The New Era Of Price Discovery

https://x.com/thelearningpill/status/2057044392075313152

ggwp to those who sold their points for $5 to 7

now its going till $30 to $50 per 1 $VAR point, after $61.8m raised, especially after now that perp dex meta is back:
- $HYPE reached ath, flipped $SOL in fdv
- $LIT listed on robinhood, both pumping

however, @variational_io still early, tge likely q3, q4

please run your own numbers but here's mine:

- current issued points: ~6.15M
- weekly issuance: ~150K
- tge expected: Q3-Q4 2026 (~21 weeks out)
- projected total points at TGE: ~9.3M
- projected FDV: $800M-$1B+
- at 25% airdrop allocation and $800M FDV: ~$21.50 per point base case
- points OTC was clearing $5-7 just weeks ago.
- after the $50M @dragonfly_xyz raise, that floor looks like it will get repriced fast
- $200m mc with an avg of $3m fees per month/$36m annualized, gives us a 5.55x MC/fee multiple.

here's one of the strategies i employ for max points:
- team clearly weights OI heavily, 5% of weekly volume typically translates to less than 0.5% of weekly points share.
- find a small-cap market with ~100K open interest
- open a 50K long
- hedge with a short on a correlated asset
- hold delta-neutral for the week
- rotate into new pairs the following week

with RWA markets now live, the same strategy extends to TradFi assets, long a stock spot, short the perp on Variational, collect the spread and the points simultaneously.

it's +EV three ways: the trade, the yield, and the airdrop.

use my ref code: for 12% more boosted points + bronze ranking: https://omni.variational.io/?ref=OMNINOX

https://x.com/arndxt_xo/status/2057383640804540670

Crypto was never for us

https://x.com/kaiski_kta/status/2057417327998844990

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw.

The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each.

1. Jane Street, janestreet/magic-trace
https://github.com/janestreet/magic-trace
5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction.

2. Goldman Sachs, goldmansachs/gs-quant
https://github.com/goldmansachs/gs-quant
Derivative pricing the GS traders use at their desks. MIT licensed.

3. JP Morgan, finos/perspective
https://github.com/finos/perspective
What JPM traders use to watch markets in real time. A $24k/year terminal, for free.

4. BlackRock, blackrock/lcso
https://github.com/blackrock/lcso
Rust optimizer for portfolio problems. Where scipy gives up, this works.

5. Hudson River Trading, hudson-trading/corral
https://github.com/hudson-trading/corral
Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms.

6. Two Sigma, twosigma/flint https://github.com/twosigma/flint
Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks.

7. D.E. Shaw, deshaw/pyflyby https://github.com/deshaw/pyflyby
Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself.

https://x.com/zostaff/status/2057141924486864926

PRESIDENT TRUMP IS EXPECTED TO HOLD A CALL AT 1:00 P.M. ET WITH LEADERS OF PAKISTAN, THE UAE, SAUDI ARABIA, QATAR, JORDAN, TURKEY AND EGYPT, REGIONAL SOURCES TELL ASHARQ NEWS

Full focus here, bidding hard on the recent dips

https://x.com/midoji7/status/2058218674503176347

Do something different this weekend.

Become a PRO in AI Model Fine-tuning.

Paste this prompt in Codex/ChatGPT/Claude/Grok.

"You are an expert AI engineer and teacher.

Your job is to teach me modern LLM engineering and fine-tuning concepts from beginner to advanced level using very simple daily-life language.

Teach me step-by-step like a real mentor. Assume I am smart but new to the topic.

https://x.com/cjzafir/status/2058281787743445416?s=46&t=hr2fbvcHJGpvp_SbDstdzg

So there’s a chance to front-run gems before the Bankr Fund invests in them. 🤣

https://x.com/0xluo/status/2058273375429230716?s=46&t=hr2fbvcHJGpvp_SbDstdzg

A guy named nbatman on Reddit accidentally built the most useful website on the internet.

It's called FMHY (Free Media Heck Yeah).

This is the website Google delisted from search for DMCA violations, Reddit shadow-banned for promoting piracy, the Motion Picture Association flagged as a top piracy threat, and the RIAA pressured hosting providers to drop. It is still online. It is still updated every month.

https://x.com/ihtesham2005/status/2058221813797446075?s=46&t=hr2fbvcHJGpvp_SbDstdzg

8 free GitHub repos for trading on Polymarket…

https://x.com/recogard/status/2057891150300623320?s=46&t=hr2fbvcHJGpvp_SbDstdzg

i am a proponent of privacy very simply because its the counter intuitive thesis to tightening regulations.

$ZEC
has led the privacy season but there are a lot more projects that are building in the privacy space.

