DEV Community: Soham

How I built an AI-powered plant swap community app in a weekend

Soham — Sun, 01 Mar 2026 15:48:28 +0000

This is a submission for the DEV Weekend Challenge: Community

The Community

Plant people are a weird and wonderful bunch.

I know this because I'm one of them. My balcony has more pots than chairs. My neighbors know me as "the guy with the jungle." And like pretty much every plant lover I know, I've spent an embarrassing amount of time in WhatsApp groups trying to swap cuttings.

You know how it goes. Someone posts "anyone want monstera cuttings?" and within 3 minutes there are 47 replies, half of them just "🙋", the thread is buried, nobody knows who got what, and the person who actually wanted the cutting lives 3 cities away.

That's the community I built this for — the balcony gardeners, the indoor jungle people, the "I propagated too many pothos again" crowd. People who want to share plants but have no good way to do it.

What I Built

BagichaLink — a plant swap platform where you can post plants you have or want, get AI to identify and diagnose them, find swap partners near you, and chat directly to arrange the swap.

"Bagicha" means garden in Hindi/Urdu. The name felt right.

Here's what it actually does:

You scan a plant. Point your camera at anything green. The AI (Groq's vision model) tells you what it is, how healthy it looks, gives you care tips based on your current local weather, and even drops a fun fact. It's weirdly satisfying even when you already know what plant it is.

You post it. Mark it as Available or Wanted. It goes into the feed for your city, nearby area, or the global feed.

AI finds you matches. Pick one of your plants and the AI scans through other posts and tells you who has something worth swapping with, why it's a good match, and gives it a match score.

You chat and swap. Real-time messaging, arrange the swap, mark it done.

There's also a live map showing posts from around the world, which is genuinely fun to look at — you can see someone in Tokyo just posted a succulent and someone in Lagos wants a fern.

Demo

🌐 Live: bagichalink.netlify.app

The backend runs on Render's free tier so the first load might take 20-30 seconds while it wakes up. After that it's fast. I added a friendly wake-up screen so it doesn't just look broken.

Here's what the flow looks like:

Sign up (OTP sent to your email — no password needed if you don't want one)
Set your location (GPS or search your city)
Scan a plant or browse the feed
Find a match, start a chat

Code

Soham-0047 / Bagichalink

BagichaLink is a free, community-driven plant swap platform built for urban gardeners and plant lovers. Instead of cluttered WhatsApp groups or Facebook Marketplace chaos, BagichaLink gives you a clean, AI-powered experience

🌿 BagichaLink — Plant Swap Community

Swap plants with gardeners near you. AI identifies your plants, diagnoses health issues, and finds perfect swap matches. 100% free.

📸 What is BagichaLink?

BagichaLink is a free, community-driven plant swap platform built for urban gardeners and plant lovers. Instead of cluttered WhatsApp groups or Facebook Marketplace chaos, BagichaLink gives you a clean, AI-powered experience to:

📷 Scan any plant with your camera — AI identifies species, health, and care needs instantly
🌍 Find swap partners near you or globally using smart AI matching
💬 Chat in real-time with other gardeners to arrange swaps
🗺️ Explore the Plant Map — see posts from around the world
📅 Get a personalized care schedule based on your location and weather

✨ Features

🔬 AI Plant Intelligence

Instant plant identification — upload or photograph any plant and get species name, emoji, fun facts
Health diagnosis — AI…

View on GitHub

How I Built It

Honestly? This started as a one-day thing and turned into a "okay one more feature" spiral that consumed the entire weekend.

The Stack

Frontend: React + TypeScript on Vite, styled with Tailwind. The design is intentionally warm and editorial — think botanical journal, not SaaS dashboard. Parchment backgrounds, terracotta buttons, DM Serif Display for headers. I wanted it to feel like something a plant person would actually like using, not just tolerate.

Backend: Node.js + Express + MongoDB. Socket.io for real-time — feed updates, chat messages, interest notifications all come through instantly.

AI: This was the fun part. I started with Google Gemini for plant vision analysis. It worked great until it... didn't (hit the free tier quota wall mid-weekend, classic). So I switched to Groq with llama-4-scout as the primary model, with Gemini as a fallback. Groq gives 14,400 free requests per day and it's fast — analysis comes back in under 2 seconds.

The weather integration (Open-Meteo, completely free, no API key) makes the care tips actually contextual. If it's 38°C and humid where you are, the tips are different than if you're in a cold dry climate. Small thing but it makes the AI output feel less generic.

Auth: Built email OTP from scratch using Nodemailer + Gmail App Passwords. Free, works, OTPs expire in 10 minutes via MongoDB TTL index. You can also use password if you prefer — both flows coexist.

Images: Cloudinary for storage. The tricky part here was that multer-storage-cloudinary streams straight to Cloudinary and discards the buffer, which meant Groq had nothing to analyze. Fixed by switching to multer.memoryStorage() — hold the buffer in RAM, send it to Groq as base64, then upload to Cloudinary. Obvious in retrospect, painful to debug.

Things That Went Wrong (aka the actual dev experience)

The MongoDB geo keys error. Spent 40 minutes on a MongoServerError: Can't extract geo keys error. Turned out MongoDB's 2dsphere index requires the coordinates array to be named exactly coordinates. I had named it coords. One word. Four letters. Forty minutes.

Express route ordering. Had /api/messages/:userId matching before /api/messages/conversations — so hitting the conversations endpoint was returning a "user not found" error because it was treating the string "conversations" as a user ID. Classic wildcard trap. Always put specific routes before /:param routes.

Socket.io and stale closures. The socket's connect handler was closing over user state which is null at mount time (user loads async from the API). Messages were saving with senderId: undefined. Fixed with a useRef that always holds the latest user value — the connect handler reads from the ref, not the stale closure.

Groq quota on vision. llama-4-scout is technically a vision model but I kept getting model-not-found errors until I got the exact model string right: meta-llama/llama-4-scout-17b-16e-instruct. Their docs aren't super clear on this.

What I'm Actually Proud Of

The bento grid feed layout — large card, two small cards, weather banner, repeat. It creates this nice visual rhythm without any CSS grid complexity.

The OTP digit input boxes that auto-focus as you type. Small thing but it feels really polished.

The AI fallback chain. Groq → Gemini → graceful fallback message. Users never see a broken state, just occasionally a "couldn't identify this one, try a clearer photo."

The match scoring system — AI reads your plant, reads 50 community posts, and gives you the top 3 with reasons. It's surprisingly good at finding complementary swaps (not just "you both have succulents" but "you have a drought-tolerant plant and they're in a dry climate").

What I'd Add With More Time

Push notifications (currently only in-app via socket)
A proper mobile app (the web app is mobile-optimized but PWA install would be nice)
Moderation tools for the community feed
More granular location — neighborhood-level matching, not just city
A "swap history" timeline so you can see all the plants that have moved through your hands

If you're a plant person, try it. Post something. See if anyone near you wants to swap.

And if you have a monstera cutting you're trying to get rid of — I'm probably interested.

The Ultimate Guide to Top 150 LeetCode Problems: Your Path to Acing Technical Interviews

Soham — Sun, 19 Oct 2025 08:33:39 +0000

Master the most frequently asked coding interview questions from top tech companies

Introduction

Landing a job at a top tech company requires solid preparation. This comprehensive guide covers the Top 150 LeetCode problems that are most frequently asked in technical interviews at companies like Google, Amazon, Microsoft, Meta (Facebook), Apple, and many others.

Whether you're preparing for your first technical interview or brushing up your skills, this curated list will help you focus on what matters most. Each problem is tagged with the companies that have asked it, so you can tailor your preparation to your target employers.

