Suhrid Mathur

Software Engineer @ShareChatApp

[Suhrid Mathur @MathurSuhrid]

@cards_wizard Couldn't agree more.

[Suhrid Mathur @MathurSuhrid]

@YugalGarg6 @ccg33k @CardNiti @AmazingCreditC I was in the same boat too, but closed Gold Charge. MRCC at least has a fee waiver, and 20K/month gives ~2.8K points, effectively making it free by year-end. Gold’s 5X looks lucrative, but doing 6×₹1K txns every month is a headache IMO. Plus, Amex isn’t waiving fees.

[Suhrid Mathur @MathurSuhrid]

@wittysiddharth Disneyland day in Hong Kong last year.

[Suhrid Mathur @MathurSuhrid]

@vaibhavluthra Go Weligama/Mirissa 4N & Colombo 2N. Did the same last year unrushed, loved it.

[Preetam Rao @Preetam_M_Rao]

Lol these South Indians elect film actors as their CM. Such low IQ behaviour.

They should learn from us. We have elected Gangsters & Crime Lords as our ministers & CMs.

[Dr. Rajeshwari Iyer @RajeshwariRW]

Bro became Chief Minister.

• His assistant is an IAS officer.
• His advisor is a CA
• His peon is a graduate.

But the Chief Minister himself is illiterate.

Long live democracy

[Suhrid Mathur @MathurSuhrid]

@luxe_explorer Earns 5 Points on Atlas?

[Suhrid Mathur @MathurSuhrid]

@ccg33k @wittysiddharth He had finally recovered from the trauma, and now you sending him right back into it again.

[Suhrid Mathur @MathurSuhrid]

@FinPaal ICICI Sapphiro; Only useful 1+1 movie benefit is also conditional now 😪

[le.hl @0xleegenz]

Walking alone through a foreign city at night and realizing how far you’ve come has to be a top 3 peak moment of all time

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Suhrid Mathur @MathurSuhrid]

@jassneetsingha They’re in completely different leagues, one requires a high TRV relationship or salary account with HSBC, while the other is a mid-tier travel card in the ₹5K segment.

[Suhrid Mathur @MathurSuhrid]

@dhimantn Lol, just last week transferred all my Axis points to Accor.

[Victoriano Izquierdo @victorianoi]

In 20 years, vibe coders will look at the Linux kernel repo the way we look at the pyramids. In awe, unable to imagine how they managed to drag all those giant stones and pile them up in the middle of the desert.

[Yuchen Jin @Yuchenj_UW]

This is so hilarious.

Nothing can make Sam and Dario hold hands, not even the Prime Minister of India!

[Jay @thetrickytrade]

Don’t forget to pay your Advance Tax by March 15.

[Suhrid Mathur @MathurSuhrid]

Imagine Amex redeems itself by adding accor as transfer partner.

#Amex #PlatTravel #AmexPlatTravel

[Dr. Sheetal yadav @Sheetal2242]

• Anurag Thakur monthly Salary - 1,24,000

• Anurag Thakur Belt Price - 86,000

Anurag Thakur is wearing a belt worth people's monthly salary.

And this is the same person who used to question the price of Rahul Gandhi's T-shirt.