QooryBeta
Baseline

Baseline

專案上升49
baseblastethereum去中心化交易所 (DEX)去中心化金融(DeFi)自動化做市商 (AMM)
官網@baselinemarketsGitHub

簡介

Baseline is an automated tokenomics engine for ERC20 tokens. By utilizing a dynamic supply model and a basic market making strategy, Baseline gives new ERC20s persistent on-chain liquidity and non-liquidatable leverage right out of the box. When a Baseline token is deployed, it automatically seeds initial liquidity into a Uniswap V3 pool. At launch, 100% of token supply is kept on-chain by the protocol and used to protect their value forever.

社交訊號
熱度rising49/100
X 粉絲11.2K
7日輿情看跌
B/USDT

暫無圖表資料

信任評分計算中…

最新資訊

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With any human-run market, you're trusting the people behind the scenes not to manipulate it. Baseline tokens don't need anyone behind the scenes.

@machibigbrother2026年6月3日
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New: @Delphi_Digital's report says $TRUMP's 2025 launch “sucked all the air out of the room,” pulling liquidity away from other DeFi coins and memecoins as traders chased the token. By April, most major meme tokens were 35% to 95% below their Jan. 17 baseline, as liquidity “did not find its way bac

@SolanaFloor2026年6月2日
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.@EVO__HQ just doubled Claude Code's accuracy on a hard benchmark In practice: Claude Code: 5/20 → 11/20 on a hard benchmark after 50-60 @EVO__HQ experiments Genome sequencing: 95% compute reduction with better results than baseline @alokbishoyi97 on what EVO does and how it works 👇 EVO looks

@alokbishoyi972026年6月2日
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Automated domain valuation tools can provide a helpful relative baseline, but don't rely on them blindly to set your final sell price. Domain valuation is 100% an art, not a science. https://t.co/nwQiVwW7Jh

@unstoppableweb2026年6月2日
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We finally hit a new ATH in LlamaAI usage Previous ath was when we gave unlimited free access to everyone, which created a huge spike that went down when we closed it again (settled above previous baseline tho) and since then it’s been a slow grind to this new ath

@0xngmi2026年6月1日
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有个研究世界模型的朋友给我安利了这个项目 👀 stable-worldmodel,一个做世界模型研究的统一平台 它的思路很清晰:把世界模型研究拆成三步—— ① 收集数据 ② 训练模型 ③ 用模型预测控制来评估 这三步在一个统一的接口下完成,不用到处拼工具 内置了常用的环境和 baseline,研究者可以只关注自己的模型创新,不用重复造轮子 pip 直接装,上手很快 感觉搞强化学习、机器人方向的同学会很喜欢 https://t.co/fCGLBAF5lD

@sss1mark2026年6月1日

KOL情緒

看跌-33

基於過去7天KOL推文的加權評分(-100至+100)