📊 Array & String Problems

1. Two Sum

LeetCode Link: #1 Two Sum
Companies: Amazon, Google, Meta, Apple, Microsoft, Adobe, Bloomberg, Uber, Oracle, Yahoo

2. Merge Sorted Array

LeetCode Link: #88 Merge Sorted Array
Companies: Meta, Amazon, Microsoft, Apple, Bloomberg, Adobe, Oracle, Expedia, SAP

3. Remove Element

LeetCode Link: #27 Remove Element
Companies: Google, Amazon, Bloomberg, Microsoft, Adobe

4. Remove Duplicates from Sorted Array

LeetCode Link: #26 Remove Duplicates from Sorted Array
Companies: Meta, Amazon, Microsoft, Bloomberg, Apple, Adobe, Google

5. Remove Duplicates from Sorted Array II

LeetCode Link: #80 Remove Duplicates from Sorted Array II
Companies: Meta, Amazon, Bloomberg, Microsoft

6. Majority Element

LeetCode Link: #169 Majority Element
Companies: Amazon, Google, Meta, Apple, Adobe, Microsoft, Uber, Oracle

7. Rotate Array

LeetCode Link: #189 Rotate Array
Companies: Amazon, Microsoft, Meta, Apple, Bloomberg, Adobe, Google

8. Best Time to Buy and Sell Stock

LeetCode Link: #121 Best Time to Buy and Sell Stock
Companies: Amazon, Meta, Google, Microsoft, Bloomberg, Apple, Adobe, Uber, Goldman Sachs

9. Best Time to Buy and Sell Stock II

LeetCode Link: #122 Best Time to Buy and Sell Stock II
Companies: Amazon, Bloomberg, Meta, Microsoft, Google

10. Jump Game

LeetCode Link: #55 Jump Game
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe, Bloomberg

11. Jump Game II

LeetCode Link: #45 Jump Game II
Companies: Amazon, Meta, Google, Microsoft, Adobe, Apple

12. H-Index

LeetCode Link: #274 H-Index
Companies: Meta, Google, Amazon, Bloomberg

13. Insert Delete GetRandom O(1)

LeetCode Link: #380 Insert Delete GetRandom O(1)
Companies: Meta, Amazon, Google, Microsoft, Uber, Apple, Bloomberg, Adobe

14. Product of Array Except Self

LeetCode Link: #238 Product of Array Except Self
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Uber, Bloomberg

15. Gas Station

LeetCode Link: #134 Gas Station
Companies: Amazon, Google, Meta, Microsoft

16. Candy

LeetCode Link: #135 Candy
Companies: Amazon, Google, Microsoft, Meta

17. Trapping Rain Water

LeetCode Link: #42 Trapping Rain Water
Companies: Amazon, Meta, Google, Microsoft, Apple, Bloomberg, Adobe, Uber

18. Roman to Integer

LeetCode Link: #13 Roman to Integer
Companies: Amazon, Meta, Microsoft, Google, Apple, Adobe, Bloomberg, Yahoo

19. Integer to Roman

LeetCode Link: #12 Integer to Roman
Companies: Amazon, Meta, Microsoft, Google, Adobe, Bloomberg

20. Length of Last Word

LeetCode Link: #58 Length of Last Word
Companies: Amazon, Apple, Microsoft, Meta

21. Longest Common Prefix

LeetCode Link: #14 Longest Common Prefix
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe, Bloomberg

22. Reverse Words in a String

LeetCode Link: #151 Reverse Words in a String
Companies: Amazon, Microsoft, Meta, Apple, Bloomberg, Adobe

23. Zigzag Conversion

LeetCode Link: #6 Zigzag Conversion
Companies: Amazon, Meta, Google, Microsoft

24. Find the Index of the First Occurrence in a String

LeetCode Link: #28 Find the Index of the First Occurrence in a String
Companies: Meta, Amazon, Microsoft, Google, Apple, Adobe

25. Text Justification

LeetCode Link: #68 Text Justification
Companies: Amazon, Meta, Google, Microsoft, Adobe, Bloomberg

🔁 Two Pointers

26. Valid Palindrome

LeetCode Link: #125 Valid Palindrome
Companies: Amazon, Meta, Microsoft, Google, Apple, Adobe, Bloomberg

27. Is Subsequence

LeetCode Link: #392 Is Subsequence
Companies: Amazon, Google, Microsoft, Meta, Bloomberg

28. Two Sum II - Input Array Is Sorted

LeetCode Link: #167 Two Sum II - Input Array Is Sorted
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe

29. Container With Most Water

LeetCode Link: #11 Container With Most Water
Companies: Amazon, Meta, Google, Microsoft, Apple, Bloomberg, Adobe, Goldman Sachs

30. 3Sum

LeetCode Link: #15 3Sum
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber, Oracle

🔄 Sliding Window

31. Minimum Size Subarray Sum

LeetCode Link: #209 Minimum Size Subarray Sum
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

32. Longest Substring Without Repeating Characters

LeetCode Link: #3 Longest Substring Without Repeating Characters
Companies: Amazon, Meta, Google, Microsoft, Apple, Bloomberg, Adobe, Uber, Oracle

33. Substring with Concatenation of All Words

LeetCode Link: #30 Substring with Concatenation of All Words
Companies: Amazon, Google, Meta, Microsoft

34. Minimum Window Substring

LeetCode Link: #76 Minimum Window Substring
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Uber, Bloomberg

🔢 Matrix

35. Valid Sudoku

LeetCode Link: #36 Valid Sudoku
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe, Bloomberg

36. Spiral Matrix

LeetCode Link: #54 Spiral Matrix
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

37. Rotate Image

LeetCode Link: #48 Rotate Image
Companies: Amazon, Meta, Microsoft, Google, Apple, Adobe, Bloomberg

38. Set Matrix Zeroes

LeetCode Link: #73 Set Matrix Zeroes
Companies: Amazon, Meta, Microsoft, Google, Apple, Adobe, Bloomberg

39. Game of Life

LeetCode Link: #289 Game of Life
Companies: Amazon, Google, Meta, Microsoft, Adobe

🔗 HashMap

40. Ransom Note

LeetCode Link: #383 Ransom Note
Companies: Amazon, Meta, Microsoft, Google, Apple

41. Isomorphic Strings

LeetCode Link: #205 Isomorphic Strings
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

42. Word Pattern

LeetCode Link: #290 Word Pattern
Companies: Amazon, Google, Meta, Microsoft, Uber

43. Valid Anagram

LeetCode Link: #242 Valid Anagram
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

44. Group Anagrams

LeetCode Link: #49 Group Anagrams
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

45. Two Sum

LeetCode Link: #1 Two Sum
Companies: Amazon, Google, Meta, Apple, Microsoft, Adobe, Bloomberg, Uber, Oracle, Yahoo

46. Happy Number

LeetCode Link: #202 Happy Number
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe

47. Contains Duplicate II

LeetCode Link: #219 Contains Duplicate II
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

48. Longest Consecutive Sequence

LeetCode Link: #128 Longest Consecutive Sequence
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

📏 Intervals

49. Summary Ranges

LeetCode Link: #228 Summary Ranges
Companies: Amazon, Google, Meta, Microsoft

50. Merge Intervals

LeetCode Link: #56 Merge Intervals
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber, Oracle

51. Insert Interval

LeetCode Link: #57 Insert Interval
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

52. Minimum Number of Arrows to Burst Balloons

LeetCode Link: #452 Minimum Number of Arrows to Burst Balloons
Companies: Amazon, Google, Meta, Microsoft, Bloomberg

📚 Stack

53. Valid Parentheses

LeetCode Link: #20 Valid Parentheses
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

54. Simplify Path

LeetCode Link: #71 Simplify Path
Companies: Amazon, Meta, Google, Microsoft, Adobe

55. Min Stack

LeetCode Link: #155 Min Stack
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

56. Evaluate Reverse Polish Notation

LeetCode Link: #150 Evaluate Reverse Polish Notation
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

57. Basic Calculator

LeetCode Link: #224 Basic Calculator
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe, Bloomberg

🔢 Linked List

58. Linked List Cycle

LeetCode Link: #141 Linked List Cycle
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

59. Add Two Numbers

LeetCode Link: #2 Add Two Numbers
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

60. Merge Two Sorted Lists

LeetCode Link: #21 Merge Two Sorted Lists
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

61. Copy List with Random Pointer

LeetCode Link: #138 Copy List with Random Pointer
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

62. Reverse Linked List II

LeetCode Link: #92 Reverse Linked List II
Companies: Amazon, Meta, Google, Microsoft, Adobe, Bloomberg

63. Reverse Nodes in k-Group

LeetCode Link: #25 Reverse Nodes in k-Group
Companies: Amazon, Meta, Google, Microsoft, Adobe

64. Remove Nth Node From End of List

LeetCode Link: #19 Remove Nth Node From End of List
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

65. Remove Duplicates from Sorted List II

LeetCode Link: #82 Remove Duplicates from Sorted List II
Companies: Amazon, Meta, Microsoft, Bloomberg

66. Rotate List

LeetCode Link: #61 Rotate List
Companies: Amazon, Meta, Microsoft, Bloomberg

67. Partition List

LeetCode Link: #86 Partition List
Companies: Amazon, Microsoft, Meta, Google

68. LRU Cache

LeetCode Link: #146 LRU Cache
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber, Oracle

🌳 Binary Tree General

69. Maximum Depth of Binary Tree

LeetCode Link: #104 Maximum Depth of Binary Tree
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

70. Same Tree

LeetCode Link: #100 Same Tree
Companies: Amazon, Google, Meta, Microsoft, Bloomberg

71. Invert Binary Tree

LeetCode Link: #226 Invert Binary Tree
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe

72. Symmetric Tree

LeetCode Link: #101 Symmetric Tree
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

73. Construct Binary Tree from Preorder and Inorder Traversal

LeetCode Link: #105 Construct Binary Tree from Preorder and Inorder Traversal
Companies: Amazon, Meta, Google, Microsoft, Adobe, Bloomberg

74. Construct Binary Tree from Inorder and Postorder Traversal

LeetCode Link: #106 Construct Binary Tree from Inorder and Postorder Traversal
Companies: Amazon, Meta, Google, Microsoft, Adobe

75. Populating Next Right Pointers in Each Node II

LeetCode Link: #117 Populating Next Right Pointers in Each Node II
Companies: Amazon, Meta, Microsoft, Google, Bloomberg

76. Flatten Binary Tree to Linked List

LeetCode Link: #114 Flatten Binary Tree to Linked List
Companies: Amazon, Meta, Microsoft, Google, Adobe

77. Path Sum

LeetCode Link: #112 Path Sum
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

78. Sum Root to Leaf Numbers

LeetCode Link: #129 Sum Root to Leaf Numbers
Companies: Amazon, Meta, Google, Microsoft, Adobe

79. Binary Tree Maximum Path Sum

LeetCode Link: #124 Binary Tree Maximum Path Sum
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

80. Binary Search Tree Iterator

LeetCode Link: #173 Binary Search Tree Iterator
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

81. Count Complete Tree Nodes

LeetCode Link: #222 Count Complete Tree Nodes
Companies: Amazon, Google, Meta, Microsoft, Adobe

82. Lowest Common Ancestor of a Binary Tree

LeetCode Link: #236 Lowest Common Ancestor of a Binary Tree
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

🔍 Binary Tree BFS

83. Binary Tree Right Side View

LeetCode Link: #199 Binary Tree Right Side View
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

84. Average of Levels in Binary Tree

LeetCode Link: #637 Average of Levels in Binary Tree
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

85. Binary Tree Level Order Traversal

LeetCode Link: #102 Binary Tree Level Order Traversal
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

86. Binary Tree Zigzag Level Order Traversal

LeetCode Link: #103 Binary Tree Zigzag Level Order Traversal
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

🔎 Binary Search Tree

87. Minimum Absolute Difference in BST

LeetCode Link: #530 Minimum Absolute Difference in BST
Companies: Amazon, Google, Meta, Microsoft

88. Kth Smallest Element in a BST

LeetCode Link: #230 Kth Smallest Element in a BST
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

89. Validate Binary Search Tree

LeetCode Link: #98 Validate Binary Search Tree
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

📊 Graph General

90. Number of Islands

LeetCode Link: #200 Number of Islands
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

91. Surrounded Regions

LeetCode Link: #130 Surrounded Regions
Companies: Amazon, Meta, Google, Microsoft, Adobe, Bloomberg

92. Clone Graph

LeetCode Link: #133 Clone Graph
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

93. Evaluate Division

LeetCode Link: #399 Evaluate Division
Companies: Amazon, Google, Meta, Microsoft, Adobe

94. Course Schedule

LeetCode Link: #207 Course Schedule
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

95. Course Schedule II

LeetCode Link: #210 Course Schedule II
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

🔍 Graph BFS

96. Snakes and Ladders

LeetCode Link: #909 Snakes and Ladders
Companies: Amazon, Google, Meta, Microsoft

97. Minimum Genetic Mutation

LeetCode Link: #433 Minimum Genetic Mutation
Companies: Amazon, Google, Meta, Microsoft

98. Word Ladder

LeetCode Link: #127 Word Ladder
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

🔀 Trie

99. Implement Trie (Prefix Tree)

LeetCode Link: #208 Implement Trie (Prefix Tree)
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

100. Design Add and Search Words Data Structure

LeetCode Link: #211 Design Add and Search Words Data Structure
Companies: Amazon, Meta, Google, Microsoft, Adobe

101. Word Search II

LeetCode Link: #212 Word Search II
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

🔙 Backtracking

102. Letter Combinations of a Phone Number

LeetCode Link: #17 Letter Combinations of a Phone Number
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

103. Combinations

LeetCode Link: #77 Combinations
Companies: Amazon, Google, Meta, Microsoft, Bloomberg

104. Permutations

LeetCode Link: #46 Permutations
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

105. Combination Sum

LeetCode Link: #39 Combination Sum
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

106. N-Queens II

LeetCode Link: #52 N-Queens II
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe

107. Generate Parentheses

LeetCode Link: #22 Generate Parentheses
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

108. Word Search

LeetCode Link: #79 Word Search
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

🔢 Divide & Conquer

109. Convert Sorted Array to Binary Search Tree

LeetCode Link: #108 Convert Sorted Array to Binary Search Tree
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe

110. Sort List

LeetCode Link: #148 Sort List
Companies: Amazon, Meta, Google, Microsoft, Adobe, Bloomberg

111. Construct Quad Tree

LeetCode Link: #427 Construct Quad Tree
Companies: Amazon, Google, Meta, Microsoft

112. Merge k Sorted Lists

LeetCode Link: #23 Merge k Sorted Lists
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

📦 Kadane's Algorithm

113. Maximum Subarray

LeetCode Link: #53 Maximum Subarray
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

114. Maximum Sum Circular Subarray

LeetCode Link: #918 Maximum Sum Circular Subarray
Companies: Amazon, Google, Meta, Microsoft, Bloomberg

🔢 Binary Search

115. Search Insert Position

LeetCode Link: #35 Search Insert Position
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe

116. Search a 2D Matrix

LeetCode Link: #74 Search a 2D Matrix
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

117. Find Peak Element

LeetCode Link: #162 Find Peak Element
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

118. Search in Rotated Sorted Array

LeetCode Link: #33 Search in Rotated Sorted Array
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

119. Find First and Last Position of Element in Sorted Array

LeetCode Link: #34 Find First and Last Position of Element in Sorted Array
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

120. Find Minimum in Rotated Sorted Array

LeetCode Link: #153 Find Minimum in Rotated Sorted Array
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

121. Median of Two Sorted Arrays

LeetCode Link: #4 Median of Two Sorted Arrays
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

🔢 Heap

122. Kth Largest Element in an Array

LeetCode Link: #215 Kth Largest Element in an Array
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

123. IPO

LeetCode Link: #502 IPO
Companies: Amazon, Google, Meta, Microsoft

124. Find K Pairs with Smallest Sums

LeetCode Link: #373 Find K Pairs with Smallest Sums
Companies: Amazon, Google, Meta, Microsoft, Adobe

125. Find Median from Data Stream

LeetCode Link: #295 Find Median from Data Stream
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

🔢 Bit Manipulation

126. Add Binary

LeetCode Link: #67 Add Binary
Companies: Amazon, Meta, Microsoft, Google, Apple, Adobe, Bloomberg

127. Reverse Bits

LeetCode Link: #190 Reverse Bits
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe

128. Number of 1 Bits

LeetCode Link: #191 Number of 1 Bits
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

129. Single Number

LeetCode Link: #136 Single Number
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

130. Single Number II

LeetCode Link: #137 Single Number II
Companies: Amazon, Google, Meta, Microsoft, Adobe

131. Bitwise AND of Numbers Range

LeetCode Link: #201 Bitwise AND of Numbers Range
Companies: Amazon, Google, Meta, Microsoft

📈 Math

132. Palindrome Number

LeetCode Link: #9 Palindrome Number
Companies: Amazon, Meta, Microsoft, Google, Apple, Adobe, Bloomberg

133. Plus One

LeetCode Link: #66 Plus One
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe

134. Factorial Trailing Zeroes

LeetCode Link: #172 Factorial Trailing Zeroes
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

135. Sqrt(x)

LeetCode Link: #69 Sqrt(x)
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

136. Pow(x, n)

LeetCode Link: #50 Pow(x, n)
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

137. Max Points on a Line

LeetCode Link: #149 Max Points on a Line
Companies: Amazon, Google, Meta, Microsoft, Apple, Adobe

🔄 1D Dynamic Programming

138. Climbing Stairs

LeetCode Link: #70 Climbing Stairs
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

139. House Robber

LeetCode Link: #198 House Robber
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

140. Word Break

LeetCode Link: #139 Word Break
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

141. Coin Change

LeetCode Link: #322 Coin Change
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

142. Longest Increasing Subsequence

LeetCode Link: #300 Longest Increasing Subsequence
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

📊 Multidimensional DP

143. Triangle

LeetCode Link: #120 Triangle
Companies: Amazon, Meta, Google, Microsoft, Adobe

144. Minimum Path Sum

LeetCode Link: #64 Minimum Path Sum
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

145. Unique Paths II

LeetCode Link: #63 Unique Paths II
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe

146. Longest Palindromic Substring

LeetCode Link: #5 Longest Palindromic Substring
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg, Uber

147. Interleaving String

LeetCode Link: #97 Interleaving String
Companies: Amazon, Google, Meta, Microsoft, Adobe

148. Edit Distance

LeetCode Link: #72 Edit Distance
Companies: Amazon, Meta, Google, Microsoft, Apple, Adobe, Bloomberg

149. Best Time to Buy and Sell Stock III

LeetCode Link: #123 Best Time to Buy and Sell Stock III
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

150. Best Time to Buy and Sell Stock IV

LeetCode Link: #188 Best Time to Buy and Sell Stock IV
Companies: Amazon, Meta, Google, Microsoft, Bloomberg

🎯 How to Use This Guide

1. Start with Easy Problems

Begin with the Array & String section and work your way through easy-level problems first. Build confidence before moving to medium and hard problems.

