I am completely amazed at how well voice cloning works locally on my machine.
Components
I already had a local MLX Whisper model running, which I use to generate subtitles from video. I thought that was all I needed. Then I asked Codex to build me a voice cloning tool in Python, and it went much deeper than I expected.
The tool ended up using:
Python
FFmpeg and FFprobe
MLX Audio text-to-speech
MLX Whisper transcription
Hugging Face model snapshots
Warning
Voice cloning needs to be handled responsibly. Permission, disclosure, and context matter. For this experiment, I used a historical public figure rather than cloning the voice of a living person.
What the Tool Does
Lets me choose a reference audio or video file
Lets me choose a speech text file
Detects and skips leading silence (FFmpeg)
Extracts and cleans a reference voice sample (FFmpeg)
Transcribes the cleaned reference using an MLX Whisper model (whisper-large-v3-mlx)
Generates new speech from text using a local MLX Audio model (higgs-audio-v2-3B-mlx-q8)
The wild part is that all of this runs locally. No cloud service. No subscription API. Just my Mac, some open-source tools, and a Python script that suddenly feels a little too powerful.
I have been experimenting with local AI for a practical reason: I wanted to make my personal photo and video archive searchable without paying cloud AI prices indefinitely. Cloud AI is faster and easier, but costs money each time and sends files to outside servers. Local AI runs on your own computer, so it is slower but more private and cheaper for repeated processing.
My collection is large enough to make this interesting: roughly 30,000 photos and videos gathered over many years. I originally stored them on Flickr, but the cost and export limitations became frustrating. One issue that stood out was Flickr’s migration tooling, including limits such as being unable to export albums with more than 500 photos at a time.
AI coding tools changed the equation for me. With Claude and Codex, I was able to migrate the media to AWS S3 and create apps to browse the collection on the web and on Apple TV.
The Problem
Moving the files was only part of the project. The bigger goal was search.
I did not want to search only by filename, date, or folder. I wanted to search by what was actually in the media:
“beach sunset”
“birthday party”
“dog in the backyard”
“old vacation video”
“kids opening presents”
“mountains with snow”
That requires descriptions, tags, and metadata for each item. For photos, this is fairly straightforward. For videos, it is more complicated because the AI needs representative frames or some kind of visual summary.
The Pricier Fix
A few months ago, I started a project to generate tags and short descriptions for the entire collection and store the results in MySQL.
My first version used cloud AI. I wrote Python scripts that sent images to the OpenAI API, received tags and descriptions, and saved the results back into the database.
Using an older model, GPT-5.2, I was able to generate about 30,000 sets of tags and short descriptions for the images. That worked, but it cost around $80 in API calls, and that was only for photos, not videos.
That cost was not outrageous for a one-time project, but it made me think differently. If I wanted to reprocess the archive, get more detailed descriptions, include videos, or run future batches, the cost would keep adding up.
Enter Local AI
While working on this, I started experimenting with local AI. On my machine, I don’t expect it to replace ChatGPT, Claude, or Codex, but I’m hoping to match that older GPT-5.2 model I paid for.
My local setup is simple:
Main AI machine
Apple M2 Max MacBook Pro, 2023
32 GB RAM
1 TB SSD
macOS
Long-running worker machine
Apple MacBook Pro, 2016 Intel i7
16 GB RAM
500 GB SSD
Ubuntu
The newer Mac runs the AI in macOS. The older Mac runs the long Python jobs in Ubuntu, so my main computer is not tied up all day and I can still use it for light processing tasks. The Ubuntu machine sends requests over the local network to the AI server running on macOS.
At first, I tried the usual local AI tools for Mac:
Ollama
LM Studio
AnythingLLM
I downloaded large Apple Silicon-optimized models, often in the 8 GB to 30 GB range, and tested them with chat, coding, and image-description experiments. On my hardware, the experience was not great. The models were slow, and the results were not impressive enough to justify the hassle.
Then I found this video and it changed everything!
That led me to try oMLX. It intelligently manages memory, which is the biggest bottleneck of running local AI.
Why oMLX Worked Better for Me
Some of the same models I had already tried felt much faster and more usable when loaded through oMLX compared with Ollama, LM Studio, and AnythingLLM.
