you have an Android phone, check your phone charger!), or a USB-C power cable if you’re using the Raspberry Pi 4.

Most kits will come with all of the cables and adapters that you need, so look in the box first before you start rummaging around your home for spare cables.

Once the power cable is connected, your Raspberry Pi will turn on. If it doesn’t, check that your SD card is inserted correctly and your cables are pushed in fully.

Still in doubt? Here’s Sally Le Page with more:

How to use a Raspberry Pi ft. Dr Sally Le Page

What is a Raspberry Pi and what do you need to get started? Our ‘How to use a Raspberry Pi’ explainer will take you through the basics of your #RaspberryPi, and how you can get hands-on with Raspbian and #coding language tools such as Scratch and Mu, with our host, Dr Sally Le Page.

Once on, the Raspberry Pi will direct you through a setup process that allows you to change your password and connect to your local wireless network.

And then, you’re good to go!

Now what?

Now what? Well, that depends on what you want to do with your Raspberry Pi.

Many people use their Raspberry Pi to learn how to code. If you’re new to coding, we suggest trying out a few of our easy online projects to help you understand the basics of Scratch — the drag-and-drop coding platform from MIT — and Python — a popular general-purpose programming language and the reason for the “Pi” in Raspberry Pi’s name.

The components of a virtual analogue Raspberry Pu synthesiser


Maybe you want to use your Raspberry Pi to set up control of smart devices in your home, or build a media centre for all your favourite photos and home movies. Perhaps you want to play games on your Raspberry Pi, or try out various HATs and add-ons to create fun digital making projects.

Sally Le Page

Whatever you want to do with your Raspberry Pi, the internet is full of brilliant tutorials from the Raspberry Pi Foundation and online creators.

Some places to start

Get involved with the Raspberry Pi Foundation

From community events and magazines to online learning and space exploration – there are so many ways to get involved with the Raspberry Pi Foundation.

The Raspberry Pi community is huge, and spreads across the entire globe, bringing people together to share their love of coding, digital making, and computer education. However you use your Raspberry Pi, know that, by owning it, you’ve helped the non-profit Raspberry Pi Foundation to grow, bringing more opportunities to kids and teachers all over the world. So, from the bottom of our hearts this festive season, thank you.

We can’t wait to see what 2020 brings!

 

The post How to set up and use your brand-new Raspberry Pi appeared first on Raspberry Pi.

Improving the Getting Started experience with AWS Lambda

Post Syndicated from Eric Johnson original https://aws.amazon.com/blogs/compute/improving-the-getting-started-experience-with-aws-lambda/

A common question from developers is, “How do I get started with creating serverless applications?” Frequently, I point developers to the AWS Lambda console where they can create a new Lambda function and immediately see it working.

While you can learn the basics of a Lambda function this way, it does not encompass the full serverless experience. It does not allow you to take advantage of best practices like infrastructure as code (IaC) or continuous integration and continuous delivery (CI/CD). A full-on serverless application could include a combination of services like Amazon API Gateway, Amazon S3, and Amazon DynamoDB.

To help you start right with serverless, AWS has added a Create application experience to the Lambda console. This enables you to create serverless applications from ready-to-use sample applications, which follow these best practices:

  • Use infrastructure as code (IaC) for defining application resources
  • Provide a continuous integration and continuous deployment (CI/CD) pipeline for deployment
  • Exemplify best practices in serverless application structure and methods

IaC

Using IaC allows you to automate deployment and management of your resources. When you define and deploy your IaC architecture, you can standardize infrastructure components across your organization. You can rebuild your applications quickly and consistently without having to perform manual actions. You can also enforce best practices such as code reviews.

When you’re building serverless applications on AWS, you can use AWS CloudFormation directly, or choose the AWS Serverless Application Model, also known as AWS SAM. AWS SAM is an open source framework for building serverless applications that makes it easier to build applications quickly. AWS SAM provides a shorthand syntax to express APIs, functions, databases, and event source mappings. Because AWS SAM is built on CloudFormation, you can specify any other AWS resources using CloudFormation syntax in the same template.

Through this new experience, AWS provides an AWS SAM template that describes the entire application. You have instant access to modify the resources and security as needed.

CI/CD

When editing a Lambda function in the console, it’s live the moment that the function is saved. This works when developing against test environments, but risks introducing untested, faulty code in production environments. That’s a stressful atmosphere for developers with the unneeded overhead of manually testing code on each change.

Developers say that they are looking for an automated process for consistently testing and deploying reliable code. What they need is a CI/CD pipeline.

CI/CD pipelines are more than just convenience, they can be critical in helping development teams to be successful. CI/CDs provide code integration, testing, multiple environment deployments, notifications, rollbacks, and more. The functionality depends on how you choose to configure it.

When you create a new application through Lambda console, you create a CI/CD pipeline to provide a framework for automated testing and deployment. The pipeline includes the following resources:

Best practices

Like any other development pattern, there are best practices for serverless applications. These include testing strategies, local development, IaC, and CI/CD. When you create a Lambda function using the console, most of this is abstracted away. A common request from developers learning about serverless is for opinionated examples of best practices.