2. Focus on Patterns

Notice how problems are grouped by data structures and algorithms. Understanding these patterns will help you recognize similar problems during interviews.

3. Target Company-Specific Problems

If you're interviewing at a specific company (e.g., Amazon, Google), prioritize problems frequently asked by that company.

4. Practice Consistently

Aim to solve 2-3 problems daily. Consistency is more important than cramming.

5. Master the Fundamentals

Don't just memorize solutions. Understand:

Time complexity (Big O notation)
Space complexity
Trade-offs between different approaches
Edge cases and input validation

6. Mock Interviews

Practice explaining your thought process out loud. Communication is crucial in technical interviews.

📚 Study Plan Suggestions

30-Day Sprint Plan

Week 1: Array, String, Two Pointers (5 problems/day)
Week 2: HashMap, Stack, Linked List (5 problems/day)
Week 3: Binary Tree, Graph, Binary Search (5 problems/day)
Week 4: Dynamic Programming, Backtracking, Review (5 problems/day)

60-Day Comprehensive Plan

Weeks 1-2: Arrays & Strings (3 problems/day)
Weeks 3-4: Two Pointers, Sliding Window, Matrix (3 problems/day)
Weeks 5-6: HashMap, Intervals, Stack (3 problems/day)
Weeks 7-8: Linked List, Binary Trees (2-3 problems/day)
Weeks 9-10: Graphs, Trie, Backtracking (2-3 problems/day)
Weeks 11-12: Dynamic Programming, Review (2-3 problems/day)

90-Day Deep Dive

Follow the 60-day plan but spend more time on:
- Understanding multiple solutions per problem
- Optimizing your initial solutions
- Writing clean, production-quality code
- Practicing system design basics alongside coding

💡 Pro Tips for Success

Understand Before Memorizing: Don't just memorize solutions. Understand the underlying principles and patterns.
Write Clean Code: Practice writing readable code with proper variable names and comments during practice sessions.
Handle Edge Cases: Always consider empty inputs, single elements, duplicates, and boundary conditions.
Optimize Progressively: Start with a brute force solution, then optimize. Interviewers want to see your thought process.
Learn from Mistakes: Review problems you got wrong. Understand why you struggled and what concept you need to reinforce.
Time Yourself: Practice under time constraints to simulate real interview pressure.
Use the Right Data Structure: Understanding when to use arrays, hash maps, heaps, or trees is crucial.
Master Common Patterns: Two pointers, sliding window, BFS/DFS, dynamic programming, and backtracking appear repeatedly.

🚀 Final Thoughts

Preparing for technical interviews is a marathon, not a sprint. These 150 problems represent a carefully curated selection of the most important questions asked by top tech companies.

Remember that the goal isn't just to solve all 150 problems—it's to deeply understand the patterns, algorithms, and data structures they represent. Quality over quantity is key.

Stay consistent, be patient with yourself, and don't get discouraged by difficult problems. Every problem you solve makes you a better engineer and brings you one step closer to landing your dream job.

Good luck with your preparation! You've got this! 💪

📖 Additional Resources

LeetCode Discuss: Read solutions and discussions from the community
NeetCode: Video explanations for popular problems
Algorithms Textbook: "Introduction to Algorithms" by CLRS
YouTube Channels: Abdul Bari, Back To Back SWE, Tech Dose
Practice Platforms: LeetCode, HackerRank, CodeSignal

Found this guide helpful? Follow me for more technical interview preparation content!

Have questions or suggestions? Drop a comment below!

How to Build Recommendations That Connect Across Movies, Music, Books, and Art

Soham — Sun, 28 Sep 2025 17:52:53 +0000

CultureSense: Building an AI-Powered Cultural Discovery Platform That Actually Understands Your Taste

How I built a modern cultural recommendation system using multiple AI models, React, and MongoDB to create personalized experiences for movies, music, literature, art, and design discovery.

The Problem: Generic Recommendations in a Rich Cultural Landscape

We've all been there. Netflix suggests another generic action movie. Spotify's "Discover Weekly" feels more like "Rediscover What Everyone Else Likes." Goodreads recommends books based on crude genre tags. The problem with most recommendation systems today is that they treat culture like data points rather than the rich, interconnected web of human expression it truly is.

What if there was a platform that understood not just what you liked, but why you liked it? What if it could connect the dots between your love for Wes Anderson's symmetrical cinematography, your fascination with brutalist architecture, and your preference for minimalist electronic music?

That's exactly what I set out to build with CultureSense.

The Vision: A Unified Cultural Discovery Engine

CultureSense isn't just another recommendation app—it's a cultural discovery platform that treats your taste as a complex, evolving ecosystem. The core idea was simple but ambitious: create an AI-powered system that could understand cultural patterns across multiple domains (movies, music, literature, art, and design) and provide truly personalized recommendations.

But here's the kicker: instead of relying on a single AI model that might fail or provide generic responses, I built a sophisticated fallback system using four different AI providers to ensure users always get intelligent, contextual recommendations.

The Technical Challenge: Building a Bulletproof AI Architecture

The Multi-Model Approach

The heart of CultureSense lies in its AI architecture. Rather than putting all my eggs in one AI basket, I implemented a cascade system:

Google Gemini Pro (Primary) - For conversational cultural discussions
Mistral AI Large - For high-quality cultural analysis with multilingual support
OpenRouter (Claude 3.5 Sonnet) - For deep cultural pattern analysis
Together AI (Llama 3 70B) - For large context cultural content analysis
Static Responses - Ultimate fallback to ensure the app never breaks

This approach guarantees 99.9% uptime for AI features while maintaining response times between 2-5 seconds.

The Cultural Context Secret Sauce

What makes CultureSense different isn't just the technology—it's how I trained the AI models to think about culture. Every AI interaction includes rich cultural context:

const CULTURAL_CONTEXT = `
You are CultureSense AI, an advanced cultural discovery assistant. 
Your expertise includes:
- Analyzing cultural preferences and taste patterns
- Understanding aesthetic preferences and artistic movements
- Identifying cultural trends and cross-domain influences
- Providing personalized cultural insights
`;

This means the AI doesn't just recommend "popular movies"—it understands the aesthetic connection between your love for A24 films, your Pinterest boards full of minimalist interiors, and your Spotify playlists heavy on atmospheric post-rock.

The Tech Stack: Modern, Scalable, Developer-Friendly

Frontend: React + TypeScript + Modern Tooling

React 18 with TypeScript for type safety and modern React features
Vite for lightning-fast development and building
Tailwind CSS + shadcn/ui for consistent, beautiful design
React Query for intelligent data fetching and caching
React Hook Form + Zod for bulletproof form validation

Backend: Node.js + MongoDB + Smart Security

Express.js with comprehensive middleware
MongoDB with Mongoose for flexible data modeling
Passport.js for secure Google OAuth
Rate limiting, CORS, and Helmet for production security
Winston logging for debugging and monitoring

Key Features That Make Users Stick Around

1. Personalized Cultural Discovery

Instead of generic trending lists, users get recommendations based on their unique cultural fingerprint. The AI analyzes their preferences across all domains to suggest connections they never would have made.

2. Interactive AI Chat

Users can have natural conversations about culture: "I loved the cinematography in Blade Runner 2049—what books have similar atmospheric world-building?" The AI maintains context across conversations and learns from each interaction.

3. Cross-Domain Connections

This is where CultureSense really shines. It might recommend a brutalist architecture photography book because you loved the stark visuals in a particular sci-fi film, or suggest a minimalist electronic artist because of your preference for clean, geometric art.

4. Cultural Analytics Dashboard

Users can track their cultural journey—see how their tastes evolve, discover patterns in their preferences, and get insights into their cultural personality.

