TL;DR: Got laid off, failed my Google onsite interviews, visa didn’t come through, but still happy.

September: Optifly faced investment issues due to a major competitor. They started layoffs but spared me.

October: Round two of layoffs—this time, I was on the list. Still, Optifly remains the kindest company I’ve worked for.

Visa: Applied for a Croatian nomad visa over 3 months ago. Still no response.

October 17th: My Google recruiter informed me my freeze period ends in November. I decided to take the chance. Interviews scheduled to finish by October 30th.

November 15th: Had 4 interviews (rescheduled 5 times). Solved all problems but struggled with follow-ups due to speed.

November 28th: Feedback—technical performance was satisfactory but could be better. The dealbreaker? Googleyness. My answers weren’t well-structured. Final decision: I can reapply in 3 months as a special case, thanks to rescheduling inconveniences.

Initially, I was disappointed, especially about the Googleyness interview. Took a few days off and reflected: I have savings, freedom to sleep, travel, and learn. Why rush? Started learning AWS for fun. 😺

I’m going to actively attend codeforces contests. Join me. It’ll definitely boost my problem solving speed. Here is my profile with the lowest rating, and I’ll use it for the contests. My prediction: the rating might get as low as 1400, but eventually will go up to 1800.

Well, I’m doing something I don’t usually do… solving easy problems on LeetCode. I remember many of you asked for explanations of easy problems too. I can stream if you want to solve them with me.

Facebookning Meta Hacker Cup 2024 da qatnashdingizmi? Katta kontestlar zo’r-a? O’zingizni zo’rman deb o’ylaysiz, lekin bunaqa global kontestlarda tushunib yetasizki “ещё есть куда стремиться”. Har narsaga kengroq qarang, o’sishda yordam beradi.

Natijalarni ko’raylikchi?

Keyingi roundga o’tdim - 💯
Qatnashdim, lekin o’tmadim - 💯
Qatnashmadim - 🗿

#algo

Randomized Algorithms
Bu postda radnomized algorithms to'g'risida umumiy ma'lumot beraman. Qachon ishlatiladi va nima uchun kerak, shuningdek, qanday turlari borligini bilib olasiz. Keyingi postda esa haqiqiy algoritmlarda misol ko'ramiz.

Randomized algoritmlar zamonaviy kompleks muammolarga o’ziga xos yechim berishi bilan yaxshi tanish. Bunday algoritmlar o’z jarayonida hech bo’lmaganda bir marta random sonlarga asoslanib biror vazifani bajaradi va bu orqali yechimni ajoyib, taxmin qilib bo’lmas va worst-case scenarioda deterministic algoritmlardan ko’ra ancha tez ishlaydigan qilib beradi.

➡️Turlari
Las Vegas:
Tassavur qiling, juda katta stavkali kazino o’yinlaridan biridasiz va sizga aniqlik juda muhim. Siz algoritmdan xatolik ehtimoli nol bo’lishini hoxlaysiz. Ha, to’g’ri taxmin qildingiz, bekorga kazinoni misolda keltirmadim. Chunki bu turdagi randomlashtirilgan algoritmlar o’z kazinolari bilan mashxur bo’lgan shahar nomiga qo’yilgan va Las Vegas algoritmi deyiladi. Aniqlik deganda kerakli natijaga erishishning iloji bo’lmasa “Failure” yoki “Infeasibility” to’g’risida ogohlantirish ham nazarda tutiladi. Bunga eng yaxshi misollar: Randomized Quick Sort, Randomized Binary Search.
Monte Carlo:
Endi esa tasavvur qiling, sizga qo’yilgan muammoda aniqlik juda qimmatga tushadi (computationally intensive), lekin aniqlik tarafdan kompromisga boriladi. Qisqa qilib aytganda aniqlikni tezlikka trade-off qilib, ba’zida inaccurate natija beradigan algoritmlar Monte Carlo deb ataladi. E’tibor bering, xatolikka ruxsat berilgani bilan, buning ehtimoli yetarlicha kam bo’ladi. Ya’ni deyarli doim aniq natija olamiz, lekin aniqlikka 100% kafolat bermaymiz. Aslida bu nom ham kazinolar bilan bog’liq (qiziqqan o’qib oladi ?). Eng taniqli misollar: Karger–Stein algoritmi va Fast Monte-Carlo Test for Primality.

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