When you choose Create application, the application uses many best practices, including:

  • Managing IaC architectures
  • Managing deployment with a CI/CD pipeline
  • Runtime-specific test examples
  • Runtime-specific dependency management
  • A Lambda execution role with permissions boundaries
  • Application security with managed policies

Create an application

Now, lets walk through creating your first application.

  1. Open the Lambda console, and choose Applications, Create application.
  2. Choose Serverless API backend. The next page shows the architecture, services used, and development workflow of the chosen application.
  3. Choose Create and then configure your application settings.
    • For Application name and Application description, enter values.
    • For Runtime, the preview supports Node.js 10.x. Stay tuned for more runtimes.
    • For Source Control Service, I chose CodeCommit for this example, but you can choose either. If you choose GitHub, you are asked to connect to your GitHub account for authorization.
    • For Repository Name, feel free to use whatever you want.
    • Under Permissions, check Create roles and permissions boundary.
  4. Choose Create.

Exploring the application

That’s it! You have just created a new serverless application from the Lambda console. It takes a few moments for all the resources to be created. Take a moment to review what you have done so far.

Across the top of the application, you can see four tabs, as shown in the following screenshot:

  • Overview—Shows the current page, including a Getting started section, and application and toolchain resources of the application
  • Code—Shows the code repository and instructions on how to connect
  • Deployments—Links to the deployment pipeline and a deployment history.
  • Monitoring—Reports on the application health and performance

getting started dialog

The Resources section lists all the resources specific to the application. This application includes three Lambda functions, a DynamoDB table, and the API. The following screenshot shows the resources for this sample application.resources view

Finally, the Infrastructure section lists all the resources for the CI/CD pipeline including the AWS Identity and Access Management (IAM) roles, the permissions boundary policy, the S3 bucket, and more. The following screenshot shows the resources for this sample application.application view

About Permissions Boundaries

This new Create application experience utilizes an IAM permissions boundary to help further secure the function that gets created and prevent an overly permissive function policy from being created later on. The boundary is a separate policy that acts as a maximum bound on what an IAM policy for your function can be created to have permissions for. This model allows developers to build out the security model of their application while still meeting certain requirements that are often put in place to prevent overly permissive policies and is considered a best practice. By default, the permissions boundary that is created limits the application access to just the resources that are included in the example template. In order to expand the permissions of the application, you’ll first need to extend what is defined in the permissions boundary to allow it.

A quick test

Now that you have an application up and running, try a quick test to see if it works.

  1. In the Lambda console, in the left navigation pane, choose Applications.
  2. For Applications, choose Start Right application.
  3. On the Endpoint details card, copy your endpoint.
  4. From a terminal, run the following command:
    curl -d '{"id":"id1", "name":"name1"}' -H "Content-Type: application/json" -X POST <YOUR-ENDPOINT>

You can find tips like this, and other getting started hints in the README.md file of your new serverless application.

Outside of the console

With the introduction of the Create application function, there is now a closer tie between the Lambda console and local development. Before this feature, you would get started in the Lambda console or with a framework like AWS SAM. Now, you can start the project in the console and then move to local development.

You have already walked through the steps of creating an application, now pull it local and make some changes.

  1. In the Lambda console, in the left navigation pane, choose Applications.
  2. Select your application from the list and choose the Code tab.
  3. If you used CodeCommit, choose Connect instructions to configure your local git client. To copy the URL, choose the SSH squares icon.
  4. If you used GitHub, click on the SSH squares icon.
  5. In a terminal window, run the following command:
    git clone <your repo>
  6. Update one of the Lambda function files and save it.
  7. In the terminal window, commit and push the changes:
    git commit -am "simple change"
    git push
  8. In the Lambda console, under Deployments, choose View in CodePipeline.codepipeline pipeline

The build has started and the application is being deployed .

Caveats

submit feedback

This feature is currently available in US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), EU (Ireland), and Asia Pacific (Tokyo). This is a feature beta and as such, it is not a full representation of the final experience. We know this is limited in scope and request your feedback. Let us know your thoughts about any future enhancements you would like to see. The best way to give feedback is to use the feedback button in the console.

Conclusion

With the addition of the Create application feature, you can now start right with full serverless applications from within the Lambda console. This delivers the simplicity and ease of the console while still offering the power of an application built on best practices.

Until next time: Happy coding!

Getting started with your Raspberry Pi

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/getting-started-raspberry-pi/

Here on the Raspberry Pi blog, we often share impressive builds made by community members who have advanced making and coding skills. But what about those of you who are just getting started?

Getting started with Raspberry Pi

For you, we’ve been working hard to update and polish our Getting started resources, including a brand-new video to help you get to grips with your new Pi.

Getting started with Raspberry Pi

Whether you’re new to electronics and the Raspberry Pi, or a seasoned pro looking to share your knowledge and skills with others, sit back and watch us walk you through the basics of setting up our powerful little computer.

How to set up your Raspberry Pi || Getting started with #RaspberryPi

Learn how to set up your Raspberry Pi for the first time, from plugging in peripherals to loading Raspbian.

