lingyugonga16

@lingyugonga16

145 Following

14 Followers

lingyugonga16 pfp

The official definition of DCA (Dollar-Cost Averaging) account use cases: - Accumulating at the bottom (bear market accumulation) - Selling at the top (bull market profit taking) - Splitting large orders - Exiting low liquidity tokens Moreover, using DCA for buying will show 'no trading' on the holders panel, which is why some whales and Dog Zhuangs love to use DCA accounts for accumulation.

0 reply

2 recasts

2 reactions

lingyugonga16 pfp

For DCA, we only need to remember two points (you'll be quizzed later): 1⃣ Every use of DCA sends a 0.1% fee to a fixed address of Jup: CpoD6tWAsMDeyvVG2q2rD1JbDY6d4AujnvAn2NdrhZV2（DCA Fee Vault Authority） 2⃣ After buying, the DCA Account (transit address) sends the tokens back to the Dog Zhuang's main address How to find the accumulation wallet? Many experts have introduced @arkham before, but I recently found their visualization tool to be faster and stronger:

0 reply

2 recasts

2 reactions

lingyugonga16 pfp

#1/2 Type1: High-frequency accumulation address cluster 1⃣ Go to Arkham, click on the visualization tool Then follow the instructions in the screenshot to find high-frequency trading addresses: For example, the address with the densest buying: 5qYcbjeHFR2sw3ZdJh86FFC5jwa6bteWkKVa5d2MDUpc 2⃣ (5qYc) Funds source: JD1dHSqYkrXvqUVL8s6gzL1yB7kpYymsHfwsGxgwp55h JD38n7ynKYcgPpF7k1BhXEeREu1KqptU93fVGy3S624k JD25qVdtd65FoiXNmR89JjmoJdYk9sjYQeSTZAALFiMy 3⃣ Accumulation address cluster: We find that these three funding sources all transferred goods to several overlapping addresses, and these addresses pay fees to the DCA Fee Account at a top frequency: Ultimately, through 6 related addresses, they accumulated 3.37 million tokens in the last two days, approximately 0.3% of the total chips.

0 reply

2 recasts

2 reactions

lingyugonga16 pfp

#1/2 Type1: High-frequency accumulation address cluster 1⃣ Go to Arkham, click on the visualization tool Then follow the instructions in the screenshot to find high-frequency trading addresses: For example, the address with the densest buying: 5qYcbjeHFR2sw3ZdJh86FFC5jwa6bteWkKVa5d2MDUpc 2⃣ (5qYc) Funds source: JD1dHSqYkrXvqUVL8s6gzL1yB7kpYymsHfwsGxgwp55h JD38n7ynKYcgPpF7k1BhXEeREu1KqptU93fVGy3S624k JD25qVdtd65FoiXNmR89JjmoJdYk9sjYQeSTZAALFiMy 3⃣ Accumulation address cluster: We find that these three funding sources all transferred goods to several overlapping addresses, and these addresses pay fees to the DCA Fee Account at a top frequency: Ultimately, through 6 related addresses, they accumulated 3.37 million tokens in the last two days, approximately 0.3% of the total chips.

0 reply

2 recasts

2 reactions

lingyugonga16 pfp

For DCA, we only need to remember two points (you'll be quizzed later): 1⃣ Every use of DCA sends a 0.1% fee to a fixed address of Jup: CpoD6tWAsMDeyvVG2q2rD1JbDY6d4AujnvAn2NdrhZV2（DCA Fee Vault Authority） 2⃣ After buying, the DCA Account (transit address) sends the tokens back to the Dog Zhuang's main address How to find the accumulation wallet? Many experts have introduced @arkham before, but I recently found their visualization tool to be faster and stronger:

0 reply

2 recasts

2 reactions

lingyugonga16 pfp

The official definition of DCA (Dollar-Cost Averaging) account use cases: - Accumulating at the bottom (bear market accumulation) - Selling at the top (bull market profit taking) - Splitting large orders - Exiting low liquidity tokens Moreover, using DCA for buying will show 'no trading' on the holders panel, which is why some whales and Dog Zhuangs love to use DCA accounts for accumulation.

0 reply

2 recasts

2 reactions

lingyugonga16 pfp

First, most disks that didn't exceed 5m during the first wave of FOMO don't go through the stages of washing, receiving, and accumulating chips. Some Zhuangs simply leave to open new disks. Only those that are truly hot with a sustained narrative have the value of washing, capable of creat ing a second or even third segment.

