Herry

[Multi @Multi_mike01]

𝗧𝗥𝗢𝗡 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗕𝘂𝘆𝗯𝗮𝗰𝗸 & 𝗕𝘂𝗿𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀: $𝗝𝗦𝗧, $𝗕𝗧𝗧 & $𝗪𝗜𝗡 𝗥𝗲𝘃𝗲𝗻𝘂𝗲, 𝗧𝗼𝗸𝗲𝗻 𝗕𝘂𝗿𝗻𝘀 𝗮𝗻𝗱 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗩𝗮𝗹𝘂𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆

Three major TRON ecosystem projects are connecting service revenue to token supply reduction. $JST, $BTT and $WIN each operate in a different part of the ecosystem, but their buyback-and-burn programs share a common approach.

1/ 𝗪𝗜𝗡: 𝗣𝗿𝗶𝗰𝗲 𝗧𝗿𝗲𝗻𝗱 𝗮𝗻𝗱 𝗙𝗶𝗿𝘀𝘁 𝗕𝘂𝗿𝗻

From around $0.000021 in early July 2026, WIN traded near $0.000053 to $0.000054 by October 19, an approximate increase of 150%. Prices vary by exchange and timestamp, and this movement cannot be attributed to buybacks alone.

On October 19, WINkLink completed its first burn, removing 67.36 billion WIN, around 6.74% of the initial supply. The program allocates 100% of WINkLink service revenue to WIN buybacks.

Transaction: https://t.co/kzc3JwkNLw

2/ 𝗝𝗦𝗧: 𝗗𝗲𝗙𝗶 𝗥𝗲𝘃𝗲𝗻𝘂𝗲

On October 16, JustLend DAO completed its fifth burn, destroying 170.72 million JST, valued at approximately $23.53 million. Cumulative burns reached 1.882 billion JST, or 19.01% of the original supply.

The round was funded through third-quarter net income and historical revenue, connecting the token mechanism to JustLend’s DeFi business.

Transaction: https://t.co/0nqMSbO7ER

3/ 𝗕𝗧𝗧: 𝗗𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲

Also on October 16, BitTorrent burned 21.62 trillion BTT, valued at approximately $7.6 million, representing about 2.18% of the supply. The first quarterly burn used 100% of third-quarter revenue from decentralized services.

Transaction: https://t.co/EzgIjtLKUO

𝗧𝗵𝗲 𝗦𝗵𝗮𝗿𝗲𝗱 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺

$JST powers the DeFi financial layer, $BTT supports decentralized infrastructure, and $WIN delivers oracle data services.

Across all three, the model links product usage to revenue, revenue to buybacks, and buybacks to permanent token burns. This creates a measurable supply-reduction mechanism, but long-term price performance still depends on demand, adoption, liquidity and market conditions.

The next BTT and WIN round data is expected in January 2027, while an update on the next phase of JST’s mechanism is expected soon.

#TRONEcoStar @justinsuntron @DeFi_JUST @BitTorrent @WinkLink_Oracle

[BEN Gurain🥇 @sanmiastar]

𝗪𝗜𝗡𝗸𝗟𝗶𝗻𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝘀 𝗜𝘁𝘀 𝗙𝗶𝗿𝘀𝘁 𝗕𝘂𝘆𝗯𝗮𝗰𝗸 & 𝗕𝘂𝗿𝗻!

WINkLink has completed Round 1 of its $WIN Buyback & Burn program, launched in Q3 2026.

The result? 67,357,364,510 WIN burned, valued at $3.59 million!

But what does this mean for WINkLink, its token economy, and the wider TRON ecosystem? Let’s break down the mechanism, revenue sources, transparency, and future implications.

𝗛𝗼𝘄 𝗗𝗼𝗲𝘀 𝗜𝘁 𝗪𝗼𝗿𝗸?

WINkLink generates revenue through oracle and protocol-related services. Under this program, 100% of the revenue allocated under the policy is used for WIN buybacks.

➠ Protocol revenue is calculated.
➠ Revenue purchases WIN tokens.
➠ Tokens are consolidated and sent to a black hole address for permanent removal.
➠ Results and transaction records are publicly disclosed.

This connects protocol revenue with systematic token buybacks and burns.