We’ve tried to make this video as easy to follow as possible, with only the essential explanations and steps.

getting started with raspberry pi

As with everything we produce, we want this video to be accessible to the entire world, so if you can translate its text into another language, please follow this link to submit your translation directly through YouTube. You can also add translations to our other YouTube videos here! As a thank you, we’ll display your username in the video descriptions to acknowledge your contributions.

New setup guides and resources

Alongside our shiny new homepage, we’ve also updated our Help section to reflect our newest tech and demonstrate the easiest way for beginners to start their Raspberry Pi journey. We’re now providing a first-time setup guide, and also a walk-through for using your Raspberry Pi that shows you all sort of things you can do with it. And with guides to our official add-on devices and a troubleshooting section, our updated Help page is your one-stop shop for getting the most out of your Pi.

getting started with raspberry pi

For parents and teachers, we offer guides on introducing Raspberry Pi and digital making to your children and students. And for those of you who are visual learners, we’ve curated a collection of our videos to help you get making.

As with our videos, we’re looking for people whose first language isn’t English to help us translate our resources. If you’re able to donate some of your time to support this cause, please sign up here.

The forums

We’re very proud of our forum community. Since the birth of the Raspberry Pi, our forums have been the place to go for additional support, conversation, and project bragging.

Raspberry Pi forums

If your question isn’t answered on our Help page, there’s no better place to go than the forums. Nine times out of ten, your question will already have been asked and answered there! And if not, then our friendly forum community will be happy to share their wealth of knowledge and help you out.

Events and clubs

Raspberry Pi and digital making enthusiasts come together across the world at various events and clubs, including Raspberry Jams, Code Club and CoderDojo, and Coolest Projects. These events are perfect for learning more about how people use Raspberry Pi and other technologies for digital making — as a hobby and as a tool for education.

getting started with raspberry pi

Keep up to date

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Build your own weather station with our new guide!

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/build-your-own-weather-station/

One of the most common enquiries I receive at Pi Towers is “How can I get my hands on a Raspberry Pi Oracle Weather Station?” Now the answer is: “Why not build your own version using our guide?”

Build Your Own weather station kit assembled

Tadaaaa! The BYO weather station fully assembled.

Our Oracle Weather Station

In 2016 we sent out nearly 1000 Raspberry Pi Oracle Weather Station kits to schools from around the world who had applied to be part of our weather station programme. In the original kit was a special HAT that allows the Pi to collect weather data with a set of sensors.

The original Raspberry Pi Oracle Weather Station HAT – Build Your Own Raspberry Pi weather station

The original Raspberry Pi Oracle Weather Station HAT

We designed the HAT to enable students to create their own weather stations and mount them at their schools. As part of the programme, we also provide an ever-growing range of supporting resources. We’ve seen Oracle Weather Stations in great locations with a huge differences in climate, and they’ve even recorded the effects of a solar eclipse.

Our new BYO weather station guide

We only had a single batch of HATs made, and unfortunately we’ve given nearly* all the Weather Station kits away. Not only are the kits really popular, we also receive lots of questions about how to add extra sensors or how to take more precise measurements of a particular weather phenomenon. So today, to satisfy your demand for a hackable weather station, we’re launching our Build your own weather station guide!

Build Your Own Raspberry Pi weather station

Fun with meteorological experiments!

Our guide suggests the use of many of the sensors from the Oracle Weather Station kit, so can build a station that’s as close as possible to the original. As you know, the Raspberry Pi is incredibly versatile, and we’ve made it easy to hack the design in case you want to use different sensors.

Many other tutorials for Pi-powered weather stations don’t explain how the various sensors work or how to store your data. Ours goes into more detail. It shows you how to put together a breadboard prototype, it describes how to write Python code to take readings in different ways, and it guides you through recording these readings in a database.

Build Your Own Raspberry Pi weather station on a breadboard

There’s also a section on how to make your station weatherproof. And in case you want to move past the breadboard stage, we also help you with that. The guide shows you how to solder together all the components, similar to the original Oracle Weather Station HAT.

Who should try this build

We think this is a great project to tackle at home, at a STEM club, Scout group, or CoderDojo, and we’re sure that many of you will be chomping at the bit to get started. Before you do, please note that we’ve designed the build to be as straight-forward as possible, but it’s still fairly advanced both in terms of electronics and programming. You should read through the whole guide before purchasing any components.

Build Your Own Raspberry Pi weather station – components

The sensors and components we’re suggesting balance cost, accuracy, and easy of use. Depending on what you want to use your station for, you may wish to use different components. Similarly, the final soldered design in the guide may not be the most elegant, but we think it is achievable for someone with modest soldering experience and basic equipment.

You can build a functioning weather station without soldering with our guide, but the build will be more durable if you do solder it. If you’ve never tried soldering before, that’s OK: we have a Getting started with soldering resource plus video tutorial that will walk you through how it works step by step.

Prototyping HAT for Raspberry Pi weather station sensors

For those of you who are more experienced makers, there are plenty of different ways to put the final build together. We always like to hear about alternative builds, so please post your designs in the Weather Station forum.

