Founded in 2018, Lokal is on a mission to strengthen hyperlocal communities by enabling its mostly non-English users to share, interact and transact in their regional language

Lokal

How We Localized Our Website in 9 Languages in Under a Week — And So Can You

The mobile apps market is snowballing. As a side effect, it is becoming more and more competitive. Nowadays, you can't hit the top of app store lists after mastering some kind of ASO magic. You need more, deeper analysis, as well as creative marketing campaigns. Today we will talk about methods for promoting your app, from the beginning of the project to the later stages of its life. For each step, we will review relevant, helpful tools and websites. Let's go! Step #1. Create a community of early testers Giving a product a boost at the very beginning is always a great idea. If you manage to attract some attention to your app just after it is released, it will help you to grow your audience in the future. You can try to solve this task using specialized websites for startups promotion. The top site in this niche today is Product Hunt. It is a community where users share links and descriptions of nice products they've built or just discovered. A successful release at Product Hunt may lead to hundreds and even thousands of users, mostly from the US and other English-speaking countries. All this means that you might not only attract some beta testers, but paying users too. Here are several tips on conducting a successful Product Hunt launch: . The rules for launching on Product Hunt are described in a written by the PH team. Make sure you read it. Don't try to cheat and ask for upvotes, you'll get banned guide All subscribers of a specific user get a notification when he or she publishes ("hunts") a new product. This is why it is a good idea to find a power user ("hunter") to post your startup. This will give you a boost from the very beginning. Try to find a hunter. . The new day on the website starts at 00:00 PST. You need to be online for the next 24 hours to reply to comments and questions. This is the best way to get to the top of the page for the day. Launching on PH is a 24 hour job . For example, there are pages with alternatives to popular products. If you manage to get onto such lists, it will bring you a steady flow of traffic for the future. You can use the website for long-term promotion There are also other websites where you can post your mobile app: Hacker News Betalist Betapage Post your announcements on each of these sites as this will help you in building a community of your apps' early adopters just after the release. Step #2. "Stealing" your competitors' ad traffic Project announcements are great, but to really succeed with your app in the long-term, you'll need a thorough marketing strategy. It will be much easier to come up with an approach by analyzing your competitors' activities. To get more traffic and paying users to your app, you can't just use organic methods: paid traffic is a must-have as well. Struggling with ad networks and campaign settings by yourself is not the best idea. Repeating the effective moves of your competitors is a much more effective strategy. If they run a similar app and the specific campaign worked for them, chances are a similar campaign will do well for you as well. This will save you tons of money on experiments. is one of the best tools for analyzing in-app campaigns for mobile apps. It can be helpful for analyzing: Apptica the top advertisers and apps used for ad placements on different markets; The most effective ad campaigns and even types of ads (a large gallery of ads in various formats for multiple networks, including Facebook and Instagram); The most popular ad networks with all relevant stats on impressions, traffic sources, etc. All these will be super helpful in developing a comprehensive marketing and monetization strategy for your app, all of which will affect the number of your users. Step #3. Using an innovative marketing approach To be competitive, you need to find  cost-effective yet attractive methods for the  promotion of your app. Surprisingly, one such way nowadays is by using QR codes. This technology has been on the in recent years, and it allows users to download and install new apps quickly just by scanning a code. This makes QR codes a perfect solution for a mobile marketer. rise There are multiple ways of using QR codes for driving installs. First of all, you need to create the code using the . QR Code Generator tool After that, you can use your code for your app ads. Place it along with the description: you can set it either offline, or online. There are use cases of companies using QR codes with links to their apps on business cards, banners, adverts inside malls, as an element of an email signature, contextual ads, etc. Step #4. Localization One of the best and simplest ways of attracting new users for a mobile app is to localize it for multiple languages. According to , the number of app downloads in many markets will grow fast. So, translating your app from, say, English to Portuguese, Spanish, and Russian, will give you access to millions of potential users. Statista The best way to solve the localization task is by using the tool. Lokalise This service has teamwork functionality, webhooks export, API, and built-in suggestions for translation. In using this software you will be able to localize both iOS and Android apps, as well as, update apps for your users over-the-air. That's it for today, thanks for reading! Share your insights and useful tools for apps promotion in the comments!

