Products – Sean Shadmand

Updated Review of LLM Based Development

I tried developing using GPT mid-2022. While I was amazed by the potential, I was not impressed enough to add it to my daily development workflow. It fell off my radar as a development tool, outpaced by a far more impactful use of text generation and image creation. A toolset that has significantly changed my day-to-day productivity.

Recently, a group of peers convinced me to give coding with LLM another shot. They loved using A.I. to develop code in languages they were not comfortable with. Or, as a manager, a way to better explain what they wanted to see in a project from their team. What convinced me to try it again was their highlighting of how well the results were formatted, syntactically correct, and well documented from the get-go. While they admitted the development of code may not be faster, the prospect of all those benefits culminating into a cleaner, well formatted final product convinced me to develop with GPT again in earnest.

I began my reexamination of the tooling via demos, as we often do. I was very impressed. I converted code into PowerShell (which I don’t know well) and re-created functionality I came across in weeks prior. I was so impressed, I showed my team examples of how the work they completed in the past could’ve been done with the help of GPT instead.

After those successes, I committed to using GPT to develop. Over the next few weeks I made sure to use it on projects I was working on.

While the technology showed incredible advancements since I tried it last year, it still hasn’t become my go-to in the same way using ChatGPT has for writing content.

Here are some areas I was both impressed with but left wanting:

Code completion
- Pro: Impressive. The look-ahead came in handy similarly to code-completion functionality of the past, with the added benefit of more contextual relevance that was not just a “cookie cutter” snippet.
- Con: It gave me a useless hint quite a bit and I found myself typing almost as much as before with the incumbent “dumb completion”. I think it is because my mind is moving ahead to what I want the code to do, not necessarily what it is doing on the console at the moment. In the end, it is using patterns to make predictions. So, any new code that is a result of changes to my approach, or my on-the-fly reworking to fix a bug (that was not due to syntax issues) took as much time to develop as non-GPT-based code completion.
Testing
- Pro: When it comes to testing an existing application, the A.I. hits it out of the park. Ask it to “write a test for myFunction() using Jest” it creates an awesome base test case that I would have hated to write for each function.
- Con: Some of the same issues outlined in the “Code Completion” and Functional Development” can be problematic here. It doesn’t always create a great test for code I haven’t written yet. (i.e. TDD) However, if the code is already there, it uses that context I’ve provided and its LLM to unpack what it the function is suppose to do and generate all the mocks and assertions needed to create a well written unit test.
Functional Development
- Pro: Much like helping me get past the dreaded blank page in text generation, I found it more useful than Google searches and StackOverflow reviews to develop a series of functions I wanted, without developing entirely from scratch. Better than code snippets, the snippets A.I. gave were pre-filled based on my prompts, variables, and existing object definitions. That was appreciated. I didn’t have to review the documentation to tweeze out the result I wanted. The A.I. pulled it all together for me.
  Additionally, the fact that it almost always has an answer goes under appreciated in other reviews I’ve read. The part that makes it so advanced, is it fills in a lot of grey area even if I (as a stupid human) carelessly leave out an instruction that is critical in generating a solution. If I were to get the response, “could not understand your request” due to my laziness, I would never use it. The assumptions it makes are close enough to my intent that I am either using the solution, learn a new path, or see what my explanation is missing so I can improve how I communicate with it.
- Con: The end result did not work out of the gate most of the time. Sometimes it never got it correct and I had to Google the documentation to figure the issue. This was due to what I think was more than one documentation existing for various versions of the library I was using. I’m not sure. While the syntax was correct, the parameters it assumed I needed, or the way the calls were made to interface with a library/API led to errors.
Debugging
- Pro: Per the “functional development” points above, I was impressed at how I could respond to a prompt result with “I got this error when using the code above: [error]”. It recognized where it went wrong, and attempted to rewrite the code based on that feedback.
- Con: Each response had a different result than the original. So, instead of fixing what I found was wrong (like a param missing) it also added or removed other features from the code that were correct. This made the generated result difficult to utilize. In some cases, it could never understand the issue well enough to generate working code.

One limitation I am not too surprised about, and am hopeful to see evolve in the future, is the AI’s understanding of a project in its entirety. Done so in a way that context is used in its function creation, making the solutions it provides “full stack”. Imagine a Serverless.com config, for an AWS deployment, that generates files and code that creates and deploys workflows using Lambda, DynamoDB, S3 and so on, all being developed based on prompts. With the yearly (and more recently) weekly leaps, I don’t think we are to far away.

