Having been in the industry for more than two decades, I’ve seen several “revolutions” come and go—the shift to web-native apps, the rise of cloud computing, and the transition to mobile-first development. Each of these changed what we built. However, the current AI boom is fundamentally different because it is changing how we build.

The rapid advancement of Large Language Models (LLMs) and Generative AI has moved beyond simple autocomplete. We are no longer looking at just a “better Google search”; we are looking at a collaborative partner that sits inside our IDE. For “old school” developers like myself, it takes time to fully embrace this shift and ride along with a totally different style of problem-solving.

From Hesitation to Full Embrace

It took me a few months to truly integrate the AI programming model into my daily workflow. My journey followed three distinct stages:

• Stage 1: Initially, I was only using (and enjoying) multi-line autocompletes from the Copilot agent.
• Stage 2: A few months ago, I started using “Agent mode” in the IDE, allowing the AI to access context from multiple files to write about 10–15% of my daily code.
• Stage 3 (Current): I am now using tools like Claude Code to handle nearly 60% to 80% of my professional coding tasks.

I must admit, I was originally quite skeptical of its usefulness. I kept thinking, “How much can you really expect from a ‘next-word predictor’ model?” It sounds like glorified autocomplete on paper. But as with many things in engineering, you have to actually use it in the trenches to believe it.

Am I loving it? Absolutely. Here is why I believe this shift falls into three high-value categories:

1. Reducing Cognitive Load

It is common for modern developers to juggle multiple projects, frameworks, and languages in a single day, often switching contexts within hours. Having AI agents embedded in the IDE means I no longer need to manually recall specific syntax. For example, I often struggle with the nuances of foreach loops across different languages. Now, 90% of the time, the AI agent fills the block as soon as I type the keyword. This makes programming less stressful and significantly more fun.

2. Accelerated Problem Solving

I’ve always loved the “puzzle” of coding—it’s why I still spend 70% of my work week and my free weekends programming. You might ask: if an AI is solving the problem, where is the fun? The balance comes down to delegation. If a problem is already “solved” or lacks a unique challenge, I treat the AI like a highly capable junior developer. I let it handle the implementation while I focus my energy on the overall architecture and high-level design.

3. Bridging the Skill Gap

This is best explained by a recent experience. A few days ago, I was debugging network congestion over TCP traffic. Without deep, specialized knowledge of the TCP traffic controller, I fed the following output to my AI agent:

tc class show dev bond0

Within seconds, the AI analysed the output and identified a significant bottleneck caused by Hierarchical Token Bucket (HTB) rate limiting, which was choking my WDT (Warpspeed Data Transfer) performance. It even provided proposals to isolate and fix the issue.

Could I have found this myself? Yes—I’ve done far more complex analyses in my 20-year career. But it would have taken hours of research to understand every parameter and its “normal” range. This is where AI shines: it acts as a domain expert, providing directional hints that allow you to skip the tedious learning curve and jump straight to the “fun” part of analysis and isolation.

Tips & Tricks

Enough with the theory—let’s talk about the actual “mechanics” of how I’ve shifted my workflow to speed up development. If you want to move from 10% AI-assisted code to 80%, you have to stop treating the AI as a search engine and start treating it as a specialized member of your team.

1. Provide Deep Context (The “Project Knowledge” Hack)

As with any conversation, context is key. This is especially true with modern LLMs, which are highly tuned to orchestrate task plans based on the information they can “see.” While the current file is enough for a simple inline function or a small autocomplete block, it isn’t enough for an AI Agent to perform tasks spanning multiple files. For that, a compact and relevant context becomes essential.

The most effective way to provide this is via “AGENT.md” markdown files. These files act as a project’s “brain” or “memory,” providing persistent context and instructions for tools like Claude Code. They help the AI understand project-specific standards, workflows, and architecture across different sessions.

Your context files should be a living repository of:

• Terminal commands: Common build, test, or run scripts.
• Workflows: Step-by-step instructions (e.g., “When creating a new API endpoint, plan the changes and seek confirmation before writing code”).
• Coding standards: Specific conventions for naming, indentation, or comments.
• Domain-specific jargon: Definitions for project-specific terms.
• Architectural decisions: High-level overviews of the project structure or design patterns.