How to Promote A Mobile App in 2019: 4 Practical Methods + Useful Tools

Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less.
Reliability: No reliance on third-party services reduces failure chances.
Scalability: No extra costs as usage grows. Cons Resource Intensive: Each app instance consumes significant memory.
More Dependencies: Requires packages for decoding and looking up GeoIP data.
Challenging Updates: Updates require manual intervention or another self-hosted service. Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources.
Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint.
Fewer Dependencies: Often only requires an HTTP client.
Updates without hassle: Third-party will handle updates Cons Slower Responses: Calls to third-party services can introduce significant latency.
Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes.
Scaling Costs: Costs can escalate as usage increases. Introducing Atlas: Combining the Best of Both Worlds Faced with these challenges, I developed Atlas—a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Cost Efficiency: No additional costs with scale in terms of third-party fees. Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md. Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/. Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8: > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage.


Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement.


Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request.


Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable.


Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration! Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less. Reliability: No reliance on third-party services reduces failure chances. Scalability: No extra costs as usage grows. Speed : IP lookups in milliseconds or less. Speed Reliability : No reliance on third-party services reduces failure chances. Reliability Scalability : No extra costs as usage grows. Scalability Cons Resource Intensive: Each app instance consumes significant memory. More Dependencies: Requires packages for decoding and looking up GeoIP data. Challenging Updates: Updates require manual intervention or another self-hosted service. Resource Intensive : Each app instance consumes significant memory. Resource Intensive More Dependencies : Requires packages for decoding and looking up GeoIP data. More Dependencies Challenging Updates : Updates require manual intervention or another self-hosted service. Challenging Updates Using External GeoIP APIs (Often Paid) Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources. Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Fewer Dependencies: Often only requires an HTTP client. Updates without hassle: Third-party will handle updates Minimal Resource Use : Outsourcing GeoIP data and logic doesn't burden your app's resources. Minimal Resource Use Ease of Implementation : Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Ease of Implementation Fewer Dependencies : Often only requires an HTTP client. Fewer Dependencies Updates without hassle : Third-party will handle updates Updates without hassle Cons Slower Responses: Calls to third-party services can introduce significant latency. Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes. Scaling Costs: Costs can escalate as usage increases. Slower Responses : Calls to third-party services can introduce significant latency. Slower Responses Increased Unreliability : Greater likelihood of failures due to network issues or third-party downtimes. Increased Unreliability Scaling Costs : Costs can escalate as usage increases. Scaling Costs Introducing Atlas : Combining the Best of Both Worlds Introducing Atlas Faced with these challenges, I developed Atlas —a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Atlas Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency : Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates : It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates Cost Efficiency: No additional costs with scale in terms of third-party fees. Cost Efficiency : No additional costs with scale in terms of third-party fees. Cost Efficiency Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API : A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free : Atlas is Open Source and freely available under Apache-2.0 License. Open and Free By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Why Rust? Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md . Atlas README.md Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/ . https://atlas-rs.fly.dev/swagger-ui/ Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8 : Fly.io 8.8.8.8 > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas : The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues : If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features : Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation : As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word : If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. special thanks to oschwald oschwald maxminddb Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration!

IP Geo-Location: Local Database vs. Remote API—Finding the Middle Ground With Atlas