As of today, I find myself going to GPT when filling in starter templates for a new project. I find it’s a much better starting point than starting from cookie cutter function as I set up my core, early, “re-inventing the wheel”-type, skeleton.

For example, I will use a Gitlab template for my infrastructure (be it GL Pages, Serverless, React, nodejs or Python and on and on), then fill in the starter code an tests via a few GPT prompts, and copying them over. Beyond that copy, I find myself detaching from GPT for the most part, and returning to occasionally “rubber duck” new framework functions.

Examples referenced above

Here I asked for a non-3rd-party use of promises (only await/async) which worked. Then I asked to modify the code by adding a zip task, and it re-introduced the promisify utility when it added the zip process.

I finally mastered my reading list!

Over the years, I’ve tried a number of ways to plow through the never ending suggestions of books that I “need to read”. I’ve kept lists in paper notebooks, Facebook books, Goodreads, my iOS Notepad, and even as a Reminders list. The list keeps getting longer. I buy books I don’t end up liking or reading, or just forget to place one in the barrel next time a get some free time to read.

Recently I discovered a way to automatically manage my list and get the book in my hands in almost any format or device — for free! Here’s how:

First, download the Libby app.

Libby is an app by Overdrive that helps make checking out books from the library easy. No, don’t worry, it’s not a way to checkout paper books. Libby is focused on helping you download audiobooks and digital books and allows you to push them to your Kindle, iBooks or whatever works for you.

Now, before you disregard the power of your local library (the institution your tax dollars pay for) let’s flip the script. Libby allows you to grab books you’re interested in.

So, think of how it plays out: You hear about a book that “you need to read.” You search for it on your Libby app, and you place a hold on it. Yes, there is a wait list for your book, and popular ones often have longer wait lists. But, guess what? You don’t care!

This is your reading list!

When books are available, they pop onto you phone or Kindle. If you don’t have time to read it, just put it back into the hold lists for the next go around. If you want to read a few chapters and put it away, that works too. The hold queue isn’t just some arbitrary list you keep that is disconnected from the act of reading — they are one and the same.

I have been doing this for the past year and love the fact that I don’t need to feel bad about falling behind on my reading. I know I’ll just read the next book that becomes available, and not think twice about my queue.

It took a while to get to “reading zen”, so I thought I’d share it. Hope it works for you too!

How this Google Home app helped my father after his stroke

About a year ago my father had a stroke. After 70 years of work as a salesman, 6 days-a-week for 12 hours-a-day, this deficiency forced him into retirement. Hoping to get back to work, he received speech therapy but never fully recovered.

Now in retirement, his typical quiet demeanor at home has kept him from exercising his neural network to reroute his audio connections. He is not tech savvy, so my attempts to get him using games on Luminosity have been unsuccessful.

This Thanksgiving, during my visit to my parents house, I decided to see how he would fair with a Google Home. So far it has been great! Even practicing the wake word “Hey, Google” was a challenge at first, but he is improving dramatically.

Excited, I went through all the games I could find. I quickly realized just how unintuitive and disorganized the App side of Google Home still is. Some apps worked, and some didn’t. Either an app was “Not Found” or “Not Responding” when I tried to activate it. Sometimes an app would unexpectedly quit mid use. Even more frustrating were the multiple steps needed to try the above search for a working app over and over again after hitting a dead end. For example:

Me: “Hey Google” (Google Lights up)

Me: “Talk to X Game” (Wait)

Google: “Sorry, I could not find X Game” (wait for light to go off)

….Start over with another game name”

Navigation through the voice-UI was frustrating as well, and for my Dad it was impossible. To work around the issue, I went through all the games I could find online, and wrote down the ones that worked from the ones that did not. Then, I wrote our an old-school paper cheat-sheet that listed each game and its commands.

“Hey Google, – Let’s play a game”

“Hey Google, – Play 1-2-3 game”

….

What made it more complicated was that some trigger words required the user to say “Play” while others required the words “Talk to”. There is no reasoning I could find as to why there was a differences. What I realized is that these nuances were terrible difficult to retain for my non-tech savvy Dad. So, listing them out distinctly, on paper, and placing the paper next to the Google Home Device, was the best way I could provide the info to him.

One thing my Dad has retained since coming to the US is his keen memory of the US Presidents. I imagine he studied american history proudly and tirelessly when he moved here and sought his citizenship. Unfortunately, the Presidents Quiz, which I found listed in Google Home’s marketplace, and one I was sure he would like, was one of the games that was “Not Responding”.