To make these documents most effective, follow these suggestions:

Layer your instructions: You can use multiple files at different levels (User, Project Root, or Subdirectories) to manage complexity and provide specific context only when the agent enters a particular module.

Keep it concise: I cannot stress this enough. Long files consume more context tokens, which can make the AI less effective and prone to side-tracking. Keep instructions compact; agents can explore the details as needed.

Cross-reference, don’t repeat: Instead of listing every coding style, point to your .editorconfig or .eslintrc. The agent can pull those files into its context automatically.

Trust the AI’s “Pre-training”: You don’t need to document every library. If you’re using Keycloak or Entity Framework, just mention it. The AI already knows the standard flows and constraints; only document where your implementation deviates from the norm.

Use Version Control: Check your AGENT.md files into Git. This ensures your entire team—and their respective AI agents—are operating from the same “playbook.”

2. Build Your AI Agent’s “Skillset”

While modern agents are increasingly capable, they often lack the specific “procedural knowledge” required to perform complex, real-world tasks reliably. This is where Skills come in. By defining a skillset, you give your agent access to company-, team-, and user-specific context that it can load on demand.

An AI agent equipped with the right skills can extend its capabilities far beyond just writing code. A skill could be anything that integrates into your daily workflow, such as:

• Infrastructure Automation: Starting up Docker Compose to run a specific suite of integration tests.
• Project Management: Fetching and summarizing task details from your company’s issue-tracking system (like Jira or GitHub Issues).
• Deployment: Triggering a specific CI/CD pipeline or checking the status of an AKS cluster.

The good news is that you don’t have to build these from scratch alone. Modern AI agents can actually help you write their own skillsets—or at least provide the initial draft. Once the foundation is laid, you can fine-tune these skills based on the agent’s performance. It is an iterative process of trial and error, but with a bit of effort, you will end up with an agent that is perfectly comfortable with your specific development workflows and can automate them end-to-end.

If you have never created AI Skills before, I would suggest going through the Agent Skills tutorial. If you created a few before, it might be a good idea to look through their Best Practices section to make sure you are making optimal use of these skills.

3. Architect It Yourself; Implement via Agent

AI Agents, at least for now, are excellent at solving localized problems but often struggle with designing complex, large-scale architectures. Furthermore, much of the code available online—the data these models were trained on—is not always “A+” quality. If you let an AI build a solution from A to Z without guidance, it might work, but it likely won’t be the most maintainable or architecturally sound codebase.

While the definition of “maintainable” might shift as AI handles more of our code modification, we are still in a transition stage where code quality and readability remain paramount.

The approach that works best for me is splitting the task into manageable, high-level chunks. Using the Slurm Microservice as an example, my process looks like this:

A. The Design Phase (The Architect)

I start by defining the boundaries. Instead of asking the AI to “build the service,” I provide a focused prompt to establish the contract:

“I am implementing a Slurm Job API management service. This service needs methods to: Get all jobs (with optional filtering/pagination), get details for a specific ID, and handle management actions like Hold, Requeue, and Delete. Create a C# interface for this service.“

B. The Review & Brainstorm (The Lead)

I review the generated interface and DTOs. If they need adjustment, I either suggest changes or tweak them manually. Once I’m happy with the contracts, I move to the implementation stage—but I don’t just hit “go.” I ask the agent to:

“Brainstorm the implementation of this interface. Ask me any clarifying questions about the underlying CLI wrapping strategy or error handling before you start writing code.”

C. The Implementation (The Agent)

I treat the AI exactly like a junior developer. We go back and forth until I am confident it has all the context it needs. The agent then works for a few minutes, and the end product is something I am 90–95% happy with. A few final manual tweaks later, and a task that would have taken several hours is done in less than one.

4. Parallelize Your Agents with Git Worktrees

Once you have an AI agent actively handling 80% of your coding tasks, you will quickly notice a new kind of bottleneck: AI Latency. A sophisticated agent like Claude Code can take several minutes to process large files, orchestrate a task plan, and generate new code. While it is still far faster than writing it yourself, you are often left waiting, effectively blocked from making progress.