AI-Powered Localization Using an Open-source Project as a Case Study

How to Run Your Own Local LLM: Updated for 2024 - Version 2

Hi! My name’s Amir. I’m a Web3 Community Manager and a podcast host. To improve the reach of my podcast, 2 months ago, I tried the Rask.ai platform, and have edited over 10 hours of footage since. In this review, I’ll go into the challenges Rask.ai can solve for you, its features, and the pros/cons of the platform. What Is Rask.ai, and Why Did I Buy It? Essentially, Rask.ai is a platform that uses AI for video/audio localization and repurposing. By repurposing, I mean auto reframing to vertical format, and auto-editing long videos to capture the most interesting moments. Rask.ai poses itself as a platform that has all the AI features for content makers in one place, a one-stop-shop of some sort. In one subscription, you get all the AI tools you would need to make your video content better. The main purpose that I had in mind when checking out Rask.ai was to increase the reach of my podcast on TikTok and YouTube. The podcast originally is in Kazakh, but to expand the potential audience and potential advertisers, I was looking for ways to target the English-speaking market as well. Price, Interface, and Functionality The platform is very flexible when it comes to pricing: you have 3 options to choose from, which vary by the price, the amount of minutes, and other available features. Honestly, I think the only metric you should really look at is the amount of minutes, as it will influence your work the most. I’ve picked the second plan, Creator Pro. It includes 100 minutes of edited footage per month and costs me $120. They also offer a plan for $50 and 25 minutes, as well as a corporate plan - $600 and 500 minutes. The biggest plan the platform offers is enterprise, which starts at 2000 minutes with a custom price. Note that all prices are correct if you pay annually - plans with monthly payments will be ~15% more expensive. Last month, I had 2 podcasts instead of one, and I ran out of minutes on my regular plan. However, you can always buy additional minutes at a $3 rate. Setting up the account is very easy (as it should be in 2024): you can use your Google account to log in or create the account with your email. The interface is very clean and isn’t overloaded by different buttons and links. You have FAQ and customer support at the top right corner, accessible from any page, so if you get a bit confused, there’s always that. I can’t say that I’m a tech guru of any kind, but I got used to the website in a matter of minutes. I’ve never worked with a mobile version of the site as I always store all raw footage on my Mac, but if you’re editing a small TikTok/Reels, you can easily do it from your phone. What Does Rask.ai Do, and How Do I Use It? The platform has 2 main features: localization and shorts. Let’s start with the first one, which became a selling point for me. You download a video from your device or paste a link from YouTube or Google Drive, choose the number of speakers, original language, and the language you want to translate the video to, and click “translate.” After that, the magic begins. In a matter of minutes (depending on the length of the video), you get the same video but translated to a different language. The voice-over keeps the voice’s depth, tone, and emotion, and even creates a lip sync, so the video footage is similar to the audio. How do I use that feature? Before discovering Rask.ai, in order to reach the English-speaking market, I was adding EN subtitles to all my podcasts. However, that approach was far from ideal as I was always encountering many barriers: Time-consumption. The current speech-to-text AI solutions are very far from perfect. On 100 AI-generated subtitles, I was getting ~30 errors that I needed to edit manually. Scale that to hours of footage and thousands of text, and you get yourself many hours of manual work.


Accessibility. Subtitles can’t be a permanent solution to expand your foreign audience, especially when it comes to podcasts. When was the last time you paid attention to video footage on a podcast? People listen to them while exercising, cooking, cleaning, and working. My podcast gets 40% more listeners on Spotify and Google Podcasts (which don’t have video) vs YouTube (which has video), meaning that people aren’t looking at the screen and subtitles when listening.