𝗥𝗼𝘂𝗻𝗱 𝟭 𝗕𝘆 𝘁𝗵𝗲 𝗡𝘂𝗺𝗯𝗲𝗿𝘀

➝ WIN burned: 67,357,364,510
➝ Reported value: $3,590,256.63
➝ Initial total supply burned: 6.74%
➝ Completed burn events: 1

These figures establish the program’s first milestone. However, burning tokens does not guarantee price appreciation; demand, liquidity, market conditions, and supply dynamics also matter.

𝗪𝗵𝗲𝗿𝗲 𝗗𝗼𝗲𝘀 𝘁𝗵𝗲 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗖𝗼𝗺𝗲 𝗙𝗿𝗼𝗺?

WINkLink provides oracle infrastructure that helps blockchain applications access external data, including price information.

Potential revenue opportunities include:

♤ Oracle service fees from data feeds and related services.

♤ Ecosystem demand from the wider TRON environment, including JustLend DAO and USDD.

♤ Future expansion through additional services, multichain deployments, and real-world asset (RWA) data.

The goal is to connect buybacks to protocol revenue rather than relying solely on announcements or speculation. Future results depend on adoption and actual revenue growth.

𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗠𝗮𝘁𝘁𝗲𝗿?

WINkLink’s dedicated Buyback & Burn page lets users explore:

✅ Total WIN burned and reported value.
✅ Historical announcements and records.
✅ Individual burn events.
✅ On-chain transaction details for verification.

The first recorded event, dated 19 October 2026, documents 67,357,364,510 WIN burned, valued at $3,590,256.63.

Public records help holders verify activity instead of relying entirely on social media announcements. Transparency cannot eliminate market risk, but it improves accountability.

𝗪𝗵𝗮𝘁 𝗖𝗼𝘂𝗹𝗱 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻 𝗳𝗼𝗿 $𝗪𝗜𝗡?

If the program continues as described, future buybacks could link protocol revenue growth with additional purchases and permanent burns.

Three things to watch:

➠ Revenue growth: Is WINkLink generating more service revenue?
➠ Execution: Are future burns completed and publicly verifiable?
➠ Market dynamics: How do demand, liquidity, and circulating supply evolve?

Long-term impact depends on consistent execution, sustainable revenue, and ecosystem growth. Reduced supply alone does not guarantee higher prices.

For now, Round 1 establishes the mechanism, delivers the first reported burn, and makes records publicly accessible.

🔥 67.35 billion WIN burned. One completed round. A transparent mechanism to follow.

🔗 Explore: https://t.co/XTS92IHOUO

@justinsuntron @WinkLink_Oracle

#WINkLink #WIN #TRONEcoStar

[Multi @Multi_mike01]

𝗪𝗵𝗮𝘁 𝗪𝗜𝗡𝗸𝗟𝗶𝗻𝗸 𝗣𝗿𝗶𝗰𝗲 𝗙𝗲𝗲𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗱𝗼𝗲𝘀

A smart contract can execute exactly as programmed and still make the wrong decision if the data it relies on is inaccurate, outdated, or unavailable.

That is one of the problems oracle infrastructure is designed to solve.

A DeFi application may need to know the current price of BTC, TRX, USDT, JST or another asset before it can determine collateral value, borrowing limits, liquidations or other on-chain actions.

But smart contracts don't naturally know what those assets are worth in external markets.

So how does that price get on-chain?

This is where WINkLink Price Feed Service comes in.

WINkLink provides on-chain price data that DApps and smart contracts can read and use.

Its price-feed system collects observations from multiple independent price-feed nodes and aggregates them before publishing the result on-chain. For its price feeds, 7 nodes participate in the reporting process, with the observations combined using a median calculation.

So the basic idea is:

Market data → multiple nodes → aggregated price → on-chain feed → DApp or smart contract

And developers get more than just a price.

A WINkLink price feed can provide:

• The latest asset price
• When that price was last updated
• The round in which the price was reported
• Historical price data through previous rounds
• The feed's decimal precision

That information gives applications the context they need to use the data correctly.

For example, latestRoundData() provides the latest price along with an updatedAt timestamp. A developer can use that timestamp to check whether the price is still fresh enough for the application's requirements.

The decimal precision also matters because different feeds can use different decimal formats. Developers can query decimals() rather than assuming every price feed uses the same precision.

This becomes particularly important in DeFi.

If a lending protocol is calculating collateral value or deciding whether a position should be liquidated, the price it receives isn't just informational. It can directly influence what the smart contract does.