While you could use that time to read documentation or answer emails, a much better use of that time is to tackle another engineering problem you’ve been deferring. This is where combining AI Agents with Git Worktrees becomes a game-changer.

If you haven’t utilized this powerful Git feature before, here is a quick overview:

What is Git Worktree?

git worktree is a native command that allows you to maintain multiple working directories simultaneously from a single repository. Each directory (a “worktree”) has its own unique branch or commit checked out. This means you can work on two different tasks (e.g., a complex refactor and an urgent hotfix) concurrently, without the constant need to stash changes, switch branches, and rebuild your environment in a single directory.

The AI Workflow Parallelization

Instead of cloning the entire repository multiple times (which is wasteful), Git Worktree lets you check out multiple branches inside the same project directory.

The immediate benefit for an AI-driven workflow is that you never have to close your AI session or interrupt its context to work on a separate bug. You can have one Claude Code session running a lengthy refactoring task in Worktree_A, and immediately open a second worktree (Worktree_B) to investigate a crash report, without your agent losing the complex context it just built.

Bonus: Most of the specialized coding agents are now introducing native support for Git Worktrees. If you are using Claude Code, for example, I highly recommend reading their documentation on Running Parallel Claude Code sessions with Git worktrees. It is the closest you can get to multi-threading your own productivity.

Conclusion

Obviously, this list is not extensive, and I will hopefully keep updating it with new tips and trick as I learn myself. If you have any of own, please do share.

Chat-GPT (Generative Pre-trained Transformer) is a type of natural language processing (NLP) model developed by OpenAI, based on the GPT architecture. It is a large language model trained on a vast amount of data to generate human-like responses to natural language inputs. Chat-GPT can perform a variety of NLP tasks such as language translation, text summarization, question-answering, and conversational agent…

And while all its language-processing capabilities are amazing (sometimes unbelievable), here we are mostly interested in its “translation” function and how we can use it to translate subtitles. As many of you know, I have been developing Online Subtitle Translator for a few years now, and it supports various machine translation engines and APIs like Google, Azure, DeepL, etc. All of these are machine translations, which, while doing a great job – if you keep in mind these are not being done by humans – machine translation is still far away from being perfect. But the gap is shrinking quite fast, and Large Language Model-based AI models are probably the future way to handle machine translations. Chat-GPT by OpenAI is one of these LLM models and is the main host of today’s blog.

Being a user of various translation APIs, I have been keeping an eye on GPT translation capabilities for a few months now, and I must say I’m super impressed with some initial results. The GPT translation is coming on par and sometimes exceeding when compared with some long-term translation veterans like Google or DeepL (as discussed here and here), which are specifically designed to do only a single task i.e. language translation. What’s even more interesting is these models were never targeted to perform content translation; rather, this comes as a side-effect of their powerful natural language processing capabilities – more like human brains do these translations?

I have been playing with the language translation capabilities of Chat GPT for subtitles, and I found that it often generates far superior translations than Google and Azure translators for the English <-> Urdu translation pair. I can’t personally say how it competes with DeepL, as DeepL does not currently support my native language for translation. Therefore, I will let my readers answer this for me.

The integration of Chat-GPT in the Online Subtitle Translator is not that different to other paid translators like Microsoft Azure or Google, as shown below:

After you select the Translator and Target Language for your subtitles, the user interface shows the following parameters:

From Language: This one is optional, as in most cases, GPT can detect the correct source language from given content. However, providing the correct language may improve the overall translation quality.

OpenAI Model: The choice of the OpenAI language model affects the overall translation quality and cost. You can read more about it here. For now, only a single model is supported, but support for other language models will be added soon.

Translation Type: The Free mode is for demo or evaluation and is limited to 30 lines only. This one is a good choice if you want to just get an idea of the overall translation quality and experience of this new translator before paying for it.

As you may notice, the OpenAI translator is substantially cheaper (sometimes 5-10 times) when compared to other paid translators. But then it’s also painfully slow. More on this is below.

Why is it so slow?