Retention. Even if a person bothers to actively read the subtitles, they still are getting less than people watching the original. Many intonations, jokes, or facial expressions are just not working when perceptive through text. Listeners are too busy reading to actually relax and enjoy the podcast. The Rask.ai platform solved all these problems for me. The amount of manual work I had to do had decreased to practically 0, since in 2 months of using the platform, I haven’t encountered any errors in translations. The retention improved a lot as well: YouTube has statistics that show you how many people watch your videos until the end, and this number significantly improved, since your listeners can actually LISTEN instead of reading. Another feature that I didn’t even know about when getting the subscription to Rask.ai is Shorts. Basically, it takes your hour-long footage and splits the most interesting moments into many short vertical videos. As you probably know, one of the best ways to promote your podcast in 2024 is to share these videos on TikTok, Shorts, and Reels, because these platforms have much better recommendation systems than YouTube, and putting the link to the full podcast in your bio. That’s how I personally discovered many of my favorite podcasts and began to host them myself. Before discovering that feature, I had a friend on a part-time salary doing that for me, because apart from the podcast, I also have a full-time job in Web3 that is taking a significant part of my day. Creating 15 short videos from one podcast took us about 5-6 hours of manual work since you have to fully watch the podcast, then edit all shorts separately, and add some graphics and subtitles. When I discovered a shorts feature on Rask.ai, the same amount of work took me from 5 to 10 minutes (again, depending on the length of the footage). This let me cut my editorial costs down to the sole price of my Rask.ai plan, without having to pay the salary to a whole person doing that. And you say AI won’t take our jobs :) There are a couple of smaller features that I haven’t really tested a lot until today, but I’m planning on exploring them in the future as well. For example, subtitles generation, and API for translations. The company also plans on integrating some features that I’m really excited about: AI video generation (Like MidJourney, but videos), and Zapier integration. Pros, Cons, and Who Can Use It? In conclusion, Rask.ai won’t make your video content more interesting or helpful and won’t make you a superstar overnight. However, with the right distribution strategy and exciting content, Rask.ai will significantly cut your editorial costs, saving you hundreds of dollars on people who translate and edit your content for you, as well as save you A LOT of time, letting you focus on the stuff that really matters: scripts, topics, guests, and other things that make your content better. Rask.ai is a great solution for podcast hosts like myself, social media or community managers producing content for their companies, or TikTok and Instagram influencers that focus on short video content. Pros: Clean and simple interface
Precise translations
Multiple features in one subscription Cons: Relatively high prices
Not all features are ready yet Hi! My name’s Amir. I’m a Web3 Community Manager and a podcast host. To improve the reach of my podcast, 2 months ago, I tried the Rask.ai platform, and have edited over 10 hours of footage since. In this review, I’ll go into the challenges Rask.ai can solve for you, its features, and the pros/cons of the platform. What Is Rask.ai, and Why Did I Buy It? What Is Rask.ai, and Why Did I Buy It? Essentially, Rask.ai is a platform that uses AI for video/audio localization and repurposing. By repurposing, I mean auto reframing to vertical format, and auto-editing long videos to capture the most interesting moments. Rask.ai poses itself as a platform that has all the AI features for content makers in one place, a one-stop-shop of some sort. In one subscription, you get all the AI tools you would need to make your video content better. The main purpose that I had in mind when checking out Rask.ai was to increase the reach of my podcast on TikTok and YouTube. The podcast originally is in Kazakh, but to expand the potential audience and potential advertisers, I was looking for ways to target the English-speaking market as well. Price, Interface, and Functionality Price, Interface, and Functionality The platform is very flexible when it comes to pricing: you have 3 options to choose from, which vary by the price, the amount of minutes, and other available features. Honestly, I think the only metric you should really look at is the amount of minutes, as it will influence your work the most. I’ve picked the second plan, Creator Pro. It includes 100 minutes of edited footage per month and costs me $120. They also offer a plan for $50 and 25 minutes, as well as a corporate plan - $600 and 500 minutes. The biggest plan the platform offers is enterprise, which starts at 2000 minutes with a custom price. Note that all prices are correct if you pay annually - plans with monthly payments will be ~15% more expensive. Last month, I had 2 podcasts instead of one, and I ran out of minutes on my regular plan. However, you can always buy additional minutes at a $3 rate. Setting up the account is very easy (as it should be in 2024): you can use your Google account to log in or create the account with your email. The interface is very clean and isn’t overloaded by different buttons and links. You have FAQ and customer support at the top right corner, accessible from any page, so if you get a bit confused, there’s always that. I can’t say that I’m a tech guru of any kind, but I got used to the website in a matter of minutes. I’ve never worked with a mobile version of the site as I always store all raw footage on my Mac, but if you’re editing a small TikTok/Reels, you can easily do it from your phone. What Does Rask.ai Do, and How Do I Use It? What Does Rask.ai Do, and How Do I Use It? The platform has 2 main features: localization and shorts. Let’s start with the first one, which became a selling point for me. You download a video from your device or paste a link from YouTube or Google Drive, choose the number of speakers, original language, and the language you want to translate the video to, and click “translate.” After that, the magic begins. In a matter of minutes (depending on the length of the video), you get the same video but translated to a different language. The voice-over keeps the voice’s depth, tone, and