
Learning Daily | Clicking buttons @N3RDROOF | @Pythnetwork Maxi
Blips and Chitz
Joined May 2022
- Tweets6.3K
- Following1.2K
- Followers1.1K
- Likes4.5K
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I built a DVR for financial markets using Pyth Pro.
Markets move in milliseconds but most tools only show the final chart after everything is aggregated. When volatility spikes or spreads explode it is almost impossible to inspect the exact sequence of events.
Market DVR records the market so it can be replayed frame by frame.
Live at dvr.masterwattson.site
Wattson retweeted
BREAKING: Pyth is now an external distributor of Nasdaq Basic.
@Nasdaq's real-time quote and trade product for U.S. equities, distributed through the Pyth Data Marketplace 🧵
Quick clarification I’ve been getting DMs about an NFT project after I was mistakenly identified as its founder.
I’m not the founder and I’m not affiliated with the project or team in any way. Just a mix up, please disregard 🙏
This is exactly why casinos should use @PythNetwork’s Entropy (decentralized verifiable randomness) instead of opaque private seeds.
Proper on chain entropy would prevent the house from previewing and serving losing outcomes
I was invited into a private, "provably fair" VIP game at a crypto casino (@Winna / Winna.com) 🚨
I lost $7,299,672 ($7.3M) - 61 of 62 bets.
Then the casino's own owner confirmed, in writing, it was possible to hand me an unfavourable seed pair.
This isn't a callout. It's one fair ask: @Winna, prove the game was provably fair - or refund my money.
Here's all of it 🧵
(Read tweet 11 for technical evidence + context and a suspicious amount of coincidences - Expanded case in Github)
Wattson retweeted
Took 5 years but here we are
Blockchain oracles are dead
Long live (1st party) financial data providers
Pyth keeps expanding beyond crypto-native assets.
Cocoa, Coffee, Raw Sugar, and Live Cattle futures feeds are now live on Pyth Pro.
Softs, grains, livestock, energy, metals, crypto, FX, equities one market data layer for builders who need real-world prices onchain.
Built Walrus Feedback for @WalrusProtocol Sessions.
It’s a Walrus-native form + feedback platform with walletless submissions, Walrus storage, Sui provenance, admin triage, CSV export, team roles, and optional Seal-encrypted private responses.
Live app: form.masterwattson.site
Wattson retweeted
The Pyth Community Hackathon is complete 🔮
A 6-week global build sprint exploring what becomes possible when real market data is open, accessible, and programmable.
The result: high-quality submissions across trading, analytics, prediction markets, and some weirdness in between.
Proud to announce the hackathon winners below:
🥇 Ouroboros - @Pstar
🥈 Pyth Correlation - @xzolmoney
🥉 Market DVR - @masterwattson
🏆 Most Creative Use of Pyth Pro - @scp_web3
🎓 Best Educational Content - @droo4you
The Pythians turned up, got creative, and built with impact.
Wattson retweeted
our boi @masterwattson built a dopamine simulator for financial markets.
83,000 data points archived. Every Pyth Pro tick across 16 assets — crypto, forex, commodities — with millisecond timestamps.
Shit your pants at 3am with a god candle? Rewind it frame by frame. First party data doesn't lie.
dvr.masterwattson.site
I built a DVR for financial markets using Pyth Pro.
Markets move in milliseconds but most tools only show the final chart after everything is aggregated. When volatility spikes or spreads explode it is almost impossible to inspect the exact sequence of events.
Market DVR records the market so it can be replayed frame by frame.
Live at dvr.masterwattson.site
Wattson retweeted
Introducing Pyth Pro X.
Institutional market data built for exchanges.
One subscription. Every asset class. No exchange fees, no redistribution barriers, no vendor lock-in.
Powering @coinbase, @cryptocom, @tradexyz, @Dreamcash, @bitget, @BitMEX, @LMAX, and others.
Wattson retweeted
🚨 BREAKING: Pyth has secured $100B in volume on Hyperliquid
Always-on, 24/7 markets are powered by Pyth on @tradexyz and @Dreamcash
Pyth Pro makes this possible because of the resolution and the confidence signal.
High frequency updates combined with confidence data allow the system to capture market structure changes that normal feeds smooth out.


