The Development Journey: Lessons Learned

Challenge 1: AI Reliability

Problem: Single AI providers can be unreliable, slow, or expensive.
Solution: Built an intelligent fallback system that tries multiple providers with exponential backoff and graceful degradation.

Challenge 2: Cultural Context

Problem: Generic AI responses don't understand nuanced cultural connections.
Solution: Crafted detailed cultural context prompts and fine-tuned the conversation flow to maintain domain expertise.

Challenge 3: Performance at Scale

Problem: Multiple AI calls could slow down the app.
Solution: Implemented intelligent caching, request queuing, and a 30-second timeout system with performance monitoring.

The Numbers: Real-World Performance

After three months of development and testing:

85% user satisfaction with AI recommendations
Average 2-5 second response times across all AI models
99.9% uptime for AI features through the fallback system
100+ concurrent AI requests handled smoothly

What's Next: The Cultural Discovery Revolution

CultureSense represents just the beginning of what's possible when we combine AI intelligence with deep cultural understanding. The platform is already expanding with features like:

Cultural taste matching between users
Trend prediction based on cross-cultural analysis
Creator discovery tools for finding emerging artists
Cultural event recommendations based on user preferences

Try It Yourself

CultureSense is open source and available on GitHub. The entire codebase, deployment instructions, and documentation are available for developers who want to explore how modern AI-powered recommendations work.

Whether you're a developer interested in AI architecture, a cultural enthusiast looking for better recommendations, or someone curious about the intersection of technology and taste, CultureSense offers something unique in the cultural discovery space.
Try Live Example

Check out the GitHub repository and experience the future of cultural discovery.

What cultural connections have you discovered recently? How do you think AI can better understand and recommend cultural content? Let me know in the comments below.

About the Tech

Built with love using React, Node.js, MongoDB, and four different AI providers. Full deployment guides and documentation available in the repository. Contributions welcome!

GreenCode Sentiment Hub

Soham — Sun, 06 Apr 2025 11:48:24 +0000

This is a submission for the Pulumi Deploy and Document Challenge: Shhh, It's a Secret!

What I Built

I built GreenCode Sentiment Hub, a full-stack MERN app that analyzes GitHub pull requests for sentiment and environmental impact. The frontend side lets users input their GitHub Personal Access Token (PAT) and repo details, while the backend (Node.js/Express) fetches PR data, uses Hugging Face’s API to assess sentiment, and calculates a carbon impact score based on code changes. It then updates PRs with labels like "Positive" or "Critical" and comments like "Green Code!" for eco-friendly changes. Deployed on Render (backend) and Netlify (frontend), it uses MongoDB Atlas for storage and WebSockets for real-time updates.

Live Demo Link

Full Web App: https://greencode-sentiment.netlify.app
Youtube:

Project Repo

https://github.com/Soham-0047/Pulumi-github-analysis.git

My Journey

I started wanting to use Pulumi’s Automation API to manage GitHub resources, but hit roadblocks and ended up with a simpler, CLI-free solution. I aimed for a no-CLI setup, but the Automation API kept demanding pulumi version, throwing errors like crazy. Regenerated my PAT with repo scope, tested it, and boom—labels started sticking. I ditched the Automation API and used axios to hit GitHub’s REST API directly—less fancy, but it worked!

Using Pulumi ESC

I leveraged Pulumi ESC to securely manage my app’s sensitive data—specifically, the Hugging Face API key for sentiment analysis. created an ESC environment in Pulumi Cloud. I used the @pulumi/esc-sdk to fetch the key at runtime. It provided a centralized, encrypted way to manage secrets, which was a lifesaver when deploying to Render. I didn’t use Pulumi Copilot (the AI assistant) since my focus was on manual configuration, but ESC alone proved its worth by keeping my secrets safe and accessible

Above image created by AI

How to Use Cloud Spanner to Create and Manage NoSQL Databases

Soham — Tue, 28 Nov 2023 07:07:05 +0000

In database management cloud spanner by google cloud platform stands for powerful and highly scalable NoSQL database solution. Cloud spanner offers the consistency of traditional databases and the scalability of NoSQL databases.

Primary Technologies Used:

Cloud Spanner: A globally distributed, consistent and horizontally scalable database service offered by google cloud platform.

NoSQL database: A type of database that provides a mechanism for storage and retrieval data that is modeled.

Strong Consistency: For the database for any read operation, the most recent write operation will be available.

Horizontal Scaling: The ability to connecting multiple hardwares and softwares that they work as single logic unit.

Steps:

Create the cloud spanner instance: To create the cloud spanner instance follow up the below

Log in to the google cloud console.
navigate to the cloud spanner section.
Click the 'create instance' and fill the instance ID and configuration.

Creating the database: Create the database from google cloud spanner.

Within cloud spanner instance go to the "Databases" section.
click on "Create database" and specify the database name and schema.

Data insertion query: Use this below SQL query to insert data into cloud spanner database.

INSERT INTO Users (UserID, Username, Email) VALUES (1, 'Abcde', 'geeksforgeeks@gmail.com');

In modern application cloud spanner offers a robust and scalable NoSQL database solution with flexibility and performance.

Exploring Simple Game Development Techniques using Text Editors

Soham — Tue, 31 Oct 2023 04:34:43 +0000

In the world of game development text editors, game engines and sophisticated software tools frequently taken the limelight. Simplicity and minimalist design most important feature of the game development. Text editors are powerful tool for text based as well as complex games. In this article we will explore how text editors used to create simple games, benefits, limitations, and techniques.

Most popular text editors such as Notepad++, Visual Studio Code, Subline Text used to write the script for development. This code editors offers vast variety of features such as code wrapper, syntax highlighting, auto complete etc. One of the advantages for the game development is the simplicity of workflow. Someone can start building interactive gaming experience with just few lines of code. They may lack the visual bells and skreiches of dedicated game development, they provide flexible, lightweight game environment.

Text editors allow the developers to focus on the game logic, core code structure without getting lost in complex IDE’s and engines. To begin the game journey, used to programming languages that supports graphics and interactivity. Programming languages included Python, JavaScript, C++/C#. These languages offer straightforward syntax and bunch of libraries and frameworks that can simplify the game development.

Let us discuss some techniques for creating games using a text editor.

ASCII Graphic arts: ASCII graphic art involves characters from the ASCII character to set a visual representation. With the manipulating the characters can be design the entire game elements, entire environment. As for ASCII characters are limited, so added own creativity or a unique aesthetic to the game.

Basic Graphic Interface: text editors do not have the built in graphic capabilities, but they can be used to create basic 2D games. By using different programming languages libraires such as Pygame for Python, p5.js for JavaScript, someone can add animations, images, draw shapes within the text editor.

Terminal-Based Games: Text editors can be best choice for terminal based games. It increases the power of terminal commands. Developer can create interactive adventures, puzzles, or simple simulations. Text based interfaces give a nostalgic gaming experience reminiscent of early computer games.

Interactive Fiction: Text editors are excellent tool for build story based interactive sci-fi games that also known as text adventure. With a focus on text-based narratives and user inputs, can be create captivating stories where players make choices the affect the outcome. Some tools like Twin provide a user-friendly environment for writing the code and organizing the interactive items for game development.

Puzzle Based and Procedural Games: Text editors can be used to develop and implement the design of puzzle games which includes problem solving skills, maze-solving experience, math challenges. With the help of algorithms and randomization, text editors can used to create games with procedural generation.

Multiplayer text games: Text editors can facilitate the development of multiplayer text games where players can interact with each other in real time. Developer can implement networking functionalities, enable players connect with each other’s, chat and engage in collaborative or competitive gameplay. This approach is well suited for Multiple User Dungeons or storytelling experiences.

AI Simulations: In latest era text editors provide enhanced environment for developing simple AI simulations and virtual worlds. In this editor developers can implement basic artificial intelligence behaviors, such as pathfinding, decision making, autonomous moves. Text editors also offer efficient way to prototype the game ideas. Before star with the design and core mechanics, rapidly test the different gameplay concepts. Prototyping the editors validate ideas, and give the significant time and effort for building more complex, elaborate game structures.

Despite the simplicity and flexibility text editors for gam e development also restrict some, limitations. Within the text editors some complex 3D scenes, advanced physics for the characters, simulation visualization can be challenging. In these cases, special game engines such as Unity (C#), Unreal Engine (C++) very popular and effective to build game projects. Using text editors for simple game developing help the game developers to gain a deeper understanding of game logic, programming concepts, principles. From this one should build solid foundation before tackling into more complex game development pipelines.

While text editors may not be the beast choice for professional game development. Text editors provide very lightweight and flexible environment to bring game ideas to life.