emotion, and even creates a lip sync, so the video footage is similar to the audio. How do I use that feature? Before discovering Rask.ai, in order to reach the English-speaking market, I was adding EN subtitles to all my podcasts. However, that approach was far from ideal as I was always encountering many barriers: Time-consumption. The current speech-to-text AI solutions are very far from perfect. On 100 AI-generated subtitles, I was getting ~30 errors that I needed to edit manually. Scale that to hours of footage and thousands of text, and you get yourself many hours of manual work. Accessibility. Subtitles can’t be a permanent solution to expand your foreign audience, especially when it comes to podcasts. When was the last time you paid attention to video footage on a podcast? People listen to them while exercising, cooking, cleaning, and working. My podcast gets 40% more listeners on Spotify and Google Podcasts (which don’t have video) vs YouTube (which has video), meaning that people aren’t looking at the screen and subtitles when listening. Retention. Even if a person bothers to actively read the subtitles, they still are getting less than people watching the original. Many intonations, jokes, or facial expressions are just not working when perceptive through text. Listeners are too busy reading to actually relax and enjoy the podcast. Time-consumption. The current speech-to-text AI solutions are very far from perfect. On 100 AI-generated subtitles, I was getting ~30 errors that I needed to edit manually. Scale that to hours of footage and thousands of text, and you get yourself many hours of manual work. Time-consumption. The current speech-to-text AI solutions are very far from perfect. On 100 AI-generated subtitles, I was getting ~30 errors that I needed to edit manually. Scale that to hours of footage and thousands of text, and you get yourself many hours of manual work. Accessibility. Subtitles can’t be a permanent solution to expand your foreign audience, especially when it comes to podcasts. When was the last time you paid attention to video footage on a podcast? People listen to them while exercising, cooking, cleaning, and working. My podcast gets 40% more listeners on Spotify and Google Podcasts (which don’t have video) vs YouTube (which has video), meaning that people aren’t looking at the screen and subtitles when listening. Accessibility. Subtitles can’t be a permanent solution to expand your foreign audience, especially when it comes to podcasts. When was the last time you paid attention to video footage on a podcast? People listen to them while exercising, cooking, cleaning, and working. My podcast gets 40% more listeners on Spotify and Google Podcasts (which don’t have video) vs YouTube (which has video), meaning that people aren’t looking at the screen and subtitles when listening. Retention. Even if a person bothers to actively read the subtitles, they still are getting less than people watching the original. Many intonations, jokes, or facial expressions are just not working when perceptive through text. Listeners are too busy reading to actually relax and enjoy the podcast. Retention. Even if a person bothers to actively read the subtitles, they still are getting less than people watching the original. Many intonations, jokes, or facial expressions are just not working when perceptive through text. Listeners are too busy reading to actually relax and enjoy the podcast. The Rask.ai platform solved all these problems for me. The amount of manual work I had to do had decreased to practically 0, since in 2 months of using the platform, I haven’t encountered any errors in translations. The retention improved a lot as well: YouTube has statistics that show you how many people watch your videos until the end, and this number significantly improved, since your listeners can actually LISTEN instead of reading. Another feature that I didn’t even know about when getting the subscription to Rask.ai is Shorts. Basically, it takes your hour-long footage and splits the most interesting moments into many short vertical videos. As you probably know, one of the best ways to promote your podcast in 2024 is to share these videos on TikTok, Shorts, and Reels, because these platforms have much better recommendation systems than YouTube, and putting the link to the full podcast in your bio. That’s how I personally discovered many of my favorite podcasts and began to host them myself. Before discovering that feature, I had a friend on a part-time salary doing that for me, because apart from the podcast, I also have a full-time job in Web3 that is taking a significant part of my day. Creating 15 short videos from one podcast took us about 5-6 hours of manual work since you have to fully watch the podcast, then edit all shorts separately, and add some graphics and subtitles. When I discovered a shorts feature on Rask.ai, the same amount of work took me from 5 to 10 minutes (again, depending on the length of the footage). This let me cut my editorial costs down to the sole price of my Rask.ai plan, without having to pay the salary to a whole person doing that. And you say AI won’t take our jobs :) There are a couple of smaller features that I haven’t really tested a lot until today, but I’m planning on exploring them in the future as well. For example, subtitles generation, and API for translations. The company also plans on integrating some features that I’m really excited about: AI video generation (Like MidJourney, but videos), and Zapier integration. Pros, Cons, and Who Can Use It? Pros, Cons, and Who Can Use It? In conclusion, Rask.ai won’t make your video content more interesting or helpful and won’t make you a superstar overnight. However, with the right distribution strategy and exciting content, Rask.ai will significantly cut your editorial costs, saving you hundreds of dollars on people who translate and edit your content for you, as well as save you A LOT of time, letting you focus on the stuff that really matters: scripts, topics, guests, and other things that make your content better. Rask.ai is a great solution for podcast hosts like myself, social media or community managers producing content for their companies, or TikTok and Instagram influencers that focus on short video content. Pros: Pros: Clean and simple interface Precise translations Multiple features in one subscription Clean and simple interface Precise translations Multiple features in one subscription Cons: Cons: Relatively high prices Not all features are ready yet Relatively high prices Not all features are ready yet