Our plans for the guide

Our next step is publishing supplementary guides for adding extra functionality to your weather station. We’d love to hear which enhancements you would most like to see! Our current ideas under development include adding a webcam, making a tweeting weather station, adding a light/UV meter, and incorporating a lightning sensor. Let us know which of these is your favourite, or suggest your own amazing ideas in the comments!

*We do have a very small number of kits reserved for interesting projects or locations: a particularly cool experiment, a novel idea for how the Oracle Weather Station could be used, or places with specific weather phenomena. If have such a project in mind, please send a brief outline to [email protected], and we’ll consider how we might be able to help you.

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Measuring the throughput for Amazon MQ using the JMS Benchmark

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/measuring-the-throughput-for-amazon-mq-using-the-jms-benchmark/

This post is courtesy of Alan Protasio, Software Development Engineer, Amazon Web Services

Just like compute and storage, messaging is a fundamental building block of enterprise applications. Message brokers (aka “message-oriented middleware”) enable different software systems, often written in different languages, on different platforms, running in different locations, to communicate and exchange information. Mission-critical applications, such as CRM and ERP, rely on message brokers to work.

A common performance consideration for customers deploying a message broker in a production environment is the throughput of the system, measured as messages per second. This is important to know so that application environments (hosts, threads, memory, etc.) can be configured correctly.

In this post, we demonstrate how to measure the throughput for Amazon MQ, a new managed message broker service for ActiveMQ, using JMS Benchmark. It should take between 15–20 minutes to set up the environment and an hour to run the benchmark. We also provide some tips on how to configure Amazon MQ for optimal throughput.

Benchmarking throughput for Amazon MQ

ActiveMQ can be used for a number of use cases. These use cases can range from simple fire and forget tasks (that is, asynchronous processing), low-latency request-reply patterns, to buffering requests before they are persisted to a database.

The throughput of Amazon MQ is largely dependent on the use case. For example, if you have non-critical workloads such as gathering click events for a non-business-critical portal, you can use ActiveMQ in a non-persistent mode and get extremely high throughput with Amazon MQ.

On the flip side, if you have a critical workload where durability is extremely important (meaning that you can’t lose a message), then you are bound by the I/O capacity of your underlying persistence store. We recommend using mq.m4.large for the best results. The mq.t2.micro instance type is intended for product evaluation. Performance is limited, due to the lower memory and burstable CPU performance.

Tip: To improve your throughput with Amazon MQ, make sure that you have consumers processing messaging as fast as (or faster than) your producers are pushing messages.

Because it’s impossible to talk about how the broker (ActiveMQ) behaves for each and every use case, we walk through how to set up your own benchmark for Amazon MQ using our favorite open-source benchmarking tool: JMS Benchmark. We are fans of the JMS Benchmark suite because it’s easy to set up and deploy, and comes with a built-in visualizer of the results.

Non-Persistent Scenarios – Queue latency as you scale producer throughput

JMS Benchmark nonpersistent scenarios

Getting started

At the time of publication, you can create an mq.m4.large single-instance broker for testing for $0.30 per hour (US pricing).

This walkthrough covers the following tasks:

  1.  Create and configure the broker.
  2. Create an EC2 instance to run your benchmark
  3. Configure the security groups
  4.  Run the benchmark.

Step 1 – Create and configure the broker
Create and configure the broker using Tutorial: Creating and Configuring an Amazon MQ Broker.

Step 2 – Create an EC2 instance to run your benchmark
Launch the EC2 instance using Step 1: Launch an Instance. We recommend choosing the m5.large instance type.

Step 3 – Configure the security groups
Make sure that all the security groups are correctly configured to let the traffic flow between the EC2 instance and your broker.

  1. Sign in to the Amazon MQ console.
  2. From the broker list, choose the name of your broker (for example, MyBroker)
  3. In the Details section, under Security and network, choose the name of your security group or choose the expand icon ( ).
  4. From the security group list, choose your security group.
  5. At the bottom of the page, choose Inbound, Edit.
  6. In the Edit inbound rules dialog box, add a role to allow traffic between your instance and the broker:
    • Choose Add Rule.
    • For Type, choose Custom TCP.
    • For Port Range, type the ActiveMQ SSL port (61617).
    • For Source, leave Custom selected and then type the security group of your EC2 instance.
    • Choose Save.

Your broker can now accept the connection from your EC2 instance.

Step 4 – Run the benchmark
Connect to your EC2 instance using SSH and run the following commands:

$ cd ~
$ curl -L https://github.com/alanprot/jms-benchmark/archive/master.zip -o master.zip
$ unzip master.zip
$ cd jms-benchmark-master
$ chmod a+x bin/*
$ env \
  SERVER_SETUP=false \
  SERVER_ADDRESS={activemq-endpoint} \
  ACTIVEMQ_TRANSPORT=ssl\
  ACTIVEMQ_PORT=61617 \
  ACTIVEMQ_USERNAME={activemq-user} \
  ACTIVEMQ_PASSWORD={activemq-password} \
  ./bin/benchmark-activemq

After the benchmark finishes, you can find the results in the ~/reports directory. As you may notice, the performance of ActiveMQ varies based on the number of consumers, producers, destinations, and message size.