S tier:
$ZEC
- the bitcoin for privacy outperformed every asset
$NIL - humanity first blind computer
$NEAR
- one of the strongest architectures for AI Agents right now, agentic payments, low fees, chain abstraction
@Grayscale
onboard

A tier:
$CC
- institutional private chain, huge beneficiary of regulation
$AZTEC - leading privacy-first Layer 2 on Ethereum
$OCTRA - open-sourced the core HFHE implementation with public benchmarks

B tier:
$H
- zk verification of proof of humanity
$STRK
- zk execution Layer scaling Ethereum
$ROSE
- makes any application private and verifiable, no matter where it runs

C tier:
$DUSK
- privacy-preserving blockchain infrastructure for institutional-grade finance

https://x.com/arndxt_xo/status/2058962785518924159

https://threadingontheedge.substack.com/p/the-privacy-bid

Sectors I'm bullish on:

https://x.com/OnlyHades_/status/2058945900530241728

Introducing: Prediction Market Atlas

What most people see in prediction markets is only the surface.

The categories now shipping include futarchy, distribution markets, multiverse markets, opinion markets, attention markets, decision markets, and info-finance.

Each has its own pricing logic, regulatory lane, and audience. PM Atlas maps that landscape across 108 platforms, 12 mechanisms, and 90 concepts, from industry structure down to the actual products being built.

A way to track who is building what, how these markets differ, and where the next primitives are emerging.

http://pmatlas.xyz

https://x.com/embrron/status/2059372115263094901

news arb is fully exploited in tradfi but prediction markets have a much lower bar

one second-order effect of pms might be that we'll see way more boutique quant firms as the entry bar lowers

imagine how many college students are building polymarket bots from their dorms rn

https://x.com/iatskar/status/2059330774004805972?s=46&t=hr2fbvcHJGpvp_SbDstdzg

I think this is partially why @OpenRouter just raised $113M+ by @CapitalG

If you are an inference provider you can raise a ton of money now off revenues and amass capital to buy GPUs. You can use these GPUs to offset chinese models going closed source by using them for model training/fine tunes in the future

Inference provider revenues are driven by deploying open source models in an easy and safely accessible way. You can pay 1/100 to 1/1,000 the cost to access GLM/Qwen etc vs centralized APIs and not send your data directly to chinese APIs. Yay I get cheap intelligence and China doesn't see my full conversation history on giving my friend breakup advice.

You leverage that revenue growth (API spend) to buy a capital asset (GPUs) for the future If @alexatallah buys GPUs with this $113M it solves two things. The first is he can lower inference costs even more through owning the hardware (and get a nice multiple on it and take loans against them, and repeat) and second he could use those models for training runs or fine tunes in the future if China goes fully closed source

https://x.com/shaughnessy119/status/2059391793679003987?s=46&t=hr2fbvcHJGpvp_SbDstdzg

This lawyer made $150,000 selling portable offline AI.

It analyzes docs that can’t legally be shown on the web.

The whole setup costs $50 and he sells it for $999.


https://x.com/coinsh0t/status/2059316915688906804?s=46&t=hr2fbvcHJGpvp_SbDstdzg

AN INDIAN DEV JUST KILLED THE ENTIRE "DOCUMENT PARSING API" INDUSTRY.

PDFs. PowerPoints. Word docs. Images. Videos. Audio. Web pages. One repo eats all of it and spits out clean Markdown ready for your RAG pipeline.

It's called OmniParse. Built by Adithya S Kolavi. 6.8K stars. 537 forks. Sitting on GitHub. Free.

Unstructured.io raised $65M to do this. LlamaParse charges per page. AWS Textract bills you by the request. Azure Document Intelligence wants $1.50 per 1,000 pages forever.

https://x.com/sukh_saroy/status/2059523822441939308?s=46&t=hr2fbvcHJGpvp_SbDstdzg

Bro, this shit is wild!

A Chinese dude on GitHub whipped up a free "money printer" called MoneyPrinterTurbo, with over 13,000 stars, still getting updates 44 minutes ago—it's blowing up.

You toss in a keyword, and it auto-writes scripts, scrapes high-def copyright-free footage, adds subtitles, music, and voiceovers, spitting out a full short video on the spot.

Isn't this the exact full pipeline for raking in $6K to $10K a month on TikTok Shop and faceless YouTube channels? They pay for tools, you get it for free.

1️⃣ Hook up Claude/DeepSeek/GPT—pick your poison
2️⃣ 9:16 vertical or 16:9 horizontal, one-click switch
3️⃣ Batch generate and cherry-pick the best
4️⃣ Runs local, Docker, Colab—all supported

No watermarks, no subscriptions, no limits. What are you waiting for?

Link:

https://x.com/nftcps/status/2059447582230000124?s=46&t=hr2fbvcHJGpvp_SbDstdzg