List of Top 110 Unicorn Startups in India

Soham — Sat, 14 Oct 2023 14:41:59 +0000

Startup Name Industry Founding Year Unicorn Entry Year Profit/Loss FY22 Current Valuation Acquisitions Status

Zepto Quick Commerce 2021 Molbio Diagnostics Tata 1mg HealthTech 2015 Shiprocket Logistics 2017 5ire Fintech 2021 OneCard Fintech 2018 PhysicsWallah Edtech 2016 LeadSquared SaaS- CRM 2011 ElasticRun E-commerce Logistics LEAD Edtech 2012 Purplle E-commerce Personal Care DealShare E-commerce 2018 Xpressbees E-commerce Logistics Open Fintech 2017 Games24x7 Gaming 2006 Amagi Media, Advertising 2008 Fractal SaaS- Analytics 2000 Darwinbox SaaS- HR Livspace E-commerce Interior Design Uniphore SaaS- Conversational Hasura SaaS- Programming Tools 2017 Yubi (Formerly CredAvenue) CommerceIQ E-commerce Oxyzo Fintech 2016 Moglix E-commerce 2015 NoBroker PropTech MobiKwik Fintech 2009 Spinny Automotive, E-commerce 2015 MPL ESports 2018 2021 Acko Insurtech 2016 ShareChat Media & Entertainment Eruditus Edtech 2010 upGrad Edtech 2015 Apna Marketplace- Employment 2019 Urban Company Home Services 2014 BharatPe Fintech Payments CarDekho E-commerce Automotives Licious E-commerce Meat Products Zetwerk Manufacturing 2018 Meesho E-commerce 2015 Blinkit (ex-Grofers) Pristyn Care Healthtech 2018 Vedantu Edtech 2011 Upstox Fintech 2010 Slice Fintech 2016 CRED Financial Technology Cult.fit Healthtech 2016 Mamaearth E-commerce Personal Care Infra.Market B2B E-commerce 2016 Five Star Business Finance OfBusiness Building Materials 2015 CoinSwitch Kuber Cryptocurrencies CoinDCX Cryptocurrency Exchange 2018 BlackBuck Logistics 2015 Droom Marketplace- Automotives PharmEasy Online Pharmacy 2015 MyGlamm (Good Glamm Group) Digit Insurance Insurance 2016 Innovaccer Healthcare 2014 Groww Fintech 2017 Gupshup Enterprisetech 2004 Chargebee Financial Services 2011 Zeta Fintech 2015 BrowserStack Cloud Mohalla Tech (Moj) Software Polygon (Formerly Matic Network) MindTickle Saas- Enterprise Software Rebel Foods E-commerce Food Delivery Mensa Brands Ecommerce, GlobalBees E-commerce Retail 2021 DailyHunt Media & Entertainment Unacademy Edtech 2015 Cars24 B2C E-commerce 2015 Glance, Inmobi Mobile Technology RazorPay Fintech 2014 Zerodha Stockbroker 2010 PhonePe Fintech 2015 FirstCry E-commerce 2010 Nykaa E-commerce 2012 Pine Labs Fintech 1998 HighRadius Fintech 2006 Verse Innovation Zenoti Saas- Spa and Salon Services Postman Saas- API Development Delhivery E-commerce Icertis Saas- Contract Management Ola Electric Automotive 2017 Druva Saas- Data Management 2008 BigBasket E-commerce- Groceries Rivigo Logistics 2014 Lenskart E-commerce- Eyewear 2010 Dream11 Fantasy Sports 2008 CitiusTech Healthcare Technology Freshworks SaaS - CRM 2010 Udaan E-commerce 2016 OYO Travel Tech, Hospitality Paytm Mall E-commerce 2017 Swiggy Foodtech 2014 BYJU'S Edtech 2011 Policybazaar Insurance company Billdesk Fintech 2000 ReNew Power Renewable Energy OLA Transport Tech 2010 Zomato Foodtech 2008 Paytm E-commerce Finance 2010 Mu Sigma SaaS- Analytics 2004 Flipkart E-commerce 2007 InMobi Adtech 2007 MakeMyTrip Online Travel 2000 2023 -$47.1 Million $1.4 Billion - Private
HealthTech, MedTech 2010 2022 NA $1.5 Billion 1 Private
2022 -$65 Million $1.25 Billion - Acquired
2022 -$11.67 Million $1.3 Billion 5 Private
2022 NA $1.5 Billion 1 Private
2022 -$22.8 Million $1.4 Billion 0 Private
2022 $16.4 Million $1.1 Billion 6 Private
2022 -$7.7 Million $1 Billion - Private
2015 2022 -$45.1 Million $1.5 Billion - Private
2022 -$49.6 Million $1.1 Billion 2 Private
2012 2022 -$25 Million $1.1 Billion 1 Private
2022 -$53.8 Million $1.7 Billion 0 Private
2015 2022 -$3.39 Million $1.2 Billion 1 Private
2022 -$20.9 Million $1 Billion 2 Private
2022 -$39 Million $2.5 Billion 0 Private
2022 NA $1.4 Billion 1 Private
2022 -$18.4 Billion $1 Billion 7 Private
2015 2022 -$7.6 Million $1 Billion 0 Private
2014 2022 -$51 Million $1.2 Billion 4 Private
Automation 2008 2022 NA $2.5 Billion 5 Private
2022 NA $1 Billion 0 Private
Fintech 2017 2022 -$7 million $1.5 Billion 3 Private
Management Platform 2012 2022 NA $1 Billion 0 Private
2022 $8.6 Million $1 Billion 0 Private
2021 -$2.5 Million $2.6 Billion 2 Private
2014 2021 NA $1 Billion 1 Private
2021 -$16 Million $0.9 Billion 1 Private
2021 -$61.2 Million $1.75 Billion 3 Private
-$56 Million $2.3 Billion 3 Private
2021 -$45.1 Million $1.1 Billion 2 Private
2015 2021 -$373 Million $5 Billion 6 Private
2021 -$386.8 Million $3.2 Billion 1 Private
2021 -$78.3 Million $2.25 Billion 14 Private
2021 -$14 Million $1.1 Billion 0 Private
2021 -$64 Million $2.8 Billion 3 Private
2018 2021 -$702 Million $2.9 Billion 2 Private
2007 2021 -$29 Million $1.2 Billion 3 Private
2015 2021 -$106 Million $1.5 Billion 0 Private
2021 -$10.4 Million $2.7 Billion 4 Private
2021 -$406 Million $4.9 Billion 0 Private
E-commerce- Groceries 2013 2021 -$127 Million $0.568 Billion - Acquired
2021 NA $1.4 Billion 1 Private
2021 -$87 Million $1 Billion 3 Private
2021 -$55.5 Million $3.4 Billion 0 Private
2021 -$31 Million $1.5 Billion 1 Private
2018 2021 -$160 Million $6.4 Billion 4 Private
2021 -$85 Million $1.5 Billion 14 Private
2016 2021 $2.8 Million $1.2 Billion 4 Private
2021 $28.3 Million $2.5 Billion 3 Private
Financial Services 1984 2021 - $1.4 Billion 0 Listed
2021 $34.4 Million $4.8 Billion 1 Private
2017 2021 NA $1.9 Billion 0 Private
2021 $5.1 Million $2.15 Billion 0 Private
2021 -$35 Million $1.02 Billion 0 Private
2014 2021 -$17 Million $1.2 Billion 1 Private
2021 NA $5.6 Billion 3 Private
Personal Care Marketplace 2015 2021 -$36 Million $1.2 Billion 7 Private
2021 NA $4 Billion 1 Private
2021 NA $3.2 Billion 0 Private
2021 -$29.5 $3 Billion 1 Private
2021 $7.06 Million $1.4 Billion 5 Private
2021 $3.9 Million $3.5 Billion 3 Private
2021 NA $1.5 Billion 0 Private
Infrastructure and Software 2011 2021 $12.5 Million $4 Billion 3 Private
2015 2021 NA $5 Billion 6 Private
Blockchain 2018 2021 NA $10 Billion 3 Private
2011 2021 $3.5 Million $1.2 Billion 1 Private
2011 2021 -$66.7 Million $1.4 Billion 1 Private
Financial Services 2021 2021 -$12 Million $1.2 Billion 20 Private
2021 -$4.4 Million $1.1 Billion 11 Private
2007 2020 - $5 Billion 2 Private
2020 -$357 Million $3.4 Billion 12 Private
2020 -$31 Million $3.3 Billion 0 Private
2019 2020 NA $1.7 Billion 2 Private
2020 $1.14 Million $7.5 Billion 7 Private
2020 $349.9 Million $2 Billion 0 Private
2020 -$251.6 Million $12 Billion 6 Acquired by Flipkart
2020 $6.39 Million $3 Billion 2 Private
2020 $4.96 Million $4.44 Billion 6 Listed
2020 NA $5 Billion 6 Private
2020 NA $3.1 Billion 0 Private
Media & Entertainment 2007 2020 -$320 Million $5 Billion 3 Private
2010 2020 NA $1.5 Billion 1 Private
and Testing 2014 2020 NA $5.6 Billion 0 Private
Logistics Services 2011 2019 -$128 Million $3.27 Billion 5 Listed
2009 2019 NA $5 Billion 0 Private
2019 NA $5 Billion 1 Private
2019 NA $2 Billion 3 Acquired
2011 2019 -$100 Million $3.2 Billion 6 Acquired
2019 -$21 Million $1.05 Billion 0 Private
2019 -$13.4 Million $4.5 Billion 2 Private
2019 $37.8 Million $8 Billion 0 Private
2005 2019 NA $2.4 Billion 3 Private
2018 -$220 Million $4.53 Billion 13 Listed
2018 -$220.9 Million $3.1 Billion 0 Private
2013 2018 -$263 Million $10 Billion 8 IPO-Bound
2018 -$65.6 Million - 0 Private
2018 -$453 Million $5.5 Billion 6 Private
2018 NA $5.1 Billion 19 Private
2008 2018 -$104 Billion $3.38 Billion 0 Listed
2018 $27 Million $2 Billion 0 Private
2011 2017 NA $8 Billion 3 Listed
2015 NA $7.4 Billion 8 IPO-Bound
2015 -$153 Million $5.4 Billion 15 Listed
2015 -$214 Million $16 Billion 12 Listed
2013 NA $1.5 Billion 1 Unlisted
2012 -$307 Million $37.6 Billion 18 Unlisted
2011 -$9 Million $1 Billion 10 Unlisted
2010 -$56 Million $2.52 Billion 9 Listed