So you tried Chat-GPT and liked the translation quality, but I’m sure you will notice that it’s extremely slow. Like, even for a 20-30 line translation, it sometimes takes 30-40 seconds. And it takes ages for some reasonably large subtitle files for videos and movies. The reason it’s so slow is mainly due to the following two factors:

1. The first and most important one is the overall infrastructure overload. OpenAI Chat-GPT users went from thousands to millions in a very short period. They are working hard, but it may take some time to adjust their infrastructure to handle all this demand.
2. The second is how these LLM models work on paragraph completion tasks (in our case, translation) in general. These algorithms mostly work on given content word-by-word (or token-by-token to be exact). Making it hard to parallelise the completion of tasks.

But the good thing is that research and work are being done on both hardware and algorithm parts, and I’m sure things will improve substantially in the near future.

What’s Next?

Integrating the first LLM-based translator for subtitle translation was an interesting and challenging task. Unlike other translation APIs, here, instead of passing the correct source and target language parameters, I had to build a translation task prompt/command to achieve the correct translation. I also have to split and join the subtitle lines to minimise tokens and keep context between lines for improved quality. There is probably still room for improvement here, so I will keep reviewing and improving these parts.

Another interesting point of discussion is whether I should expose various AI model tuning parameters to users or not. Like, should I expose model turning parameters (e.g. to control creativity vs predictability) to the translation user interface? Open to suggestions here.

Also, there are a few more interesting LLM models than OpenAI Chat-GPT (from Google and Facebook, to name a few). Probably time to explore a few of those, too? If you have any suggestions or requests, please feel free to let me know as well.

While this was possible before using my Online Subtitles Translator too, the process itself was quite laborious – where the user has to upload a single file, but for each target language translation, do the steps like these:

1. Selected target language
2. Click the “Translate” button to begin translation.
3. Wait for it to finish (a few seconds to a minute or so depending on length).
4. Do any post-translation edits.
5. Save-As the file

And then repeat the above steps for each new target language. The new enhancements to the Subtitle Translation tool make it quite convenient to translate to multiple languages too. All you have to do is upload one or more subtitle files (.SRT, .SUB, .ASS and .VTT formats supported) by dragging these to the drop file area:

Once that’s done, select your target translator like Google, DeepL, Microsoft, etc – yes, it works with both free and paid translators. And select your target language. At this point, now you can add multiple translation languages by clicking the button, and selecting one or more target languages as shown below:

Tip: You can save your language selection by clicking the “Save” icon at the bottom of the list. This way, the next time you upload subtitle file(s), you can restore your last selection by clicking the icon next in the Translator bar.

Once you are happy with the language selection, just click the “Translate” button as you normally do for single-language translation, and wait for it to finish. Once the translation is done, you can review – and edit subtitles – if you like by selecting the translated language from the drown-down above the translation area as shown below:

And that’s mostly it! Once you are done with the review process, you can download all these translated files in a single ZIP file format by just clicking the “Save All” button:

Hope you find this a time-saving feature, and that it helps promote your videos. As always, I’m looking forward to your suggestions and feedback.

As you may have guessed, unfortunately, this one is also a paid translation service, and is based on the same payment model as already supported Microsoft Azure translation. But I think this will still be way cheap option compared to human translation services, and hopefully its quality is somewhat acceptable too.

The translation process is not very different from the previous free options. Once you select the “DeepL” translation options and target language (the source language is auto-detected), you get the info about how much it will cost and can also select the content length i.e. Full or Preview as shown below:

Tip: If you are trying a new language pair, or you just want to compare different translators, selecting the “Preview” option will only translate first hundred lines and thus only charge you for these lines. So this one is very economic option to compare the various translators.

Once you are happy with language and cost, just click the Pay Now button to make the payment using very secure PayPal payment system:

Once the payment is made, translation should start almost instantly. Depending on the content of the file size, this translation process may take from few seconds to a minute or two, but should not take very long. Plus you will see translation progress too. Once the translation is complete, the translated subtitles are displayed in the preview/editor table as normal:

That’s all for now. I hope you find this new translation service of help. As always, if you have any other suggestions or comments, please do let me know.