At first I was disappointed, but then realized this was the perfect opportunity to try and build a Home App! I set out to create a US Presidents Quiz on Google Home for my Dad. 🙂

There are many ways to build a Google Home app. The two I explored were DialogFlow (https://console.dialogflow.com – formerly app.ai) and the “Actions” console (https://console.actions.google.com/u/0/). Dialog Flow had a great UI that made it seems like it would be simple to set up an interaction, but the concept of Intents, Events, Entities, Training Phrases and Responses was complex. What fed into what, and where I was suppose to handle requests from users and deliver responses did not come easily.

Google Actions is amazingly simple and perfect for those looking to build a game or quiz. WhileDialogFlow has many samples (https://developers.google.com/actions/samples/github) and plenty of docs, I decided Actions made the most sense, and I would leave DioalogFlow for another project; by using Actions, I could spin up an entire game in a single night. Interested in creating your own? Just follow this extremly simpley one-pager: https://developers.google.com/actions/templates/trivia. No code required!

The more labor intensive part of this project was listing out the hundreds of questions, correct answers, and purposefully wrong answers for multiple choice, I needed to seed the game.

You can check it our yourself, by saying:

“Hey Google, Talk to US Presidents Quiz”
Or by opening it in the directory here.

UPDATE:

Here is a print out for commands if you have a similar situation.

Firebase is 🔥

I’ve had the pleasure to watch the Firebase product grow from an idea our office buddies had as a startup, into a formidable product, and then to a suite of products at Google. I’ve been really impressed with what the founders have done. Hats off to them.

This is not a fluff piece for a friend though. To be honest, and for whatever reason, I never really used the platform until about a year ago; just didn’t have a need.

That has all changed, and, today, I see firebase as more than just a cool product, but one that I truly love and have received tremendous value from. Here is how I got there and why I feel that way.

Remember Parse? Facebook acquired the DB as a service in April 2013, and shut them down in Jan 2017. If I remember correctly, Firebase served as Google’s way to address that chasm and provide a novel, cloud-based, data platform that was especially friendly to mobile developers.

A lot has changed since, on the Firebase platform. Their systems is more than just a websocket based, real-time, hash database. It is a veneer to the plethora of services that sit locked away in Google’s not-so-friendly-to-use ecosystem.

It was very unlikely that I move from what I know in AWS, to what I do not know, and can not easily navigate, Google Cloud Platform. My initial need for a database that handled live-reloads on data update, grew into me using their storage, auth, hosting, serverless/functions, and logging services. In fact, it didn’t hit me that they were just tapping into GCP until I had to edit some auth/keys in the system; that’s just how seamless it is.

Out of curiosity, I tried to copy the same functionality of my Firebase system by setting up a GCP-only clone. It was a crappy experience! One I would never have taken the time to ramp up on otherwise.

With firebase, if you want storage, boom you got it. Want to right some serverless functions, easy. Checkout logs and crash analytics, yup you’re covered. Create a key to allow access to your system? No problem. In just a few click or a few lines of code, you can get up and running easily, and have the power of Google (without the admin overhead) behind you.

When it comes to filler features to help keep moving quickly, Firebase is there for you. Whether it is a beautiful auth flow (without a bias to only using Google auth), an invite system, or “who is logged in now”, Firebase does not say “that is not core – go some place else or build it yourself”. I have found myself coming back to them, even when a live-db is not a requirement for the ease in implementing those filler features alone.

If there was a critique, it would be that their use of storage for video is not top notch. They lag behind AWS for their ability to pull content seamlessly. Not much else.

GitLab and My Transition from GitHub

I was a heavy Github user. That is to say, I used them exclusively for my code projects. For a long time, there was no question in my mind of who to give my projects to. Even when Gitlab entered the market, my first thought was, these guys are just copying GH, why would I convert? Not to mention, hearing the rumors that the CEO is was a jerk didn’t entice me to rush to adopt.

A few crucial moments, and Gitlab releases, changed that way of thinking within a year.

The Conversion

Initially, it was sheer curiosity that got me clicking around on their product. That and the very low barrier to entertain that curiosity.

I had reached my “private repo” limit on Github, and of my private repos few were businesses and mostly projects that I experimented ideas with and/or coded up prototypes. So, I had reached that limit right when I had another idea I wanted to flesh out, and upgrading for a cost didn’t seem worth it. Out of curiosity, I went to GitLab and logged in.

