LeetCode Solutions

🇺🇿 The 2026 International Olympiad in Informatics starts today in Tashkent. 🏆 The IOI is the ultimate coding competition for high-school students. Years ago, many members of the Telegram team won medals there. 🤝 To support the best young programmers in the world, Telegram will award the 235 winners exclusive digital collectibles — Algorithm Cups — worth at least $117,000 in total. 🕌 It’s fitting that IOI is hosted in Uzbekistan — the homeland of Al-Khwarizmi, the father of algebra whose name became the word “algorithm." Let the best algorithm win! 💪

I recently had a great conversation with Shakhzod aka with 25+ years of experience about startups, sales, and MVPs. Really practical insights and a solid perspective on building and selling. He had recently opened his own channel and started sharing more of his experience through his new channel. If you’re into startups, business, product development, sales, or entrepreneurship, I’d recommend checking it out. 👉 @akhmedov_tech

I've published my System Design and Architecture notes on rustamz.com Mastering these concepts is essential for every engineer. In fact, system design is becoming more important because of AI, not less. My Notion includes my structured framework, core fundamentals, and learning resources page: books, videos, and articles to advance your skills. Share this with someone who might find useful. 🔗 How to access: rustamz.com -> System Design

O'zbekiston uchun yaxshi yangilik. International Conference on Machine Learning (ICML) shu sohadagi top 3 konferensiyalardan biri hisoblanadi (NeurIPS, ICML, ICLR). AI revolutsiyasiga hozirgacha hissa qo'shgan muhim ilmiy ishlarning katta qismi shulardan birida chop etilgan. Ammo, agar maqolangiz qabul qilinsa, mualliflardan kamida bir nafari konferensiyaga qatnashib maqolani taqdim etishi kerak edi (har yili har xil joyda o'tkaziladi). ICML birinchilardan bo'lib bu siyosatni bekor qilibdi. Albatta bu O'zbekiston kabi ilmiy budjeti cheklangan davlatlar uchun yaxshi xabar. Yangiliklardagi xabarlarga qaraganda O'zbekiston sun'iy intellektini rivojlantirishga katta urg'u berishi boshlangan. AI startaplar va AI muhandislikdan tashqari, mahalliy institutlarning AI/ML fundamental tadqiqotlari ham yaqin kelajakda katta xalqaro sahnalarni ko'radi deb umid qilaman. @lazizabdullaev

Hey friends! 👋 One of my friends is doing a quick survey about AI and digital transformation in companies, how it impacts performance and leadership. Your insights are extremely valuable! It only takes a few minutes and your answers are completely confidential. 👉 The survey Thanks a lot for helping out!

10–14 noyabr kunlari LLM masshtablash bo‘yicha bepul onlayn intensiv bo‘lib o‘tadi. Zamonaviy multimodal modellar tobora ko‘payib bormoqda. Ular bilan samarali ishlash uchun nimalar kerak? 📌 5 kun ichida Yandex muhandislari bilan LLM'lar qanday ishlashini bilib olasiz: — LLM asosidagi muhandislik va matematik g‘oyalar — Distributed learning (DL) va Inference'ni tezlashtirish usullari — Yandex yetakchi mutaxassislaridan masshtablash bo‘yicha amaliy yondashuvlar — Q&A-sessiyalar va ishtirokchilarning umumiy chati Intensiv bitta GPU doirasidan chiqib, zamonaviy LLM qanday tuzilganligini chuqurroq tushunishni istagan ML muhandislari, tadqiqotchilari va dasturchilari uchun foydali bo‘ladi. 🗓 13-noyabrgacha ro‘yxatdan o‘ting: havola. Workshoplar rus tilida o'tadi. @cracking_maang

