Not having enough $PYTH in the big 2026 should be considered a red flag atp

Brazil’s Election Night Brought a Spike in EWZ Perp Trading EWZ is a US listed ETF that tracks Brazilian stocks. Even while Brazil’s stock market was closed, traders were still trading EWZ perpetual futures, which let them trade its price movements without buying the ETF itself. As election results came in hourly perp volume rose from $1M to $52.8M between 19:00 and 00:00 UTC. A total of $218M was traded from poll close to the US morning. Pyth provided pricing through its 24/5 EWZ feed and 24/7 EWZ index, with 99% of the perp volume priced on Pyth.

Pyth Pro has extended post-market pricing to four Korean stocks: SK Hynix, Samsung Electronics, Hyundai Motor and Samsung Electro-Mechanics. Their prices now update from 16:00 to 20:00 KST, adding four hours beyond Seoul's regular 09:00 - 15:30 session. That matters for RWA perps, which keep trading after the underlying stock market closes. Memory stocks accounted for 43.5% of August's RWA perp volume, with SK Hynix and Samsung among the most traded. With Pyth keeping prices updated beyond regular market hours, traders have better access to price data while these markets remain active. Pyth Pro now has 18 Korean equity and ETF feeds, with more names to follow 🤤 Full blog: pyth.network/blog/four-korea…

Pyth Karaoke Night 🎤 Time to ruin some songs together Everyone better show up and bring your mics! Btw Halls has the best voice out of all of us Even if you're not planning to sing, come listen to Halls sing. Trust me, you don't wanna miss it 🎶

Found both of them vibing to the beat while PIRB was busy with Pro MCP 1.1

𝘛𝘳𝘢𝘥𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘴𝘵𝘰𝘤𝘬 𝘦𝘹𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘩𝘢𝘷𝘦 𝘧𝘪𝘹𝘦𝘥 𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘩𝘰𝘶𝘳𝘴, 𝘣𝘶𝘵 𝘰𝘯𝘤𝘩𝘢𝘪𝘯 𝘮𝘢𝘳𝘬𝘦𝘵𝘴 𝘤𝘢𝘯 𝘬𝘦𝘦𝘱 𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘣𝘦𝘺𝘰𝘯𝘥 𝘵𝘩𝘦𝘮. 𝘛𝘩𝘢𝘵 𝘮𝘢𝘬𝘦𝘴 24/7 𝘱𝘳𝘪𝘤𝘦 𝘥𝘢𝘵𝘢 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵. @PythNetwork 𝘯𝘰𝘸 𝘩𝘢𝘴 26 𝘪𝘯𝘥𝘪𝘤𝘦𝘴 𝘵𝘳𝘢𝘤𝘬𝘪𝘯𝘨 𝘪𝘯𝘥𝘪𝘷𝘪𝘥𝘶𝘢𝘭 𝘴𝘵𝘰𝘤𝘬𝘴 𝘢𝘤𝘳𝘰𝘴𝘴 𝘵𝘩𝘦 𝘜𝘚, 𝘒𝘰𝘳𝘦𝘢 𝘢𝘯𝘥 𝘑𝘢𝘱𝘢𝘯, 𝘸𝘪𝘵𝘩 24/7 𝘱𝘳𝘪𝘤𝘪𝘯𝘨. 𝘐𝘯 𝘘3, 𝘗𝘺𝘵𝘩 𝘱𝘳𝘪𝘤𝘦𝘥 $1.26𝘛 𝘪𝘯 𝘦𝘲𝘶𝘪𝘵𝘺 𝘱𝘦𝘳𝘱𝘴, 𝘸𝘩𝘪𝘤𝘩 𝘪𝘴 98.3% 𝘰𝘧 𝘵𝘩𝘦 𝘵𝘰𝘵𝘢𝘭, 𝘢𝘭𝘰𝘯𝘨 𝘸𝘪𝘵𝘩 $458𝘉 𝘪𝘯 𝘤𝘰𝘮𝘮𝘰𝘥𝘪𝘵𝘺 𝘱𝘦𝘳𝘱𝘴 (91.6%) 𝘢𝘯𝘥 $317𝘉 𝘪𝘯 𝘪𝘯𝘥𝘦𝘹 𝘱𝘦𝘳𝘱𝘴 (91.2%), 𝘵𝘰𝘵𝘢𝘭𝘭𝘪𝘯𝘨 $2.03𝘛 𝘢𝘤𝘳𝘰𝘴𝘴 𝘢𝘭𝘭 𝘵𝘩𝘳𝘦𝘦 𝘢𝘴𝘴𝘦𝘵 𝘤𝘭𝘢𝘴𝘴𝘦𝘴. 𝘈𝘴 𝘰𝘯𝘤𝘩𝘢𝘪𝘯 𝘮𝘢𝘳𝘬𝘦𝘵𝘴 𝘨𝘳𝘰𝘸, 𝘩𝘢𝘷𝘪𝘯𝘨 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘦 𝘱𝘳𝘪𝘤𝘦𝘴 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦 𝘢𝘳𝘰𝘶𝘯𝘥 𝘵𝘩𝘦 𝘤𝘭𝘰𝘤𝘬 𝘮𝘢𝘵𝘵𝘦𝘳𝘴 𝘮𝘰𝘴𝘵, 𝘢𝘯𝘥 𝘗𝘺𝘵𝘩 𝘪𝘴 𝘣𝘶𝘪𝘭𝘵 𝘵𝘰 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘵𝘩𝘢𝘵 𝘥𝘢𝘵𝘢 𝘧𝘢𝘴𝘵𝘦𝘳, 𝘮𝘰𝘳𝘦 𝘴𝘦𝘤𝘶𝘳𝘦𝘭𝘺, 𝘢𝘯𝘥 𝘢𝘵 𝘢 𝘭𝘰𝘸𝘦𝘳 𝘤𝘰𝘴𝘵.

