For students and working engineers

Stop watching tutorials. Start getting good.

Placements are coming. Learn the patterns interviewers actually test, and prove you understand them instead of memorising them.

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● 6 day streak
✓ Two Sum mastered
◆ LLMs: lesson 3 of 27
Stop 1 · How it works

The video course

Lecture 14 of 212: Hashing, part 3

▶
17:52 / 47:102x speed
  • Someone else solves it while you watch.
  • You never commit to a guess, so you're never wrong.
  • “Completed” means you reached the end of the video.

A Cruxion lesson

Step 1 · Observe

Watch it fail, then find the idea

Crux:

  • +You make the call before the answer appears.
  • +Wrong guesses are the point. Crux notes them.
  • +“Mastered” means you wrote it, fixed it and defended it.
Stop 2 · DSA

8

stages to mastery

3

languages: Python, C++, Java

0

videos to sit through

Two Sum

Return the indices of the two numbers that add up to target.

2 ≤ n ≤ 10⁴−10⁹ ≤ nums[i] ≤ 10⁹Exactly one answerNo reusing an element

nums target = 10

3

0

8

1

11

2

4

3

6

4

lookups 0brute force on this input 10

seen value → index

empty

Frame: read the constraints

n can reach 10,000. Checking every pair is about 50 million checks. The constraint is quietly telling you: one pass.

Step 1 of 12

Stop 3 · AI & LLMs

Live demo · The sampling dial

next-token probabilities

The interview went ___

well
60%
fine
25%
badly
8%
long
5%
viral
2%
0.80
precisecreative

The usual range. Mostly sensible, with a little variety.

Live now27 lessonsAbout 4 hours3 tiers

The LLM Engineer

  1. Apprentice · How the model actually works

    • How LLMs actually think
    • Embeddings as a coordinate system for meaning
    • Prompts as programs
  2. Engineer · Make it know your data

    • Retrieval-augmented generation, done right
    • Supervised fine-tuning with LoRA and QLoRA
    • Preference alignment: RLHF and DPO
  3. Architect · Ship it and keep it honest

    • Evaluation, and LLM-as-judge
    • Inference at scale: KV cache, quantization
    • LLMOps: from a script to a live service
Stop 4 · System design

Coming nextPreview · numbers are illustrative
◎Users
⇄Load balancer
App servers
⚡Cache
▤Database

p99 latency

38ms

Database load

22%

All green. The cache is answering nine reads out of ten.

High-level design

Scaling, caching, queues and consistency on request-flow diagrams you can break and fix.

Low-level design

Classes, interfaces and patterns, taught by extending real code until the design holds or cracks.

What you master in DSA carries straight into both.

Stop 5 · Habits

Streaks that forgive one bad day

Miss a day and a banked freeze covers it automatically. No button, no guilt.

M
T
❄
W
T
F
S
S

Wednesday: freeze used automatically. Streak: 6 days.

Mastery you actually earned

✓ Mastered

A problem counts only once all eight stages pass. So the tick means something.

Frame

Observe

Predict

Control

Implement

Debug

Defend

Transfer

Two Sum · all eight stages cleared

Crux notices the problem behind the problem

Not “time to practise!” nudges. Crux tracks the kind of mistake you keep making and brings you back to it.

Start the drillLater

Crux grows with you

Earn XP, level up, and watch your companion glow brighter.

Level 3 → 4

Stop 6 · Companies

GoogleAmazonMicrosoftMetaAdobeUberGoldman SachsFlipkartBloombergWalmartSwiggySalesforceMorgan StanleyLinkedInGoogleAmazonMicrosoftMetaAdobeUberGoldman SachsFlipkartBloombergWalmartSwiggySalesforceMorgan StanleyLinkedIn
GoogleAmazonMicrosoftMetaAdobeUberGoldman SachsFlipkartBloombergWalmartSwiggySalesforceMorgan StanleyLinkedInGoogleAmazonMicrosoftMetaAdobeUberGoldman SachsFlipkartBloombergWalmartSwiggySalesforceMorgan StanleyLinkedIn
  • easyTwo Sum
    AmazonGoogleMicrosoftAdobe
  • mediumTwo Sum on a Sorted Array
    AmazonMetaMicrosoft
  • easyBalanced Brackets
    AmazonMicrosoftBloombergMeta
  • easyFind the Target
    GoogleMicrosoftAmazon
  • mediumLongest Run of Unique Characters
    AmazonGoogleBloombergMicrosoft
  • easyMajority Element
    AmazonGoogle
  • easyStock Buy & Sell
    WalmartSwiggyGoogle
  • mediumKadane's Algorithm
    MicrosoftMeta
  • medium4Sum
    AdobeOYOUber
  • mediumNext Permutation
    UberGoldman SachsAdobe
  • mediumMerge Overlapping Intervals
    Google
  • mediumWord Search
    OlaGoldman SachsGoogle
  • hardCount Inversions
    GoogleAmazonSalesforce
  • hardSliding Window Maximum
    FlipkartGoogleMicrosoft

Company names are trademarks of their owners, shown only to indicate where a question has been reported. No affiliation implied.

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