As the name implies, GitLab did not shy away from their copy-cat beginnings as a GH clone. Because of that, I was able to login using GH credentials and import all my private repos for free. The conversions was instant and easy, and my access to an unlimited store of private repos sure did help. The copy-cat look and feel played to my advantage since there was no ramp-up required. What was different about the site were things I hated about GH. Like the wording on PRs (“MRs” in GitLab), or how I could create new files from within the UI.

All in all, an unexpectedly pleasurable experience.

Top of the Hill

My first experience was my gateway drug. Each new idea/project I started, I started in GitLab. It wasn’t too long after that I used them almost exclusively. Gradually, feature after feature, GitLab took that initial win with me and solidified it with feature I really loved having all in once place, like CI and CD.

Successful startups typically take one of two approaches: innovating on one thing and the rest is copy and paste, or, finding innovation as a combination of many non-innovations and putting them together in a beautiful way. For example, the first utensil was not a spork, and sliced bread did nothing more than combine bread and a knife in a novel, simple, and less expensive way.

GitLab is like sliced bread in that, they took a few things I already used (docker, git, CI/CD), and combined them seemlesless, and cost effectively, as their innovation.

I can very easily go from a concept-project, into a full blown production sized deployment suite in a matter of minutes. In its most basic form, GitLab is very easy to use and can be entirely free.

What keeps me happy is that they keep pumping useful improvements out; and I emphasize useful. It is not getting cluttered with features that get in the way, or as a way to prove they are hard at work. Rather, they seem to have a pulse on the dev community.

Where are they Still Losing?

One thing that has yet to change is the stronghold GitHub has on the community driven aspects of development. Their attention to open-source, from links to NPM package repos, to issues for projects, all keep me returning to GH on my google searches.

Will GitLab take that on next? We will have to wait and see!

Digging into the Monte Carlo Algorithm

After hearing about the Monte Carlo Algorithm over beers with friends one night, I decided to get a better understanding of how it works and learn a bit more about poker along the way. For me, there is no better way to understand a problem than coding up and launching a product around it.

Have you ever watched a Texas Hold ’em Poker Champion on T.V.? Every time a set of cards are laid out on the table the odds of each player’s hands is provided to the audience (for example, Lindh has a 75% chance of winning with his K and 9 of clubs above). Advanced poker players have become quite good at predicting the odds as a gut instinct and is partly why mathematicians enjoy the game so much.

In order to practice my ability to develop a second-sense for poker odds, I figured repetition was the key. The game I set out to create would lay out a set of cards and allow the user to predict the percentage probability of converting that to a winning hand, quickly, over and over again.

Of couse, there are far fewer total combinations of game-plays for a poker game as compared to a game of chess; so it isn’t rocket science. However, the variation in the number of players combined with a 52 card deck does create enough variation to make things interesting.

In order to make the solution robust, I used a Monte Carlo algorithm to generate thousands of possible outcomes randomly and recorded the statistical output for “player 1” to win. Once the algorithm was completed in Python, I built a Google Polymer app to present the probability guessing game.

You can test your ability to guess your probability of winning a text hold ’em hand in the game here.

GoGong: An open-source, non-SaaS, screen capture & share platform

There are many awesome Saas-based screen capture & share services in the market today. Typically they offer a client-app that, when installed, listens in the background for all your screen captures. Once a screen capture is taken, the app seamlessly uploads the image to the cloud and provides the user with a URL (added to their clipboard) that they can easily share with others. (For example, you can checkout two captures I’ve taken with Sketch and CloudApp.)

I love those apps! 99% of the time they fill my use cases perfectly. However, recently I was working on an intranet with hundreds of users and no access to a public internet. Of all the capture & share services I knew of, none could accommodate a closed network system. Do to that environment, I was forced to manually upload my screenshots as attachments when massaging my peers – which was a real PIA!

Enter GoGong.

I created GoGong as an open-source project to provide those working on a closed network access to a screen captire & share system; without concern of having any copied material exposed to the outside world. You can read more about the project, download the server and mac DMG, and contribute to the effort here:

https://sshadmand.github.io/GoGong/

In short, GoGong provides:

An installable DMG OSX client
A server to receive and host your uploaded captures
A completly open-sourced project
A platform that do not require a public internet connection

Hope you find it useful!

TuneTime: This Game App Takes Music Trivia to the Next Level

Now that I’ve learned how to natively develop for iOS, I’m starting to have some fun with it! In this case I’ve built a piano based music trivia game.