What seems to make the difference is not just raw model speed, but how the stack uses Apple Silicon. MLX is built around the Mac’s unified memory architecture, and oMLX appears to make better use of that while also treating older context more like cacheable state than something that must stay fully resident in RAM at all times. For my workload, that matters more than benchmark bragging rights. I am sending resized images and a handful of video frames in long unattended batches, so a systemthat manages memory well and stays responsive under pressure is more useful than one that only feels fast in short interactive chats.
I am not running the latest Mac with 128 GB of memory – I am using a 32 GB M2 Max MacBook Pro from 2023, so memory pressure matters.
The tipping-point model for me was this model, on my machine, in oMLX:
Qwen3.6-35B-A3B-4bit
This model had barely been functional on my machine using Ollama/LM Studio/AnythingLLM. With oMLX, it became very usable – enough for long-running image and video description jobs.
It’s nowhere near today’s cloud models, but still useful enough for some tasks that don’t require higher thinking. It reminds me of where cloud models were roughly a year ago- around the time OpenAI was transitioning from GPT4 to GPT5.
The Cheaper Fix
The oMLX app makes it easy to startup the server, download models, and tweak the performance. Once I had the local AI server running, I started building Python scripts to query it.
The goal was to generate richer, larger descriptions for both images and videos, then save those descriptions into a new database. For videos, the script uses FFmpeg to extract sample frames and sends those frames to the local model as visual context. A simpler operation will follow to migrate that data where I host the image information.
The workflow looks like this:
The Ubuntu machine runs a Python script.
The script reads media records from MySQL.
For images, it resizes the image to a practical size before sending it to the AI model.
For videos, it extracts five frames with FFmpeg.
It also extracts and stores exif data using ExifTool.
The script sends the image or video frames to the oMLX server running on the Mac.
The local model returns a content description and tags.
The script stores the result in MySQL.
If the script stops, it can resume later without starting over.
Here is the basic idea of the video prompt:
Local AI Test
Before I started building this out, I used OpenCode to test the local AI model’s ability with the prompt above. I captured the session in the clip below, which also reveals the video content that was analyzed.
Result
The frames show a young boy wearing a white suit dancing at an outdoor nighttime birthday party. A decorated banner reading “Happy 50th Birthday Minnie” with colorful illustrations is hung on a block wall behind him. Colored party lights—red and green—cast spots on the wall as the boy moves across the concrete patio, appearing to dance. Several other people, including adults and possibly other children, stand to the right watching, with one person in a plaid shirt partially visible. Large leafy plants or bushes are visible to the right of the wall.
The Plan
The full plan is to let the Ubuntu machine run for days if necessary while the M2 Max handles the local AI requests.
The script will:
Traverse the database of roughly 30,000 images and videos.
Resume safely if the process is stopped or interrupted.
Use ExifTools to collect camera and location metadata where available.
Use FFmpeg to extract five representative frames from each video.
Query the local oMLX AI server for image and video content descriptions.
Store the generated descriptions and metadata in a new MySQL table.
The important part is that I am no longer paying per image or per video, and can run more computationally expensive queries such as full 1-2 paragraph image descriptions. Once the local AI setup is working, the cost is mostly electricity, heat, and time.
Challenges
This was not plug-and-play. I had to do a fair amount of tuning.
One issue was image size. Some of the images being sent to the local AI model were too large, which created context window and memory problems. That may also explain why the earlier cloud API run became more expensive than expected.
I asked Codex to recommend a practical image size for tagging and description generation. The script now resizes images before sending them to the AI server. The resized images are still good enough for recognition, but they are much easier on the model.
I also used ChatGPT to analyze my oMLX server logs and help tune performance settings, including:
Context window
Maximum tokens
Concurrent requests
Heat was another issue. During long runs, the MacBook Pro can get hot from sustained CPU, GPU, and memory usage. To reduce stress on the machine, I added a five-minute cooldown after every 500 requests.
This makes the 6-day job take 4 hours longer, but that is acceptable. The whole point of this setup is that it can run unattended.
Current Results
It is working.
The current run is slow, but steady. With around 30,000 media items and an average of roughly 22 seconds per item, the full job should take 6 days or more with breaks.
That sounds terrible compared with cloud processing, but it changes the economics. I can stop, tweak prompts, retry batches, and improve the database without watching a meter run.