How to Localize Your Shorts in 3 Clicks

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 2 RELATED WORK As stated before, we will limit our discussion to local search heuristics. For future reference, we will specifically refer to local search heuristics when discussing heuristics. 2.1 Classical Heuristics In this subsection, we explore works that can solve diverse CO problems without polynomial time reduction. In their influential work, Khalil et al. (2017) introduced Structure-to-Vector GNN trained with Deep Q-Networks (DQN) to address various CO problems. The resulting S2V-DQN algorithm showcased strong performance across diverse CO problems by effectively generalizing across graphs of varying sizes and topologies. Building on the work of Khalil et al. (2017) , Manchanda et al. (2019) initially trained an embedding Graph Convolution Network (GCN) in a supervised manner. Subsequently, they trained a Qnetwork using reinforcement learning (RL) to predict action values for each vertex. Their method employed GCN embeddings to prune nodes unlikely to be part of the solution set, significantly enhancing scalability compared to S2V-DQN. However, it is not applicable to problems where nodes cannot be pruned, including fundamental CO problems like the Max-Cut problem. 2.2 Generalized Learned Heuristics Barrett et al. (2020) proposed ECO-DQN, a SOTA RL algorithm for Max-Cut. ECO-DQN improves the initial solution by navigating between the local optimal points. However, ECO-DQN uses a costly GNN at each decision step, resulting in worse scalability than S2V-DQN. To address scalability challenges in ECODQN, Barrett et al. (2022) proposed ECORD. This approach limited the costly GNN to a pre-processing step and introduced a recurrent unit for fast-action selection. 2.3 Learned Heuristics for SAT In the domain of Boolean Satisfiability, the application of machine learning to solving SAT problems is not a new idea (Battiti and Protasi, 1997; Haim and Walsh, 2009; Flint and Blaschko, 2012; Grozea and Popescu, 2014; Ganesh et al., 2009; Liang et al., 2016). In recent years, there has been a trend in integrating GNNs with SAT solvers(Yolcu and P´oczos, 2019; Lederman et al., 2019; Kurin et al., 2020; Jaszczur et al., 2020), aiming to improve search heuristics by leveraging predictions from GNNs. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 2 RELATED WORK As stated before, we will limit our discussion to local search heuristics. For future reference, we will specifically refer to local search heuristics when discussing heuristics. 2.1 Classical Heuristics In this subsection, we explore works that can solve diverse CO problems without polynomial time reduction. In their influential work, Khalil et al. (2017) introduced Structure-to-Vector GNN trained with Deep Q-Networks (DQN) to address various CO problems. The resulting S2V-DQN algorithm showcased strong performance across diverse CO problems by effectively generalizing across graphs of varying sizes and topologies. Building on the work of Khalil et al. (2017) , Manchanda et al. (2019) initially trained an embedding Graph Convolution Network (GCN) in a supervised manner. Subsequently, they trained a Qnetwork using reinforcement learning (RL) to predict action values for each vertex. Their method employed GCN embeddings to prune nodes unlikely to be part of the solution set, significantly enhancing scalability compared to S2V-DQN. However, it is not applicable to problems where nodes cannot be pruned, including fundamental CO problems like the Max-Cut problem. 2.2 Generalized Learned Heuristics Barrett et al. (2020) proposed ECO-DQN, a SOTA RL algorithm for Max-Cut. ECO-DQN improves the initial solution by navigating between the local optimal points. However, ECO-DQN uses a costly GNN at each decision step, resulting in worse scalability than S2V-DQN. To address scalability challenges in ECODQN, Barrett et al. (2022) proposed ECORD. This approach limited the costly GNN to a pre-processing step and introduced a recurrent unit for fast-action selection. 2.3 Learned Heuristics for SAT In the domain of Boolean Satisfiability, the application of machine learning to solving SAT problems is not a new idea (Battiti and Protasi, 1997; Haim and Walsh, 2009; Flint and Blaschko, 2012; Grozea and Popescu, 2014; Ganesh et al., 2009; Liang et al., 2016). In recent years, there has been a trend in integrating GNNs with SAT solvers(Yolcu and P´oczos, 2019; Lederman et al., 2019; Kurin et al., 2020; Jaszczur et al., 2020), aiming to improve search heuristics by leveraging predictions from GNNs. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license.

