# Solutions Published articles for Solutions. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A practical approach to end-to-end Solvency II reporting in Databricks DevFeed: [A practical approach to end-to-end Solvency II reporting in Databricks]() Original publisher: [Read original article]() Author: Laurence Ryszka; Jack Yallop Published: 2026-09-09T16:20:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [DataOps](), [Orchestration](), [data](), [systems]() Tags: [business](), [data](), [databricks](), [eu](), [financial](), [financial-services](), [governance](), [industries](), [monitor](), [orchestration](), [platform](), [regulatory](), [solutions](), [uk](), [workflow]() ### AI overview The article presents Databricks as a governed data, orchestration, and reporting layer for end-to-end Solvency II reporting. It covers ingestion, data quality, actuarial reserving, capital calculation, QRT production, ORSA drafting, governance, approvals, and disclosure while allowing insurers to retain established actuarial and capital modeling systems. ### Source excerpt Solvency II reporting is not only a regulatory submission. It is a business process... ## Secure by default is your only way forward DevFeed: [Secure by default is your only way forward]() Original publisher: [Read original article]() Author: Vishrut Iyengar Published: 2026-08-31T13:00:00Z Content type: opinion Language: en Sources: [Docker]() Topics: [AI-assisted coding]() Tags: [agents](), [code](), [community](), [enterprise](), [images](), [products](), [security](), [solutions](), [supply-chain]() ### AI overview The article argues that AI coding agents amplify existing software supply-chain risks because they build and ship code using unexamined dependencies. It advocates a hardened, minimal, source-built foundation with accountable security practices. ### Source excerpt The newest worker on your team builds with whatever it finds and never asks what deserves your trust. Our answer is a hardened foundation and a boundary built for agents. ## Moving from Minimus to Docker Hardened Images DevFeed: [Moving from Minimus to Docker Hardened Images]() Original publisher: [Read original article]() Author: Vishrut Iyengar Published: 2026-08-25T22:27:06Z Content type: article Language: en Sources: [Docker]() Topics: [Docker Hardened Images](), [migration](), [Docker](), [supply-chain-security](), [Docker Hub](), [Open Source](), [Debian](), [Artificial Intelligence]() Tags: [apache](), [ci](), [community](), [debian](), [docker](), [docker-hardened-images](), [docker-hub](), [enterprise](), [guide](), [migration](), [open-source](), [products](), [security](), [solutions](), [supply-chain](), [vulnerabilities]() ### AI overview Docker explains how Minimus customers can migrate to Docker Hardened Images before the Minimus registry goes offline on October 22, 2026. The article highlights the 60-day maintenance window, free migration assistance, DHI's open-source Apache 2.0 catalog, and a drop-in migration process centered on updating Dockerfile FROM lines. ### Source excerpt The Minimus registry goes offline on October 22. Here is the migration path, the free help Docker is offering, and where to start. ## MinIO End of Life: How to Stay Patched and Audit-Ready with Docker ELS DevFeed: [MinIO End of Life: How to Stay Patched and Audit-Ready with Docker ELS]() Original publisher: [Read original article]() Author: Vishrut Iyengar Published: 2026-08-24T13:00:00Z Content type: article Language: en Sources: [Docker]() Topics: [Docker Hardened Images](), [open-source-security](), [Security](), [migration](), [Docker](), [Open Source](), [Go]() Tags: [community](), [docker](), [docker-hardened-images](), [enterprise](), [go](), [migration](), [open-source](), [open-source-security](), [production](), [products](), [releases](), [security](), [solutions]() ### AI overview Docker's Extended Lifecycle Support keeps MinIO patched and audit-ready after the project's upstream end of life. The article explains how Docker maintains hardened images, tracks CVEs across MinIO and its Go dependencies, backports fixes, and supports teams that cannot immediately migrate their production object storage. ### Source excerpt MinIO reached end of life in February 2026. Docker Extended Lifecycle Support (ELS) keeps end-of-life software like it patched, compliant, and audit-ready for up to five years, covering versions upstream no longer supports all the way up to entire projects. ## 17,600 Actions: Agent Security Is a Systems Problem DevFeed: [17,600 Actions: Agent Security Is a Systems Problem]() Original publisher: [Read original article]() Author: