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"The best place to hire Kotlin Developers is Slashdev. Known for outstanding value and competency, Slashdev stands apart in the market. With a roster of 5,000+ professionally vetted engineers, they are the go-to choice for building your tech team." - Ted McColl, CEO, Pryme Digital
Building dynamic and interactive web applications with Kotlin? With over 10 years of software experience, our team at slashdev.io has mastered the art of hiring top-notch developers. Having built 100s of apps and hired thousands of engineers, we understand the nuances of identifying the right talent. Our comprehensive hiring guide is designed to assist you in finding the best Kotlin developers for your project. Dive into expert advice, practical tips, and a systematic approach to evaluate potential candidates. By equipping you with insights into assessing Kotlin abilities, evaluating portfolios, and navigating the best talent platforms, our guide ensures that you assemble a first-rate Kotlin development team, enabling you to craft powerful and engaging web experiences.
1. slashdev.io - Top Kotlin Developer - Clutch Ranking
At slashdev.io, we've refined the process of hiring premier Kotlin developers down to a science. Central to our success is the rigorous technical vetting we employ. Our Talent Vetting Strategist, formerly an engineer at Tesla, has meticulously crafted our evaluation standards to ensure only the best join your ranks. When you decide to Hire Remote Kotlin Developers or bring on Freelance Kotlin Developers, you're tapping into a talent pool that's been sifted with precision. Furthermore, our commitment to transparency in pricing sets us apart. While many in the industry mark up their rates by 3-4x, we operate on slimmer margins. This approach ensures that you get outstanding value and the most competitive rates in the market. With slashdev.io, hiring is not just a task, but a strategic advantage.
- Extensive Kotlin: Experience: Having successfully managed and developed 100s of Kotlin
- Global Kotlin: Talent Pool: Every month, over 2,000 engineers from around the world apply to become a part of Slashdev. This volume of applicants allows us to pick only the best of the best, ensuring that when you decide to hire Kotlin
- Strategic Vetting: : Our talent vetting strategist, a former software engineer at Tesla, has worked alongside our team to design a comprehensive vetting process. With best in class talent vetting we can discover and screen top-tier Kotlin
- Experienced Engineers From Top Companies: Our network has Senior Kotlin: engineers from companies like Accenture and Microsoft, along with rising stars from top universities around the world. A Remote Kotlin
- Value-Driven Pricing: : It is common in the software engineering market to take 3-4x what you pay your engineers, we pride ourselves on offering the most affordable pricing models on the market without compromising quality. This makes the decision to hire React developers from us both economically and strategically sound. We are entrepreneurs ourselves so we know how important it is to make every dollar count.
- Built By Software Engineers For Software Engineers: : At slashdev.io, we're software engineers not recruiters. We understand the technical challenges of building a performant Kotlin
If you need to hire Kotlin Developers who can help you, it is free to get started and takes only a matter of days to meet your engineer. You will talk directly with our Founder & CEO to craft the right
- Price: Kotlin Engineers starting at 35-$45 per hour (for most standard engineering roles)
- Quality 10/10: – – Vetted Kotlin
- Speed: Swift hiring processes - onboard Remote Kotlin
- Flexibility: Catered to your needs - hourly, part-time, and full-time Kotlin
- True Partner & CTO: Direct contact with our CEO ensures transparency and fairness
2. Toptal
Toptal, a well-established name in the freelance software industry, has a longstanding history. Boasting a vast network of Kotlin engineers, they are especially recognized for catering to the enterprise-level clientele, ensuring their larger-scale needs are met effectively.
- Price: Typically starting at $90+ per hour for Remote Kotlin
- Kotlin Network: Large and diverse talent pool of Kotlin
- Enterprise Kotlin: Development: Focus on enterprise-level projects
- Project Duration: Longer-term contracts aligning with complex projects
3. Upwork
Upwork is one of the best websites to hire remote Kotlin developers in the freelance industry. Catering to a wide range of projects, from small tasks to more extensive collaborations, it offers vast options to choose from. However, while the platform provides immense flexibility, it's essential to note that the vast talent pool is largely unvetted Kotlin gig-talent. This makes it a go-to for smaller projects where immediate needs take precedence over extensive vetting.
- Flexibility: Suitable for various project sizes and durations
- Talent Pool: Expansive Kotlin
- Best For: Smaller Kotlin
- Price Range: Highly variable, depending on the Remote Kotlin
Twitter’s “The Algorithm” Recommendation Engine Finally Unveiled And It Is Incredible/
Patrich
Patrich is a senior software engineer with 15+ years of software engineering and systems engineering experience.
0 Min Read
After much anticipation, Twitter has finally unveiled its Recommendation Algorithm, known as “The Algorithm.” The platform’s cutting-edge engine is responsible for suggesting tweets and users to follow, ensuring a tailored experience for every user. In this article, we will explore the primary components of this incredible algorithm, breaking down the various elements and their roles in providing personalized content.
The Main Components of Twitter’s Recommendation Algorithm is
- Feature: simclusters-ann
The simclusters-ann component is responsible for community detection and sparse embeddings into those communities. It helps identify groups of users with similar interests, allowing Twitter to recommend tweets and users that fit within a user’s preferences.
- TwHIN
TwHIN, or Dense Knowledge Graph Embeddings, creates dense representations of users and tweets, enabling the algorithm to understand relationships and connections better.
- Trust and Safety Models
These models are designed to detect and filter out NSFW or abusive content, ensuring a safer and more enjoyable experience for users.
- Real-graph
The real-graph component predicts the likelihood of a user interacting with another user, helping the algorithm to understand potential connections and recommend relevant content.
- Tweepcred
Tweepcred is a Page-Rank algorithm that calculates the reputation of Twitter users. The algorithm considers this reputation score when recommending users and content.
- Recos-injector
The recos-injector is a streaming event processor responsible for building input streams for GraphJet-based services, which play a significant role in Twitter’s recommendation engine.
- Graph Feature Service
This service provides graph features for a directed pair of users, such as how many of User A’s followers liked tweets from User B.
- Candidate Source: search-index
The search-index component is responsible for finding and ranking in-network tweets, making up approximately 50% of tweets in the recommendation engine.
- CR-mixer
The CR-mixer acts as a coordination layer for fetching out-of-network tweet candidates from underlying compute services.
- User-Tweet-Entity-Graph (UTEG)
UTEG maintains an in-memory user-to-tweet interaction graph, finding candidates based on graph traversals. This component is built on the GraphJet framework, which includes other features and candidate sources.
- Follow Recommendation Service (FRS)
FRS offers users recommendations for accounts to follow and tweets from those accounts, ensuring a personalized experience.
- Ranking
Twitter’s recommendation algorithm uses two ranking models, light-ranker and heavy-ranker. The light-ranker model is used by the search index to rank tweets, while the heavy-ranker is a neural network responsible for ranking candidate tweets after candidate sourcing.
- Tweet Mixing and Filtering
The home-mixer, built on product-mixer, is the main service used to construct and serve the Home Timeline. Visibility-filters ensure legal compliance, product quality, user trust, and revenue protection through hard-filtering, visible product treatments, and coarse-grained downranking.
- Software Framework
Twitter’s recommendation algorithm is built on various software frameworks, including Navi (a high-performance machine learning model serving written in Rust), product-mixer, and the legacy machine learning framework, TWML, built on TensorFlow v1.
Check out https://github.com/twitter/the-algorithm for more information and to clone or fork the source code.
If you are interested in building your own algorithm for your company please check out slashdev.io with an excellent selection of high quality, english speaking engineers for as low as $28/h.