For my use case, local AI does not have to be instant. It just has to be good enough, reliable enough, and cheap enough to keep running.
What I Learned
Local AI (for my hardware) is more powerful than I expected, but is much slower than cloud-models and takes more setup. Even so, local AI has its own advantages:
No per-request cost
Ability to reprocess data without paying again
Useful performance on consumer Apple Silicon hardware
A good fit for long-running batch jobs
For a project like tagging and describing a lifetime of personal photos and videos, that tradeoff makes sense.
Another surprising part is that older hardware still has a role. My 2016 Intel MacBook Pro is not running the AI model, but it is perfect as a worker machine that can run Python scripts all day. The M2 Max does the AI work, and the Ubuntu laptop keeps the pipeline moving.
That combination turned out to be exactly what I needed: one machine for local AI, one machine for automation, and no cloud bill for every experiment.
Built with Swift, SpriteKit, and an AI-assisted workflow (Claude + Codex)
This week I released a new game on the App Store: Alien Barrage. The project took about three months to build and ultimately grew into a roughly 20,000-line Swift/SpriteKit game.
While AI tools played a major role in accelerating development, the project still required significant engineering work: planning systems, designing gameplay, testing, debugging, integrating platform services, managing the AI workflow, and continuously refining the experience based on iteration and feedback.
The game itself was inspired by the classic arcade shooters I grew up playing. I combined elements I enjoyed from several games, added my own mechanics and pacing ideas, and let the gameplay evolve naturally throughout development. In many ways, Alien Barrage became both a technical experiment and a throwback to the arcade era.
Why I Chose Native iOS Development
I considered building the game in Unity, but ultimately decided to use Apple’s SpriteKit framework and develop the project natively in Swift.
Part of that decision was practical: I wanted to build more directly against modern Apple-native frameworks and services, including:
Swift and Xcode workflows
SpriteKit
Game Center leaderboards and achievements
In-App Purchases
Native App Store deployment
Localization pipelines
The game and App Store content were ultimately translated into 14 languages.
Coming from years of cross-platform development using Xamarin, .NET MAUI, React Native, and Adobe AIR, I wanted to push further into modern native Apple development and build more directly against platform-native frameworks and tooling.
Development with AI
The project was developed using a custom AI-assisted workflow built primarily around Claude and Codex.
Rather than treating AI as a “one click app generator,” I approached it more like structured pair programming. I directed architecture, gameplay systems, feature planning, debugging, testing, iteration, and project organization, while AI accelerated implementation and repetitive development tasks.
One of the biggest lessons I learned was that workflow design matters just as much as prompting.
Phase-Based Development
Before writing production code, I used AI to help generate a full phase-based development outline for the game.
Each phase had:
a clearly defined goal
implementation scope
testing criteria
isolated Git branches
completion checkpoints
The workflow looked something like this:
Plan a phase
Scope prompts tightly
Let AI implement the feature
Review and test manually
Refine edge cases
Merge the branch
Move to the next phase
This created a surprisingly clean development history with structured progression and meaningful commit messages.
No production code was generated until the overall structure of the game had been planned first.
What Actually Worked
Model Switching (Claude ↔ Codex)
I frequently switched between Claude and Codex depending on context limits, reasoning quality, or implementation drift.
This ended up having several unexpected advantages:
reduced long-context degradation
lower overall cost
forced re-grounding between phases
improved planning discipline
Different models also had different strengths depending on the task.
Human Validation Loops
AI would implement a feature and then stop, often providing testing instructions or validation steps.
I reviewed, tested, and refined features continuously rather than allowing large unverified code changes to accumulate.
That tight feedback loop helped keep the project stable even as the scope grew.
Git Discipline
Each phase was isolated into its own Git branch before being merged back into the main project.
That structure made experimentation safer and kept development organized as the game evolved.
AI Beyond Coding
AI was used for more than gameplay implementation.
The workflow also included:
asset generation
image processing
sound integration
video generation
documentation generation
command-line automation
Tools like ImageMagick and ffmpeg were integrated into the workflow with AI assistance, alongside ChatGPT for image generation and other production tasks.
Me vs. Me + AI
Realistically, I probably would not have had the time to build a project of this size entirely on my own within a few months while balancing everything else.
What AI changed for me was not the need for engineering judgment—it changed the speed of execution.