• New integrated map view for both static and mobile locations.
• Easy access to permission and log page for mobile locations.
• Smart and secure location sharing protocol.
• Andy many small bug fixes…

Thanks to your feedback and patience, this build contains most (if not all) reported issues, plus lot of user-interface tweaks to make it easier to use this app, and quickly access various areas. From the new settings drawer to the direct map view, the goal is to just make the features accessible with as less clicks as possible.

While I strongly encourage you to try this app yourself, but to tempt you even more, here are some screenshots of the new layout:

Hope you enjoy this updates, and as always, looking forward to your feedback and comments.

– Some clients were using Deflated XML while others were not.
– Encryption Algorithms choice per client was quite different.
– There were differences in the NameID format being used.
– Differences in how SAML attributes were being communicated/passed.

Now when I look back, I think it may have been best if we have instead opted for an existing C# library to support SAML. If you ask for my recommendation on C# SAML 2.0 libraries, I would suggest these (in listed order):

Microsoft SAML 2.0 (WIF)
ComponentSpace.com SAML v2.0
C# SAML 2.0

Even though I now think using a pre-built library would have been the rather best option, the custom implementation was a great learning experience, and also provided us more control on enforcing some custom integration rules for our clients. If you want to implement SAML or just are exploring existing libraries, here are some of the tools/tips I would like to share:

1. XML Schema Definition Tool

The XML Schema Definition (Xsd.exe) tool generates XML schema or common language runtime classes from XDR, XML, and XSD files, or from classes in a runtime assembly. What I did in my case was to give this tool an XML Schema for the SAML 2.0, and it automatically generated all the C# classes for the SAML 2.0 object hierarchy. This was of great help in overall implementation.

2. SAMLTool

This website not only provides very good documentation on the SAML, but its Online Tools are of great help in debugging and resolving various SAML integration issues. The common tools you would use during development and debugging are Base64 Encoder/Decoder, XML Inflate/Deflate, GZip, and Encrypt/Decrypt XML. Another tool worth exploring is the meta-data generator for Identity Provider and Service Provider.

3. TestShib – SAML Online Testing 

Once you have developed and deployed your SAML Identity or Service Provider, it’s common that you will run into issues while working with various clients who are using different implementations of SAML 2.0 protocol. This can be due to some configuration issue on your/client end, or a problem in your implementation of SAML. In any case, I think this online testing tool is worth exploring and trying. To use this too, all you have to do is upload your metadata file, and you are ready to test your Identity or Service Provider.

Tip: When testing your Identity Provider, if you get “Unable to locate metadata for identity provider” error after just uploading an new Metadata file, then all you have to do is wait for few minutes. My experience is that it usually takes 2-3 minutes before metadata is accessible to their testing server after uploading.

In the end, I would say it doesn’t matter if you either do your custom implementation for SAML, or decide to use an existing library, but with the right development, debugging and testing tools this tricky project becomes a lot of fun and is a great learning experience.

Based on these lines, this new Jack Sparrow version brings this magical property to your beloved compass as well, and now it can not only point to a static geo-locations, but can also point accurately to any moving object or person. So, for example, if you want to track where your friend or family member is at this time, or where is your car at the exact current moment, using this app, you can get target mobile object exact location, distance and direction from your current position.

To add a new mobile location, you start by selecting the “Add Mobile” option from the target location drawer as shown below:

Add Mobile User

When you click this, the App should display you a list of all your saved Contacts. You should now select your mobile contact whose location you want to track or request using this App.

Warning: For this to work correctly, the target user Android phone must also have the latest version of the Jack Sparrow Compass app installed so that it can process the location requests.

Once you select the target contact, the App sends an SMS to the target user to let him/her know that you want to query about that his location. Since, sending the SMS may cause chargers on your mobile account, before application does this, it asks for your permission with following confirmation dialog:

Location Request Message Confirmation

If you “allow” the App, it will send a location request message to the target user.

Tip: At this point you can also select the value from remember dropdown/spinner control to remember this preference for selected time period. If you have data plan with free SMS messages and/or you want to track this user location frequently, you can select the “Remember choice forever” option so that App remembers this info, and doesn’t display this confirmation dialog again for this user in future.

Now if the target user has the latest version of the Jack Sparrow Compass app installed on their Android phone, within few seconds (sometime it may take longer due to some issues on service provider’s end), he/she should get the location request which is shown in the Notification area if the Jack Sparrow Compass is not the active App at that time.