Jin Kim Published: 2026-08-18T16:00:00Z Content type: opinion Language: en Sources: [Docker]() Topics: [AI Agent](), [Security](), [incident](), [Securing AI](), [Incident response](), [Vulnerabilities](), [Benchmark](), [benchmarking]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml](), [benchmark](), [community](), [company](), [dhi](), [docker](), [docker-ai-governance](), [docker-hardened-images](), [engineering](), [evaluation](), [hugging-face](), [incident](), [infrastructure](), [model](), [network](), [openai](), [opinion](), [persistence](), [sandboxes](), [security](), [solutions](), [systems](), [vulnerabilities]() ### AI overview The article argues that AI-agent security is a systems problem, using the OpenAI/Hugging Face incident and its approximately 17,600 attacker actions to show why human approval and ordinary alert triage cannot control persistent, high-rate workloads. It emphasizes constraining authority, credentials, network access, state, and execution across environments. ### Source excerpt The OpenAI/Hugging Face incident exposed a new challenge for AI agent security. 17,600 attacker actions show why AI agent security can't rely on human review. Explore the controls needed to constrain, observe, and govern agents at speed. ## How Neuron Systems Served 2.3 Million Fans Across 104 World Cup Matches with AI on Confluent DevFeed: [How Neuron Systems Served 2.3 Million Fans Across 104 World Cup Matches with AI on Confluent]() Original publisher: [Read original article]() Author: Shalini Ananda, PhD Published: 2026-08-14T06:48:05Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Artificial Intelligence](), [Kafka](), [Streaming](), [real-time](), [observability](), [systems]() Tags: [agents](), [ai](), [architecture](), [confluent](), [kafka](), [observability](), [production](), [real-time](), [scale](), [solutions](), [streaming]() ### AI overview Neuron Systems used six agents and Confluent's Data Streaming Platform to provide multilingual live commentary for all 104 FIFA World Cup 2026 matches. The platform processed 42.1 million production events, maintained a median glass-to-glass latency of 42 milliseconds, and supported millions of fans while keeping commentary synchronized. ### Source excerpt Every one of the 42.1 million production events flowed through Confluent's Data Streaming Platform and that single decision is what let a small team serve a global tournament without the commentary ever falling out of sync. ## We're launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks DevFeed: [We're launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks]() Original publisher: [Read original article]() Author: Dr. Ramine Tinati Published: 2026-05-21T19:46:42Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google](), [Frontier AI](), [Google AI](), [Artificial Intelligence]() Tags: [ai](), [energy](), [frontier-ai](), [global](), [google](), [google-ai](), [growth](), [none](), [scale](), [science](), [solutions](), [startups]() ### AI overview Google DeepMind is launching a three-month Asia-Pacific accelerator focused on using frontier AI for environmental challenges. Startups, research teams and nonprofits will receive mentorship, tailored support and help integrating Google AI and science AI models into climate, nature, agriculture and energy projects. ### Source excerpt The Asia-Pacific region is a global engine for economic growth, but it's also highly vulnerable to climate change. While green technologies are gaining momentum, a recen... ## Partnering with industry leaders to accelerate AI transformation DevFeed: [Partnering with industry leaders to accelerate AI transformation]() Original publisher: [Read original article]() Author: David Thacker Published: 2026-04-21T14:54:15Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Frontier AI](), [AI Strategy](), [AI Development](), [Google](), [Cloud]() Tags: [agentic-ai](), [ai](), [cloud](), [data](), [enterprise](), [finance](), [frontier-ai](), [global](), [google](), [industry](), [leadership](), [management](), [manufacturing](), [media-and-entertainment](), [models](), [partnerships](), [real-time](), [responsibility-safety](), [retail](), [scale](), [solutions]() ### AI overview Google DeepMind describes partnerships with Accenture, Bain & Company, BCG, Deloitte, and McKinsey to help organizations adopt frontier AI at scale. The initiative focuses on industry-specific AI capabilities, early access to frontier models including Gemini, and leadership support for enterprise transformation across sectors such as finance, manufacturing, retail, and media and entertainment. ### Source