Yandex [ CUP ] / 25 Deadline is 29th Oct -> To Participate @leetcodesolve

Yandex ML School endilikda Toshkentda! 👩‍🎓🤖 Agar sun’iy intellekt (AI), machine learning (ML) va data science sizni qiziqtirsa — bu siz uchun katta imkoniyat. Yandex ML School: bu bepul, oflayn va ingliz tilida o‘qitiladigan, ikki semestrlik intensiv ta’lim dasturi. 🤔 ML School nimadan iborat? — Matematika va statistikadan tortib ML-modellar arxitekturasi va algoritmlarigacha o'rganasiz. — Sanoatdan olingan real muammolar, uyga vazifalar, workshoplar va mentorlar bilan ishlaysiz. — Darslar Yandex muhandislari, yetakchi OTMlar o‘qituvchilari va ML-industriyasi mutaxassislari tomonidan olib boriladi. — Haftasiga 20–30 soat: kechki mashg‘ulotlar, qolgani eaa mustaqil o‘qish. Qanday roʻyxatdan oʻtish kerak? 🚩Onlayn test topshirasiz. Sentyabrda Toshkentda yuzma-yuz suhbatdan o'tasiz. 🗓 Arizalar 10-sentyabrgacha qabul qilinadi! Ro‘yxatdan o‘ting. ❗️Bepul (ha-ha, rostdan ham). Darslar oktyabr oyida boshlanadi. @cracking_maang

💡 Coding Project Ideas for Learning & Leveling Up Programming Skills https://dev.to/kishansheth/200-project-ideas-from-beginner-to-advanced-with-open-source-contributions-3g6a MORE: 1. 50 project ideas, 100 simple coding tasks 2. https://codingchallenges.fyi/challenges/intro/ 3. https://github.com/codecrafters-io/build-your-own-x @cracking_maang

Phew! What a crazy period it was. I've honestly never worked so hard! The models are launched, I'm on an airplane and it is time for a post ✏️. I think it's ok if I talk a bit about what I built without revealing confidential stuff. Very roughly speaking, there are two big phases of training a model: - During pretraining (PT) the model acquires base knowledge from the internet text, code, images and whatever else the Tokens team has. The goal is to predict next token, so this is supervised learning. - During Reinforcement Learning (RL), the pretrained model is put into various RL "environments" where it is given some goal and it generates "action tokens" that should get it closer to the goal. For example, if this was a game of Chess, the environment would a chess board and the action tokens would be moves. After a model performs an action, the environment state might change and the model can reassess the situation and produce new actions. The goal here is not to predict some specific token, but generate action tokens such that some reward goes up, for example it wins a game or all tests pass. An action can also be a tool invocation, e.g. call Bash or edit a file. I like to think of RL Envs as classrooms where Claude studies and we have a lot of different ones. An RL loop is basically doing some sampling, computing the rewards, computing gradients, updating the model, rinse and repeat. The more training we do and the more diverse representative environments we have, the smarter the model gets. We use word "sampling" for token generation (because we "sample" from the probability distribution that the model represents) and at our RL scale we need A LOT of identical sampling replicas/deployments, each consisting of multiple VMs. One critical part of an RL step is, each time the model is updated, we need to to redistribute the new version to all sampling replicas, as fast as possible. I cannot talk about the specifics of the scale, but it is a proportional to the product of the model size (Opus is gigantic) and the number of sampling replicas (we want a LOT). We are multi cloud, so the solution must work on both GCP and AWS, and unfortunately GCS doesn't scale to our needs 🤷🏻‍♂️ . Note that we must do this transfer after each RL step, so saving a gigantic model to an object store just for a brief period of time is not very efficient. In fact we want so many AI chips for RL that no single data center has enough AI chips, so the sampling servers reside in multiple data centers. The bandwidth across data centers is not the same as within the data center, so ideally the transfer solution is aware of the network topology. By data center I don't necessarily mean a region, but more abstractly, e.g. even a single AZ has non-uniform bandwidth within. To add some complexity to this, the model is large enough that it doesn't fit one machine, obviously, so it has to be sharded, but trainers and samplers want it sharded along different dimensions. To give an oversimplified example, imagine that N training machines have rows of a 2D matrix but M sampling machines (of one sampling replica) want columns. So, not only we need to transfer a model, we also want to reshard it. I've designed and implemented the model transfer solution that covers all above, and led a couple of people to implement some parts of it. I think I've rewritten it twice at this point and the latest version is in Rust. It is used in production RL since Sonnet 3.7. I can't talk about the actual solution and will leave this as an exercise for the reader 😄. How would you solve it? The optimization metric is transfer latency. The constraints of the problem are: - gigantic model - a lot of destination replicas - each destination replica has multiple machines - multiple tensors of different shapes and sizes - source and destination shard the tensors differently - non-uniform network topology - the model params comes from/goes to AI chip memory (HBM) - potentially multiple NICs - must support cross-region transfers - Linux