Exploring Classical and Learned Local Search Heuristics for Combinatorial Optimization

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials Abstract In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization. 1 INTRODUCTION Designing effective heuristics or approximation algorithms for NP-hard combinatorial optimization (CO) problems is a challenging task, often requiring domain knowledge and extensive trial-and-error. Thus, the idea of automating this demanding and tedious design process through machine learning to learn algorithms that exploit the inherent structure of a problem has gained significant interest among researchers (Bello et al., 2016; Khalil et al., 2017; Bengio et al., 2021; Dong et al., 2021). Particularly, a considerable portion of these works (Khalil et al., 2017; Barrett et al., 2020; Yolcu and P´oczos, 2019) have concentrated on employing Graph Neural Networks (GNNs) for CO problems. Despite the computational demands, these GNN-based approaches have demonstrated competitive performance compared to SOTA heuristics tailored to specific problems. While these potential advancements offer great promise, some concerns remain. The superior performance of the learned heuristics can be attributed to the selection of specific instances and baselines. Specifically, if the baselines are weak, the learned heuristics can easily outperform them. Without hard instances and proper baseline selection, the learned heuristics can easily show comparable performance with the SOTA heuristics, and this can lead to an overestimation of the actual capabilities of the learned heuristics. Moreover, comparisons with SOTA heuristics are occasionally left out due to scalability challenges with deep learning architectures. A subset of learning-based approaches (Khalil et al., 2017; Yolcu and P´oczos, 2019; Barrett et al., 2020, 2022) incorporate the functionality or behavior of traditional heuristics, potentially offering improved or enhanced performance by integrating heuristics principles with machine learning components. If a comprehensive comparison with the integrated heuristics and an ablation study of the deep learning architecture are lacking, it becomes challenging to determine the specific contribution of the deep learning architecture. Consequently, if the integrated heuristics are robust, the learned heuristics can seamlessly outperform the baseline heuristics, while the deep learning architecture plays a little role. Another great achievement of learned heuristics (Khalil et al., 2017; Barrett et al., 2020; Toenshof et al., 2019), initially trained on smaller and specific instances from a particular distribution, exhibits impressive performance when tested on larger instances from different distributions. This achievement stands as a significant accomplishment, aligning with the core objective of employing learning-based approaches: to mitigate the necessity for extensive customization and domain-specific knowledge often required by heuristics. Although classical heuristics with hyperparameters may also encounter challenges if they are fine-tuned for a specific distribution, they may also generalize across different distributions. The primary inquiry revolves around whether the learned heuristics indeed demonstrate superior generalizability compared to classical heuristics. A thorough and insightful comparison of learned heuristics against classical heuristics provides valuable insights into the generalizability of learned heuristics. Learned heuristics often lack theoretical guarantees, making empirical evaluation the sole method to comprehend the strengths and limitations of proposed methodologies. We believe that several fundamental, yet pivotal questions remain unexplored in the empirical evaluation of these works. While these inquiries are pertinent to all types of learned heuristics, we ask and answer them by analyzing highly cited and recent peer-reviewed publications focused on learning local search heuristics. Our goal is not to provide exhaustive benchmarks for the CO problems discussed in our work but rather to assist future researchers in evaluating their research. Concretely, we ask and answer the following questions: 1. Can learned heuristics be over-parameterized? Absolutely. Replacing the GNN in ECO-DQN with linear regression and utilizing a subset of features from ECO-DQN (Barrett et al., 2020) that links to Tabu Search (Glover, 1990), we introduce a pruned version of ECO-DQN termed SoftTabu. Our study demonstrates that SoftTabu showcases superior performance and generalizability in comparison to ECO-DQN for the NPhard Maximum-Cut (Max-Cut) problem. 2. Can baseline bias be attributed to the superior performance of learned heuristics? Yes, we demonstrate that SoftTabu, a vanilla learned heuristic, can outperform S2V-DQN (Khalil et al., 2017) for the Max-Cut problem and GNNSAT (Yolcu and P´oczos, 2019) for Boolean Satisfiability (SAT). *3. Can learned heuristics demonstrate superior generalizability owing to instance selection bias?*Yes, we demonstrate that ECO-DQN shows poor generalization on harder instances and easily gets trapped in the search space. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials Abstract In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization. 