The combination of:
real-world software engineering experience
mobile development background
architecture planning
debugging ability
product direction
and AI-assisted implementation
turned out to be extremely effective.
To me, the process felt less like “AI replacing programming” and more like advanced pair programming with a very fast collaborator.
Cross-Platform vs. Native Development
For years I leaned heavily into cross-platform development.
My background includes:
Xamarin
.NET MAUI
React Native
Adobe AIR
The biggest advantage was always efficiency: one codebase and one primary skill set for multiple platforms.
But AI-assisted development changes that equation somewhat.
Recently I’ve been focusing heavily on native apps in Swift and Kotlin while using AI-assisted workflows to accelerate implementation, experimentation, and iteration.
That has made native development significantly more appealing than it once was.
My AI Coding Journey
I started experimenting seriously with AI coding tools in 2025 using Codex and later Claude.
Since then, I’ve:
rebuilt my Xamarin-based iOS app TimesX in native Swift
integrated Apple Intelligence-powered content generation into TimesX
replaced the original Xamarin version on the App Store
created a native Android version in Kotlin optimized for Chromebooks
built supporting websites and tooling
developed Alien Barrage using Swift and SpriteKit
experimented with Apple TV and Mac OS native applications for personal use
In just a few months, I’ve been able to build and ship substantially more software than I could previously as a solo developer.
I’ll admit it: I’m hooked on AI-accelerated development.
Not because it removes the need for engineering—but because it amplifies what experienced developers can accomplish.
Final Thoughts
One thing this project reinforced for me is that AI is most powerful when paired with real development experience.
Architecture decisions, debugging, testing, workflow design, platform knowledge, and product direction still matter enormously.
AI simply compresses the distance between idea and execution.
For experienced developers willing to adapt, that combination feels less like a threat and more like a significant advantage.
FFmpeg is one of those tools everyone knows is powerful, but can be complicated to use. It can do almost anything with video, but the learning curve is steep, and the syntax is unforgiving. Even after years of using it, I still find myself searching for examples or reusing old commands.
Recently, I experimented with using Claude as a kind of “translator” between what I want to do in plain English and what FFmpeg actually needs. The result was surprisingly effective.
The Problem
I had a simple goal, at least conceptually:
Take a screen recording of my iOS app
Turn it into a square video for Instagram
Use a slow-moving 4K cloud video as a background
Speed up both videos
Center the app video with padding
Add a QR code in the bottom corner linking to the App Store
Output a single, Instagram-ready MP4
The Approach
Instead of building the FFmpeg command myself, I described the entire process in plain English to Claude and let it handle the mechanics:
Trim the background video to skip the black frames at the start
Resize it slightly larger than the app video to allow padding
Match its duration to the foreground video
Speed everything up 2×
Center the app video both vertically and horizontally
Overlay a QR code in the bottom-right corner with padding
Name the output file
What stood out immediately was that Claude didn’t just generate a command—it ran and verified the output. If multiple steps were needed, it handled them without me having to reason about intermediate files or filter chains.
The Result
Less than a minute later, I had exactly what I wanted:
A square video
Animated cloud background
App video perfectly centered
QR code placed cleanly with spacing
Ready to upload to Instagram
I previewed it in VLC, and everything matched the mental image I had when I wrote the prompt.
Why This Matters
I’ve tried doing this same task in traditional video editors like iMovie, and ironically, it was harder. Tools with visual timelines can struggle once you step outside their expected workflows.
What made this interesting wasn’t just that AI “saved time.” It removed friction from a task that usually discourages experimentation. I didn’t have to remember FFmpeg syntax or worry about getting one parameter wrong—I could focus entirely on the outcome.
This also wasn’t really “programming” in the traditional sense. It was intent-driven tooling: describing a result and letting the system figure out the steps.
Takeaway
If you already know what FFmpeg can do but avoid it because of complexity, pairing it with an AI assistant like Claude is a game changer. It lowers the barrier without limiting capability—and it encourages you to try things you might otherwise skip.
Hopefully this opens up a few ideas for how you might use AI tools in your own workflows, even outside of coding.
A decade after TimesX was first released, the 2026 version receives a full rewrite in native Swift, along with a major new feature: AI-generated word questions.
