Locale is a Control Tower for Ops Observability & Actionability which empowers operations and business teams to leverage their data and set up a simple monitoring and alerting system of record.

LocaleAI

If you are a two-degree marketplace like Uber, you cater to millions of users requesting a ride through your driver partners accepting and fulfilling those requests. For a three-degree marketplace like Swiggy, there is another static component added (like restaurants or stores), where delivery partners pick up the orders. Borrowing from the quote, “Everything happens somewhere” — all of these events and actions described take place at a specific location! Often, companies end up not leveraging the in their data and running their analyses at the city level. But, cities are too large, geographically diverse and the parameters vary way too much! lat/long component Area level polygons are much more practical but still broad. They don’t have uniform shapes or sizes and are subject to changes very frequently. Even the zones or clusters drawn by the operations teams’ local knowledge require updating and have arbitrary edges. Making sense of your spatial data and deriving precise insights requires these analyses to become more granular and uniform. brings that fine granularity to the table. It works brilliantly in bucketing all your lat-longs into “cells”. These cells can also be clustered to represent a particular neighborhood or area and can be aggregated at different levels. Grid system Hence, this system becomes critical to crunch large spatial data sets to match the supply & demand fragmented across the city. What is meant by grids? In Spatial Data Science, we use grids that are regular polygons repeating over a surface, edge to edge to cover any space without overlaps and gaps — a phenomenon called Each cell can be assigned a unique id for spatial indexing (aggregating the points inside that cell). tessellation . A wide variety of grids of different shapes have been proposed including squares, rectangles, triangles, hexagons or diamonds. The covers the entire surface of the earth. global grid system If you are a hyperlocal, on-demand company, grids as small as can be very useful to run models that work in context to location and in include surge pricing in high demand areas, promotions in low demand areas & distribution models of delivery folks on the ground. 0.5 sq. km real-time. Examples What are the different grid types? There are just three types of grids or that can tessellate: squares, equilateral triangles & hexagons. 1. Square grids: The most common application of square grids occurs in raster datasets and geohashes. For the scope of this piece, we will focus on geohashes. is a hierarchical data structure to transform a 2D spatial point (lat & long) into a short string of alphabets and numbers. They divide the world into a grid of 32 cells with 4 rows and 8 columns. Geohash You can keep splitting up each cell into a grid of 32 cells. Thus, the longer the string of the geohash, the higher the accuracy! You can also easily identify if geohashes are close together if they can have a common prefix. So, the longer the common prefix, the closer they are. For example, the coordinate pair near the tip of the peninsula of Jutland, Denmark produces a slightly shorter hash of (57.64911, 10.40744) u4pruydqqvj. [1] 2. Triangular grids: Triangular grids are not very commonly used. Besides, their unfamiliarity, one of the reasons for that is they have a large perimeter and a small area which means it’s harder to piece them together on the map. Another reason is that each triangle is connected to only three adjacent triangles, which limits the number of options to move and to make connections. (Check the image below) Moreover, while for hexagons and squares, there are always two faces parallel to each other, for triangles, there are two directions in which lines are parallel centered from the axis of movement. Thus, in a way, there are not completely symmetrical. [2] 3. Hexagonal grids: Besides looking appealing, hexagons are more symmetric than geohashes. They are very close to circles in terms of shape to provide a more accurate sampling. [3] As a result, this system has been increasingly adopted by companies like Uber. A hex grid and triangular grids are a dual of each other — putting a dot in the center of each hexagon & connecting them to all the adjacent ones, you get a triangular grid and vice versa! Fun fact: [4] Moving on the vertices of a hexagonal grid is equivalent to playing in the spaces on a triangular grid. Square grids, on the other hand, are a dual of themselves. Why hexagons? At , one question that we get a lot from our customers is, “why are we using hexbins and not geohashes?” Locale Well, the choice depends on your exact use case and you would have to make some tradeoffs, no matter what you use. So, let’s take some parameters and deep dive. This diagram shows the distance of the center of triangles, square & hexagon to its neighbors. Distance from Nearest Cells: A triangle has three kinds of distance (through the edge, vertex and across the center of the edge), a square has two (across the edge & the diagonal) and hexagon only has one — another reason why triangles are not really favored. This property of hexagons makes it very easy to perform analysis and is preferred when your analysis includes aspects of connectivity or movement. [4] All the neighbors in a hexagon form a ring around it with equal radius. The function provides grid cells within distance “k” of an origin index. In the diagram below, here is the of the shaded hexagon and square. kRing 1st kRing Fitting on Curved Surfaces: Hexagons are the densest way to pack circles in tessellation and reduce . (Circles have the least perimeter to area ratio but can’t form a continuous grid). edge effects The more similar a polygon is to a circle, the closer the points near the border area are to the center. Thus, any point inside a hexagon is closer to its center as compared to an equal-area square or triangle. Now, when large areas come into play and the curvature of the earth is important to consider, hexagons are therefore better suited to fit the curvature and suffer less distortion. [5] Explicit Patterns in Data: Hexagons allow any in the data to show easily and explicitly because they break up lines. curvature of the patterns For linear figures like squares and rectangles, this becomes tricky. These shapes draw our attention to the straight, unbroken and parallel lines which hinder the patterns present in the data. Refer to the diagram below. [6] Why geohashes? That brings me to my next question, “When would someone use fishnets or square grids?” Aggregation/ Division of Cells: Different kinds of models require different granularity and that’s where aggregation and division become important. If you need to increase the spatial resolution of a square grid, you just need to divide it into 4. Similarly, to aggregate, you need to combine four grids into one. For hexagons, aggregations and divisions are not uniform at different scales as shown in the image below. The finer cells are only approximately contained in the parent cell. [7] Squares are preferred over hexagons for hierarchical analysis. Combining square grids is fairly simple. No spatial operations are necessary for combing multiple grids built on the same template — you can use matrix algebra. Very Intuitive & Familiar: We also think in terms of “ ”. Up, down, left, right are simple to understand. We have built cities and civilizations on squares and rectangles. Since our primary coordinate system is squared, people find it difficult to work on other systems. squares They are also sometimes used for connectivity analysis as they have eight neighbors (including diagonals). Some Real-Life Examples Hexagons are found widely in nature. For example, honeycombs, graphite, benzene, silicene etc. Chinese Checkers is played on a hex grid and several variants of chess have also been invented for a hex board. The hex grid is a distinguishing feature of the games from many wargame publishers, and a few other games (such as The Settlers of Catan)! [8] About Locale: At Locale, we are building an “ operational ” analytics platform using location data for supply and operations teams in on-demand companies. If you want to delve further, check our website out or get in touch with me on LinkedIn or Twitter . Originally posted here . Read Similar: Geospatial Clustering: Types and Use Cases [ ] Link A Guide to Kickstart with Geospatial Analysis [ ] Link References: If you wish to read more, check out the links to delve further: [1] https://en.wikipedia.org/wiki/Geohash [2] https://boardgames.stackexchange.com/questions/633/why-are-there-fewer-board-games-with-a-triangular-grid [3] https://www.redblobgames.com/grids/hexagons/ [4] http://www-cs-students.stanford.edu/~amitp/game-programming/grids/ [5] https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-whyhexagons.htm [4] https://www.sciencedirect.com/science/article/pii/S0304380007001949 [5] http://strimas.com/spatial/hexagonal-grids/ [6] https://gis.stackexchange.com/questions/82362/what-are-the-benefits-of-hexagonal-sampling-polygons [7] https://en.wikipedia.org/wiki/Hex_map [8] https://en.wikipedia.org/wiki/Hexagonal_tiling%5C Previously published at https://blog.locale.ai/spatial-modelling-tidbits-honeycomb-or-fishnets/

