Ankit Kumar

Building @ml_xero
Compliance observability for every AI agent in finance
IIT Bombay Alum

[Malav @malav399]

Compliance stack isn’t ready to tackle the speed agentic finance will move speed.

Compliance and broadly cannot be reactive.

We change it to preventive.

https://t.co/2MciVDG7F8

[Ankit Kumar @a_kmrx]

@isaac_tolley17 Building Xeroml that helps banks and fintechs keep their financial AI agent decision within the rules and compliant and ready for audit.

[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.

[Ankit Kumar @a_kmrx]

@shivambhadani_ tech people are too busy fixing the stuff marketers are writing threads about

[Ankit Kumar @a_kmrx]

@hamptonism this looks like duolingo got a phd in linear algebra

[Ankit Kumar @a_kmrx]

@signulll openai bout to get judgeGPT’d by 12 people who still think “chatgpt” means google with extra steps

[Ankit Kumar @a_kmrx]

@kimmonismus months behind? thats just a firmware update away

[Ankit Kumar @a_kmrx]

@signulll fighting for a “good job” in an economy built on bad ones is the funniest dystopia ever

[Ankit Kumar @a_kmrx]

@Adriksh maybe the real hack was ignoring every buzzword and still ending up running the buzzword machines

[Ankit Kumar @a_kmrx]

@theJayAlto so just 2 weeks of hyperfixation and i can quit my job got it

[Ankit Kumar @a_kmrx]

@gokulr vertical ai founders about to learn the hard way that slope > moat

[Ankit Kumar @a_kmrx]

@0xDesigner people out here treating AI copilots like a calculator when it's literally a genie with version control

[Ankit Kumar @a_kmrx]

@Codie_Sanchez same, it's like my weekly reality check and ego destruction session in one chart

[Ankit Kumar @a_kmrx]

@almonk somehow every mac dev hits that point where they rebuild xcode’s tabs but sexier

[Ankit Kumar @a_kmrx]

@TheGeorgePu quiet money > loud panels

[Ankit Kumar @a_kmrx]

@0xgaut peak 2026 productivity is letting the ai code, design, argue with itself and hoping one of them ships something before monday

[Ankit Kumar @a_kmrx]

@ChShersh waiting for the CTO to say “we’re Claude native now” like it’s a real architecture shift

[Ankit Kumar @a_kmrx]

@chrisgpt devs bout to go from coding to prompt engineering full time once that thing hits max speed

[Ankit Kumar @a_kmrx]

@HSVSphere bluetooth was clearly designed by someone who never used a mouse mid-fight in apex

[Ankit Kumar @a_kmrx]

@SahilBloom fun is the cheat code they forgot to mention in business school