Once that user clicks on that notification, App will open, and will show all the pending notifications requiring your approval. Like for the above request, following location request confirmation dialog will be displayed to the recipient user:

Location Request Warning for Target User

Once the target user allow this request, then his/her latest location will be calculated, and sent back to you via SMS message. Once you get the message back from the user, the App will process that message, and if it contains a valid location data, it’s displayed in your target locations as shown below:

Mobile Location in List

Once the mobile location is visible, it should work like normal Geolocation, and you can select it any time from the target location drawer. Once Mobile location is selected, the App should show its distance, address and Compass will also point to that location. The App also shows the last time the location was received from the user in the top bar as shown below:

Mobile Location View

Please note that Mobile locations are not automatically or live updated. For example, if you requested a user location, and then he moves away from that location after few minutes, the App will still point to the old location. If you want to get the latest location, you can do this by clicking on the “Refresh” icon in the action bar.  For this, App will send again an SMS to the target user, and thus chargers may apply on this action.

With all these new features, a couple of new screens and preferences have been added in the Application settings to give you full control on the saved permission and location tracking. The screenshot and the details of these preferences is discussed below:

Location Settings

Location Notification
This preferences controls whether or not to show the location request and response notification in the Android. If you frequently send and track the locations, you may prefer to select the “Hide” or “Show only for Unapproved Requests” option to avoid unnecessary notifications in your phone.

Permissions
This setting page shows all your saved permissions. If you wrongly selected a permission options, or have change your mind and want to alter some previously assigned permission for a user, this is where you should look for.

Message Log
This page shows list of all the request and responses messages sent and received by the App. It’s mainly used for diagnostic purpose (when setting up some new location tracking), or for auditing of the App.

Location Tracking
A “Peace of Mind” setting. If you want to temporarily disable all location requests e.g. run out of the SMS package or don’t want your location to be shared temporarily, this is what you can “un-check”.

Communication Protocol
For most of you the “Data SMS” should work fine, but in case, it doesn’t work, you can try choosing the “Standard SMS” option. The one main disadvantage of the “Standard SMS” messages is that these messages are also displayed in your native Messaging App, so make it cluttered.

The only thing that’s changed is everything.

Or if I say this in my own wording, then “The only thing that’s not changed is Jack Sparrow Compass legendary dial.” If you don’t believe me just look at the following screenshots of the new App:

Now if you are normal user, then this info should be enough for you. You can try downloading the new version, play with it, explore and learn as you seem fit and get free time. I hope that new interface is intuitive enough to let you learn all features by exploring. It’s more fun this anyway, and you will be Columbus on your first voyage!

For those of you how prefer to know each ins and outs of the new features, you can find most of the information below:

1. User Interface Changes

Most of these user interface changes should be noticeable visually anyway, but in any case, on the main Compass page, the interface is changed to be more standard e.g.

• Android native header is  now being used.
• All the action icons have been changed to Android standard look, size and behaviour.
• Toggle drawer has been added to let you easily view the target location, and switch easily between them.
• Current location source i.e. GPS, Network and Cell Tower is now displayed in the top status bar.
• New location type specific icons have been added in various lists and selection dialogs to make them easily distinguishable.

Plus few other fixes and tweaks along with improvement for smoothness of the compass dial have been done. Hope you like all these changes.

2. Location Search

Pick a Place

Now using the new search icon (magnifying glass) in the header, you can search for all the geographical locations, and then add them easily form the Google maps. For searching the locations, the App supports following two modes:

2.1 Best Match (Default)

As the name suggest, in this mode, the App will try to find the best matching location for the given search string. For example, if you search for “Golden Gate Bridge”, then the returned search results page will show the map of Golden Gate Bridge in “California, United States” no matter what you current location is. This most of the time makes sense because you are searching for popular locations anyway.

2.2 Proximity

Now suppose what you want to find out actually is the nearest ATM machine so that you can quickly go there, and get some cash. In this scenario, all the world popular ATM locations in USA are of no interest to you, and that’s where this search mode comes in place. When the search mode is set to “Proximity”, the App prefers the locations matching the given string which is nearest to you instead of its popularity or best match.