Amazon MQ architecture

The last bit that’s important to know so that you can better understand the results of the benchmark is how Amazon MQ is architected.

Amazon MQ is architected to be highly available (HA) and durable. For HA, we recommend using the multi-AZ option. After a message is sent to Amazon MQ in persistent mode, the message is written to the highly durable message store that replicates the data across multiple nodes in multiple Availability Zones. Because of this replication, for some use cases you may see a reduction in throughput as you migrate to Amazon MQ. Customers have told us they appreciate the benefits of message replication as it helps protect durability even in the face of the loss of an Availability Zone.

Conclusion

We hope this gives you an idea of how Amazon MQ performs. We encourage you to run tests to simulate your own use cases.

To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

Use Slack ChatOps to Deploy Your Code – How to Integrate Your Pipeline in AWS CodePipeline with Your Slack Channel

Post Syndicated from Rumi Olsen original https://aws.amazon.com/blogs/devops/use-slack-chatops-to-deploy-your-code-how-to-integrate-your-pipeline-in-aws-codepipeline-with-your-slack-channel/

Slack is widely used by DevOps and development teams to communicate status. Typically, when a build has been tested and is ready to be promoted to a staging environment, a QA engineer or DevOps engineer kicks off the deployment. Using Slack in a ChatOps collaboration model, the promotion can be done in a single click from a Slack channel. And because the promotion happens through a Slack channel, the whole development team knows what’s happening without checking email.

In this blog post, I will show you how to integrate AWS services with a Slack application. I use an interactive message button and incoming webhook to promote a stage with a single click.

To follow along with the steps in this post, you’ll need a pipeline in AWS CodePipeline. If you don’t have a pipeline, the fastest way to create one for this use case is to use AWS CodeStar. Go to the AWS CodeStar console and select the Static Website template (shown in the screenshot). AWS CodeStar will create a pipeline with an AWS CodeCommit repository and an AWS CodeDeploy deployment for you. After the pipeline is created, you will need to add a manual approval stage.

You’ll also need to build a Slack app with webhooks and interactive components, write two Lambda functions, and create an API Gateway API and a SNS topic.

As you’ll see in the following diagram, when I make a change and merge a new feature into the master branch in AWS CodeCommit, the check-in kicks off my CI/CD pipeline in AWS CodePipeline. When CodePipeline reaches the approval stage, it sends a notification to Amazon SNS, which triggers an AWS Lambda function (ApprovalRequester).

The Slack channel receives a prompt that looks like the following screenshot. When I click Yes to approve the build promotion, the approval result is sent to CodePipeline through API Gateway and Lambda (ApprovalHandler). The pipeline continues on to deploy the build to the next environment.

Create a Slack app

For App Name, type a name for your app. For Development Slack Workspace, choose the name of your workspace. You’ll see in the following screenshot that my workspace is AWS ChatOps.

After the Slack application has been created, you will see the Basic Information page, where you can create incoming webhooks and enable interactive components.

To add incoming webhooks:

  1. Under Add features and functionality, choose Incoming Webhooks. Turn the feature on by selecting Off, as shown in the following screenshot.
  2. Now that the feature is turned on, choose Add New Webhook to Workspace. In the process of creating the webhook, Slack lets you choose the channel where messages will be posted.
  3. After the webhook has been created, you’ll see its URL. You will use this URL when you create the Lambda function.

If you followed the steps in the post, the pipeline should look like the following.

Write the Lambda function for approval requests

This Lambda function is invoked by the SNS notification. It sends a request that consists of an interactive message button to the incoming webhook you created earlier.  The following sample code sends the request to the incoming webhook. WEBHOOK_URL and SLACK_CHANNEL are the environment variables that hold values of the webhook URL that you created and the Slack channel where you want the interactive message button to appear.

# This function is invoked via SNS when the CodePipeline manual approval action starts.
# It will take the details from this approval notification and sent an interactive message to Slack that allows users to approve or cancel the deployment.

import os
import json
import logging
import urllib.parse

from base64 import b64decode
from urllib.request import Request, urlopen
from urllib.error import URLError, HTTPError

# This is passed as a plain-text environment variable for ease of demonstration.
# Consider encrypting the value with KMS or use an encrypted parameter in Parameter Store for production deployments.
SLACK_WEBHOOK_URL = os.environ['SLACK_WEBHOOK_URL']
SLACK_CHANNEL = os.environ['SLACK_CHANNEL']