1 INTRODUCTION Designing effective heuristics or approximation algorithms for NP-hard combinatorial optimization (CO) problems is a challenging task, often requiring domain knowledge and extensive trial-and-error. Thus, the idea of automating this demanding and tedious design process through machine learning to learn algorithms that exploit the inherent structure of a problem has gained significant interest among researchers (Bello et al., 2016; Khalil et al., 2017; Bengio et al., 2021; Dong et al., 2021). Particularly, a considerable portion of these works (Khalil et al., 2017; Barrett et al., 2020; Yolcu and P´oczos, 2019) have concentrated on employing Graph Neural Networks (GNNs) for CO problems. Despite the computational demands, these GNN-based approaches have demonstrated competitive performance compared to SOTA heuristics tailored to specific problems. While these potential advancements offer great promise, some concerns remain. The superior performance of the learned heuristics can be attributed to the selection of specific instances and baselines. Specifically, if the baselines are weak, the learned heuristics can easily outperform them. Without hard instances and proper baseline selection, the learned heuristics can easily show comparable performance with the SOTA heuristics, and this can lead to an overestimation of the actual capabilities of the learned heuristics. Moreover, comparisons with SOTA heuristics are occasionally left out due to scalability challenges with deep learning architectures. A subset of learning-based approaches (Khalil et al., 2017; Yolcu and P´oczos, 2019; Barrett et al., 2020, 2022) incorporate the functionality or behavior of traditional heuristics, potentially offering improved or enhanced performance by integrating heuristics principles with machine learning components. If a comprehensive comparison with the integrated heuristics and an ablation study of the deep learning architecture are lacking, it becomes challenging to determine the specific contribution of the deep learning architecture. Consequently, if the integrated heuristics are robust, the learned heuristics can seamlessly outperform the baseline heuristics, while the deep learning architecture plays a little role. Another great achievement of learned heuristics (Khalil et al., 2017; Barrett et al., 2020; Toenshof et al., 2019), initially trained on smaller and specific instances from a particular distribution, exhibits impressive performance when tested on larger instances from different distributions. This achievement stands as a significant accomplishment, aligning with the core objective of employing learning-based approaches: to mitigate the necessity for extensive customization and domain-specific knowledge often required by heuristics. Although classical heuristics with hyperparameters may also encounter challenges if they are fine-tuned for a specific distribution, they may also generalize across different distributions. The primary inquiry revolves around whether the learned heuristics indeed demonstrate superior generalizability compared to classical heuristics. A thorough and insightful comparison of learned heuristics against classical heuristics provides valuable insights into the generalizability of learned heuristics. Learned heuristics often lack theoretical guarantees, making empirical evaluation the sole method to comprehend the strengths and limitations of proposed methodologies. We believe that several fundamental, yet pivotal questions remain unexplored in the empirical evaluation of these works. While these inquiries are pertinent to all types of learned heuristics, we ask and answer them by analyzing highly cited and recent peer-reviewed publications focused on learning local search heuristics. Our goal is not to provide exhaustive benchmarks for the CO problems discussed in our work but rather to assist future researchers in evaluating their research. Concretely, we ask and answer the following questions: 1. Can learned heuristics be over-parameterized? Absolutely. Replacing the GNN in ECO-DQN with linear regression and utilizing a subset of features from ECO-DQN (Barrett et al., 2020) that links to Tabu Search (Glover, 1990), we introduce a pruned version of ECO-DQN termed SoftTabu. Our study demonstrates that SoftTabu showcases superior performance and generalizability in comparison to ECO-DQN for the NPhard Maximum-Cut (Max-Cut) problem. 1. Can learned heuristics be over-parameterized? Absolutely. 2. Can baseline bias be attributed to the superior performance of learned heuristics? Yes, we demonstrate that SoftTabu, a vanilla learned heuristic, can outperform S2V-DQN (Khalil et al., 2017) for the Max-Cut problem and GNNSAT (Yolcu and P´oczos, 2019) for Boolean Satisfiability (SAT). 2. Can baseline bias be attributed to the superior performance of learned heuristics? *3. Can learned heuristics demonstrate superior generalizability owing to instance selection bias?*Yes, we demonstrate that ECO-DQN shows poor generalization on harder instances and easily gets trapped in the search space. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license.

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