How it Works:
Each team attempts to play a secret song on the provided keyboard (via a simple play-by-numbers music score). The player gets no indication towards the timing or rhythm of the song, just the notes. The team must figure out what the name of the song is before the 30 second timer runs out.

Updates/Feedback

First time use of app user is very confused as to what to do next. Usually they click solo first, and skip over instructions.
1. Potential Solutions: A intro screen and walk through of the game would be helpful. Though that could be skipped too. So offering an “easy” version of the game that grows in complexity over time may be more helpful. e.g. less keys, keys to play light up etc
2. Note: Users wanted to keep the keyboard as the point of interaction.
Even when the instruction are read there is a lot of confusion on how the workflow of the game works. This was expected as Jackie and I even got confused playing it ourselves. Just didn’t have ideas yet.
1. Potential Solutions: One user used the game in a way that gave me an idea. Before I thought the lack of providing a rhythm would make the game fun, ergot I set it up so the player didn’t know the song name before playing (this add to a very complex workflow).
  This user wanted to know the song name they played ahead of time and seemed to have a lot more fun trying to communicate the song name through the keyboard, to the other players. This workflow would be MUCH easier to present and understand. I’m just not sure if it would be too easy. Maybe there are other ways to add complexity over time (per #1)
People who have never played the piano have no idea what “#” means. Though I suspected it wouldn’t matter since all the keys are labeled, it was to much information to digest on a first time use.
1. Potential Solutions: per #1. A “how to use the kayboard and music sheet” before presenting the keyboard would help. As well as lighting up the keyboard for first time game use.
Keyboard was a bit too small
1. Potential Solutions: Not sure how I can have many song with few keys. The original keyboard had bigger keys but couldnt get much out of it. This could again play into harder level, more songs and smaller keyboard.
When the game got going people had some laughs and fun, so the potential is there, just needs a much smoother and well informed experience.

BitLang: My First iOS App

You may have read how I recently learned to develop in Swift for iOS. Well, here’s a link to the app (BitLang) I set out to build during that learning process; now available on the iTunes AppStore.

More About the Inspiration and Background for Why I Chose to Build BitLang:

While on our “year long trip around the world” I found myself going back and forth between Duolingo (a language learning app) and Google Translate (a phrase translation app). While I love both products, they had a few shortfallings when it came to a couple of specific needs I had.

First, I wanted to learn from a pre-organized set of phrases that pertained to my immediate needs. I would then want to build upon that set of phrases with custom phrases of my choice.

Although I enjoy the general Duolingo-made learning process, it is built with a long-term lesson plan in mind. I am required to learn phrases like “Your horse ate my apples” to get familiar with the grammar of a language. What I want, however, is to learn phrases I can use for my one week tour through France. For example, due to travel plans this weekend I may just want to prepare myself to say, “May I have a local beer on tap?”, “Check please”, or “Two more red wines.”

Of course, I can accomplish my phrase-by-phrase translation needs using Google Translate, but it doesn’t do a great job organizing the translations by category or language. Also, it isn’t built to help me practice the phrases once I’ve looked them up.

I was frustrated by those gaps left between my favorite products. So, I posted feature requests to both product’s sites and got a “[not in our roadmap]” answer. (Which I completely understand and respect.) At first I was disappointed, but then I realized, “Hey, wait a sec, I know how to build things. Maybe I should just create a solution myself.” And so, BitLang was born.

The app is still a work in progress and is growing from its humble MVP beginnings. Here are the designs I mocked up for the first few iterations. Currently it just looks up phrases and allows the users to bookmark them into a single folder. It currently only translates for three languages: French, Spanish and German.

In the next few versions users will be able to login, view pre-made translation packages, and bookmark those packages. Beyond that I will start digging into deeper learning based workflows (quizzes and tests) as well as some community based features.

You can read more about the iterations I took in fleshing out the BitLang concept below.

Phase I

At first, I focused heavily on the learning part of the concept. Trying to simplify the lessons into premade (but pertinent) Q&A with very simple phrases. Users would translate a phrase one word at a time.

Phase II

Phase I became very complex, and the questions ended up getting pretty redundant. Also, I was missing the whole aspect of being able to generate a list of phrases that interest the user the most. For it all to work, the system would have to be made up of a custom lesson, not UGC. So for the next iteration I focused more on the “looking up of phrases” side of things. To make things even more challenging decided to build it while learning Polymer 1.0.