Why Do We Use Hexagons And Not Sqaures to Aggregate Location Data

Geo is practically everywhere today! With the prevalence of GPS all around us & the rise of smartphones, location data is being collected in abundance. Legend almost all of the data (~80%) that companies store has a location component. says Our lives are now filled with products and services available at the tap of a phone, getting things delivered in minutes to our doorstep! Today, it is hard to find an app which doesn’t ask for your location permissions. There is a famous in the GIS (Geographic Information Systems) world which says When you place an order from any food delivery app, every event starting from placing of the order — to delivery executives (DE) getting assigned to — the DE picking it up from the restaurant — to him delivering happens at a certain lat-long! quote “everything happens somewhere.” Which brings me to my next point: Why are we obsessed with “location” as a dimension? Firstly, there is no “mobility” without dynamic location. In other words, location is a fundamental aspect whenever there is movement of assets (people, vehicles, cargo, parcel) on the ground. After all, in cities and towns where things change at every sq km in a city, it’s important to add the “context” of those areas. For the on-demand economy that we live in, analyzing location data across all your dimensions (users, stores, partners) becomes in real-time. This is because you have to match your supply with demand, do location-based pricing, promotions or faster and more accurate deliveries at scale! critical And finally, the most successful for consumers are based on sequential activities at a very granular level: Where are you? Where do you need to go? What do you want to do when you get there? This becomes important when you need to acquire customers by targeting ads and retaining and engaging them by their behavior. location-based experiences studying Location is not only about a point on a map. It is about a line. It is about movement. But all this time, the location component has been particularly in business decisions. This is because conventional analytics tools are fantastic for the treasure trove of statistical data that businesses have but somewhere, they fall short of . neglected location-based decisioning You might be wondering: “But, why?” Before we deep-dive into that, let’s understand how geospatial data is different from statistical data. Location is exciting! If played right, it can drive significant revenue improvements (case in point: Uber & Airbnb). Its high usage and real-time nature make it really valuable and sticky. What sets geospatial data apart from statistical data? Just like text, sound, image are different kinds of data, Latitude-Longitude is a different kind of data that can add immense depth, meaning and insights to statistical data in a space-time context. Statistical data comes in a tabular format and usually, is comprised of two elements: values across time. (also called geographic data or location data) is often formatted as points (latitude-longitude coordinates), polygons or polylines. Geospatial data It incorporates the third dimension on top: values across time and dynamic location — which requires a completely different approach and treatment. Computing metrics for static assets involves plotting points on a map and calculating metrics for that. Movement analytics concerns itself with how do we visualize, analyze and optimize how things move on the ground! Two special properties of geospatial data are autocorrelation and non-stationarity, which make it difficult to meet the assumptions and requirements of traditional (nonspatial) statistical methods, like OLS regression. means how similar (correlated) are my measures between nearby observations. It falls in-line with the first law of geography as well- Spatial Autocorrelation “ everything is related to everything else, but near things are more related than distant things ”. is a condition in which a simple “global” model cannot explain spatial relationships between the variables. The nature of the model alters over space and behaves differently in different parts of the study area. Spatial nonstationarity Let’s answer our pending question now: Why are current statistical tools not apt for spatial analysis? Today, the current way of dealing with location data inside companies is broken. Due to the dearth of any location analytics products out there, most of the businesses have no choice but to rely on traditional BI and analytics tools. It’s not puzzling that this is a highly ineffective strategy because these tools are not really meant for geo-analysis. 1. Formats Most companies collect location log data in the form of . The way to analyze the patterns they encompass is to have an infrastructure that can ingest these pings in real-time — something that platforms like Periscope or Tableau don’t cater to. “ pings ” Moreover, location data is present across disparate databases, in different structures. Hence, slicing and dicing variables across tables in real-time comes even more complex. 2. Visualizations All of us know that a statistical dashboard contains bars and charts which sprout from carrying out operations (sum, count, divide, average, etc) on variables. While getting live trend updates through spikes and dips on graphs might be helpful, these charts are work better on aggregated historical data. Adding or dividing lat-longs and creating bars and charts on them is pretty futile. To make sense of these lat-longs, you need to have a map by your side to understand their spatial distribution! Another important aspect that governs all the properties of a geospatial dashboard is a . Not having this layering mechanism which you can use to display multiple types of data points on the map, sort of misses the point. For instance, layer layers help in viewing how my orders (first layer) and partners locations (second layer) are distributed across my area clusters (third layer). Maps are also more insightful to draw inferences than bars and charts if there is movement of components on the ground involved. Hence, real-time geographic analysis when everything is dynamic becomes fundamental. 3. Aggregation Traditional BI tools like and offer the capability to plot points on a map. However, just plotting points on a map is not adequate as billion dots in space is not very intuitive. Moreover, PowerBI Geckoboard Location intelligence is so much more than tracking and plotting points on a map! Strategies like clustering, heat mapping, aggregation, indexing, etc. come in handy to absorb a large number of points. Some tools like and also allow the creation of heat maps — a fantastic way to depict the patterns of metrics. The disadvantage of heatmaps is that they are only a visual representation, thus restricting you to do any sort of analysis on it. Tableau Periscope To know more about why heatmaps don’t work, check out! this A more efficient way to do aggregation is indexing them on or . Once you analyze the pattern of your metrics (such as demand and supply) across grids, you can use the cells as a single unit in your models as well. Indexing also helps to go very granular in your analysis. hexagonal grids geohashes 4. Data Preparation Using and , you can clean your statistical data by taking care of the blanks, spaces, data formats, NAs etc whereas cleaning of GPS data involves snapping it back to the nearest road or correcting for . (You must have observed often while using Google Maps, GPS goes off very far randomly.) It is safe to say that GPS as a technology still has miles to go! Cleaning: Periscope Thoughtspot spatial outliers Platforms like or allow you to merge two tables using an inner join, left join or a right join on the basis of a common identifier. It’s quite difficult to do spatial merges using these platform if, for instance, you have data across three dimensions: users, delivery partners, and stores (sometimes they come in different formats). Merging: Tableau Prep Metabase A involves inserting columns from one feature table of a layer to another in the spatial perspective. spatial join For example. merging a Kormangala area polygon with all the ride pick up points inside it. Enriching of any data implies adding new layers of information and merging it with third-party or external data sources. In the GIS world, we enrich spatial data for a better context of areas in which the points are present. This means adding the environmental layer of roads, traffic, weather, points of interest, demographics, buildings, etc. Enriching: The internal solutions not working out? Some companies, of course, realize this and hack around open-source tools (like or ). But these open source tools come with their own list of constraints and limitations. Kepler.gl QGIS However, the issue doesn’t get resolved here because geospatial data itself comes with a bucket full of challenges. Performing geospatial queries on streaming data become very compute-intensive and legacy technologies (like ArcGIS) provide very little support. The complexity increases with visualizing large geospatial datasets with any sort of interactivity at scale. Sometimes developers also build their own internal tools, but most of the times they are not well suited for all different audiences inside the company. Since the tools are not built in a scalable way, maintaining these suck up a lot of developer bandwidth often! A lot of times there is even a repetition of effort and the wheel keeps getting re-invented over and over again. As Jeff Lawson from Twilio — “It is easier than ever to build software but harder than ever to operate it”. said Why are we building Locale.ai? It all started with a personal problem. As data scientists working with geospatial data, the existing analytics products were futile in our daily workflows. Hence, we had to build our own tools and libraries for our everyday workflows. We then realized data scientists around the globe face similar problems when it comes to location data. As a result, businesses are struggling to attain operational efficiency! At , we plan on solving these problems once and for all. We want to make all of these processes less painful and building a world where it is very easy to get all your geospatial answers in minutes! That’s why we are going back to the drawing board and handcrafting this experience completely from scratch. Locale So, the next time you want to order medicines in case of an emergency, you won’t hopefully read on the screen, “ ” Next time the delivery guys won’t have to stand idle in the scorching heat, cold or rains waiting for the orders to come. Delivery guys are not available. Please check again later. They can be incentivized to move to high demand areas and can earn more money. The push notifications that you get won’t be spam — they will be shot to you at the right place and right time. Read Similar: Bridging Supply-Demand Gaps in Last-Mile Delivery Companies Geospatially [ ] Link Carto vs Kepler vs Locale: Which product to use for geospatial analytics? [ ] Link

Why Do We Need a New Product for Geospatial Analytics for Mobility

How to Run Your Own Local LLMs: Updated for 2024 with 8 New Open Source Tools!

