How I Learnt to Code in 9 Months & Got a $250K Job!

Main ideas / lessons conveyed

• Getting a software engineering job in “big tech” depends on two separate bottlenecks:
  1. Getting interviews
  2. Passing interviews
• In software engineering, success is not guaranteed by degrees or “applying a few jobs.” People may apply to hundreds of roles without getting interviews.
• Your path to interviews differs depending on where you are in life (high school/university/new grad/career switcher), but the video focuses mainly on the high school → university → new grad / internship route.
• To maximize your chances:
  • Choose a degree with real demand (computer science or closely related engineering/math fields).
  • Attend a “target” school (where recruiters actively recruit).
  • Get an internship before leaving school, ideally at a big-name company—because internships make interview access and future offers much easier.
• Passing technical interviews is framed as a distinct skill from day-to-day programming work.
  • Interviews test deeper algorithmic knowledge (e.g., time/space complexity, patterns, algorithms) that may not appear in regular work.
• The speaker claims their improvement came from studying interview fundamentals from the top (very basic programming concepts → data structures/algorithms → system design), building notes, and practicing heavily.
• The speaker promotes a product/course approach called TraceDev, claiming it helps compress and structure this learning path into a shorter, more efficient timeline (e.g., ~6–8 months).

Methodology / instructions presented (detailed)

A) How to get interviews (strategy framework)

• Don’t rely on typical degree entitlement
  • Software engineering hiring does not work like other careers where a degree automatically leads to interview calls.
• Choose an employable degree
  • Prefer degrees in computer science or closely related fields (the speaker mentions):
    • math
    • electrical & computer engineering
    • other closely related engineering fields
  • The speaker advises avoiding “random” degrees that feel useless in the job market.
• Pick the right university (“target schools”)
  • Even if curriculum overlaps across schools, the key advantage is recruiter attention and authority.
  • The speaker cites an example comparison in Canada:
    • University of Waterloo vs. a less “target” option (the subtitle suggests “Mckewan/McKewan,” though the name may be imprecise).
  • Suggested practical check:
    • Use LinkedIn to compare how many Google/Meta/Amazon hires come from each school.
• Get an internship before graduating
  • The speaker emphasizes internship timing: get it in third or fourth year if still in school.
  • Interning at large companies (examples listed: Amazon, Google, Apple) increases:
    • resume authority (“cracked resume”)
    • callback likelihood
    • ease of switching companies later (less friction if laid off)

B) How to pass interviews (technical preparation approach)

• Understand the nature of technical interviews
  • Technical interviews often include algorithmic questions and concepts like:
    • identifying patterns
    • time/space complexity
    • complex algorithms
  • These differ from day-to-day work, so knowing how to do the job isn’t sufficient by itself.
• Study from fundamentals upward (the speaker’s personal approach)
  • Start at “basics” (e.g., variables, scope, printing)
  • Then build toward:
    • data structures & algorithms
    • system design concepts
    • scalability-related topics
    • concepts around common interview requirements (the subtitle includes “Big O notation,” sorting, CAP-like theory, compliance-like topics—some may be mis-transcribed)
• Use deliberate learning + documentation
  • The speaker describes:
    • writing/typing detailed personal notes
    • studying anything they don’t understand immediately
    • keeping notes updated for interview-specific recall (including “hints” and reminders)
• Practice via lots of problem solving
  • The speaker claims to have solved a large number of interview-style questions (referenced as ~307).
  • They describe gaps as learning cycles: when they encounter unknown types, they pause and study, then return.
• Avoid being unready when interview rules restrict retries
  • Top companies may require long waiting periods after a fail (example given: 12 months).
  • Therefore, when you get an opportunity, you must be ready to “deliver.”
• Focus on interview execution behaviors
  • The speaker’s notes mention guidance such as:
    • don’t give overly high-level answers—enter detail appropriately
    • ask clarifying questions
    • code should be readable
    • attempt brute force first (when appropriate) to structure thinking

C) What TraceDev is positioned to do (as an instructional product)

• The platform is described as:
  • starting from coding fundamentals (so you aren’t missing basics)
  • moving into interview-relevant data structures and algorithms
  • preventing skipping by requiring proficiency at each level (no moving on until confident)
  • including system design topics and mock interviews
  • emphasizing competitive readiness in ~6–8 months (claimed)
• The speaker’s stated motivation for TraceDev:
  • their own learning was slow and fragmented due to trial-and-error
  • TraceDev is meant to make concepts easier to understand and harder to “skip,” reducing the “what am I missing?” phase

Notable claims / examples mentioned

• Personal timeline and career outcome (high level)
  • International student in Canada (arrived around 2017–2018, age 17)
  • Worked part-time during school; also worked full-time in summers
  • Later salaries grow (subtitles mention approx. $75k, then $175k, with a later job market disruption and changes)
• Interview process descriptions
  • Online assessment followed by multiple rounds
  • Onsite with several consecutive technical interviews (sometimes 4–5)
  • Coding sessions with interviewers joining/leaving on a clock
• A specific failure → realization story
  • Amazon interview call after prior internships/smaller companies
  • Failed the online assessment
  • Concluded the issue wasn’t coding on the job—it was missing how to pass technical interview formats
• Tools/learning examples referenced
  • AWS concepts tied to system design (load balancers, decoupling, etc.)
  • Data structure distinctions (example: queue vs. linked list framing, with performance implications when “popping from the front”)

Speakers / sources featured

• Main speaker (video creator): The person telling their personal story and promoting TraceDev (name not provided in the subtitles)
• Companies mentioned as interview/resume examples / recruiters:
  • Amazon, Google, Meta, Apple, Uber, Twitter/X, Netflix, Stanford
  • University of Waterloo
  • McKewan/McKewan University (university name may be inaccurate due to subtitle transcription)