There is a clear industry trend toward empowering handheld devices with artificial intelligence, visible across personal computers, phones, and wearables such as Meta glasses. Apple began including NPUs (Neural Processing Units) in its chips starting with the M1 (Macs and iPads) and later the A17 Pro (iPhone 15 Pro). This enabled new AI capabilities on iOS devices—such as face detection and image classification—but also introduced support for an on-device Large Language Model (LLM ), similar in concept to ChatGPT.
How Does This Affect TimesX?
Since its creation, the app supported only two question types: Multiple Choice and Type the Answer. With the 2026 rewrite, a third question type—Word Questions—has been added.
This rewrite made it easier to access Apple’s on-device LLM directly in code. On supported hardware, TimesX can now generate fresh word questions for every quiz using Apple Intelligence. An important benefit for security-conscious parents is that the AI runs entirely on-device and does not require an internet connection. Once installed, TimesX can operate completely offline.
What About Devices Without Apple Intelligence?
For devices that do not support Apple Intelligence, TimesX includes a pre-generated bank of word questions. The AI feature can also be disabled in Settings, in which case the app will always use the question bank instead.
What Else Is New?
Dozens of refinements have been made across layout, imagery, and usability. Some of the most impactful improvements are on the Error Counts screen.
Imagine a child using TimesX to practice multiplication tables across dozens of short tests each day. The app tracks questions that have been answered incorrectly at least twice and surfaces them on this screen. The update adds visibility into how many times each question has also been answered correctly.
When a child starts a Test from the Error Counts screen, the quiz is built entirely from these problem areas. Over time, as accuracy improves, a happy face appears next to questions that have been answered correctly more often than incorrectly—clear feedback that focused practice is paying off.
Conclusion
If you—or someone you know—has a child in elementary school where multiplication tables are part of the curriculum, TimesX offers a more focused and adaptive practice experience than traditional methods or most existing apps.
The audio in this video contains several languages and subtitles were generated using the process described in this post.
I recently watched a movie on Netflix with scenes in multiple languages: English, Korean, French, and Italian. During the foreign language scenes, there was no translation, just the name of the language spoken, like “[Korean]”. How disappointing…
In a nerdy fit of revenge I decided to fix this myself. So, I obtained an .mp4 video file of the movie and went to work. The tech I’m about to describe uses AI to listen to your movie’s audio, translate it from almost any language, and create subtitles. You could also use these tools for other tasks such as generating lyrics for music.
The tools involved are a combination of ffmpeg and mlx-whisper – a version of OpenAI’s Whisper model optimized to take advantage of Apple Silicon chips. The hour and a half movie I mentioned took less than 5 minutes to generate subtitles on my Apple M2 Max Macbook Pro with 32Gb of memory. I asked ChatGPT what makes mlx-whisper faster on Apple Silicon chips and this is what it said:
Unified Memory Architecture (UMA): Apple Silicon chips use a unified memory architecture, allowing the CPU, GPU, and Neural Engine to access the same memory pool. This reduces latency and improves data transfer speeds, which are crucial for machine learning tasks.
Metal Performance Shaders (MPS): MLX leverages Metal Performance Shaders, Apple’s framework optimized for machine learning on Apple Silicon. This ensures efficient execution of operations compared to traditional frameworks like PyTorch with MPS backend.
Specialized Machine Learning Accelerators: The Apple Silicon chips include dedicated machine learning accelerators that provide hardware-level support for common algorithms and models, significantly boosting performance for tasks like transcription in Whisper.
Batched Decoding: Implementations like Lightning Whisper MLX utilize features like batched decoding, which improves throughput by processing multiple inputs simultaneously.
Neural Engine: The Neural Engine on Apple Silicon is designed for AI tasks, offering high-speed performance for operations related to speech recognition and transcription.
Optimized Operations: MLX uses custom-implemented operations that are optimized for Apple Silicon, outperforming traditional CUDA and other GPU-based setups in some benchmarks.
What you’ll need
A modern Mac using Apple Silicon
The Terminal app
This is how you get ffmpeg and mlx-whisper on your Mac.