Welcome to this issue of the Activation Function Newsletter. For those of you who are new, it’s pretty simple - Every other week, I introduce you to a new and exciting open-source backend technology (that you’ve probably only kind of heard about ) and explain it to you in 5 minutes or less so you can make better technical decisions moving forward. In this issue, we’ll explore K8sGPT, an open-source tool that combines expert knowledge and Generative AI to help you identify, understand, and troubleshoot your Kubernetes cluster in plain English. Before you dismiss it as “just another ChatGPT wrapper,” let me assure you that it’s not. It’s much more than that. I think it’s a thoughtful implementation of GenAI that has the potential to bring tangible benefits and make your life a bit easier. Alright, Let’s dig in! Quick Facts: K8sGPT was launched at KubeCon Amsterdam 2023. K8sGPT is open-sourced under an Apache-2.0 license. K8sGPT was accepted to CNCF on December 19, 2023, at the Sandbox maturity level. K8sGPT now has over 4K GitHub stars and 50 contributors. Introduction For years, we’ve used ML for APM and Observability to identify patterns, trends, and anomalies (e.g., behavioral changes in applications or infrastructure), anticipate performance issues, generate early warnings, etc. But so far, it hasn’t been great at actually helping us understand and fix issues. The story goes something like this. You get an alert from your ML-enabled monitoring tool and then spend a lot of time looking through and interpreting complex error messages, trying to communicate those to your team, and iterating through remediation steps until you fix the problem. Enters K8sGPT. Its primary function is to help you quickly triage and remedy issues by giving hints and shortcuts in plain English. It does this by leveraging expert knowledge, Natural Language Processing (NLP), and Generative AI. How K8sGPT Works K8sGPT uses contain codified knowledge, which is essentially a series of rules/checks that an experienced Site Reliability Engineer (SRE) might use to diagnose Kubernetes issues such as pod crashes, service failures, and ingress misconfigurations, etc. Analyzers that It was a good decision to avoid using GenAI at this stage to prevent speculative guesses, hallucinations, and, ultimately, a lot of noise. Thought: Once K8sGpT is connected to your Kubernetes cluster through the API Server, the will start scanning your cluster and looking at logs, error messages, configurations, and pod status to identify potential issues. Analyzers : K8sGPT has a set of , but you will be able to write your own analyzers. Technical Note analyzers that are built in It then tries to correlate issues and run additional checks if necessary to add more context and figure out the root cause before packaging the data and sending it to the AI backend provider to get a simple and descriptive text of the issue and potential remediation steps. As you can see, the use of Gen AI is targeted and practical. Of course, the AI can hallucinate and provide incorrect remediation steps, but that’s a minor inconvenience you can spot and ignore easily. Technical Note: Getting Started with K8sGPT The easiest way to get started is using a by running a brew command on Linux/Mac or downloading the Windows Bianary if you’re on Windows. CLI installation Once installed, you’ll need to , and within a few minutes, you’ll be ready to run your first scan. authenticate your chosen AI backend K8sGPT is designed to work with various AI providers, including , , , , , , and . Technical Note: OpenAI Cohere Amazon Bedrock Amazon SageMaker Azure OpenAI Google Gemini LocalAI You can find how to configure different backends . here Once everything is configured, you can start using K8sGPT directly from your CLI using the following set of commands:‍ Usage:
  k8sgpt [command]

Available Commands:
  analyze     This command will find problems within your Kubernetes cluster
  auth        Authenticate with your chosen backend
  cache       For working with the cache the results of an analysis
  completion  Generate the autocompletion script for the specified shell
  filters     Manage filters for analyzing Kubernetes resources
  generate    Generate Key for your chosen backend (opens browser)
  help        Help about any command
  integration Integrate another tool into K8sGPT
  serve       Runs k8sgpt as a server
  version     Print the version number of k8sgpt

Flags:
      --config string        Default config file (default is $HOME/.k8sgpt.yaml)
  -h, --help                 help for k8sgpt
      --kubeconfig string    Path to a kubeconfig. Only required if out-of-cluster.
      --kubecontext string   Kubernetes context to use. Only required if out-of-cluster.