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    print("Received event: " + json.dumps(event, indent=2))
    message = event["Records"][0]["Sns"]["Message"]
    
    data = json.loads(message) 
    token = data["approval"]["token"]
    codepipeline_name = data["approval"]["pipelineName"]
    
    slack_message = {
        "channel": SLACK_CHANNEL,
        "text": "Would you like to promote the build to production?",
        "attachments": [
            {
                "text": "Yes to deploy your build to production",
                "fallback": "You are unable to promote a build",
                "callback_id": "wopr_game",
                "color": "#3AA3E3",
                "attachment_type": "default",
                "actions": [
                    {
                        "name": "deployment",
                        "text": "Yes",
                        "style": "danger",
                        "type": "button",
                        "value": json.dumps({"approve": True, "codePipelineToken": token, "codePipelineName": codepipeline_name}),
                        "confirm": {
                            "title": "Are you sure?",
                            "text": "This will deploy the build to production",
                            "ok_text": "Yes",
                            "dismiss_text": "No"
                        }
                    },
                    {
                        "name": "deployment",
                        "text": "No",
                        "type": "button",
                        "value": json.dumps({"approve": False, "codePipelineToken": token, "codePipelineName": codepipeline_name})
                    }  
                ]
            }
        ]
    }

    req = Request(SLACK_WEBHOOK_URL, json.dumps(slack_message).encode('utf-8'))

    response = urlopen(req)
    response.read()
    
    return None

 

Create a SNS topic

Create a topic and then create a subscription that invokes the ApprovalRequester Lambda function. You can configure the manual approval action in the pipeline to send a message to this SNS topic when an approval action is required. When the pipeline reaches the approval stage, it sends a notification to this SNS topic. SNS publishes a notification to all of the subscribed endpoints. In this case, the Lambda function is the endpoint. Therefore, it invokes and executes the Lambda function. For information about how to create a SNS topic, see Create a Topic in the Amazon SNS Developer Guide.

Write the Lambda function for handling the interactive message button

This Lambda function is invoked by API Gateway. It receives the result of the interactive message button whether or not the build promotion was approved. If approved, an API call is made to CodePipeline to promote the build to the next environment. If not approved, the pipeline stops and does not move to the next stage.

The Lambda function code might look like the following. SLACK_VERIFICATION_TOKEN is the environment variable that contains your Slack verification token. You can find your verification token under Basic Information on Slack manage app page. When you scroll down, you will see App Credential. Verification token is found under the section.

# This function is triggered via API Gateway when a user acts on the Slack interactive message sent by approval_requester.py.

from urllib.parse import parse_qs
import json
import os
import boto3

SLACK_VERIFICATION_TOKEN = os.environ['SLACK_VERIFICATION_TOKEN']

#Triggered by API Gateway
#It kicks off a particular CodePipeline project
def lambda_handler(event, context):
	#print("Received event: " + json.dumps(event, indent=2))
	body = parse_qs(event['body'])
	payload = json.loads(body['payload'][0])

	# Validate Slack token
	if SLACK_VERIFICATION_TOKEN == payload['token']:
		send_slack_message(json.loads(payload['actions'][0]['value']))
		
		# This will replace the interactive message with a simple text response.
		# You can implement a more complex message update if you would like.
		return  {
			"isBase64Encoded": "false",
			"statusCode": 200,
			"body": "{\"text\": \"The approval has been processed\"}"
		}
	else:
		return  {
			"isBase64Encoded": "false",
			"statusCode": 403,
			"body": "{\"error\": \"This request does not include a vailid verification token.\"}"
		}


def send_slack_message(action_details):
	codepipeline_status = "Approved" if action_details["approve"] else "Rejected"
	codepipeline_name = action_details["codePipelineName"]
	token = action_details["codePipelineToken"] 

	client = boto3.client('codepipeline')
	response_approval = client.put_approval_result(
							pipelineName=codepipeline_name,
							stageName='Approval',
							actionName='ApprovalOrDeny',
							result={'summary':'','status':codepipeline_status},
							token=token)
	print(response_approval)

 

Create the API Gateway API

  1. In the Amazon API Gateway console, create a resource called InteractiveMessageHandler.
  2. Create a POST method.
    • For Integration type, choose Lambda Function.
    • Select Use Lambda Proxy integration.
    • From Lambda Region, choose a region.
    • In Lambda Function, type a name for your function.
  3.  Deploy to a stage.

For more information, see Getting Started with Amazon API Gateway in the Amazon API Developer Guide.

Now go back to your Slack application and enable interactive components.

To enable interactive components for the interactive message (Yes) button:

  1. Under Features, choose Interactive Components.
  2. Choose Enable Interactive Components.
  3. Type a request URL in the text box. Use the invoke URL in Amazon API Gateway that will be called when the approval button is clicked.

Now that all the pieces have been created, run the solution by checking in a code change to your CodeCommit repo. That will release the change through CodePipeline. When the CodePipeline comes to the approval stage, it will prompt to your Slack channel to see if you want to promote the build to your staging or production environment. Choose Yes and then see if your change was deployed to the environment.

Conclusion

That is it! You have now created a Slack ChatOps solution using AWS CodeCommit, AWS CodePipeline, AWS Lambda, Amazon API Gateway, and Amazon Simple Notification Service.

Now that you know how to do this Slack and CodePipeline integration, you can use the same method to interact with other AWS services using API Gateway and Lambda. You can also use Slack’s slash command to initiate an action from a Slack channel, rather than responding in the way demonstrated in this post.