excerpt Google DeepMind partners with global consultancies to bring the power of frontier AI to organizations around the world. ## Supabase UI: Platform Kit DevFeed: [Supabase UI: Platform Kit]() Original publisher: [Read original article]() Author: Saxon Fletcher Published: 2025-07-14T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [Shadcn UI](), [Tailwind CSS](), [Database](), [Authentication](), [paas and iaas](), [Artificial Intelligence](), [cloud-computing]() Tags: [ai](), [api](), [authentication](), [components](), [database](), [infrastructure](), [library](), [platform](), [shadcn-ui](), [solutions](), [tailwind](), [ui]() ### AI overview Supabase is releasing Platform Kit, a set of Supabase UI Library components for building platforms on top of Supabase. The kit includes database, storage, authentication, and AI components, and supports customizable shadcn/ui and Tailwind CSS styling. ### Source excerpt We are launching new components in the Supabase UI Library that makes it incredibly easy to build platforms on top of Supabase. ## Staff Picks, June 2025: Summer of Showcases DevFeed: [Staff Picks, June 2025: Summer of Showcases]() Original publisher: [Read original article]() Author: Armando Sanchez-Monsivais Published: 2025-06-24T00:00:00Z Content type: opinion Language: en Sources: [Discord Blog]() Topics: [Discord](), [bug]() Tags: [2025](), [advertising](), [desktop](), [games](), [japan](), [japanese](), [media-and-entertainment](), [monitors](), [rpg](), [solutions]() ### AI overview Discord staff discuss notable games and summer highlights from June 2025 game showcases, including Hollow Knight: Silksong, Onimusha, Stranger Than Heaven, and Infinitesimals. ### Source excerpt 23 game showcases in June, and just as many recommendations! We're talking about all the big announcements and big summer vibes with Alex, Armando, Matt, and... Matt??? ## Open R1: Update #3 DevFeed: [Open R1: Update #3]() Original publisher: [Read original article]() Author: Guilherme Penedo; Lewis Tunstall; Anton Lozhkov; Hynek Kydlicek; Edward Beeching; Loubna Ben Allal; Quentin Gallouédec; Leandro von Werra; Agustín Piqueres Lajarín; Nathan Habib Published: 2025-03-11T20:40:47Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Programming](), [Code](), [Benchmark](), [dataset](), [C++](), [Python](), [Chain-of-thought](), [deepseek]() Tags: [2025](), [benchmark](), [c-plus-plus](), [chain-of-thought](), [code](), [contests](), [data](), [deepseek](), [evaluation](), [model](), [models](), [performance](), [programming](), [reasoning](), [solutions](), [training](), [update]() ### AI overview Open R1 Update #3 presents CodeForces-CoTs, a dataset of nearly 100,000 DeepSeek-R1 chain-of-thought samples for generating competitive-programming solutions in C++ and Python. It also introduces the IOI benchmark and OlympicCoder models fine-tuned on this data, reporting strong performance on challenging IOI problems. ### Source excerpt Over the last few weeks, we have focused our efforts on reproducing the competitive programming (code reasoning) aspects of the DeepSeek-R1 recipe. In this post, we are excited to share: - The construction of CodeForces-CoTs: a dataset of nearly 100k high-quality samples distilled from R1 to produce solutions in C++ and Python. - The IOI benchmark: a new benchmark of challenging problems from the 2024 International Olympiad in Informatics (IOI). ## Explore engineering solutions from Temporal Replay 2025 speakers DevFeed: [Explore engineering solutions from Temporal Replay 2025 speakers]() Original publisher: [Read original article]() Author: Lauren Bennett Published: 2025-02-13T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [Architecture & Design](), [Back end](), [legacy](), [Microservice](), [Resilience](), [Automation](), [developer velocity](), [Tech Debt]() Tags: [2025](), [architecture](), [automation](), [backend](), [community](), [developer-velocity](), [engineering](), [finance](), [legacy](), [microservices](), [orchestration](), [payments](), [php](), [resilience](), [ruby](), [solutions](), [workflow]() ### AI overview Temporal Replay 2025 features engineering talks from Salesforce, Vinted, and Datadog about workflow orchestration, durable execution, modernization, scaling, resilience, and developer velocity. ### Source excerpt Explore engineering solutions for workflow orchestration from Temporal Replay 2025, featuring experts from Salesforce, Vinted, and Datadog. ## Securing GenAI Development with Snyk DevFeed: [Securing GenAI Development with Snyk]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2025-01-09T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [genai](), [Application