Use "k8sgpt [command] --help" for more information about a command. You’ll soon be able to debug interactively by asking K8sGPT questions about any issue directly in your CLI. As for writing, this feature isn’t live. [Technical Note] K8sGPT Operator K8sGPT can also be installed as an Operator within your Kubernetes Cluster. By using the K8sGPT operator, you’ll be able to scan your cluster for errors continuously. The works by leveraging Kubernetes Custom Resources and producing reports that it stores in your cluster as YAML manifests. K8sGPT Operator You can cache your results locally or like AWS S3. [Technical Note] offload them to a remote location You can also easily customize the analysis and output of the operator to fit your needs and workflow. For example, you specify a certain namespace or resource and a specific output format like JSON. This makes K8sGPT easy to integrate into your CI/CD pipelines and other workflows. Integrations K8sGPT integrates with observability tools like Grafana and Prometheus, but the really cool thing is that it offers an that allows you to extend scanning and troubleshooting capabilities by writing your own plugins that connect it to other tools. integration feature The first one, contributed by the core team, is the plugging, which enables you to generate vulnerability scans as part of your analysis and leverage AI to help you understand and triage Common Vulnerabilities and Exposures (CVEs). Trivy Security & Privacy Alright! I’m sure this is the first or second thing that came to your mind when hearing that K8sGPT sends your data to an external AI-API. Well, the K8sGPT team thought of this and developed a nifty little feature called anonymize, which masks sensitive data like Kubernetes object names and labels before sending it to the AI backend for analysis. All you need to do is use the anonymize flag: k8sgpt analyze --explain --anonymize During the analysis, k8sGPT retrieves sensitive data and masks it before sending it to the AI backend. Once the solution is returned, the masked data is replaced with the actual Kubernetes object names and labels. Where to go from here? K8sGPT is still a nascent project and is quickly evolving thanks to an active community of contributors. As of writing, an exciting new feature on the horizon is the interactive debugging mode that lets you ask questions directly inside your terminal. Other things on the horizon include Kubernetes auto-remediation with K8sGPT, Karpenter, deep integration into AWS services, and new backends into services such as Hugging Face. If you want to dig deeper into K8sGPT, here are a few resources for you: Documentation & Repos - The main code repository k8sgpt - Documentation Website ( ) docs https://docs.k8sgpt.ai - Community-related information community Tutorials K8SGPT - Kubernetes SRE SUPERPOWERS Using AI with Kubernetes | K8sGPT Operator | DEMO Full Tutorial: K8sGPT -- SRE superpowers through AI Talks AI for Kubernetes with ChatGPT and k8sgpt Unleashing the Power of AI in Kubernetes through K8sGPT | Alex Jones Communities Slack: https://k8sgpt.slack.com Who to Follow Alex Jones (Creator of K8sGPT) on and Linkedin X K8sGPT on and LinkedIn X Thomas Schuetz (Maintainer) on and Linkedin X Matthis Holleville (Maintainer) on and Linkedin X It’s a wrap! I hope this gave you a good overview of K8sGPT and how Gen AI might make your life a bit easier when troubleshooting your Kubernetes clusters. If you want to take K8sGPT for a quick spin, I suggest you check out this . Sandbox tutorial Until next time! Also published . here

A Gentle Intro to K8sGPT: Your Kubernetes Debugging Sidekick

Introduction Hi, everyone, my name is Maksim Nechaev. I'm a Senior iOS developer at Snoonu, and also the founder of my own startup. Like many other developers, I've written a lot of apps and features on UIKit. All the global projects I've worked with have been on it. And no wonder because it is the most common set of UI elements if we talk about . Along with UIKit, we should immediately mention layout via constraint. Often, we used SnapKit to simplify this task and write more minimalistic code. iOS development But times were changing, and a new, trendy, and hip SwiftUI framework appeared on the horizon. At first, it was shunned. They looked at it with derision. Maybe it even made sense at first because SwiftUI had huge limitations and shortcomings. We couldn't do 50% of what we could do with UIKit. But years later, SwiftUI has become more powerful, so much so that many companies don't even look at UIKit when starting new projects. Why is that? It's because SwiftUI has a declarative layout style, uses convenient and fast reactive elements, and building any screen complexity takes from 30 minutes to two hours, while on UIKit, you would have spent at least a day. And what is time for business? We all know time is money. So, the first thing SwiftUI is already doing right now is saving money for businesses. Let's go deeper. What else is great about SwiftUI? The ease of creating animations. Yes, yes, if you imagine a cool app with 60+ frames per second that responds smoothly to taps, buttons shrink with animation, screens pop out of the right side of the screen, everything happens in beautiful and pleasing (for UX) animations, all of this is very quick and easy to do inside SwiftUI. You can do a lot of this in UIKit, too, to tell you the truth. But spend a lot more effort and time. I hope I'm doing well in convincing you that SwiftUI is ready for full production development and that you should be learning it right now. That's why, with my first article on this topic, I'd like to touch upon the SwiftUI View lifecycle. How a "view" is created, where it is stored and how it is updated, I would like to talk about it all in this article. Let's get started! SwiftUI View vs UIView Let's start with the fact that UIView is created once in most cases. After that, we simply update its data. A SwiftUI View can be redrawn and created dozens of times within a single screen. This is very well illustrated in the following image. You may notice that when we use UIViewRepresentable, a structure that helps us use UIView inside SwiftUI, we create it once. But further, to update it, we pull updateUIView and update it as many times as needed. SwiftUI View is created many times, while UIView is created once. Storing data in SwiftUI View Let's take a look at ways to store data inside a . After all, you're probably already thinking, how do you store data if the View is constantly being recreated and redrawn? SwiftUI View In total, we can divide it into . three parts . Let and var, the classic use of data. First . @State and @StateObject. Already more SwiftUI approach Second . @Enviroment and @EnviromentObject Third Let Absolutely classic usage; we can create some kind of constant that we pass externally and use inside our structure. struct CustomView: View {
    let text: String