Follow the resulting instructions displayed in the terminal to make brew into a command. These were mine, specific to my user name on the machine. Copy yours from the terminal
Download the script from https://bootstrap.pypa.io/get-pip.py into a folder you can run the terminal from. You can also right-click the link and save it
python3 get-pip.py
Give it a quick test by typing pip and hit return
MLX-Whisper
pip install mlx-whisper
Give it a quick test by typing mlx_whisper and hit return
You can open the .srt file with a text editor and take a look, as well as make manual edits if desired. Now, you can either overlay the subtitle into the video, or add it as a track, so you could turn it on/off when viewing the video.
Now, suppose you wanted to do this to a folder of .mp4 files. You could loop through them with a shell script. I created this one and it worked for me:
#!/bin/bash
# Loop through all .mp4, .mkv, and .m4v files in the current directory
for video in *.mp4 *.mkv *.m4v; do
# Skip if no matching files are found
[[ -e "$video" ]] || continue
# Extract the file extension and base name
ext="${video##*.}"
base="${video%.*}"
subtitle="${base}.srt"
echo "Subtitling: $video"
mlx_whisper "$video" --task translate --model whisper-large-v3-mlx --output-format srt --verbose False --condition-on-previous-text False
sleep 3
# Check if the matching .srt file exists
if [[ -f "$subtitle" ]]; then
output="${base}_subtitled.${ext}"
echo "Creating video: $output"
echo " from subtitle: $subtitle"
ffmpeg -i "$video" -i "$subtitle" -c copy -c:s mov_text "$output"
else
echo "Subtitle not found for $video"
fi
done
Because my media player can play .mp4, .mkv, and .m4v files, and they all work with these commands, I also added those formats into the loop.
This past weekend was dedicated to getting my first app store submission in order. I was overjoyed that it was accepted on the first review!
Music: Pacific Sun by Nicolai Heidlas
Originally made as a learning tool for my son who was always on his iPad, I wanted him to get more multiplication practice for grade 2. I downloaded several apps which were entertaining and gave him practice, but I wanted more. I wanted to see if he was making progress, which questions he was getting wrong the most often, and how long it took him to do a test today vs last week. So, being a programmer, I made my own app for his iPad.
Features
Tests are saved on your device with letter grade, percent score, test time and more
With more use, ‘Error Counts’ shows you where your child needs help
Choose which times tables will be on the test
Limit for selected tables x10 for younger students or x12 for older
Live timer display optional
Programming
At first, I made the app in Xamarin Forms. This was going to be the quick and easy way to put it together for him to start using it. Also later, if I decided to port it, it would be 95% ready for Android as well as iOS. When I started transitioning the app from “my son’s learning tool” to “TimesX”, I ran into issues displaying it on ALL iOS devices from the same layout. That’s when I decided to leave “forms” and move on to Xamarin iOS. The constraints system implemented by Apple for XCode was made for this, and Xamarin carried that through in it’s iOS implementation. A bit of a learning curve but it’s second nature now – constraints were a great solution that allows this app to display on every iOS device from an iPhone 4s to an iPad Pro 12″, in both Portrait and Landscape.
Earlier this year I watched the documentary “Citizen Four” about Edward Snowden’s revelations on government spying. Unlike any other documentary I’ve seen, this one had me on the edge of my seat, feeling tense, shocked, and violated all at the same time. Though I have no illegal activities to hide, I can not be comfortable with the level of access the spying agencies have to our computers, cellphones, and other connected devices. Also, the increase of private spying (hacking) is so rampant, it seemed that protection from agency spying might also be increased protection from hacking.
I decided to step up my game and see if I could maintain privacy in this Orwellian environment. A month of research later, I came up with a solution – VPN. A VPN, or Virtual Private Network – encrypts all your traffic between your device and the VPN server. Here is an example scenario of a regular connection vs a VPN connection:
Scenario: Regular Connection VS VPN
-You and I are at a mom and pop ice cream store
-We are both on our iPhones using the FREE WiFi
-mom and pop have a son with ambitions to be the next “Mr Robot” super hacker
-son set up the FREE WiFi network we are using
-son has taught himself enough Linux and Network Administrator skills to Port Scan, Traffic Sniff, and see everything you are doing on his network (urls, IMs, emails, and more)
-son can NOT see what I’m doing. All he can see is encrypted chunks of data going back and forth to one location, which he can not decrypt
This is VPN. All my data – including URLS, requests, responses, etc – flow through a server in an encrypted connection. When I visit a web page, the url is part of the encrypted data between me and the VPN server, which handles the request to that web page. “Son”, or anyone between me and the VPN (like my internet provider) can not see what pages I visit or what is in my data.