Security](), [ai-coding](), [Vulnerabilities](), [Security](), [ChatGPT](), [Cloudflare]() Tags: [ai](), [ai-coding](), [ai-tools](), [awareness](), [blog](), [chatgpt](), [cloudflare](), [coding](), [cves](), [developer](), [developers](), [development](), [genai](), [megawatt](), [securing-ai](), [security](), [security-solutions](), [snyk](), [solutions](), [speed](), [vulnerabilities](), [workflows]() ### AI overview This article explains how AI-generated code and AI coding assistants are changing developer workflows while creating security risks for AppSec teams. It describes the prevalence of tools such as ChatGPT and Copilot, the vulnerabilities that can appear in generated code, and the need for guardrails and security solutions such as Snyk. ### Source excerpt AI-generated code is impacting how AppSec teams work. Learn more about the risks of AI-generated code and how Snyk can help secure your codebase. ## Intuitive Proofs for Linked-List Loop Detection and Majority-Element Algorithms DevFeed: [Intuitive Proofs for Linked-List Loop Detection and Majority-Element Algorithms]() Original publisher: [Read original article]() Published: 2024-12-21T00:00:00Z Content type: tutorial Language: en Sources: [Vasya Drobushkov]() Topics: [Algorithms, Complexity](), [Algorithms](), [LeetCode](), [Programming]() Tags: [algorithms](), [array](), [code](), [collections](), [counter](), [examples](), [memory](), [pointers](), [solutions](), [time]() ### AI overview This tutorial explains why concise solutions to seemingly easy algorithmic problems work. It uses linked-list loop detection with slow and fast pointers and majority-element detection with a counter, emphasizing intuitive proofs over implementation alone. ### Source excerpt Blowing the dust off LeetCode once again, I found myself, as in the past, struggling with coding relatively simple algorithms. Just like with anything else, if you don't practice for years, you lose some of the hands-on experience. On the positive side, I noticed that for some problems, my new submissions were much better and more concise compared to my old ones. That's an awesome feeling--a tangible measure of growth. While tackling certain easy problems, I realized that "easy" usually just means "doesn't require much code." However, the idea behind the optimal solution might still not be very intuitive. Sure, one can use brute force or additional collections, but in most cases, this leads to either a "time limit exceeded" or an "out of memory" error. ## Introducing Nuxt Icon v1 DevFeed: [Introducing Nuxt Icon v1]() Original publisher: [Read original article]() Published: 2024-11-25T00:00:00Z Content type: article Language: en Sources: [The Nuxt Blog]() Topics: [User interface design](), [web applications]() Tags: [browsers](), [developers](), [engineering](), [flash](), [http](), [images](), [performance](), [release](), [scalability](), [scale](), [server](), [solutions](), [support](), [techniques](), [themes](), [usability](), [user-experience](), [v1](), [web]() ### AI overview The article introduces Nuxt Icon v1, an icon-rendering solution for Nuxt projects. It discusses icon requirements such as theming, scalability, bundling, dynamic loading, and SSR compatibility, and contrasts them with limitations of image-based icons. ### Source excerpt Discover Nuxt Icon v1 - a modern, versatile, and customizable icon solution for your Nuxt projects. ## Best practices for creating a reliable on-call rotation DevFeed: [Best practices for creating a reliable on-call rotation]() Original publisher: [Read original article]() Author: incident.io Published: 2024-02-26T20:37:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [site-reliability-engineering](), [Availability](), [Software](), [monitor](), [App]() Tags: [availability](), [best-practices](), [churn](), [communication](), [customers](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [monitor](), [on-call](), [outage](), [post-mortem](), [slack-incident](), [software](), [solutions]() ### AI overview This article presents best practices for creating reliable on-call rotations. It covers automated scheduling, clearly defined team responsibilities, escalation policies, resolution time limits, coverage overrides, pager-load management, transparency, and communication. The goal is to improve service reliability, reduce downtime, support teams, and protect customer satisfaction. ### Source excerpt By putting thought into how you structure your on-call rotation, you can set everyone up for success: your team and business. ## Adding Speed to Snowflake