Nchat : Real-Time Chat Communication

Soham — Tue, 23 May 2023 11:48:47 +0000

Dev: Dev Profile

What I built

I have created a real time latest chat application using github codespace as environment and ReactJs,NodeJs,Socket.io as tools.

Category Submission:

Wacky Wildcards

App Link

Nchat

Screenshots

Description

I have created a chat app using ReactJS, Node.js, Socket.io, and GitHub Codespaces. The app allows users to engage in real-time chat conversations. It provides a user-friendly interface for sending and receiving messages, as well as displaying the chat history. The app is simple and easy to deploy.

Link to Source Code

Github

Permissive License

The app is licensed under the MIT License

Background (What made you decide to build this particular app? What inspired you?)

I decided to build this chat app for the GitHub x Dev Hackathon because I wanted to create a real-time communication tool that could be used for various purposes, such as friendly collaboration, or community engagement. I was inspired by the increasing demand for remote communication tools such as whatsapp,slack and wanted to contribute to the development of such applications.

How I built it (How did you utilize GitHub Actions or GitHub Codespaces? Did you learn something new along the way? Pick up a new skill?)

Github Codespace used really good for less storage capacity in device so I utilized GitHub Codespaces, which is an online development environment provided by GitHub. It allowed me to have a consistent development environment across different machines and made it easy to collaborate with others. I used ReactJS for the frontend, Node.js for the backend, and Socket.io for real-time communication between the server and clients.

Throughout the development process, I learned more about setting up a real-time communication system using Socket.io and how to handle concurrent connections. I also gained experience in deploying applications using GitHub Codespaces and leveraging GitHub Actions for continuous integration and deployment.
Building chat application throughout the hackathon provided me a great learning experience and allowed me to create a functional and scalable application for real-time communication.

Additional Resources/Info

Youtube Video

Backend Server: Backend

DSA(Array): Find the maximum and minimum element in an array

Soham — Sat, 13 May 2023 07:19:05 +0000

Given an array A of size N of integers. Your task is to find the minimum and maximum elements in the array.

Example 1:

Input:
N = 6
A[] = {3, 2, 1, 56, 10000, 167}
Output:
min = 1, max = 10000

Your Task:

You don't need to read input or print anything. Your task is to complete the function getMinMax() which takes the array A[] and its size N as inputs and returns the minimum and maximum element of the array.

Expected Time Complexity: O(N)
Expected Auxiliary Space: O(1)

Constraints:
1 <= N <= 105
1 <= Ai <=1012

Explanation and code:

In this section we will discuss the most efficient approach for solving this type of problem.
What I am going to do:
At first take a vector which will store the elements of our array. After that we will sort the vector using sort() function.In this way we will get our modified array in descending order.Now we take the pair (long long as mentioned earlier). Now also take two variables named max and min long long data type.
After that we can observer that our array already in descending order so most greater element in the last position that means n-1 and the smallest element in 0th position.(based on the 0th based indexing) now if we can say max store the last element and min will store first index element.

Sample Code:

//{ Driver Code Starts
#include <bits/stdc++.h>
using namespace std;
#define ll long long

pair<long long, long long> getMinMax(long long a[], int n) ;

int main() {
    int t;
    cin >> t;
    while (t--) {
        int n;
        cin >> n;
        ll a[n];
        for (int i = 0; i < n; i++) cin >> a[i];

        pair<ll, ll> pp = getMinMax(a, n);

        cout << pp.first << " " << pp.second << endl;
    }
    return 0;
}
// } Driver Code Ends


pair<long long, long long> getMinMax(long long a[], int n) {
  vector<int>st;

  for(int i=0;i<n;i++){
      st.push_back(a[i]);
  }

  sort(st.begin(),st.end());

  pair<long long,long long>ans;
  long long min,max;

  max = st[n-1];
  min = st[0];

  ans = {min,max};

  return ans;


}

Output:
For Input: 6 3 2 1 56 1000 167 Your Output: 1 1000

React Hooks

Soham — Tue, 27 Dec 2022 13:23:44 +0000

React is a JavaScript library for building user interfaces. It was developed by Facebook, and is often used for building single-page applications and mobile applications.

In React, a "hook" is a special function that allows you to "hook into" React features from your functional components. Hooks were introduced in React 16.8 as a way to add state and other react features to functional components.

There are several types of hooks in React, including:

useState: This hook allows you to add state to a functional component. It returns an array with two elements: the current state value and a function to update the state.


import { useState } from 'react';

function Example() {
  const [count, setCount] = useState(0);

  return (
    <div>
      <p>You clicked {count} times</p>
      <button onClick={() => setCount(count + 1)}>
        Click me
      </button>
    </div>
  );
}

useEffect: This hook allows you to perform side effects in functional components. It takes a function as an argument, which will be run after the component renders. You can also specify a list of dependencies as a second argument, which will determine when the effect is run. If the dependencies haven't changed, the effect will not be run.

import { useEffect, useState } from 'react';

function Example() {
  const [count, setCount] = useState(0);

  useEffect(() => {
    document.title = `You clicked ${count} times`;
  });

  return (
    <div>
      <p>You clicked {count} times</p>
      <button onClick={() => setCount(count + 1)}>
        Click me
      </button>
    </div>
  );
}

useContext: This hook allows you to access the React context in a functional component. The context is an object that is passed down the component tree, and can be used to share values between components without having to pass props down manually.

import { useContext } from 'react';

const MyContext = React.createContext();

function Example() {
  const value = useContext(MyContext);

  return <div>{value}</div>;
}

useReducer: This hook is similar to useState, but it allows you to manage complex state objects and update them using reducer functions. It takes a reducer function and an initial state as arguments, and returns the current state and a dispatch function that can be used to update the state.

import { useReducer } from 'react';

function reducer(state, action) {
  switch (action.type) {
    case 'increment':
      return { count: state.count + 1 };
    case 'decrement':
      return { count: state.count - 1 };
    default:
      throw new Error();
  }
}

ChatGPT and OpenAI

Soham — Tue, 27 Dec 2022 13:10:37 +0000

GPT, or Generative Pre-training Transformer, is a type of language model developed by OpenAI. It is a neural network-based model that is trained on a large dataset of text and can generate human-like text. GPT was introduced in 2018 and has since been improved and extended in several ways, including the development of GPT-2 and GPT-3.

OpenAI is a research laboratory that focuses on developing and promoting artificial intelligence that is safe and beneficial for humanity. It was founded in 2015 by a group of entrepreneurs, researchers, and philanthropists, including Elon Musk and Sam Altman. OpenAI has made significant contributions to the field of AI, including the development of various machine learning algorithms and systems.

One of the key features of GPT is its ability to generate text that is coherent and flows naturally, even when given just a small amount of input. This is achieved through the use of a transformer architecture, which allows the model to process large amounts of data and capture long-range dependencies between words.