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

From Framework to Function: Deploying AWS Lambda Functions for Java 8 using Apache Maven Archetype

Post Syndicated from Ryosuke Iwanaga original https://aws.amazon.com/blogs/compute/from-framework-to-function-deploying-aws-lambda-functions-for-java-8-using-apache-maven-archetype/

As a serverless computing platform that supports Java 8 runtime, AWS Lambda makes it easy to run any type of Java function simply by uploading a JAR file. To help define not only a Lambda serverless application but also Amazon API Gateway, Amazon DynamoDB, and other related services, the AWS Serverless Application Model (SAM) allows developers to use a simple AWS CloudFormation template.

AWS provides the AWS Toolkit for Eclipse that supports both Lambda and SAM. AWS also gives customers an easy way to create Lambda functions and SAM applications in Java using the AWS Command Line Interface (AWS CLI). After you build a JAR file, all you have to do is type the following commands:

aws cloudformation package 
aws cloudformation deploy

To consolidate these steps, customers can use Archetype by Apache Maven. Archetype uses a predefined package template that makes getting started to develop a function exceptionally simple.

In this post, I introduce a Maven archetype that allows you to create a skeleton of AWS SAM for a Java function. Using this archetype, you can generate a sample Java code example and an accompanying SAM template to deploy it on AWS Lambda by a single Maven action.

Prerequisites

Make sure that the following software is installed on your workstation:

  • Java
  • Maven
  • AWS CLI
  • (Optional) AWS SAM CLI

Install Archetype

After you’ve set up those packages, install Archetype with the following commands:

git clone https://github.com/awslabs/aws-serverless-java-archetype
cd aws-serverless-java-archetype
mvn install

These are one-time operations, so you don’t run them for every new package. If you’d like, you can add Archetype to your company’s Maven repository so that other developers can use it later.

With those packages installed, you’re ready to develop your new Lambda Function.

Start a project

Now that you have the archetype, customize it and run the code:

cd /path/to/project_home
mvn archetype:generate \
  -DarchetypeGroupId=com.amazonaws.serverless.archetypes \
  -DarchetypeArtifactId=aws-serverless-java-archetype \
  -DarchetypeVersion=1.0.0 \
  -DarchetypeRepository=local \ # Forcing to use local maven repository
  -DinteractiveMode=false \ # For batch mode
  # You can also specify properties below interactively if you omit the line for batch mode
  -DgroupId=YOUR_GROUP_ID \
  -DartifactId=YOUR_ARTIFACT_ID \
  -Dversion=YOUR_VERSION \
  -DclassName=YOUR_CLASSNAME

You should have a directory called YOUR_ARTIFACT_ID that contains the files and folders shown below:

├── event.json
├── pom.xml
├── src
│   └── main
│       ├── java
│       │   └── Package
│       │       └── Example.java
│       └── resources
│           └── log4j2.xml
└── template.yaml

The sample code is a working example. If you install SAM CLI, you can invoke it just by the command below:

cd YOUR_ARTIFACT_ID
mvn -P invoke verify
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- maven-jar-plugin:3.0.2:jar (default-jar) @ foo ---
[INFO] Building jar: /private/tmp/foo/target/foo-1.0.jar
[INFO]
[INFO] --- maven-shade-plugin:3.1.0:shade (shade) @ foo ---
[INFO] Including com.amazonaws:aws-lambda-java-core:jar:1.2.0 in the shaded jar.
[INFO] Replacing /private/tmp/foo/target/lambda.jar with /private/tmp/foo/target/foo-1.0-shaded.jar
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-local-invoke) @ foo ---
2018/04/06 16:34:35 Successfully parsed template.yaml
2018/04/06 16:34:35 Connected to Docker 1.37
2018/04/06 16:34:35 Fetching lambci/lambda:java8 image for java8 runtime...
java8: Pulling from lambci/lambda
Digest: sha256:14df0a5914d000e15753d739612a506ddb8fa89eaa28dcceff5497d9df2cf7aa
Status: Image is up to date for lambci/lambda:java8
2018/04/06 16:34:37 Invoking Package.Example::handleRequest (java8)
2018/04/06 16:34:37 Decompressing /tmp/foo/target/lambda.jar
2018/04/06 16:34:37 Mounting /private/var/folders/x5/ldp7c38545v9x5dg_zmkr5kxmpdprx/T/aws-sam-local-1523000077594231063 as /var/task:ro inside runtime container
START RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74 Version: $LATEST
Log output: Greeting is 'Hello Tim Wagner.'
END RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74
REPORT RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74	Duration: 96.60 ms	Billed Duration: 100 ms	Memory Size: 128 MB	Max Memory Used: 7 MB

{"greetings":"Hello Tim Wagner."}


[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 10.452 s
[INFO] Finished at: 2018-04-06T16:34:40+09:00
[INFO] ------------------------------------------------------------------------

This maven goal invokes sam local invoke -e event.json, so you can see the sample output to greet Tim Wagner.