2 replies

1 recast

1 reaction

lingyugonga16 pfp

0 reply

0 recast

0 reaction

lingyugonga16 pfp

0 reply

0 recast

0 reaction

lingyugonga16 pfp

0 reply

0 recast

0 reaction

lingyugonga16 pfp

First, most disks that didn't exceed 5m during the first wave of FOMO don't go through the stages of washing, receiving, and accumulating chips. Some Zhuangs simply leave to open new disks. Only those that are truly hot with a sustained narrative have the value of washing, capable of creat ing a second or even third segment.

2 replies

1 recast

1 reaction

lingyugonga16 pfp

Xiaobai Tutorial 4: Can My Meme Be Saved? How to Find Evidence of Dog Zhuang Accumulating Chips? As a seasoned p-soldier, my daily routine is: 🤡 After catching the top and swearing to be a diamond hand, I end up drilling to the center of the earth by accident. 🤡 After being grinded by the Zhuang for half a month, I finally surrender my chips, only for them to fly the next day. All I can say is, don't try to guess the Zhuang's mind, listen less to what's said online, and look more at what's done on-chain. To play the second segment well, finding evidence of Dog Zhuang accumulating chips is a crucial judgment basis. Take the recent recovery of SSE as an example.

1 reply

1 recast

1 reaction

lingyugonga16 pfp

Xiaobai Tutorial 4: Can My Meme Be Saved? How to Find Evidence of Dog Zhuang Accumulating Chips? As a seasoned p-soldier, my daily routine is: 🤡 After catching the top and swearing to be a diamond hand, I end up drilling to the center of the earth by accident. 🤡 After being grinded by the Zhuang for half a month, I finally surrender my chips, only for them to fly the next day. All I can say is, don't try to guess the Zhuang's mind, listen less to what's said online, and look more at what's done on-chain. To play the second segment well, finding evidence of Dog Zhuang accumulating chips is a crucial judgment basis. Take the recent recovery of SSE as an example.

1 reply

1 recast

1 reaction

xiaohuiqin17 pfp

How does Sion work? Sion fundamentally changes how the Grass Network processes large-scale data retrieval by optimizing scraping efficiency and scaling infrastructure to sustain higher throughput.

0 reply

0 recast

0 reaction

xiaohuiqin17 pfp

What is Sion? Sion is a network upgrade designed to optimize web data retrieval at scale, allowing Grass to scrape over 1 petabyte of multimodal web data per day. To put this into perspective, at this scale, Grass will be scraping enough data to fill approximately 92 football fields' worth of flash drives each day. This level of compute, bandwidth, and algorithmic efficiency makes Grass the most accessible multimodal data provider in the AI industry.

0 reply

1 recast

1 reaction

xiaohuiqin17 pfp

Phase 1 - Optimizing Network Efficiency: Complete The first phase of Sion focused on improving the way public web data was scraped and processed without adding additional compute. Optimizing scraping algorithms increased efficiency, leading to a significant spike in web data retrieval. This pushed the network to its operational limits, reinforcing the need for a broader infrastructure expansion in Sion Phase 2 to sustain long-term scaling.

0 reply

1 recast

1 reaction

xiaohuiqin17 pfp

Phase 2 - Scaling Infrastructure: Rolling out in the weeks ahead With the groundwork laid in Phase 1, Phase 2 is about deploying these optimizations at scale by: Horizontally scaling compute – Distributing workloads across more machines, allowing for parallel processing and higher sustained scraping speeds. Scaling multimodal data retrieval – New adaptive scraping techniques allow for processing 4K video, images, and text without bottlenecks.

0 reply

1 recast

0 reaction

xiaohuiqin17 pfp

Expanding network bandwidth to beyond 1 terabit per second – Phase 2 removes throughput bottlenecks by distributing workloads across more machines, allowing sustained 10x data retrieval efficiency. A scale which makes Grass one of the most performant decentralized data scraping networks in the world.

0 reply

0 recast

1 reaction

xiaohuiqin17 pfp

Why Sion? The demand for multimodal data—especially video—has surged as AI advances into generative, autonomous systems, and robotics. Multimodal models require vast, high-quality datasets to improve realism and motion accuracy. From AI-generated video to autonomous perception, these systems rely on large-scale multimodal data to function effectively.

0 reply

1 recast

1 reaction

xiaohuiqin17 pfp

Yet, acquiring this data at the necessary scale remains a challenge. AI companies developing frontier models need exponentially more data to stay competitive, but the current options for sourcing it are costly, fragmented, and difficult to scale. Training the next generation of AI requires petabytes of high-quality data, yet existing solutions cannot efficiently sustain data retrieval at this magnitude.

1 reply

1 recast

1 reaction