Not All VPN’s are Safe
So what if the VPN provider decides to spy on me? Or logs all my traffic and uploads it to the N.S.A. ? This was something I dug deeper into in my research. My wish list for a VPN service provider evolved into this:
– no logging of my data
– good encryption
– good performance ( bandwidth )
– useable on my computers, phones, tablets (and all at the same time)
– decent price
– good reputation for privacy and reliability
I had VPN connections before with my work, but real privacy is something you have to pay for. On a company VPN, the company can still see your unencrypted traffic, because they operate the VPN server.
Speed
My final choice was Private Internet Access ( which I’ll refer to as PIA ). I have it set up on my Mac, PC, iPhone, Android, and Linux box. You can see PIA’s supported clients here. When not using VPN, I can download up to 12 Megabytes per second on my 100 Megabit connection. On the VPN I’ve reached up to 4 Megabytes per second, but typically cap around 2. These are good speeds considering that the VPN provider has to service many other individuals simultaneously. This is also more than fast enough for YouTube and other video streaming.
Geolocation
I have a choice of servers all over the country and all over the world. This gives me better connections where ever I am, but also allows me to “be” in other places when I need to be. For example, when in Canada, certain web sites re-direct you to the Canadian .ca versions of the page. Your IP location is used for redirection behind the scenes. On PIA, I simply connect to a US VPN server and the problem is solved. This could also hold true for people in countries with censorship and other restrictions, as the agencies blocking certain URLs and IP addresses would never see them in the encrypted VPN traffic.
Hacker Proof?
Other than being digitally safe in the ice cream shop, and being able to spoof my location, VPN has other advantages. My true IP address is never revealed when I surf the internet on VPN. If a hacker was trying to get to my computer via internet, my IP address would appear as one of PIA’s VPN servers. Getting back to my computer via internet should technically be impossible. Although there may be other ways hackers can get to your computer, blocking the passage through the internet is a big step toward safety.
Xamarin is a powerful development environment for creating apps for multiple platforms, using the same C# code base. The new Xamarin Forms technology will even allow you to use most of your UI code between different mobile platforms. Just have to say I LOVE this technology because I can make Android & iOS apps from the same code, rather than using 2 or 3 other languages.
Onto our app.. This example is meant to be simple and quick, and give you a taste of Xamarin development. We are going to make an app that shows a map, and then zooms into your current location when you click a button.
Prerequisites
You can deploy this app to the iOS Simulator or a real iOS device if you have that set up on your Mac already. Oh yeah, you’ll need a Mac, otherwise you’d have to do this somewhat differently on a PC using Visual Studio with Xamarin plugins. The app may be too big to deploy using the Xamarin Starter edition on its own. If you try to publish for iOS and get messages about the app size, take the option to start a free trial.
Create an iPhone App
First thing we want to do is create an iPhone app project in Xamarin. Choose File > New Solution
Give this solution the NameLocator. Xamarin will generate a project that should look like this:
Visually Build the Screen
Next, we visually build our app screen with some standard iOS components. To do that, go to the Solution pane on the left and double click the file MainStoryboard.storyboard. You should get a blank storyboard like this:
Note the Toolbox and Properties panes. We are going to drag components from the Toolbox onto the storyboard, size and position them, and then customize them in the Properties. Drag these components from the Toolbox onto the storyboard so it looks like the image below:a) Label
b) Button
c) Map Kit View
Customize Components and Add Hooks for the Code
To resize components, just grab an edge and drag. Let’s start with the Label first. Click on it and then go to the Properties pane in the Widgets. Change the Text property from “Label” to “Locator”
Next, lets give the Map Kit View a hook so we can access it in code. Click on it and set the Name property to “myMap”.
