As said by the title, I'm curious as in how good or bad is it for data to be centralized and aggregated to corporate AI giants like Anthropic and other ai companies

Hi everyone,

I want to learn cloth simulation from the ground up and eventually implement it myself. Could you suggest a roadmap of the topics I should study (math, physics, numerical methods, collision handling, simulation techniques, etc.) and recommend the best resources (books, papers, courses, tutorials, or open-source projects) for each?

Any guidance on what to learn first and what to avoid would be greatly appreciated. Thanks !

They loudly claim to be defenders of human creativity, subjectivity, and original thought — yet their arguments are strikingly uniform. Same talking points, same buzzwords, same outrage. It’s not a diverse range of personal perspectives; it’s mostly regurgitated algorithmic slop fed to them by their own social media bubbles.

The real irony? They’re getting played by the exact same algorithmic logic they claim to hate in AI. Their feeds optimize for engagement, pushing simplified, emotionally charged narratives that flatten nuance and reward repetition — just like the training data pipelines they criticize.

They rail against “soulless machines” while outsourcing their own thinking to recommendation algorithms. The hypocrisy is almost poetic.

Also Duckduckgo DuckAI: A wrapper around OpenAIs gpt 4.0 , wherein despite their claims for having metadata of your prompts stripped off of your identifiable personal identity, YOUR PERSONAL PROMPT DATA STILL GOES GOES TO THE OPENAI

So fucking hypocrites IF THEY WERE SO WORRIED ABOUT PERSONAL PRIVACY THEY COULD'VE FINE TUNED AN OPENSOURCE MODEL AND MAKE WRAPPER AROUND THAT BUT NO!!!!, IT'S ALL ANTI AI TRIBALISM based engagement farming and nothing else, like as in hating ai has become an identity , many people don't even have their own ideas of hating ai in general as well and also why even hate and not having a holistic debate

I cleared PGEE and interviewed for MS Research in CSE, targeting vision/ML-focused labs. The process tested research aptitude, conceptual depth, and clear communication more than rote syllabus recall. Panels started with projects, then drilled into fundamentals with board work, always asking for intuitions, derivations, and connections to real problems. The emailed topic list (one from each pool) was flexible. This write-up is especially relevant for CVIT, MLL, and similar DSAC labs, where applied ML/CV + core CSE thinking dominate. Here's the detailed day, drawn directly from what happened.

CVIT Lab Interview (~25-35 minutes)

First slot of the day. The panel had a strong vision/applied ML bent and spent significant time on projects before moving to concepts.

1. Projects: Heavy focus on YOLOv-based hazard detection for steel plant CCTV (large annotated dataset, live deployment). They asked about challenges (lighting, conditions), design decisions, and improvements—real production experience helped steer the conversation positively.
2. On-Board & Key Questions:
   • Logistic Regression: Full explanation and derivation on the board (model, loss functions, GD updates).
   • Regularization: Explicitly asked about Ridge and Lasso (including why Lasso promotes sparsity).
   • Optimization: Gradient Descent, Batch GD vs. SGD, Convex functions.
   • Other: Bias-variance elements and algorithmic thinking (e.g., independent set to clique reduction after working through it).

The vibe was probing but fair—they wanted to see why things work and how you'd apply them. Strong project grounding and derivations helped; some nervousness showed early in delivery.

MLL Lab Interview (Same Day, ~30 minutes)

Second interview. More theory-heavy ML focus, building on the warmed-up state from CVIT.

1. Projects: Discussed tabular ML/forecasting work, including reflections on method choices (e.g., symbolic regression as a potential alternative in one case). They appreciated the self-critique.
2. On-Board & Key Questions:
   • Logistic Regression: Another strong derivation on the board (Cross-Entropy loss, gradient finding, full pipeline).
   • Other ML: Bias-variance tradeoff.
   • Weaker Area: SVM basics (bluffed through it—visible to the panel).

They emphasized intuitions, trade-offs, and research thinking. The logistic regression part stood out positively again.

Cohort Experiences from the Interview Day

While waiting, others shared snippets:

1. One was asked to implement integer square root using binary search on the board (clean, efficient solution expected).
2. Common across the batch: Project deep-dives + mixed fundamentals (regression, regularization, optimization). Panels adapted questions to the candidate's background and chosen topics.

Preparation Tips (Hard-Earned Lessons)

Start early on lab-relevant topics—don’t repeat my mistake of finishing core material fairly late, which meant rushing and missing depth on several areas. Gold resources that helped massively (even if late): CampusX ML/DL playlists for intuitive explanations of regression, optimization, backprop, and practical ML. Watch them actively—pause, derive alongside, and implement small examples.

Targeted Advice for CVIT/MLL/DSAC Labs:

1. Must-Know Cold (Board-Ready): Logistic Regression full flow (including Cross-Entropy vs MSE, GD derivations), Ridge/Lasso (geometry and sparsity), Bias-Variance, GD variants (Batch vs Stochastic), Convex functions.
2. DSA/Core CSE: They mix GATE-level stuff in applied/problem-solving ways rather than pure theory. Expect graphs, trees, shortest paths, DP reductions (harder topics) over basic stacks/queues. Practice explaining solutions aloud.
3. Projects: Treat them as your strongest asset. Prepare stories on challenges, trade-offs, and "what if" improvements.
4. Practice Style: Explain everything out loud as if teaching a panel. Record yourself. Do mocks under time pressure. Verbalize thinking during tough spots.
5. General: Nerves affect delivery—breathe and own what you know. One or two weak answers (like SVM) don’t sink you if projects and core strengths shine. Post-interview, avoid immediate peer comparisons.

Overall Feel & Outcome: The interviews felt fair and research-oriented. They rewarded genuine applied experience and clear thinking over perfection. Selected for CVIT. For future candidates targeting these labs: Prioritize early, deep prep on ML fundamentals + your projects, practice board explanations relentlessly, and show reflective problem-solving. It’s challenging but very much doable. Good luck!

Research Syllabus Link- https://docs.google.com/document/d/1W_QpFFnc_DiTQhzS9TjqqAYq6xqfDh9MaT5qbgLwtUY/export?format=pdf

Finally after months of uncertainty and poor performance in gate and almost losing hope on pgee, I managed to convert the mtech into ms degree and completed my agonizing preparation for core to cse degree switch for the year 2025-26 , I don't wanna go into my personal story specifics ,but financial situation in my home weren't good and my father too had a stroke in 2024 end and i had failed in placements coupled with my personal life stuff related to bullying and social starvation, i was emotionally and mentally broken, but after all this I feel like I'm finally safe and not in an uncertain stage, if anyone is interested in the interview experience , I'll summarise it and post on other relevant platforms like goclasses/gfg and can also talk about the research perspectives as well as that is more aligned to me personally

Guys how are the courses and the placements in ME in Software Systems at Bits Pilani, i haven't seen much data about it recently and wanted to know about it as I was considering it

man like this is straight up cruel, getting 276 rank at 53.25 marks while last year a person could've easily cleared admission in 1st round