To deploy this application to AWS, you need an Amazon S3 bucket to upload your package. You can use the following command to create a bucket if you want:

aws s3 mb s3://YOUR_BUCKET --region YOUR_REGION

Now, you can deploy your application by just one command!

mvn deploy \
    -DawsRegion=YOUR_REGION \
    -Ds3Bucket=YOUR_BUCKET \
    -DstackName=YOUR_STACK
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-package) @ foo ---
Uploading to aws-serverless-java/com.riywo:foo:1.0/924732f1f8e4705c87e26ef77b080b47  11657 / 11657.0  (100.00%)
Successfully packaged artifacts and wrote output template to file target/sam.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file /private/tmp/foo/target/sam.yaml --stack-name <YOUR STACK NAME>
[INFO]
[INFO] --- maven-deploy-plugin:2.8.2:deploy (default-deploy) @ foo ---
[INFO] Skipping artifact deployment
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-deploy) @ foo ---

Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - archetype
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 37.176 s
[INFO] Finished at: 2018-04-06T16:41:02+09:00
[INFO] ------------------------------------------------------------------------

Maven automatically creates a shaded JAR file, uploads it to your S3 bucket, replaces template.yaml, and creates and updates the CloudFormation stack.

To customize the process, modify the pom.xml file. For example, to avoid typing values for awsRegion, s3Bucket or stackName, write them inside pom.xml and check in your VCS. Afterward, you and the rest of your team can deploy the function by typing just the following command:

mvn deploy

Options

Lambda Java 8 runtime has some types of handlers: POJO, Simple type and Stream. The default option of this archetype is POJO style, which requires to create request and response classes, but they are baked by the archetype by default. If you want to use other type of handlers, you can use handlerType property like below:

## POJO type (default)
mvn archetype:generate \
 ...
 -DhandlerType=pojo

## Simple type - String
mvn archetype:generate \
 ...
 -DhandlerType=simple

### Stream type
mvn archetype:generate \
 ...
 -DhandlerType=stream

See documentation for more details about handlers.

Also, Lambda Java 8 runtime supports two types of Logging class: Log4j 2 and LambdaLogger. This archetype creates LambdaLogger implementation by default, but you can use Log4j 2 if you want:

## LambdaLogger (default)
mvn archetype:generate \
 ...
 -Dlogger=lambda

## Log4j 2
mvn archetype:generate \
 ...
 -Dlogger=log4j2

If you use LambdaLogger, you can delete ./src/main/resources/log4j2.xml. See documentation for more details.

Conclusion

So, what’s next? Develop your Lambda function locally and type the following command: mvn deploy !

With this Archetype code example, available on GitHub repo, you should be able to deploy Lambda functions for Java 8 in a snap. If you have any questions or comments, please submit them below or leave them on GitHub.

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Hackspace magazine 6: Paper Engineering

Post Syndicated from Andrew Gregory original https://www.raspberrypi.org/blog/hackspace-magazine-6/

HackSpace magazine is back with our brand-new issue 6, available for you on shop shelves, in your inbox, and on our website right now.

Inside Hackspace magazine 6

Paper is probably the first thing you ever used for making, and for good reason: in no other medium can you iterate through 20 designs at the cost of only a few pennies. We’ve roped in Rob Ives to show us how to make a barking paper dog with moveable parts and a cam mechanism. Even better, the magazine includes this free paper automaton for you to make yourself. That’s right: free!

At the other end of the scale, there’s the forge, where heat, light, and noise combine to create immutable steel. We speak to Alec Steele, YouTuber, blacksmith, and philosopher, about his amazingly beautiful Damascus steel creations, and about why there’s no difference between grinding a knife and blowing holes in a mountain to build a road through it.

HackSpace magazine 6 Alec Steele

Do it yourself

You’ve heard of reading glasses — how about glasses that read for you? Using a camera, optical character recognition software, and a text-to-speech engine (and of course a Raspberry Pi to hold it all together), reader Andrew Lewis has hacked together his own system to help deal with age-related macular degeneration.

It’s the definition of hacking: here’s a problem, there’s no solution in the shops, so you go and build it yourself!

Radio

60 years ago, the cutting edge of home hacking was the transistor radio. Before the internet was dreamt of, the transistor radio made the world smaller and brought people together. Nowadays, the components you need to build a radio are cheap and easily available, so if you’re in any way electronically inclined, building a radio is an ideal excuse to dust off your soldering iron.

Tutorials

If you’re a 12-month subscriber (if you’re not, you really should be), you’ve no doubt been thinking of all sorts of things to do with the Adafruit Circuit Playground Express we gave you for free. How about a sewable circuit for a canvas bag? Use the accelerometer to detect patterns of movement — walking, for example — and flash a series of lights in response. It’s clever, fun, and an easy way to add some programmable fun to your shopping trips.


We’re also making gin, hacking a children’s toy car to unlock more features, and getting started with robot sumo to fill the void left by the cancellation of Robot Wars.

HackSpace magazine 6

All this, plus an 11-metre tall mechanical miner, in HackSpace magazine issue 6 — subscribe here from just £4 an issue or get the PDF version for free. You can also find HackSpace magazine in WHSmith, Tesco, Sainsbury’s, and independent newsagents in the UK. If you live in the US, check out your local Barnes & Noble, Fry’s, or Micro Center next week. We’re also shipping to stores in Australia, Hong Kong, Canada, Singapore, Belgium, and Brazil, so be sure to ask your local newsagent whether they’ll be getting HackSpace magazine.

The post Hackspace magazine 6: Paper Engineering appeared first on Raspberry Pi.