Warehouses for Real-Time Solutions DevFeed: [Adding Speed to Snowflake Warehouses for Real-Time Solutions]() Original publisher: [Read original article]() Author: Alejandro Martín Published: 2023-04-19T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [real-time]() Tags: [product-updates](), [real-time](), [solutions](), [speed](), [warehouse]() ### AI overview The article discusses workarounds for adding speed to Snowflake warehouses for real-time solutions without replacing the warehouse. ### Source excerpt Real-time solutions with Snowflake require workarounds. Here's how to add speed to your warehouse without replacing it. ## Lessons Learned From Running Apache Airflow at Scale DevFeed: [Lessons Learned From Running Apache Airflow at Scale]() Original publisher: [Read original article]() Author: Megan Parker Published: 2022-05-23T14:00:01Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [airflow](), [Orchestration](), [Shopify](), [Kubernetes](), [Google Cloud Platform (GCP)](), [MySQL](), [Apache Iceberg](), [Training AI Models](), [data-modeling]() Tags: [airflow](), [apache](), [cloud](), [cloud-storage](), [data-modeling](), [google-cloud](), [kubernetes](), [load](), [machine-learning](), [model-training](), [monitoring](), [mount](), [mysql](), [orchestration](), [scale](), [scheduler](), [shopify](), [solutions](), [workflows]() ### AI overview Shopify explains lessons from operating Apache Airflow at scale for more than two years. The deployment runs on Kubernetes with the Celery executor and MySQL, supporting data extraction, machine learning model training, Apache Iceberg table maintenance, and DBT-powered data modeling. The article describes scaling to more than 10,000 DAGs, over 400 concurrent tasks, and more than 150,000 daily runs, along with challenges involving file access, DAG capabilities, traffic variability, and resource contention. ### Source excerpt Shopify shares some of the lessons we learned and solutions we built in order to run Airflow at scale. ## Using Teleport to Create and Maintain Shared Demo Environments DevFeed: [Using Teleport to Create and Maintain Shared Demo Environments]() Original publisher: [Read original article]() Author: info@goteleport.com (Steven Martin) Published: 2022-02-09T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Demo](), [DevOps](), [Cloud](), [Provisioning](), [Amazon EC2](), [Kubernetes clusters](), [Security](), [audit](), [Authentication](), [Authorization](), [Jenkins](), [AWS IAM]() Tags: [audit](), [authentication](), [authorization](), [aws](), [blog](), [cloud](), [demo](), [devops](), [ec2](), [iam](), [jenkins](), [kubernetes](), [kubernetes-clusters](), [maintenance](), [provisioning](), [solutions]() ### AI overview The Teleport Solutions Engineering team describes building a shared demo environment on Teleport Enterprise. The approach provides each engineer access to demo resources while controlling provisioning costs, supporting distributed Teleport clusters, and using just-in-time requests and auditing for administrative changes. ### Source excerpt This blog shows how the Teleport Solutions Engineering team uses Teleport to create and maintain shared demo environments. ## Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 4/4 DevFeed: [Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 4/4]() Original publisher: [Read original article]() Author: Yev Kanivets Published: 2021-12-26T14:39:41Z Content type: tutorial Language: en Sources: [xorum.io - Medium]() Topics: [Advent of Code](), [Kotlin](), [3D](), [pixel]() Tags: [3d](), [advent-of-code](), [algorithms](), [array](), [beacon](), [competitive-programming](), [data-structures](), [kotlin](), [pixel](), [points](), [scanner](), [solutions]() ### AI overview A Kotlin walkthrough of Advent of Code 2021 tasks 19 and 20. It explains matching rotated point sets in 3D space using beacon intersections, then enhancing an image represented as a 2D pixel array while accounting for infinite padding and changing background values. ### Source excerpt Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 4/4 The final week of Advent of Code was, obviously, the most challenging one. To be honest I was close to being happy with just a silver star multiple times. But at the end, tenacity made the difference. 