It was starting to come together, but as I mentioned in my key learnings for iOS development, I was forcing a web app model in what was obviously better suited for a native app. My lack of skills to develop in iOS has annoyed me for years so I figured it was time to make the move. That is when the BitLang app became to be.

Updates/Feedback

Is really looking for a tool that provides gender along with translations. e.g. spanish: Cup -> Taza … should be … La Taza
Is looking forward to the helpful learning side. One users suggests getting notifications for any words looked up. The interesting thing he asked here is: why should I organize things – If i’m looking it up I want to learn it so assume it.
Big win: Tons of people find the same holes in language learning tools. I’m not the only one. People urn for crash course mixed with lookup.

Key links to finally learning iOS development

If you haven’t picked up any iOS development skills yet, now is the time. It’s never been easier. Below are my reasons to finally take the plunge (successfully), followed by some helpful links to help you learn to create your first app too.

Contrary to popular belief, I’ve never coded up an iOS app myself. My excuse? For one, hiring great iOS developers gave me more time to focus on building great teams and products for my startups. In addition, Objective-C has a unique syntax and requires a deeper understanding of handling memory, which demanded even more learning time. Finally, there was an immense level of complexity involved in testing, certifying and delivering native iOS apps to market. As a matter of fact, those higher than normal learning curves inspired many startups (including a few that I launched) to focus on making developing apps easier.

Since I already had a strong web development background, I always found it easier to build prototypes for my ideas using the latest web-based, app-building, technologies. Year-after-year a new product claimed to have “the right stuff” needed to create an iOS app that felt fully native, without needing to learn to code directly in Objective-C. Year-after-year I found those claims to be more wishful thinking than reality. Although quicker to develop, those technologies always left the final product feeling hacky, unresponsive or limited, and, in order to go full steam ahead with a project, a fully native version would be necessary.

Earlier this year I took another shot at using a new piece of web tech to build out a mobile app idea I had. This time I learned Polymer 1.0. I loved it as a web framework, but my hopes that Google had managed to finally develop an SPA framework that translated into a smooth functioning mobile app was, yet again, overly optimistic.

It isn’t really the technology’s fault though. The rendering mechanisms for HTML/Web (et al.) just weren’t made to process smooth app-like features. It renders top to bottom, grabs all its assets remotely, makes a lot of inferences, is based on standards that try and work across an array of products made by a variety of companies, and manages general security measures that must be spread across every site. In the web world, the browser is the ad-hoc gatekeeper, and its fighting to keep up. The mission of a browser is critically different to that of apps: to allow a user to serendipitously browse a large breadth of sites in a single view, all the while protecting the user from exposure to malicious pages that are inherently sprinkled into a user’s browsing session. Native apps are different. Both the user and the developer have a strong working agreement between what the developer would like you to see and how the user would like to see it. With that level of trust the developer is able to confidently create an experience specifically tailored to the goal of the app and the interest of the user; the OS can focus on greasing the wheels.

…

Sorry, I digress. Point is, yet again I was disappointed in what the web (and web wrappers) could offer, and, almost as a yearly tradition, I took a stab at learning how to develop directly in iOS again. This time, I’m glad I did!

Maybe it was due to all the free time I had while on our year long trip, but I doubt it; it came rather easily this time around. No, I think the main contributor to my smooth transition is that Apple has done a stellar job incrementally improving the life of an iOS developer over the years. I think the real turn was the release of Swift in 2014. The language is a natural leap from other common languages, as compared to its Objective-C counterpart. Also, there is no longer a heavy requirement to understand how to manage an app’s memory and delegations. The other power ally in creating ease for iOS developers is XCode’s more powerful yet simplified environment, along with interactive interfaces like Storyboards, segues, IB Designables and more. In addition, now that TestFlight is fully integrated with iTunes Connect and Xcode, testing an app on a device, releasing it to external testers, and pushing it to the App Store is only a few clicks worth of effort; fairly brainless really.

All this added up to a surprisingly easily made V1 of my very first fully native iOS app! Yay! This will be fun 😀

Links to Learning iOS

Here are some key links I bookmarked while learning Swift in Xcode 9.0, including: vides, Q&As on StackOverflow, and tutorials. I strongly recommend learning the language by working toward implementing an idea you want to bring to life. Not only does it give you an inherent direction in what needs to be learned, but it also helps you push through the tough parts of learning that would otherwise spell defeat. The app I built used APIs, JSON, CoreData, Table Views (for listing data), Audio, and more. Hope this list helps!