That's why the quality, freshness and structure of price data matter.

So, in simple terms, WINkLink Price Feed Service connects external market prices with on-chain applications, giving smart contracts a structured way to access and work with asset price data.

The price itself may look like just a number.

But for DeFi applications, that number can determine what happens next.

Explore more: https://t.co/UGjJQFy3Jq

#TRONEcoStar @justinsuntron #TRON #WINkLink @WinkLink_Oracle

[Multi @Multi_mike01]

𝗛𝗼𝘄 𝘁𝗵𝗲 𝗪𝗜𝗡𝗸𝗟𝗶𝗻𝗸 𝗽𝗿𝗶𝗰𝗲 𝗳𝗲𝗲𝗱 𝘄𝗼𝗿𝗸𝘀

When a DeFi application needs the current price of an asset, getting that information onto the blockchain isn't as simple as checking a market price.

Smart contracts cannot directly access external market data. They need an oracle to bring that information on-chain in a form the application can read and use.

This is where the 𝗪𝗜𝗡𝗸𝗟𝗶𝗻𝗸 𝗣𝗿𝗶𝗰𝗲 𝗙𝗲𝗲𝗱 comes in.

So, how does it actually work?

It starts with 𝗺𝗮𝗿𝗸𝗲𝘁 𝗱𝗮𝘁𝗮.

WINkLink's price-feed system uses multiple independent nodes to observe and report relevant price information. For its price feeds, 𝟳 nodes participate in the reporting process.

Each node provides its own observation.

Those observations are then brought together and 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲𝗱 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝗮𝗻.

This is an important part of the design. The reported price isn't simply taken from one individual node. The observations from the participating nodes contribute to the final result.

Once the price has been aggregated, the report is 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗱 𝗼𝗻-𝗰𝗵𝗮𝗶𝗻 and made available through the price-feed contract.

That's where 𝗗𝗔𝗽𝗽𝘀 𝗮𝗻𝗱 𝘀𝗺𝗮𝗿𝘁 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁𝘀 can access the data.

But the feed doesn't only provide the latest price.

Developers can also retrieve:

• 𝗧𝗵𝗲 𝗹𝗮𝘁𝗲𝘀𝘁 𝗮𝘀𝘀𝗲𝘁 𝗽𝗿𝗶𝗰𝗲
• 𝗪𝗵𝗲𝗻 𝘁𝗵𝗮𝘁 𝗽𝗿𝗶𝗰𝗲 𝘄𝗮𝘀 𝗹𝗮𝘀𝘁 𝘂𝗽𝗱𝗮𝘁𝗲𝗱
• 𝗧𝗵𝗲 𝗿𝗼𝘂𝗻𝗱 𝗶𝗻 𝘄𝗵𝗶𝗰𝗵 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝘄𝗮𝘀 𝗿𝗲𝗽𝗼𝗿𝘁𝗲𝗱
• 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗽𝗿𝗶𝗰𝗲 𝗱𝗮𝘁𝗮
• 𝗧𝗵𝗲 𝗳𝗲𝗲𝗱'𝘀 𝗱𝗲𝗰𝗶𝗺𝗮𝗹 𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻

That last point is easy to overlook.

Different WINkLink feeds can use different decimal formats, so developers should query the feed's decimals() value rather than assume every feed uses the same precision.

The update timestamp is also important because a price isn't useful simply because it exists. Applications need to know whether the data is still fresh enough for their particular use case.

So, behind the price you see inside a DeFi application, there is a process involving 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗻𝗼𝗱𝗲𝘀, 𝗽𝗿𝗶𝗰𝗲 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻𝘀, 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻, 𝗺𝗲𝗱𝗶𝗮𝗻 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻 and 𝗼𝗻-𝗰𝗵𝗮𝗶𝗻 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴.

The user may only see a number on the screen.

But that number has gone through several steps before a smart contract can actually use it.

𝗧𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗶𝗱𝗲𝗮 𝗯𝗲𝗵𝗶𝗻𝗱 𝗪𝗜𝗡𝗸𝗟𝗶𝗻𝗸 𝗣𝗿𝗶𝗰𝗲 𝗙𝗲𝗲𝗱: taking external market price information, processing it through multiple nodes, putting the resulting data on-chain, and making it accessible to applications that need it.