50 stars are mine this year 🌟 Ideas and Solutions for tasks 1 to 6 can be found here, for tasks 7 to 12 -- here, for tasks 13 to 18 -- here. Day 19: Beacon Scanner We are provided with several sets of points in 3D space. Those points are rotated differently in each set, so we don't know how X, Y, and Z coordinates are aligned between sets. Our goal is to merge all those sets into a single group by matching points (sub-task one) and find the most significant distance between coordinate origins after they are matched. Here is the complete task. This is an exciting task! I've initially learned about this idea from The Three-Body Problem book, which introduced the concept of locating any star in the Universe by the distances from a few nearest stars -- in our case, "a few" means 12. Think about it as a star fingerprint ;) So to match the sets of points in 3D space, we need: check all combinations of rotations in three dimensions (in our task, the step is 90 degrees, so there are 48 combinations) for the whole set assume that each pair of points from the first set and the second set is an intersection by adjusting all points to that pair check how many adjusted points intersect between the two sets if there are more than 12 intersections, voilá -- the sets are matched The embedded Kotlin's functions like intersect, map, and mapNotNull make the solution, even for such a complex task, elegant. Day 20: Trench Map This image-enhancing task provides us with the initial photo (2D array of black and white pixels), and enhancing guidance (1D array). Enhancing happens in steps, where for each pixel, we found its enhanced value as a result of a combination of itself + all eight adjacent pixels. Here is the complete task. We ## Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 3/4 DevFeed: [Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 3/4]() Original publisher: [Read original article]() Author: Yev Kanivets Published: 2021-12-26T14:22:49Z Content type: tutorial Language: en Sources: [xorum.io - Medium]() Topics: [Advent of Code](), [Kotlin](), [Programming](), [competitive-programming](), [Graphs](), [math]() Tags: [advent-of-code](), [algorithms](), [array](), [code](), [competitive-programming](), [data-structures](), [graph-theory](), [kotlin](), [memory](), [pairs](), [programming](), [solutions](), [time]() ### AI overview This article presents high-level ideas and Kotlin solutions for Advent of Code 2021 tasks from days 13 to 15. It discusses folding a 2D array, modeling polymer growth with dynamic programming, and finding a shortest path in a 2D array. ### Source excerpt Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 3/4 The third week of Advent of Code requires more time and even some competitive programming knowledge like dynamic programming and graph theory. In this article, I share some high-level ideas and my solutions if you need a hint or a few to get that gold star. Ideas and Solutions for tasks 1 to 6 can be found here, for tasks 7 to 12 -- here. Day 13: Transparent Origami The 2D array contains two types of symbols -- . and #. This array can be folded horizontally and vertically multiple times. When folding # symbols replace . symbols, but not vice versa. Here is the complete task. This is a modeling task, which you can do on the 2D array itself, but the possible range is quite large, so you can hit a memory limit. The smarter solution would be to fold the # symbols (the initial input, actually). Such folding can be done with Kotlin's fold function using the initial value of # symbol positions and mapNotNull, which mirrors X or Y coordinates depending on the fold direction. Here is my solution. Day 14: Extended Polymerization We get the initial string (template) consisting of uppercase letters, which describe the initial state of the polymer. The list of pair insertion rules allows growing the initial polymer step by step exponentially. We need to model this growth during 10 (first sub-task) and 40 (second sub-task) steps. Here is the complete task. The first sub-task can be solved just by modeling, but even 20 steps are too long and require too much memory and time. The (much) better solution is based on dynamic programming. You can easily see that every pair grows independently of others (new elements are always added inside the pair). It means that the solution can be found for each pair separately and then combined. It doesn't speed up things sufficiently though. But makes the solution easier? Yes. Now, let's grow each pair step by step. So at each step, every pair (usually) produces two more pairs, which h ## Building a Real-time Buyer Signal Data Pipeline for Shopify Inbox DevFeed: [Building a Real-time Buyer Signal Data Pipeline for Shopify Inbox]() Original publisher: [Read original article]() Author: Ashay Pathak Published: 2021-12-13T21:24:33Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [data](), [App]() Tags: [a-b-testing](), [apache](), [app](), [apple](), [building](), [business](), [customer](), [data](), [event](), [experiment](), [insights](), [messaging](), [product](), [products](), [real-time](), [sales](), [shopify](), [solutions](), [streaming](), [testing]() ### AI overview Shopify Data built a real-time Apache Beam pipeline that surfaces cart and order-completion signals to merchants in Shopify Inbox during customer conversations. The article covers the pipeline's design, streaming challenges, Apache Beam functions, and A/B testing of merchant behavior. ### Source excerpt Shopify Data built a real-time buyer signal data pipeline to show relevant customer information to merchants while they're chatting with their customers. ## Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 1/4 DevFeed: [Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 1/4]() Original publisher: [Read original article]() Author: Yev Kanivets Published: 2021-12-13T15:31:55Z Content type: tutorial Language: en Sources: [xorum.io - Medium]() Topics: [Advent of Code](), [Kotlin](), [Programming](), [Code]() Tags: [advent-of-code](), [algorithms](), [array](), [commands](), [competitive-programming](), [data-structures](), [kotlin](), [map](), [programming](), [solutions]() ### AI overview A Kotlin-focused walkthrough of the first six Advent of Code 2021 puzzles. It explains the key solution ideas for the early tasks, including array comparisons, submarine movement modeling, and binary-number calculations, while linking to source code for further guidance. ### Source excerpt Ideas and Solutions for Advent of Code 2021 in Kotlin -- Part 1/4 There are so many things to do before Christmas, so I was always wondering how people find extra time to solve a daily programming puzzle of Advent of Code. Well, this year I'm one of those lucky folks with a bunch of spare time. Advent of Code is an annual event of Christmas-oriented programming challenges started December 2015. Every year since then, on the first day of December, a programming puzzle is published every day for twenty-four days. You can solve the puzzle and provide an answer using the language of your choice. Extra reason by JetBrains in the form of the giveaway of some Kotlin care packages has certainly contributed to my motivation. But what's the point of solving tasks without sharing your ideas and solutions with the community ;) So here we are, the first six tasks. What's special about this article? I won't be sharing the complete editorial, but the key idea only, so you can still solve the task by yourself. And if you need more guidance, there is a source code linked. Day 1: Sonar Sweep We are given an array of heights of a seafloor. We need to calculate the number of times heights (sub-task one) and sliding triples of heights (sub-task two) are increasing. Complete task is here. To solve both tasks we need to go from left to right and compare the previous element (or sliding triple) to the current one. That's pretty simple, but challenge yourself to craft the beautiful code. In Kotlin we may consider using drop and foldIndexed functions for this. Here is my solution. Day 2: Dive! The submarine can move in 2D space (horizontal position and depth) controlled by the set of commands. Moves mechanics are slightly different between subtasks, but the overall approach and goal are the same -- model moves and find the resulting position. Complete task is here. The first sub-task can be solved by folding horizontal position and depth changes, but the second sub-task should be modeled exact ## Winning AI4TSP: Solving the Travelling Salesperson Problem with Self-programming Machines DevFeed: [Winning AI4TSP: Solving the Travelling Salesperson Problem with Self-programming Machines]() Original publisher: [Read original article]() Author: Meinolf Sellmann Published: 2021-10-06T14:30:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Optimization](), [Artificial Intelligence](), [Routing (disambiguation)](), [data]() Tags: [artificial-intelligence](), [competition](), [data](), [optimization](), [routing](), [solutions]() ### AI overview Shopify optimization expert Meinolf Sellmann and collaborators won first prize in both tracks of the AI4TSP Competition. The article explains how the competition combines optimization and artificial intelligence to address the Travelling Salesperson Problem under uncertain travel times, rewards, and time windows, and why this problem is relevant to Shopify's fulfillment system. ### Source excerpt Shopify optimization expert Meinolf Sellmann collaborated with his former colleagues to win the AI4TSP Competition that focuses on solutions to the Travelling Salesperson Problem. [Next page]()