I found my Pythian bruvs doing their weekend thing

Markets move on news, but understanding the reaction takes more than reading a headline. An earnings report drops. A liquidation hits. Prices start moving. Can an AI agent track how the market reacted around that moment? @PythNetwork just upgraded its Pro MCP Server to 1.1, adding tools like get_price_range to retrieve available price updates within a window of up to 60 seconds around an event. It also brings funding rates, EMAs, interest rates, feed checks, natural pair resolution like BTC/USD, and update channels down to 50ms. Think about it. An agent can research an announcement through Exa, then use Pyth to look at the actual price movements around it. Not just reading the news, but understanding how the market responded. As AI agents become more capable, reliable financial data will matter even more. Pyth is making that data accessible to the next generation of AI-powered market tools.

A project that uses 100% of its DAO revenue to buy back solana:HZ1JovNiVvGrGNiiYvEozEVgZ58xaU3RKwX8eACQBCt3 A project that provided price data for $2.04T of $2.16T in Q3 RWA perp volume, accounting for 94.4% of the total. A project that just delivered its biggest quarter yet. $11.5M ARR. 86% growth over Q2. $9.68M from Pyth Pro. $1.81M from Pyth Indices. This is @PythNetwork

Pythenians deserve better than automated replies and weeks of silence. Hoping someone at @opensea can take a proper look at this and help get the verification sorted. It shouldn’t take this long to get a clear response. @opensea_support @dfinzer

RWA perps saw a slowdown in trading activity during September, but open interest continued to climb. Total volume fell 15% from August to $637.85B, while average daily open interest rose 12% to $11.71B OI hit a new record of $12.61B on September 30. Commodities were a major part of the story. Oil volatility pushed trading activity higher, while gold volumes surged around the Fed’s rate decision. Meanwhile, Variational climbed from 7th to 4th among venues, with Swaps accounting for 88% of its RWA volume. Through all this, @PythNetwork priced 90.29% of tracked RWA perp volume. Trading slowed, but the amount of exposure kept open continued to grow. Data: @RefractionRWA / RWA Markets dashboard by @zinnresearch — rwa-markets.xyz/ Report: pyth.network/blog/september-…

Pythenians and Pyth have given me some of my favourite memories in Web3. I’m grateful to be part of this journey, to keep building with the purple ecosystem, and to be recognized by a community that means so much to me. Always Pyth pilled 💜

PIRB never sleeps, just like Pyth, when price feeds are lying 🐦

A private company doing $1.82B in perp volume in 24 hours. And almost all of it was priced by Pyth. SpaceX perps recorded $1.82B in 24h volume across 23 of 24 tracked venues, with 98.4% of that volume priced by @PythNetwork Traders can get exposure to SpaceX through perpetual futures that trade 24/7 These markets need reliable pricing. Pyth is already playing a major role in that infrastructure. Source: rwa-markets.xyz/

Exa is testing Pyth's market data inside its AI research agents. The goal is simple: help AI agents understand why something happened in the market and verify how the market reacted using actual price data. That's where Exa and Pyth come together. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐰𝐢𝐭𝐡 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 Imagine asking an AI what happened after a company's earnings announcement and how its stock performed over the next five trading sessions. Finding the announcement is only half the job. The agent also needs accurate market data for that exact period. 𝐇𝐨𝐰 𝐏𝐲𝐭𝐡 𝐁𝐫𝐢𝐧𝐠𝐬 𝐌𝐚𝐫𝐤𝐞𝐭 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐀𝐈 Exa helps agents find news, earnings reports and company information across the web, while Pyth provides structured financial data, including current prices, historical observations and candlestick data through its MCP server. Together, they connect the story behind a market event with actual market data. Exa provides the context, Pyth provides the data, and the agent brings both together in one workflow. 𝐋𝐞𝐭'𝐬 𝐭𝐚𝐤𝐞 𝐭𝐡𝐚𝐭 𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 𝐞𝐱𝐚𝐦𝐩𝐥𝐞 An agent could use Exa to find a company's latest earnings announcement and summarise its results. It could then use Pyth to retrieve market data for the following five trading sessions. This connects the company's announcement with its actual price performance, giving a more complete picture than an earnings summary alone. 𝐖𝐡𝐲 𝐭𝐡𝐞 𝐌𝐂𝐏 𝐬𝐞𝐫𝐯𝐞𝐫 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 MCP (Model Context Protocol) provides a standardised way for AI applications to interact with external tools and data. Pyth's MCP server lets agents discover feeds and retrieve current prices, historical prices and candlestick data. Market data can become part of an agent's research workflow instead of requiring users to look it up separately. 𝐖𝐡𝐚𝐭 𝐜𝐨𝐮𝐥𝐝 𝐭𝐡𝐢𝐬 𝐞𝐧𝐚𝐛𝐥𝐞? Potential applications include: • Connecting news and filings with later price movements. • Comparing multiple assets across the same period. • Generating market reports using news and price data. • Combining portfolio monitoring with relevant market developments. • Analysing historical data and time series. These are potential use cases, not claims that every workflow is already live. 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐦𝐚𝐲 𝐮𝐬𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐚𝐭𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲 A human analyst might open charts and read articles manually. An AI agent could discover feeds, retrieve historical observations, compare assets and repeat these steps within a larger research task. That creates new requirements for how financial data is discovered, retrieved and delivered. Testing will help Exa and Pyth understand these usage patterns and where market data adds the most value. 𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐧𝐠𝐬 𝐬𝐭𝐚𝐧𝐝 This work is still in an early testing phase. The teams are exploring how Pyth's structured market data can fit into Exa's agentic search and financial research workflows. It's an exploration of what's possible, not an announcement that every application is already available. Source: pyth.network/blog/pyth-and-e… Exa brings the story behind market moves, while Pyth brings the data to verify them. Together, they could make AI financial research more reliable.