Learn more at https://t.co/J66vwXPl4d

#TRONEcoStar @justinsuntron #TRON @WinkLink_Oracle

[孙可可 @Sunkeke_AI]

https://t.co/9punqIztFf

[蜘蛛侠 | 1000X GEM @zhizhuxia22]

我最近发现个超级牛逼的网站，简直就是数字游民的福音

它直接把全世界城市的：
房租、物价、医疗、安全、气候、税率、宜居度

全部整理到一起了！

哪个城市适合长期住
哪个地方生活成本更低
哪个城市更适合远程办公
甚至连一个月大概要花多少钱，都给你算得明明白白
以后想换个城市生活、旅居几个月、或者去国外待一阵

真的不用自己到处查资料了
直接拿这个当城市对比工具就行

传送门：https://t.co/XAIp4CJeyZ

[加密狗 @jiamigou]

https://t.co/VPofntOx4g

[meme @oooodjdjd]

web3岗位薪资参考表（最全合集）

1⃣社群大使：400U-600U
难度：⭐️
优点：上手快，任务简单，适合新人入场。曝光机会多，容易积累行业人脉
缺点：收入天花板较低，工作内容重复，会比较依赖社群活跃度。

2⃣运营助理：800U-1200U
难度：⭐️⭐️

优点：能学到完整运营流程，技能成长快，未来发展方向多
缺点：琐碎事务多，临时任务频繁，容易遇到突发情况需要加班

3⃣BD助理：1000U-1200U
难度：⭐️⭐️⭐️
优点：接触项目方和合作资源，视野开阔，未来跳槽空间大。
缺点：沟通压力大，目标要求高，需要一定的表达能力和抗压能力。

4⃣客服：1200U-1500U
难度：⭐️⭐️
优点：难度不算高，薪资还不错，适合执行力强的人。
缺点：容易被用户情绪影响，被骂多了会被搞心态，重复问题非常多。

5⃣社群运营：1500U+
难度：⭐️⭐️⭐️⭐️
优点：薪资高，能力通用性强，能快速提升组织和策划能力。
缺点：责任大，需要全时段关注社群动态，容易心理疲劳。

6⃣内容编辑：1000U-1500U
难度：⭐️⭐️⭐️
优点：适合喜欢写作的人，积累作品后更易提升价值。内容可长期复用，有成就感。
缺点：需要灵感和深度思考，产出压力大，热点时段会很忙。

到这个层次的岗位，至少得3年的经验起步，而且要做出一定的成果

适合web2大厂的员工转web3

1⃣ 产品运营：2800U-3500U
难度：⭐⭐⭐⭐⭐
优点：
看得到产品从零到一的变化，成就感强。数据敏感度会快速提升，跨部门沟通能力飞升。
缺点：
事情多到像八爪鱼一样，需求永远做不完，临时紧急的调整随时插队，容易精神内耗。

2⃣ 客服经理：2200U-2500U
难度：⭐⭐⭐⭐⭐
优点：
第一线掌握用户反馈，能迅速洞察问题。团队管理经验积累快，适合往运营方向横向发展。
缺点：
压力指数上升快。负面情绪输入频繁，处理不好容易被带跑情绪。

3⃣ 运营经理：2000U-3000U
难度：⭐⭐⭐⭐⭐⭐⭐
优点：
话语权提升明显。能直接负责增长与策略，输入输出价值都很高，职业发展路径清晰。
缺点：
责任比薪水更先到位。要负责 KPI，要顾项目，还要盯团队，节奏极其紧绷。

4⃣ BD 专家：2500U-3000U
难度：⭐⭐⭐⭐⭐
优点：
能接触大量项目方与合作资源，行业人脉积累速度超快。强关系链一旦形成，跳槽和涨薪都会起飞。
缺点：
沟通量巨大。目标压力也很硬核，需要持续输出价值，嘴皮子和心态都要硬。

5⃣ 大客户经理：2800U-4000U
难度：⭐⭐⭐⭐⭐⭐
优点：
客户体量大，单个项目价值高。只要磨出专业度，收入和地位都能上一个台阶。
缺点：
对服务质量要求极高。一个细节都可能影响合作，随时处于被放大镜审视的状态。
web3岗位参考（入行人开始的第一步）