    init(text: String) {
        self.text = text
    }

    var body: some View {
        Text(text)
    }
} The only thing to keep in mind here is that usually, when we create a constant, we assume that it will not change. The situation here is that when is changed and redrawn, the constant can be changed. That is, it can be "bound" to the objects being changed. CustomView text Var This is where it gets interesting. What if we want to change a variable inside a structure? We have always done it in a very simple way, if we talk about classes in UIKit. var text: String Let's imagine that we have made our text a variable. It seems that now we can change it. But no, Swift won't let us do that. Structures are more capricious and rigid than classes, and even more so in SwiftUI. There is only one way to try to change text, and that is to create a mutating function. But unfortunately, we will get an error here, too. It will look like this: "Cannot use mutating member on immutable value.” In general, the idea with an ordinary var in the structure is very doubtful, and you should think ten times why you should do it. After all, SwiftUI has other cool tools for working with values, which we will talk about next. State The situation with the State is fundamentally the opposite. It can and must be used inside structures if we want to work with the value and change it somehow. struct CustomView: View {
    @State private var text: String

    var body: some View {
        Text(text)
    }
    
    func changeText() {
        text = "Hello, World!"
    }
} That is, if we create a structure with some local variables to which we tie local logic, we should use , so that all internal Views that use this value will be updated. Everything will work correctly, but... there is always a "but". What if we want to set the value externally? @State For example, like this struct CustomView: View {
    @State private var text: String

    init(text: String) {
        self.text = text
    }

    var body: some View {
        Text(text)
    }
} Do you think it will work, and in what cases? To be fair, it will work, but only when we set the first value. When we set all subsequent values, only the first one will be displayed inside the . body Here, it is important to realize that does not live as long as View does. That is, when we create inside View, we can change it inside View as many times as we want. The view will be updated and redrawn, but the local value will be stored. But if at the same time we try to set new values for through the initializer, they will be simply ignored. @State @State @State What is the output here? should be used only for saving and updating local data, but not those passed externally to the structure. @State StateObject Now, let's talk about using StateObject. In general, we use to set some kind of ViewModel for our screen. That is, we pair it with a class that obeys the ObservableObject protocol. It works very similarly to , with one exception: it is created after the first initialization of the structure. That is, while could somehow be passed to to put the first value, won't work. It will be deleted from memory after the initializer is called and will be created again. @StateObject @State @State init @StateObject Environment Let's move on to the consideration of . This property modifier is used to access data that is passed down the view hierarchy. Unlike , which is used to control the state of an object within a single view, allows you to access data that is shared by the entire application or a specific part of it. @Environment @StateObject @Environment Let's say you have some value that needs to be available in many places in your interface, such as custom settings or a design theme. Instead of passing this value through all levels of the hierarchy, you can use for easier access. @Environment When you mark a property in your SwiftUI view as ,SwiftUI automatically looks up the value of that property in the nearest matching context and provides it to you. If the value changes, SwiftUI also automatically updates the views that use that value. This makes a very powerful tool for managing data in SwiftUI. @Environment @Environment Example of use: struct ContentView: View {
    @Environment(\.locale) var locale: Locale

    var body: some View {
        Text("Current localization: \(locale.identifier)")
    }
} EnvironmentObject Now, let's break down . This property modifier is used to inject dependencies into SwiftUI views. Unlike , which is designed to access system settings or values defined at the application level, is used to pass and use user data between views. @EnvironmentObject @Environment @EnvironmentObject When you use , you expect the object to be provided by the parent views. This allows you to create more modular and easily scalable applications because your views do not depend on specific implementations of their dependencies. Instead, they dynamically retrieve the necessary objects from their environment. @EnvironmentObject Example usage of : @EnvironmentObject class UserSettings: ObservableObject {
    @Published var score = 0
}

