Maybe A Skull @maybeaskull

maybe a skull. maybe a coder. maybe a thinker. maybe a player. maybe a singer. Joined June 2025

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116
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31
Following

945
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157

Tanisha Pritha @tpritha03

2 days ago

You can pick only one path for the next 5 years. What are you choosing? A) Build your own SaaS B) Join an early-stage startup C) Work at Big Tech D) Freelance/consult E) Start an agency

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Maybe A Skull @maybeaskull

3 months ago

@eattmilo Do men enjoy eating pussy?

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Maybe A Skull @maybeaskull

6 months ago

@ManhoodDecoded Would love to see a gender inversed version of this picture LMAO

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Maybe A Skull @maybeaskull

6 months ago

@praisejohnn Gurl stop objectifying men 😭

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Maybe A Skull @maybeaskull

6 months ago

@sunflower_babz Psychopath

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Maybe A Skull @maybeaskull

7 months ago

@caraksha103 Marriage is a social construct and overrated

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Maybe A Skull @maybeaskull

7 months ago

@Anshgrover23 @100xDevs @kirat This picture is AI generated

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Palak @Palakonweb

8 months ago

Building in public.

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Humi @byteHumi

8 months ago

Gm! Wealth can be inherited Status can be faked Power can be negotiated But tell me why ppl uses SLACK

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Maybe A Skull @maybeaskull

8 months ago

@thealokverse There's a whole ass ragebait on this

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Maybe A Skull @maybeaskull

8 months ago

@Divyansh91565 I love 3sum

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Traditional RAG vs. HyDE, visually explained! RAG is great, but it has a major problem: Questions are not semantically similar to their answers. Consider an example where you want to find context similar to "What is ML?" It is likely that "What is AI?" will appear more similar than "Machine learning is fun." This semantic dissimilarity leads to several irrelevant contexts being retrieved during the retrieval step. HyDE solves this issue. The visual below illustrates how this approach differs from RAG. Here's how it works: - Use an LLM to generate a hypothetical answer (H) for the query (Q). This answer doesn't have to be entirely correct. - Embed the answer using a contriever model to obtain E. Bi-encoders trained with contrastive learning are generally used for this purpose. - Use the embedding E to query the vector database and retrieve relevant context (C). - Pass the hypothetical answer H, the retrieved context C, and the query Q to the LLM to produce a final answer. Done! Now, of course, the hypothetical generated will likely contain hallucinated details. But this does not severely affect the performance due to the contriever. More specifically, this model is trained using contrastive learning, and it also functions as a near-lossless compressor whose task is to filter out the hallucinated details of the fake document. This produces a vector embedding that is expected to be more similar to the embeddings of actual documents than the question is to the real documents. Several studies have shown that HyDE improves the retrieval performance compared to the traditional embedding model. But this comes at the cost of increased latency and more LLM usage. I have shared a hands-on guide to HyDE by Milvus in the replies. 👉 Over to you: Have you used HyDE in production yet? ____ Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

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Kushal Bhagia 🇮🇳 @kushalbhagia

8 months ago

Met a founder today who sold his company for ~$150M in cash in his early thirties. Only raised like a $2-3M seed. Very under the radar type guy. Grew up in a village in Rajasthan. Parents are still farmers. New India’s stories are insane. 🔥

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Arpit verma @arpit_vermaniac

8 months ago

Always remember - - India below poverty line cutoff: 35k/annum. - Twitter India below poverty line cutoff: 30LPA - Twitter India BLR below poverty line cutoff: 50LPA

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Vineeth K @DealsDhamaka

8 months ago

About ten years ago, my father lent ₹8 lakh to someone in need. It was a huge amount back then. In 2015, I got an admit from a top university in the US, but I couldn’t go because we didn’t have enough funds (70L) to support . I eventually did my MBA in India, thankfully partially sponsored by my then employer. So rest of the money(8L) which was saved for my higher education, but was give to help another family send their child to study in Canada as my MBA was sorted A couple of years later, my father passed away. Our family was left with debts and surrounded by financial struggles. We took several loans for my brother’s education, my wedding, and our household needs. It took us years of hard work to clear everything. For almost 8 years, 92 months, my mother kept calling that family, hoping they would return the money, it would have really helped us. Their child finished education, settled in Canada, got married, and is doing well now. He bought his first house in Canada now. I’ve told my mother not to ask them again. I like to think my father’s kindness helped another person build their life, and that’s a blessing in itself. ❤️

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shydev @shydev69

8 months ago

you cannot be in 20s, have a work life balance, and survive in a country with 150 crores population especially if you don't come from money. there is a scarcity of high-paying entry roles. millions have the same degree as you. if you relax, someone else is dying to replace you.

Prasanna S @myprasanna

8 months ago

It’s 10pm and the entire office is alive. No one told them to stay. Blessed to be working with a team of owners. We are looking for great people to join us!

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Maybe A Skull @maybeaskull

8 months ago

@mischavdburg Linux

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Maybe A Skull @maybeaskull

8 months ago

@pankajkumar_dev Brothaaa which internship?

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Maybe A Skull @maybeaskull

8 months ago

@Pvt_insaann @caraksha103 What's the point of living properly if you can't even make 10 LPA 🤓

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Maybe A Skull @maybeaskull

8 months ago

@salujamehak5 3 years of mechanical engineering and I can solve medium array+strings questions (working on trees and DP) and have average projects in genAI. No internships tho. Too bad?