2.3 Pick a Place

This one is actually not a search mode, but another option accessible from + button of the locations drawer on the home screen. When you select this option, App uses the Google provided “Pick a Place” widget to help you easily search the location and add it. The search results are far better than above two modes, and most of the time should match what you can find by searching on-line on the Google Maps website. It also recommends other locations based on your search query.

So, this sounds like almost perfect choice, but it’s with one catch, and that’s there is a daily limit by Google on how much locations can be searched using this widget. So, if this is not working for you, please excuse me and try using the native search

Note: when the limit has expired, App will open the blank new Add-Location dialog instead of Pick a Place widget.

3. Location Manager

Location Manager

The new Location Manager screen is a central place to manage all your saved locations. You can create new one, edit existing, or delete the ones you added by mistake or the one which are no longer need.

One new exciting feature of the Location Manager is that it also lets you export and import all you locations. So, whether you want to make a backup of all your existing locations to restore in case of data loss, or you are switching to another device, and want to copy over the locations. Import/Export is the tool you are looking for. The import and export actions are available in the screen menu, so hit they Menu key to view these options.

The locations are exported in simple CSV format (you can edit this with MS Excel along with many other on-line editing tools). If you want to create new import file from scratch, the recommend option is to create a new dummy location, export it and then see how the exported file is structured to help you add all your new locations.

You can also share you exported locations file using the Share action in the Location Manager menu to Email it, do a WhatsApp message, or send it via Bluetooth.

4. Locations Map

Locations Map

Locations Map screen, similar to the Location Manager, lists all your locations, but in a more intuitive way. On the Locations Map screen, all your saved locations appear over the Google Maps with correct geographical coordinates. So, you can visually see where a location exactly is on the world map.

When this screen loads, it tries to do a best Zoom to show all your saved locations. For example, if all you saved locations are in the Austin, Texas city, then this screen will auto zoom to Austin city so that all you saved locations are easily visible. Similarly, if your saved locations are scatter here and there all over the USA, then the map will be zoomed to show the USA map. You can also change the Map type (Normal, Satellite or Hybrid) to match you best needs.

Some times, it’s more easy to add the new location directly from the Google Map, and this is the screen which lets you do this as well. To add a new location zoom the map to that location or geo point, and click on the + button in the Action bar. App will ask for the basic info like Name, Type, Description, etc, and once that’s done touch on the exact Map location where you want to add the new location. That’s it.

If you made a mistake and clicked on the wrong place when adding new location. No worries, just touch and hold the  location icon you want to move, and it will become draggable. You can now move that icon to correct location and release it. App will set that dropped point as the new pointer for you location. This can be also handy when you are adding locations using the GPS or WiFi network, and it’s off by few meters,  so that you can manually move the newly added location marker to correct spot.

That’s all for now. There have been some other improvements as well, like Settings screen have redesigned, My Location dialog contains text tweaks, new Send a location feature to share a location with friend or family via Message or Email. You can explore these on your own.

If you run into any problems, or have any other feature requests, feel free to leave comments here.

Teaser: A new feature is also under development where you will be able to track location of moving objects as well e.g. you car, pets, friends, and logically any moving entity with either a special GPS device or an Android phone. Stay tuned for updates.

I was hoping that this would be the end of story, but looking at the daily server logs, we found that one of C# Console application was throwing following error connecting with Server:

Unhandled Exception: System.Data.SqlClient.SqlException: A connection was successfully established with the server, but then an error occurred during the pre-login handshake. (provider: Shared Memory Provider, error: 0 – No process is on the other end of the pipe.)

I knew this has to do something with TLS 1.0, and as this application was developed under .Net 3.5, so it was not able to utilize the TLS 1.2 support recently added to Microsoft .NET Framework 4.6. Fortunately, it’s quite easy to force application to run on a particular .Net version by modifying its configuration file. So, what fixed is the following new section in the Application configuration file:

And that worked flawlessly. You can read more about .Net framework runtime support options here.

I’m hoping I don’t run into any more TLS 1.0 compatibility issues. If I do, I will be posting the details here.