struct ContentView: View {
    @EnvironmentObject var settings: UserSettings

    var body: some View {
        Text("Score: \(settings.score)")
    }
} SwiftUI View Life Cycle Understanding the life cycle of a view in SwiftUI is key to creating efficient and well-performing applications. Let's explore this process in a detailed and interesting way. 1. Initialization ( ) Initialization Every SwiftUI View starts its life journey with initialization. This is when SwiftUI creates an instance of your view and sets up its initial state. It's important to realize that SwiftUI views are structures, meaning they are value types. When you create a view, you are actually creating a "snapshot" of it at this point in time. 2. State Update ( ) State Update After initialization, SwiftUI watches for changes in data that may affect your view. This data can include , , , , and . When one of these values changes, SwiftUI starts the process of updating the view. @State @Binding @ObservedObject @EnvironmentObject @Environment 3. Body Computation ( ) Body Computation In this step, SwiftUI calls the property of your view. is a computed property, and every time SwiftUI detects a change that affects the view, it recomputes . This process allows SwiftUI to determine which parts of the UI need to be updated. body body body 4. Rendering & Layout ( ) Rendering & Layout Once the body of the view has been computed, SwiftUI proceeds to the rendering and layout phase. In this stage, SwiftUI determines exactly how the interface elements should be arranged and rendered. This process involves calculating the dimensions, positions, and other layout aspects for each element on the screen. 5. View Activation ( ) View Activation This is a specific point in the lifecycle that relates to user interaction with the view. For example, it could be the activation of a text box or the appearance of a view on the screen. 6. Deinitialization ( ) Deinitialization The last step in the life cycle of a view is to deinitize it. When a view is removed from the SwiftUI view hierarchy, it is deinitialized. This is an important time to release resources or perform any cleanup. Important Points: : SwiftUI frequently recreates and reuses views. Therefore, it is important to avoid expensive operations in initializers and . Reuse body : SwiftUI is optimized to ensure that only the necessary parts of the interface are updated. This improves the performance and efficiency of the application. Efficiency Conclusions SwiftUI View is very different from the familiar UIView, but that's the point. SwiftUI is different, and that's a good thing. In this article, I tried to convey the essence of working with SwiftUI View as succinctly and clearly as possible because this is a key skill for a SwiftUI developer. You will come across it in every task, which means it's worth understanding it very deeply. You need to be conscious of what you are writing and creating. I wish everyone good luck on the path to becoming powerful developers. If you are reading this article, then you are already on the right path. Thanks for your time, bye everyone! Please respond to this article so I can write more useful material for you.