6⃣活动运营：2000U–2200U
难度：⭐️⭐️⭐️

优点：资源充足，活动影响力大，能做出真正“看得见数据”的项目。参与大型 Campaign、AMA、空投、全球活动后，履历含金量显著提升。
缺点：节奏极快，版本更迭频繁。上午需求中午改晚上上线。还经常半夜被一句“活动有个小问题”叫醒

7⃣用户运营：2000U–2500U
难度：⭐️⭐️⭐️
优点：数据体系完善，能系统化学习用户分层、生命周期运营、留存模型。接触的用户量级大，对未来做增长或产品帮助非常大。
缺点：数据指标压力明显，需要频繁做复盘。用户反馈密度高，用户问的问题永远只有三种.但是能用一百种方式问。还可能因为一个 bug 被喷到怀疑人生

8⃣上币运营（Listing Ops）：2200U–2600U
难度：⭐️⭐️⭐️⭐️
优点：能深度接触项目方、投行部门、审核流程。每一次上币都是重大里程碑，履历价值极高。对行业全链路理解最快的一类岗位。
缺点：流程严谨复杂，跨部门协作密度大。节点多、责任重，任何延误都可能影响项目上线节奏。

9⃣BD（商务拓展）：2000U–3200U
难度：⭐️⭐️⭐️⭐️⭐️
优点：能直接接触项目方、机构、生态合作伙伴。谈成一个合作就是重大成果，跳槽时薪资提升空间极大。资源沉淀快，是一线交易所最核心的岗位之一。
缺点：目标压力强烈，需要持续产出 pipeline。沟通节奏快，加班和随时沟通是常态。

🔟KOL拓展（Influencer Ops）：1800U–2200U
难度：⭐️⭐️⭐️
优点：能快速积累全球媒体/KOL资源，了解内容投放逻辑和效果评估。适合外向善沟通的人，促成合作的成就感很强。
缺点：需要不断对接不同国家的 KOL，同步时差、谈预算、改素材。遇到不配合或反馈慢的合作方会非常耗精力。

（步入斗宗强者行列）

在这个行列，基本上都是公司的大动脉了，裁员风险基本没有，但是要求也非常的高，行业经验5年起步，压力更不用说
而且项目奖金和年终奖也很高，我这里算的只是基础工资

1⃣ 全球业务负责人：7500U-10000U

难度：⭐⭐⭐⭐⭐⭐⭐⭐
优点：
站在组织最高业务视角，能直接制定全球市场策略，推动公司在多区域落地。行业洞察与资源积累呈指数级增长，履历含金量极高。
缺点：
压力堪称地狱模式。要同时对增长、团队、合作方与监管变化负责，跨时区沟通常态化，决策容错率极低。

2⃣ 产品负责人（Web3 全链路方向）：7200U-9000U

难度：⭐⭐⭐⭐⭐⭐⭐
优点：
掌控从用户体验到链上逻辑的全套产品方案，可以真正推动从 0 到 1 的创新。对行业生态理解会变得异常深刻，职业竞争力显著提升。
缺点：
业务链路极长。要协调研发、合规、市场、设计、风控等多个部门，需求变化频繁，长期处于高负荷节奏。

3⃣ 机构及场外交易主管：6000U-7800U
难度：⭐⭐⭐⭐⭐⭐⭐
优点：
深度耕耘高净值客户与机构交易，能迅速建立强关系网络。收益结构更灵活，只要专业度足够，增长速度非常快。
缺点：
OTC 业务节奏快、金额大，对风控、资金流与信用体系的敏感度要求极高，心理压力持续存在。
4⃣ 法务总顾问：7000U-9000U
难度：⭐⭐⭐⭐⭐⭐⭐
优点：
能站在合规与业务之间的最高维度，直接参与重大战略决策。越是复杂的政策环境，越能突显专业价值，属于不可替代的关键岗位。
缺点：
持续跟踪全球监管变化，信息量巨大且需要快速判断。责任沉重，任何细节疏漏都可能带来法律或财务风险。
5⃣ 机构业务负责人：7500U-9500U

难度：⭐⭐⭐⭐⭐⭐⭐⭐
优点：
能接触到全球顶级市场参与者，如基金、做市商、交易策略团队等，决策级资源大量累积。项目体量更大，成功一次即可拉升个人行业地位。
缺点：
专业要求极高。需要深刻理解交易结构、风控、合规以及机构需求，不仅要稳，还要能持续开拓。

