Video summary

Still Unplaced in 2026? Here's Exactly What To Do Next

Main summary

Key takeaways

Business

High-level business/career strategy (what the video is optimizing for)

  • The speaker frames the “first job placement” as an execution problem under market pressure—e.g., hiring windows, standardized interview rounds, and a skills roadmap (DSA + coding + modern stack).
  • Core strategy:
    • Target the right hiring periods
    • Prepare for standardized interview rounds
    • Build signals (projects/GitHub/LinkedIn)
    • Increase referral probability
    • Expand through startups, hackathons, and communities
    • Track readiness using daily quotas (e.g., DSA targets)

Market reality + urgency

  • Hiring is portrayed as tightening due to tech layoffs and AI-driven changes.
  • Practical takeaway: candidates must apply correctly, at the right time, and with the right preparation—not only “work hard”.

Placement route / “5 ways” playbook (with execution steps)

1) Campus/large company hiring cycles (“MAC route”)

Companies mentioned

  • TCS, Infosys, Accenture, Wipro, Cognizant, Tech Mahindra, Hexaware, Mindtree (as referenced), etc.

Hiring window by month (financial-year style)

  • Jan–Mar: TCS NQT / Accenture drives / Cognizant exams / similar drives
  • Apr–Jun: Wipro LT / Infosys and other drives (a June exam is implied)
  • Jul–Oct: TCS/Wipro cycle again (“bicycle moves” metaphor)
  • Nov–Dec: Tech Mahindra and others

Interview structure (typical for mass hiring)

  1. Aptitude + Reasoning (filtering round)
  2. Technical MCQs / Core CS basics (DBMS, OS, Networking, OOPs)
  3. Coding round (DSA)
  4. HR round (behavior/fit)

Actionable prep timeline (as claimed)

  • 15 days to cover aptitude + reasoning + core theory
  • 2–3 months for DSA/coding if you want to avoid struggle

2) LinkedIn + referrals as a growth channel

Why it works (referral incentive)

  • Employees receive bonuses for successful referrals, so they’re incentivized to refer.

LinkedIn “playbook”

  • Fix your profile (“get up/profile”):
    • Headline, About, education
    • Projects/achievements
  • Daily connection requests: “5 connections requests every day” (recruiters/engineers/hiring managers)
  • Posting cadence:
    • At least 1 post weekly
    • Post progress (projects, DSA progress, learnings, news/insights)
  • Referral request timing:
    • Ask only after you have proof (good level + projects), not immediately when skills are weak.
  • Expected referral funnel (approx.):
    • From 100 connection requests → ~15 accepts
    • Then message those 15
    • Only a few convert into actual referrals

Other referral platforms mentioned

  • Insta, CutShort, PeerList, Hire, and StartupHire (for startups)

3) Startups (“fast learning, fast hiring”)

Rationale

  • More end-to-end exposure: frontend/backend, deployment, founder interaction
  • “Learning curve grows exponentially.”
  • Funding-driven expansion can lead to faster hiring.

Pay examples/claims (execution signal, not investment)

  • Early starter packages mentioned: ₹5L–₹6L (could vary later via switching)

Modern startup tech emphasis (stack signals)

  • React, Next.js, Node.js, APIs, AWS, Postgres, MongoDB
  • Spring Boot is mentioned as more enterprise-leaning; startups may use different patterns.

Degree stance (signals over credentials)

In startups, “degree won’t matter”; what matters is:

  • Proof of work (GitHub/projects)
  • Deployed project
  • Ability to explain architecture and why-tech choices
  • Public presence (LinkedIn/blogs/social)

4) Hackathons / competitions as a “selection engine”

Purpose

  • Demonstrate capability under real constraints
  • Gain visibility with recruiters/companies

Examples mentioned

  • Smart India Hackathon (SIA)
  • Flipkart Grid
  • Amazon HackOn
  • AOB India Hackathon
  • Microsoft Engage
  • Platforms referenced for lists: DeFolio, Unstop, HackerEarth; also “mlh” for upcoming events

Hackathon team operating model

  • Build a functioning team with roles:
    • Front-end/UI
    • Back-end
    • Presenter/problem-solver

Behavioral recommendation

  • Participate even if you’re 30–40% prepared
  • “Fail to learn” attitude

5) Community-based hiring (Discord/Reddit/X/alumni groups)

Mechanism

  • Don’t treat it as “apply from community”.
  • Contribute → build reputation → be visible for opportunities/referrals.

Community contribution loop

  • Observe maturity/level and opportunities
  • Engage (help in chats, solve doubts)
  • Contribute work (projects, discussions)
  • When hiring/referral slots appear, you’re already in the network

Communities named

  • Reddit, Discord servers, “X tech communities”
  • Also mentions WhatsApp/Telegram groups and alumni/off-campus groups

Interview preparation: standardized rounds + required skills (execution checklist)

Interview rounds & what to prepare

  • Aptitude/Reasoning + Verbal/Group Discussion (if applicable)
    • Suggested prep: paid courses/videos; or free content inside their batch
  • Core CS theory MCQs
    • DBMS, OS, Networking, OOPs
  • Coding/DSA
    • Focus on fundamentals + common patterns:
      • Strings, hashmap, recursion
      • Stack basics
      • DP basics
      • Trees, graphs
  • HR round
    • Behavior, teamwork, leadership signals
    • Common HR questions can be looked up and practiced

Skill roadmap for 2026–27 (what to learn)

The speaker proposes 4 skills:

  1. “Mernstack development” (MongoDB + React/Node style implied)
  2. DSA + problem solving
  3. Generative AI / LLM development
    • Learn via OpenAI/Gemini APIs, LangChain, building RAG systems
    • Include vector database usage
  4. DevOps / cloud computing (AWS) for scaling
    • Manage servers/web workers, deployment/optimization

Targets / quantities

  • At least ~150 DSA questions (mix of easy/medium; some hard acceptable)

  • Daily execution target:

    • 2–3 Lead Code questions daily (best case 5)
  • Core knowledge timeline claim:
    • 15 days for aptitude/reasoning + core theory

Marketing/sales angle (candidate branding as “go-to-market”)

  • Treat candidates like products:
    • Signals: LinkedIn profile quality, posts, project explanations, deployed repos
    • Distribution: connection requests + weekly posts + community engagement
    • Conversion lever: referrals and recruiter visibility

Action plan (directly recommended by the speaker)

  • Immediately: fix LinkedIn profile.
  • Daily: solve 2–5 DSA/LeetCode questions and study concepts in parallel.
  • Build projects: web projects + ML/AI projects + optional blockchain project.
  • Practice explaining architecture: tech choices, libraries, database decisions, why/how.
  • Apply in windows: Jan–Mar, Apr–Jun, Jul–Oct, Nov–Dec for large companies.
  • Don’t delay referral requests: ask after you have proof.
  • Add proof-of-work everywhere: GitHub + deployed project + public presence.

Presenters / sources mentioned

Presenter/primary source

  • Sarthak Sharma (Sherian Scoring School / Sherians)

Other referenced entities/resources (not necessarily presenters)

  • Companies: TCS, Infosys, Accenture, Wipro, Cognizant, Tech Mahindra, Oracle, Amazon, Microsoft, Meta, LinkedIn-related recruiting context
  • Platforms/communities: LinkedIn, CutShort, InstaHire, PeerList, Hire, Wellfound, Y Combinator, DeFolio, Unstop, HackerEarth, mlh, Reddit, Discord, X (Twitter-like tech community), WhatsApp/Telegram groups
  • Books/resources: RS Agarwal’s book, PrepInst (as referenced)
  • Training programs: “Code”/bootcamp and aptitude/DSA course references (Sherians batch)

Original video