Understanding the SwiftUI View Lifecycle and Data Management

In this tutorial, you will learn to create your app with Flutter, which can help you calculate tips (when you buy chips, ha-ha). In this process, you will explore a few interesting things from the Flutter stack: What is a ? widget What method does? build - one of the most exciting features of Flutter Hot Reload Ready? Let’s start! A Little Theory is an entity, which can have some state. Also, this entity is a way to declare and construct the UI of your app. As the Flutter team says: Widget All is Widget The Widget can be small and plain - only one word in the text can be a separated Widget. But, on the other hand, Widget can be huge with much complex logic. Well, to start, you need to have installed Flutter and Dart. I hope you have done this. Next - create a Flutter application from the template with the command: flutter create. The 'tip app' will consist of only one screen - in which you can see two simple text fields - one for the overall amount of your order and the second input for the tip percentage. The screen also has two text lines with a tip in dollars and a total amount of charge, which equals the overall amount + tip amount. Source Code ; ; ; ; ; ; ; ; ; ; ; ; ; ; { OrderView({
    Key? key,
  }) : (key: key); _OrderViewState createState() => _OrderViewState();
} { late OrderState orderState; initState() { .initState(); orderState = OrderState()
      ..registerHook(() {
        setState(() {});
      })
      ..initState();
  } dispose() {
    orderState.dispose(); .dispose();
  } Widget build(BuildContext context) { infoStyle = TextStyl.bold(context); Padding(
      padding: EdgeInsets.all(Pad.l1),
      child: Column(
        children: [ TextFormField(
            decoration: InputDec.outline8.copyWith(
              labelText: Locale.billTotalLabel,
            ),
            keyboardType: TextInputType.number,
            controller: orderState.billTotalController,
            inputFormatters: [ NumberFormatter(),
            ],
          ), VDivider(), TextFormField(
            decoration: InputDec.outline8.copyWith(
              labelText: Locale.tipPercentLabel,
            ),
            keyboardType: TextInputType.number,
            controller: orderState.tipAmountController,
            inputFormatters: [
              NumberFormatter( : ),
            ],
          ), VDivider(level: DividerLevel.l2), Row(
            children: [
              Text(
                Locale.tipAmountTitle,
                style: infoStyle,
              ),
              AnimatedText(
                Utils.formatMoney(orderState.order.tipAmount),
                style: infoStyle.copyWith(fontWeight: FontWeight.bold),
                useOpacity: ,
              ),
            ],
          ), VDivider(), MoneyRow(
            title: Locale.totalAmountTitle,
            money: orderState.order.totalWithTipAmount,
          ), Expanded(
            child: Center(
              child: Column(
                mainAxisSize: MainAxisSize.min,
                crossAxisAlignment: CrossAxisAlignment.center,
                children: [
                  Text(Locale.changeTipHint, style: TextStyl.bold(context)),
                  Row(
                    mainAxisSize: MainAxisSize.min,
                    children: [
                      IconButton(
                        icon: Icon(Icons.add),
                        onPressed: orderState.increaseTip,
                        color: Colors.green,
                        highlightColor: Colors.transparent,
                        splashColor: Colors.green.withOpacity( ),
                      ), HDivider(),
                      IconButton(
                        icon: Icon(Icons.remove),
                        onPressed: orderState.reduceTip,
                        color: Colors.red,
                        highlightColor: Colors.transparent,
                        splashColor: Colors.red.withOpacity( ),
                      ),
                    ],
                  ), VDivider(level: DividerLevel.l5),
                ],
              ),
            ),
          )
        ],
      ),
    );
  }
} import 'package:anitex/anitex.dart' import 'package:flutter/material.dart' import 'package:flutter/services.dart' import 'package:flutter/widgets.dart' import 'package:tipsalc/order/view/money_row.dart' import '../../constants/input.dart' import '../../constants/padding.dart' import '../../constants/text_style.dart' import '../../locale/locale.dart' import '../../utils/formatters.dart' import '../../utils/utils.dart' import '../../widgets_helpers/horizontal_divider.dart' import '../../widgets_helpers/vertical_divider.dart' import '../state/order.state.dart' class OrderView extends StatefulWidget const super @override < > class _OrderViewState extends State OrderView will /// We use Flutter 2.0 with Dart 2.12 you /// which offer null-safety @override void super will /// For state management we /// use simple [setState] method /// But to prevent placing business logic will /// in UI layer - we take out it in /// separate class [OrderState] @override void super @override final return const /// This is first field with just /// amount of order will /// We using formatter /// for prevent input non-digit symbols /// in our input const /// Second field with amount of tips /// in percents int true const /// This is two ours text lines with /// total amount of tips in dollars false const the /// And second row with total amount just /// of all order ( order + tips in dollars) a /// Well, there we have visual helper to manipulate you /// tips quantity - can simple increase or reduce the /// amount of tips with step in 5% const 0.15 const const 0.15 const StatefulWidget From this sample, you can see that we use a for our single screen because it has its own state, where you can place some data which you can rule. We place the data in a separate class, StatefulWidget OrderState. The logic is placed there too, but the UI layer's renewal is with - state of - , which can refresh UI by calling method. _OrderViewState OrderView StatefulWidget setState initState() { .initState(); orderState = OrderState()