Promotion oldim 🎉 Dropbox'da ish boshlaganimga ko'p bo'lmadi. Shu paytgacha Core Team a’zosi sifatida 2 ta katta loyiha ustida 2 jamoa bilan ishladim: Network Engineering Team va Infrastructure Team. Ha Big Tech'da (hammasida ham emas) siz asosiy va qo'shimcha loyiha sifatida yana bir loyiha bilan ishlasangiz bo'ladi va biz uni ToD (Tour of Duty) deb ataymiz. Polshada hiring juda ko'paydi va bu interviewer bo'lish uchun zo'r imkoniyat ochdi. Hozirgacha 5 ta interview o'tkazdim. 2 tasida shadow va 3 ta interviewer sifatida. Yaxshi tomoni ko'p narsa o'rgandim. Yomon tomoni men o'tkazgan intervyulardan kandidatlar yaxshi perform qila olishmadi (yaxshi kandidat uchramadi). Sizni o'sishingiz doim ham siz ishlayotgan loyihaga bog'liq emas, balkim menejeringizga ham juda katta bog'liqligi bor. Meni omadim kelib, yaxshi menejerlar uchrab qoldi. Va bugun ularni menga bergan yordamlari bilan Staff Software Engineer (IC5) lavozimiga ko'tarilayabman (Avgust oyidan). Yaxshi loyihada ishlash yaxshi, ammo loyihani o'sishini (company impact) va loyiha menejerini jamoaga bo'lgan e'tiborini ham inobatga olish juda muhim ekan. Shuning uchun intervyuda faqat kompaniya/menejer sizni emas, siz ham kompaniya/menejerni intervyu qilishingiz muhim. To'g'risi, agar kimdir "24 yoshingda Staff Software Engineer bo'lib ishlaysan" deyishsa "qo'ysangizchi-ye" degan bo'lardim. Yig'ilgan tajribalarni esa tez kunlarda 42.uz va otabek.io da bo'lishib o'tamiz.

1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Create indexes on frequently queried columns to speed up data retrieval. 2. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Upgrade your database server by adding more CPU, RAM, or storage to handle increased load. 3. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Store frequently accessed data in-memory (e.g., Redis, Memcached) to reduce database load and improve response time. 4. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Distribute data across multiple servers by splitting the database into smaller, independent shards, allowing for horizontal scaling and improved performance. 5. 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Create multiple copies (replicas) of the database across different servers, enabling read queries to be distributed across replicas and improving availability. 6. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Fine-tune SQL queries, eliminate expensive operations, and leverage indexes effectively to improve execution speed and reduce database load. Original source

Otabek studied in Poland and received offers from Meta, IBM, Google, and Dropbox. He is now a Senior Software Engineer at Dropbox. A very useful podcast for those who are studying in Poland or planning to. ⸻ 🇺🇿 Otabek Polshada o’qib Meta, IBM, Google, va Dropbox’dan offer olgan, hozir Dropbox'da Senior Software Engineer. Polshada tahsil olayotgan yoki rejalashtirayotgan talabalar uchun juda foydali podcast. 🔗 https://www.youtube.com/watch?v=UKUHx5P3ISk

European Championship 2025 - Online Mirror (Unrated, ICPC Rules, Teams Preferred) will take place on the 2nd of March at 10:35 UTC. Please, join by the link https://codeforces.com/contests/2068

Google is hiring Junior Engineers (L3)! 🚀 🇺🇿 O‘zbekistondan turib men kabi ariza topshirib "offer" olishingiz mumkin. Men shu oyda O‘zbekistondan turib suhbatdan o‘tgan 3 nafar nomzodni bilaman. Yevropa bo‘ylab 30 ta bo‘sh ish o‘rni mavjud! • 6 tasi Germaniyaning Munich shahrida 🇩🇪 • 8 tasi Shveysariyaning Zurich shahrida 🇨🇭 • Yana bir nechtasi Polshada 🇵🇱 Intervyuga qanday tayyorgarlik ko‘rganim haqida post shu yerda.

JetBrains dan offer oldim 🎉 Maqola uchun havola. Bollar biz yutamiz!

Books must read list: — Cracking the coding interview — Grokking Algorithms — Introduction to Algorithms — Programming Interview Exposed — Designing Data-Intensive Applications

Good articles to read during weekends: — How Google Search Works — I Worked More and Achieved Less. How To Get Your Team Engaged — Can We Please Avoid Over-Engineering - YAGNI — Even Data can't Escape Physics.. — Some Undesirable Behaviors in Software Engineers — The best way to test Web APIs — Why your job search is failing as an engineer or a manager — Session Management Demystified — Design spotify system design interview Original post