Markets running 24/7 with non-stop pricing was a 1996 fantasy, now Pyth makes it real. Traditional markets might sleep on weekends, but Perp DEXs and crypto never stop, That's why platforms rely on @PythNetwork for 24/7 continuos pricing across equities, FX, and commodities keeping trading alive around the clock.

𝗣𝘆𝘁𝗵 𝗗𝗔𝗢 𝗶𝘀 𝗽𝘂𝘁𝘁𝗶𝗻𝗴 𝗮𝗹𝗹 𝗶𝘁𝘀 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝘁𝗼𝘄𝗮𝗿𝗱 $𝗣𝗬𝗧𝗛 The Pyth DAO has decided to use 100% of the funds it receives from Pyth products to buy $PYTH for its Reserve. The interesting part is how this works and what changed from the previous system. Let’s get into it. 𝗣𝘆𝘁𝗵’𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝘀 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 In September 2026, Pyth reached $11.5M in ARR. Pyth Indices also reached $1.81M in fixed ARR. Pyth is expanding across equities, commodities, indices and other financial data products. Some of the biggest names in finance are now paying to use its data. So what happens to the money the DAO receives from this business? 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗢𝗣-𝗣𝗜𝗣-𝟭𝟯𝟲 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻. Earlier, the DAO used one third of its non-PYTH treasury balance for monthly PYTH purchases. Each transfer needed a separate vote. Now, the DAO has approved one standing authorization to use 100% of the funds it receives from Pyth products to buy $PYTH. The commitment is clear and there’s no need to vote on every monthly purchase. 𝗪𝗵𝗶𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗰𝗼𝘃𝗲𝗿? The rule applies to the DAO’s revenue from four product lines: • Pyth Pro • Listing as a Service • Data Marketplace • Pyth Indices One thing worth clarifying: the DAO receives 60% of revenue from the products it owns. It’s that share the DAO is committing to PYTH purchases, not all the revenue generated across Pyth. 𝗧𝗵𝗲𝗿𝗲’𝘀 𝗿𝗲𝗮𝗹 𝘂𝘀𝗮𝗴𝗲 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗶𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 The blog reports that more than 94% of tracked RWA perp volume used Pyth data over the past quarter, accounting for over $2 trillion in trading volume. Pyth is also being used across exchanges and prediction markets, while its Data Marketplace is expanding through products like Nasdaq Basic. These aren’t just new integrations to talk about. They’re part of the business Pyth is building. 𝗪𝗵𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗽𝘂𝗿𝗰𝗵𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁? The process is simpler, but the safeguards haven’t changed. • 5% slippage cap • $25,000 limit per transaction • Aggregator routing • Public onchain proofs • Monthly reporting Stablecoins received by the DAO are exchanged for PYTH on the open market under predefined instructions. Purchases remain publicly trackable. 𝗧𝗵𝗲 𝗣𝗬𝗧𝗛 𝗥𝗲𝘀𝗲𝗿𝘃𝗲 The first purchases under OP-PIP-136 were executed on September 30, 2026. The blog reports around 42 million PYTH in the Reserve following these purchases. You can check the holdings, monthly purchases and transaction links here: pyth.scptech.xyz/reserve 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗳𝗶𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲. Pyth is building a business around financial data, and the DAO has now committed all the funds it receives from those products to buying $PYTH. It doesn’t guarantee a higher token price. But it does give the DAO a clear, onchain way to turn the revenue it receives into PYTH holdings. Now it’ll be worth watching how the business grows and how the Reserve changes over time. Full blog : pyth.network/blog/the-100-ru…

PIRB ON FIRE after knowing 100% of Pyth DAO revenue goes to $PYTH buybacks