    ..registerHook(() { setState(() {});
    })
    ..initState();
} @override void super will /// For state management we /// use simple [setState] method /// But to prevent placing business logic will /// in UI layer - we take out it in /// separate class [OrderState] /// We pass method [setState] to our /// OrdersState class, where this method will /// called after any data updates StatelessWidget The second type of that you can see in this example is a . In opposite to - that type hasn’t its own state - the only UI without and logic. But you also can use methods and data, which you must pass to your from its parents. Widget StatelessWidget StatefulWidget StatelessWidget For example, our widget places two identical widgets with the same goal - show the user two similar rows with text. Here they are: OrderView Row(
  children: [
    Text(
      Locale.tipAmountTitle,
      style: infoStyle,
    ),
    AnimatedText(
      Utils.formatMoney(orderState.order.tipAmount),
      style: infoStyle.copyWith(fontWeight: FontWeight.bold),
      useOpacity: ,
    ),
  ],
), false You can also simplify the code as follows: MoneyRow(
  title: Locale.tipAmountTitle,
  money: orderState.order.tipAmount,
), You create your own . StatelessWidget ; ; ; ; { MoneyRow({
    required .title,
    required .money,
    Key? key,
  }) : (key: key); title; money; Widget build(BuildContext context) { infoStyle = TextStyl.bold(context); Row(
      children: [
        Text(
          title,
          style: infoStyle,
        ),
        AnimatedText(
          Utils.formatMoney(money),
          style: infoStyle.copyWith(fontWeight: FontWeight.bold),
          useOpacity: ,
        ),
      ],
    );
  }
} import 'package:anitex/anitex.dart' import 'package:flutter/widgets.dart' import '../../constants/text_style.dart' import '../../utils/utils.dart' class MoneyRow extends StatelessWidget const this this super final String final num /// We move all UI logic from /// old place (OrderView) to here you /// and in OrderView can use simple /// and small widget MoneyRow instead /// this several widgets /// you /// And also can reuse this in any place your /// of app @override final return false Build Method As you can see - we moved widgets from to separate widget . However, we placed them in a special method , which must return a widget too. What does this mean? All Widgets must have a build method and that will be called by the parent of the widget. As Flutter docs say: OrderView MoneyRow build Build method describes the part of the user interface represented by this widget. The framework calls this method when the widget is inserted into the tree in a given and when the dependencies of this widget change (e.g., an referenced by this widget changes). This method can potentially be called in every frame and should not have any side effects beyond building a widget. BuildContext InheritedWidget Super Exciting Hot Reload Hot Reload. How nice to hear these words! That feature saves your time as a developer time. You can ask - How Well - because you can change the code and see the result of your actions simultaneously. You can change the theme of all your app, and after part of one second - you will see your app in new colors. You can cut a big part of your widgets and replace them with another and see changes after splits of seconds. But you must know that you can make some changes and don’t see any difference in-app. ? For example - you add new logic in some method which called only once when your app loading or in method - in that situations, you must using feature - it is like Hot Reload, but it reloads all Flutter layer of your app without saving of state. So in some situations, you must use Hot Restart instead of Hot Reload. You will understand when to use what. initState the Hot Restart Conclusion To start creating apps with Flutter you should know how widgets work. You can use in small applications for simple state management. You can also use to separate complex UI logic into simple different widgets, from which you will assemble the interface like a constructor. StatefulWidgets StatelessWidgets You can see this application in real-time . here

How To Create a Tip Calculator with Flutter

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

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

LOCALEAI

locale.ai

11-50 employees

Since 2019

COMPANY RANKING

LOCALEAI

EVERGREEN INDEX #6870

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LocaleAI WIKI

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LOCALEAI

locale.ai

11-50 employees

Since 2019

COMPANY RANKING

LOCALEAI

EVERGREEN INDEX #6870

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