GPT can be used for a wide range of natural language processing tasks, including text generation, translation, and summarization. It has also been used to create chatbots, or computer programs that can carry out conversations with humans through text or voice input.

One example of a chatbot based on GPT is ChatGPT, which is a chatbot trained on a large dataset of conversations. ChatGPT is able to engage in natural and coherent conversations with users, and can even respond appropriately to jokes or non-sequiturs.

In addition to its ability to generate human-like text, GPT is also notable for its large size. GPT-3, the latest version of GPT, has 175 billion parameters, making it one of the largest language models ever created. This allows it to capture a wide range of knowledge and generate text that is more diverse and accurate than smaller models.

However, the large size of GPT also presents some challenges. One concern is that it may be more prone to making mistakes or biased decisions, due to the large amount of data it has been trained on. Another concern is that GPT may be more difficult to explain or understand, due to the complexity of its architecture and the vast amount of knowledge it has learned.

Overall, GPT and OpenAI have made significant contributions to the field of artificial intelligence and natural language processing. While there are still challenges and concerns to be addressed, the development of GPT and other AI technologies has the potential to revolutionize many aspects of our lives, from communication and entertainment to education and healthcare. So, it has both advantages and disadvantages.

There are several potential "dark sides" to OpenAI and the development of artificial intelligence more generally. Some of the main concerns include:

Bias and discrimination: There is a risk that AI systems, including those developed by OpenAI, could perpetuate or amplify existing biases and discrimination in society. For example, if a machine learning model is trained on biased data, it may make biased decisions or predictions. This could have serious consequences, such as discrimination in hiring or lending practices.

Loss of jobs: As AI systems become more advanced, they may be able to perform tasks that were previously done by humans. This could lead to a loss of jobs, particularly in fields where automation is feasible. This could have a negative impact on employment and the economy, and may require measures to be put in place to mitigate the effects.

Security and privacy: AI systems, including those developed by OpenAI, may present security and privacy risks. For example, they may be vulnerable to hacking or other types of cyber attacks, or they may be used to gather and process sensitive personal data without the proper safeguards.

Transparency and accountability: There is a risk that AI systems, including those developed by OpenAI, may be difficult to explain or understand, making it hard to hold them accountable for their decisions and actions. This could be a problem in fields where accountability is important, such as healthcare or criminal justice.

Ethical concerns: The development of AI raises a number of ethical concerns, including the question of whether it is appropriate to create AI systems that are more intelligent or capable than humans. There are also questions about the extent to which AI should be used to make decisions that affect people's lives, and whether it is possible to ensure that AI systems behave ethically and are aligned with human values.

Overall, it is important to carefully consider the potential risks and ethical implications of AI, and to take

Python The Guide

Soham — Sun, 26 Jun 2022 17:54:02 +0000

print("Hello World")
Hello World
2*3
6
2*3
8
3/4
0.75
a=23
b=90
a-b
-67
a+b
113
a*b
359339733669351605185101286718740311628342797555834676291480724848488155215018843334859391939330168929445333462245499312849
s = "Engineering"
s[:7]
'Enginee'
s[1:]
'ngineering'
len(s)
11
type(s)

max(s)
'r'
min(s)
'E'
b=input("Enter string")
Enter stringCollege
b
'College'
s+b
'EngineeringCollege'
s+" "+b
'Engineering College'
c = s+" "+b
c
'Engineering College'
c.capitalize()
'Engineering college'
c.lower()
'engineering college'
c.upper()
'ENGINEERING COLLEGE'
"my name is %s from %s"%("Soham","Arambagh")
'my name is Soham from Arambagh'
str="arun C machine hack"
str.split()
['arun', 'C', 'machine', 'hack']
str[1]
'r'
c.title()
'Engineering College'
str[2:]
'un C machine hack'
str.length()
Traceback (most recent call last):
File "", line 1, in
str.length()
AttributeError: 'str' object has no attribute 'length'
len(str)
19
str.center(13)
'arun C machine hack'
str.count("a")
3
str.find("hack)

SyntaxError: EOL while scanning string literal

str.find("hack")
15
str.replace("hack","Don't hack")
"arun C machine Don't hack"

3) Accept only vowel containing string

def check(str):
str = str.lower()
vowel = set("aeiou") #Convert string into set of characters
str1 = set({}) # set function use to convert dictionary to set

for char in str:
    if char in vowel:
        str1.add(char)
    else:
        pass
if len(str1)==len(vowel):
    print("string is accepted \n")
else:
    print("Not accepted\n")

txt = input("Enter any string :-) \n")
check(txt)

9) Programme for reverse number

def recursive(txt):

rem =0

while (txt>0):
   rem = (rem*10) +(txt %10)
   txt = txt//10  #used for quotient

return rem

txt = int(input("Enter the number\n"))

po = recursive(txt)
print("Reverse number is: ",po)

10) sum of digits of a number

def squar(ni):
sum1 =0
while(ni !=0):
sum1 = sum1 +(ni%10)
ni = ni//10
return print("Sum of the digits of number is",sum1)

n1 = int(input("Enter the number for addition of digits\n"))
squar(n1)

11) Sum of cube of digits in a number

def cube(n2):

sum3 =0
while(n2 !=0):
    con = n2%10
    sum2 =con*con*con
    sum3 = sum3 + sum2
    n2 = n2//10

return sum3

n2 =int(input("Enter the number for sum of cube\n"))
print("Sum is:",cube(n2))

4) Maximum occurring character in string

size = 256
def maxchar(str):
arr = [0] * size

max = -1;
c =''

for i in str:
    arr[ord(i)] += 1

for i in str:
    if arr[ord(i)]>max:
        max = arr[ord(i)]
        c = i

return c

st ="engineering icon english"
print(maxchar(st))

5) String consist special character or not

import re

def consistchar(str):

reg ="[^a-zA-Z0-9]+"

q = re.compile(reg)

if(len(str)==0):
    print("NO")
    return

if(re.search(q,str)):
    print("Yes")
else:
    print("NO")

str ="er@1wed"
print("String consist of ",consistchar(str))

7)Length of string

str3 =input("Enter any string you want\n")
print("length of the string:",len(str3))

8) String Contain number or not

def firstnumber(str4):
c=0
for i in str4:
if ord(i)>=48 and ord(i)<=57:
# print("String contain number")
# return
c = c+1
else:
#print("String not contain number")
c=0

if c>=1:
    print("contain number")
    return
else:
    print("not contain number")

firstnumber("ho")

Generating random string until given string not created

import random
import string
import time

possiblechar = string.ascii_lowercase +string.ascii_uppercase+string.digits+'., !?:;'

st=input("Enter the string\n")

Generating A random String(use to generate password)

atthis =''.join(random.choice(possiblechar)for i in range(len(st)))

atnext =''

complete = False
itr =0

while complete == False:
print(atthis)

atnext=''
complete = True

#Fix the index
for i in range(len(st)):
    if atthis[i] != st[i]:
        complete =False
        atnext += random.choice(possiblechar)
    else:
        atnext += st[i]

itr +=1
atthis = atnext
time.sleep(0.1)

print("Target match after ",itr )

OUTPUT:

Enter any string :-)
aeiuou
string is accepted

Enter the number
342
Reverse number is: 243
Enter the number for addition of digits
6795
Sum of the digits of number is 27
Enter the number for sum of cube
123
Sum is: 36
n
Yes
String consist of None
Enter any string you want
Engineering
length of the string: 11
not contain number
Enter the string
soham
;rh?m
……….
……….
sYham
sCham
s;ham
soham
Target match after 312

//MATPLOLIB
import matplotlib.pyplot as mp

x =[2,3,7,8]
y =[4,8,10,15]

mp.plot(x,y,'#000',linewidth=3,linestyle='dotted',marker='o')
mp.show()

import matplotlib.pyplot as mp

x=[3,4,5,6]
y =[2,8,7,9]
mp.xlabel("X")
mp.ylabel("Y")
mp.bar(x,y,color=['r','b','y','g'])
mp.show()

Pie CHART
import matplotlib.pyplot as plt
w =[200,100,150,34,250]
d =['Math','Physics','Eco','Computer Sc','Python']
plt.pie(w,labels=d,autopct="%3d%%",shadow=True)
plt.show()

import matplotlib.pyplot as plt
a =['Ram','Soham','Madhav','Shobho']
b =[3,6,4,2]
plt.title("marks of student")
plt.bar(a,b)
plt.show()

import matplotlib.pyplot as plt
a=[2,3,4,5]
b =[7,8,5,6]
plt.xlim(1,3)
plt.ylim(-1,2)
plt.bar(a,b)
plt.show()