6⃣ 钱包与链上业务负责人：7000U-9500U
难度：⭐⭐⭐⭐⭐⭐⭐
优点：
能深度参与链上交互标准、资产安全体系、地址体系设计等核心门槛话题，技术与业务深度都能同步提升。对未来职业路线的延展性极强。
缺点：
业务涉及安全、审计、链上异常监控等高风险领域，精力消耗巨大。每个功能都需极高谨慎度，出错成本极高。

当然不止这些，但是基本都是核心业务的核心人，大家可以作为职业发展目标

web3终极岗位参考：
（超级无敌打工皇帝）

这个就没有优缺点之分了，因为平常也难接触到这样的人

属于是卷中之卷，人中龙凤
能超过这个级别的也只有董事会了

CEO 首席执行官：90000U-100000U
难度：⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐

首席运营官 / 总裁：70000U-90000U
难度：⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
CFO 首席财务官：70000U-90000U
难度：⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
CTO 首席技术官：8000U-100000U
难度：⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
CMO 首席市场官：80000U-100000U
难度：⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
增长负责人：80000U-100000U
难度：⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐

[CoinMarketCap @CoinMarketCap]

8 hours of sleep = 2 hours sleep + 3 hours on CT + 3 hours checking the portfolio.

[Binance @binance]

Your future self is thanking you for locking in.

[Wintermute @wintermute_t]

What do you see?

[Phantom @phantom]

you met me at a very solana time of my life

[Phantom @phantom]

Timeline: Scrolled ✅
Phantom: Opened 400+ times ✅
Situation: Monitored ✅
Easter: Celebrated ✅

[鸟哥 | 蓝鸟会🕊️ @NFTCPS]

做视频搬运的可以看看这个网站，全网视频免费下载，还能去水印！
https://t.co/0WG57g5g87

[Kai @real_kai42]

最近招人，大概收了近 200 份简历，有些个人想法，分享出来，仅供参考和讨论

- 简历命名最好是：姓名-学校/公司-投递岗位，这样大家看着舒服，想去翻你简历也非常快的找到。我个人投简历的时候，甚至会加上“亮点”，如果亮点是几个字可以说清楚的话。

- 简历不要超过一页，大家筛简历的时候真的每个简历就看几个分钟（甚至不到一分钟），你给我一个三四页的简历，外加很多业务和技术需要动脑理解，过长读起来真的难顶

- 要分重点，也就是你写简历的时候，要站在读简历人的角度，思考，如果他只有 30s 看你的简历，你希望通过什么样的方式让他在 30s 就能看到你最大的亮点？可以通过加粗、图片、设计等等方式

- 简历没有任何“规定”要求可以放什么，不可以放什么。所以，任何你觉得是你亮点的都可以放，同样的，一些你觉得价值不高的，没有要求必须让你放。

[DeFi Teddy @DeFiTeddy2020]

《分享AI资讯实时站+ 节省 95% token的skill》

AI新闻现在多到刷不过来，后来刷到 https://t.co/p5r8rhjtlF 这个网站，感觉非常实用：

• 专注AI领域，东西方新闻都有（包括大模型、OpenClaw等细分方向）
• 按影响力排序，能快速看到当下最热的几条
• 更新很快，几分钟就能刷到新内容
• 界面干净，纯信息流，没有广告

另外还配套的一个免费开源 Skill：会对推文、图片、视频自动做摘要。拿来喂 OpenClaw 分析的时候，输入量直接砍掉一大截，我自己测了下，小龙虾token 消耗省了80%-95%！

有在折腾 AI 信息流的朋友可以看看

[BITWU.ETH 🔆 @Bitwux]

发现一个很牛逼的学习网站，对正在学习养虾的新手非常友好，强烈推荐给你们！

👉 https://t.co/NAN8s9isDa

很多新手现在还是一头雾水不知道怎么开始，这个网站可以做到：

🔹帮助你从入门到进阶，系统化的学习指南
🔹收录了很多中文 + 英文优质教程
🔹精选推荐了按场景分类的 Skills
🔹可以按分类、语言、关键词快速找到需要的资源

感谢技术大佬们，感觉要学习的东西越来越多了！

[Mantle @Mantle_Official]

me when someone says DeFi is ded

[XinGPT🐶 @xingpt]

https://t.co/5GBtn0C4qr

[沐阳 @yyyole]

https://t.co/a5m5bcmz9J