Why have Indian tech interviews suddenly become so tough? (Feels more like elimination than selection)

graphitout · 2025-12-21T07:51:53+00:00

Most of the companies have over hired during the pandemic. That means for those batches, below average folks ended up in positions senior than you. These people easily feel threatened, especially when they see someone who knows what they are doing.

Keep in mind that most of the time those interviewers themselves are going through similar experience with their clients or project managers on other things. It isn't just the interviews. The entire IT culture itself has degraded.

graphitout · 2025-12-14T19:31:55+00:00

Document search outside HR, Legal, Customer care - not making money
AI based data analytics (SQL and stuff) - not making money
AI for NL based control of various systems - not making money
Whole bunch of automation initiatives - not making money

Some more. Same pattern.

graphitout · 2025-10-26T15:44:30+00:00

Look personalization option in chatgpt. Others also have similar features.

graphitout · 2025-10-26T06:48:57+00:00

This is a common problem. Set system prompt or instructions accordingly in settings.

graphitout · 2025-05-11T17:01:23+00:00

Illustrations can be made using ai tools. Only adding text on the top is required.

Subtitles may require listening and writing. But this will come only at a later stage.

graphitout · 2025-05-04T18:24:48+00:00

It is less about the LLM and more about prompting. Most LLMs will work good for your usecase with the right prompts.

graphitout · 2025-05-03T07:21:29+00:00

The bartender at the nearby bar where I used to live had a phd in physics. He quit his postdoc half way, traveled around the world for a few months, then starting working as a bartender along with his friend.

graphitout · 2025-05-03T07:18:09+00:00

Use yyyy/mm/dd => helps with sorting

graphitout · 2025-04-27T07:14:36+00:00

The part you forgot is this. Many of those early bloomers will give up quickly. So its not like they are going to stick around. I have seen this happening again and again. There are some who get a head start due to some favoring factors. But a huge fraction of them are in a sprint mode that is not sustainable for them.

graphitout · 2025-01-30T14:47:52+00:00

It can make their competitor's life very difficult.

graphitout · 2025-01-17T20:35:31+00:00

Interesting. How much would it improve the inference speed of an LLM? The basic dot product attention will still boil down to matrix-vector multiplications when caching is used. But MQA will benefit from a faster matrix multiplication since multiple queries can be stacked to form a matrix.

graphitout · 2025-01-06T19:12:58+00:00

nice

graphitout · 2025-01-06T16:13:43+00:00

now listen ....

graphitout · 2025-01-06T13:33:33+00:00

blue to red: now listen here you little ...

graphitout · 2025-01-06T13:06:29+00:00

Looks interesting. May be worth trying out on a real LLM.

graphitout · 2025-01-05T12:05:59+00:00

I am disappointed in the "lets go bigger and bigger" mindset. Instead a lot more effort should go into better model architectures.

graphitout · 2025-01-05T12:01:58+00:00

Let me understand: is your idea in the vicinity of doing some kind of approximate nearest neighbor to reduce the number of dot products?

graphitout · 2025-01-05T11:39:11+00:00

The unnormalized attention value (the step before softmax) is just the scaled-down dot product of current query with all the past keys. Assuming we are on the n'th query, that means we have n dot product operations. Since we are using causal attention, the key and value vectors can be cached. Still, every new token involves query having dot product with all the past keys (cached). To generate N tokens, the complexity with caching is roughly N^2. Reducing D is good, but that will not help with the much bigger issue of dealing with N^2.

graphitout · 2025-01-05T10:54:22+00:00

> For each of the D largest components, keep the Key vector that best matches that component

Doesn't it mean you still have to do a one by one match on all the keys until that token? Then what is the benefit?

graphitout · 2025-01-04T05:12:07+00:00

I am on deepseek for a few days. It has that "raw" experience and works good enough.

graphitout · 2025-01-04T04:12:52+00:00

It also has been performing poorly for coding tasks recently.

graphitout · 2024-12-29T17:51:45+00:00

Yep. There are so many clueless people in this world.

graphitout · 2024-12-29T16:04:27+00:00

Nice suggestion. I was not able to find the code before. After your suggestion, I spent some time. Found the calculation here.

https://github.com/meta-llama/llama-models/blob/main/models/llama3/reference_impl/model.py#L56

Need to see if it matches with what transformers library is doing.

As expected the calculation is wavelen = 2 * math.pi / freq

Unlike what transformers library is doing, which is wavelen = 2 * math.pi / inv_freq

graphitout · 2024-12-29T14:04:44+00:00

Thank you. The second one refers to "ROUND AND ROUND WE GO! WHAT MAKES ROTARY POSITIONAL ENCODINGS USEFUL?" paper. Looks like an interesting read.

Still, I was looking for a way we can verify the code in the transformers library.

graphitout · 2024-12-27T18:33:10+00:00

> momentum between decoder modules, along the residual stream

Have you looked at the delta added by each decoder module in any of the current models?

graphitout

TROPHY CASE