Finally, click on the Button and change the Title text to “Find Me”
Set the Name property to “findMeButton”
To detect the user pressing the button, we could set up the Events tab and write functions or we could let Xamarin generate this code for us. Double-Click the Button on the storyboard and Xamarin should switch you to the LocatorViewController.cstab, where you’ll see this yellow code hint:
Press Enter and Xamarin should generate this code:
Notice the variable type is red, which means that the class you are editing doesn’t know what a MKCoordinateRegion is. To fix, right click on it, and choose Resolve>Using MonoTouch.MapKit. If you scroll to the top of the class, you’ll see the MapKit class was imported. Now lets enter the rest of the code. The function should look like this:
You’ll notice as you type that Xamarin is suggesting code for you. This is similar to the IntelliSense feature in Visual Studio on the PC, and also several other programming IDEs. Next we need to add one more line of code to a different function. Scroll up to the ViewDidLoad function, and add the myMap line:
public override void ViewDidLoad ()
{
base.ViewDidLoad ();
// Perform any additional setup after loading the view
myMap.ShowsUserLocation=true;
}
Save your work and lets try this app out. Assuming you have the iOS simulator installed on your system, or have published to your iPhone before, press the Debug Button
Since the iOS simulator does not have a real location service like your iPhone, it will simulate one. You can change the current simulated location in the iOS Simulator using Debug > Location > Custom Location and setting the longitude and latitude. You can also simulate a moving location as demonstrated in this video:
Update for iOS 8
Just finished this post a week before iOS 8 officially rolled out and the update caused the application to break. I started getting this error message: Trying to start MapKit location updates without prompting for location authorization. Must call -[CLLocationManager requestWhenInUseAuthorization] or -[CLLocationManager requestAlwaysAuthorization] first.
A few extra steps are required to make this work in iOS 8 now:
Edit the Property List File (plist)
In the Solution pane on the left, look for the file Info.plist.
Double click it to open the tab for it
At the bottom of the window, click on the Source tab
Click on the green plus symbol to add a new entry
Change the text Custom Property to NSLocationWhenInUseUsageDescription
Click on the Value field for this entry and enter a message to prompt the user for location access such as Please allow this app to access your location.
Add some C#
Add this locationManager variable under the class definition. If the CCLocationManager is red, right click and choose Resolve>Using MonoTouch.CoreLocation
public partial class LocatorViewController : UIViewController
{
CLLocationManager locationManager;
Update the ViewDidLoad function to look like this:
public override void ViewDidLoad ()
{
base.ViewDidLoad ();
// Perform any additional setup after loading the view, typically from a nib.
locationManager = new CLLocationManager();
locationManager.RequestWhenInUseAuthorization();
myMap.ShowsUserLocation=true;
}
Again, this last section is only for iOS 8, so you would not need to do this for iOS 7.
Something I’ve been meaning to do is add a video gallery or list of videos contained in this blog. Rather than install a WordPress plugin, I decided to build one on my own using the popular JavaScript library, Angular JS, along with HTML, CSS, JavaScript, and a Lightbox library for showing videos. What I like about Angular for my Video List is that it has some functionality I would normally use PHP for, like dynamically drawing rows for each record of data. In this code screen shot below, the top half is the header row that contains the list filter and sortable title, while the bottom half is where all the other rows repeat for the data in the model.
<!-- this row has the filter input text and sortable title -->
<tr>
<td>filter: <input type="text" ng-model="searchText" /> </td>
<td>blog link <a href="" ng-click="predicate = 'title';
reverse=false">(a-z)</a>
<a href="" ng-click="predicate = '-title'; reverse=false">(z-a)</a></td>
<td>dimensions width x height</td>
<td>video link</td>
</tr>
<!-- this is where the rows repeat according to the data -->
<tr ng-repeat="vid in video_list | filter:searchText | orderBy:predicate:reverse">
<td><img src="{{video_thumb_prefix+vid.url_thumb}}"/> </td>
<td><a href="{{vid.blog}}"
target="blog"><h1>{{vid.title}}</h1></a></td>
<td>{{vid.vid_width + ' x ' +vid.vid_height}}</td>
<td>
<a href="{{video_media_prefix+vid.url_media}}"
rel="shadowbox;width={{vid.vid_width}};height={{vid.vid_height}}"
title="{{vid.title}}">{{vid.url_media}}</a>
</td>
</tr>
Notice the ng-repeat – thats the magic right there for looping through data. The other great feature is that the data is live. As you enter text in the filter, the rows render to match what you typed. Using Angular instead of a server side language enabled me to host it on my CDN as a static set of files (vs dynamic). It’s all .html, .css, .js, and .mp4 videos – no PHP. Download the source to see how it all ties together with the data:
