u/Curious-Green3301

India produces 1.5 million engineering graduates a year. Most of them figure out what to actually learn on their own, after college starts.

I'm doing B.Tech AI/ML right now. In a batch of ~250 students, maybe 20–30 are genuinely trying to build real technical capability beyond exams and placements.

The rest of the system runs on attendance, lab files, PPTs, and placement anxiety. Not on building anything real.

And most of those students aren't lazy. They're directionless. Trying to figure out what to learn, what actually compounds, and how the industry works — through YouTube, Twitter, GitHub, random seniors, and now LLMs.

Here's the strange part: students today have near-unlimited access to knowledge. But access to knowledge is not the same as access to mentorship, environment, or technical culture.

You can learn transformers online. You can't easily learn judgment online.

You can't Google whether your roadmap makes sense, whether your projects are actually good, or whether you're building depth or just collecting buzzwords. That comes from serious peers and mentors. For most Indian CS students, that environment doesn't exist.

This isn't a talent problem.

India's biggest gap isn't intelligence, curriculum, or access to content. It's the absence of high-intensity technical environments outside a tiny elite layer.

Scaler School of Technology and 100x School are genuinely attempting to fix parts of this — better peers, mentorship, industry alignment. But they can't absorb the scale, and for a large chunk of students, they're unaffordable.

So millions of students end up in the same loop: degree for signaling, self-learning for actual capability.

Other countries didn't leave this entirely to chance.

China's 2017 "New Engineering" initiative pushed universities to redesign CS education around AI and modern industry needs. Elite programs at Tsinghua and Peking University now embed students into research much earlier.

The US did it through institutional experimentation — Olin College built a project-first engineering school from scratch, Recurse Center built a self-directed programming environment that engineers often value more than a Master's degree.

In both cases, someone decided the environment had to change at a structural level.

In India, individual students are making that decision alone.

The current "winning move" is: get a degree for signaling, self-learn for actual capability, use the internet and AI to patch the gap manually.

That's not a system. That's students filling institutional holes themselves.

The future of CS education here won't be won by whoever uploads the best course videos. It'll be won by whoever actually solves mentor density, peer quality, employer trust, and real technical culture.

Not content distribution. Environment design.

Curious what actually shifted things for people here — or is everyone still in the patching-holes phase?

reddit.com
u/Curious-Green3301 — 7 days ago
▲ 8 r/developersIndia+2 crossposts

Be honest — is this upskilling plan actually good or am I just feeling productive?

6th sem AI/ML student here. Need honest advice because I genuinely don’t know if I’m going in the right direction anymore.

Right now my profile looks decent from outside:

  • built a multi-agent clinical reasoning project (med-signal.vercel.app)
  • built a movie recommender system using embeddings/vector DB
  • hackathon finalist at Meta x Scaler OpenEnv
  • decent CGPA too (8.2 GPA)

GitHub: github.com/alok943

But honestly, I feel there’s a gap between “having projects” and actually being skilled.

Most of my projects were made with heavy LLM help. I can understand the flow, debug stuff, connect APIs, deploy things, improve outputs etc. But if someone tells me to sit alone and code a lot of things from scratch without AI help, I’ll struggle.

And I don’t know how normal this is becoming now.

After end sems, I want to stop randomly jumping between things and seriously fix my fundamentals.

Current plan:

  • DSA in Python daily
  • finish Andrew Ng ML + DL courses
  • learn ML properly instead of just using libraries
  • go deeper into RAG/LLM engineering
  • improve communication skills
  • become less dependent on AI while coding

Target is AI/ML internships at Indian startups.

What I really want to know from people already in industry:

  • does DSA matter that much for AI/ML internships in India?
  • are projects like these actually valuable or do recruiters see through them instantly now?
  • am I spending too much time on courses?
  • what skills do startups actually expect from freshers now?
  • if you were in my place, what would you focus on for the next 6-8 months?

Would appreciate honest answers more than motivation 🙏

u/Curious-Green3301 — 8 days ago

​

Hey everyone,

I'm a BTech (AI/ML) student considering Claude Pro ($20/month) but want to separate the real value from the marketing.

I want to clarify what I *think* Pro includes before asking my questions — correct me if I'm wrong:

* **Within** [**claude.ai**](http://claude.ai) **(the chat UI):** higher usage limits (\~5x free), web search, sandboxed code execution, file creation, Projects for organizing context, memory across sessions

* **Claude Code (terminal CLI):** an agentic coding tool that can autonomously read/edit files, run bash, and build features — this requires at least Pro

* **What it's NOT** (unless you use the API separately): arbitrary tool-calling, hooking into your own APIs, custom agent pipelines — that's the developer API, billed separately

My use case:

* Learning ML + DSA (need a high-quality tutor I can go deep with)

* Building projects — currently a recommendation system

* Exploring **Claude Code** for agentic coding workflows

* Eventually experimenting with the API for agent pipelines

My actual questions:

  1. Is the **usage limit increase** alone worth $20/month for heavy daily use?

  2. Is **Claude Code** (via Pro) genuinely useful for a student building real projects, or is it premature without strong fundamentals?

  3. How does Claude Pro compare to just using the free tier + API pay-as-you-go?

  4. For someone not yet building production systems — is Pro the right tier, or should I just use the free tier + save up for API credits when I need them?

No hype — I want to know if it moves the needle for actual building and learning.

reddit.com
u/Curious-Green3301 — 20 days ago