AnonTrades

9+ years Profitable Daytrader | I rake in the Cash drawing Lines. Partner @Binance | @WEEX_Official best non-KYC CEX |

[AnonTrades @Anonsune]

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#Crypto #BTC

[Packy McCormick @packyM]

Mom how did we get so rich?

Back in 2026, your dad read the YC Request for Startups and just built one of the ideas on the list.

[Bold @boldleonidas]

CT is a lot of poor people pretending to be rich.

Rich people pretending to be poor.

(And one rich person pretending to be a cat).

[Gwart @GwartyGwart]

There’s not even going to be any money left for quantum computers to steal

[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.

[AnonTrades @Anonsune]

@Trader_Dante Cunt

[Lunix @SolLunix]

"When someone asks why i started trading"

Me:

[Bags @0xbags]

the ico implosion
covid crash
terra collapse
nft mania
ftx bankruptcy
10/10
2026 sell off

and I'm still fucking here

[Tom Dante @Trader_Dante]

When it comes to the age old question of whether to trade multiple markets / setups or specialise in one, remember: “A jack of all trades is a master of none but oftentimes better than a master of one”

[ZachXBT @zachxbt]

A short story.

Shame on all of the projects that ran AI slop campaigns via InfoFi platforms.

The inorganic activity / fake metrics was obvious if you have common sense and it made X borderline unusable for everyone else.

[Bold @boldleonidas]

Tap the post to pump.

[AnonTrades @Anonsune]

@ASvanevik True

[Aporia @0xaporia]

I believe one of the better ways to improve CT is quite simple: post when you actually have something meaningful to say. Most people don't follow this principle, but it's how I approach it. That's why I don't post daily, I don't always feel I have something worth articulating or capturing for eternity.

I view Twitter as a tool, almost like a public journal. It's a space where I can document thoughts that matter to me, and where others can either challenge me when I'm off track or validate when I'm onto something. I only share what feels worth preserving; ideas and observations I genuinely want to capture.

[AnonTrades @Anonsune]

If you can’t survive one week cutting losers and letting winners run, you’re not ready to scale.

[AnonTrades @Anonsune]

Profit is patience taxed at the right time.
The market doesn’t owe you conviction.

[AnonTrades @Anonsune]

Small losses compound into large lessons. Keep the size small so you can learn large.

[Watcher.Guru @WatcherGuru]

Bitcoin Price on New Year's Day:

2010: free
2011: $0.3
2012: $5
2013: $13
2014: $770
2015: $314
2016: $434
2017: $1,019
2018: $15,321
2019: $3,794
2020: $7,193
2021: $29,352
2022: $47,025
2023: $16,630
2024: $42,660
2025: $93,500
2026: $87,500