RT Kraken Pro (@krakenpro)What is scaling in futures trading❓Scaling is the art of adjusting position size over time, instead of going all in or out at once.There are three main approaches:🔹 Scaling in: Entering a trade gradually, as the setup confirms🔸 Scaling out: Exiting in stages to lock in profits and reduce risk♦️ Scaling down: Reducing position size if the trade turns against you💡 This strategy is often used with other futures approaches to smooth out entries and stay in control during volatile marke ...

RT Eli Ben-Sasson | Starknet.io (@EliBenSasson)Why Cairo *will* overcome Solidity with time.An erroneous claim folks make against Cairo, the first 100% STARK-friendly smart contract lanuage goes like this: There are way more Solidity developers than Cairo developers, and way more Dev tools for Solidity, and this gap will only widen.But this logic is flawed. According to it, there would never be any new programming language that's the new thing. Pascal, Basic, C, C++, Rust, Python, are all languages that in ...

RT Token Terminal 📊 (@tokenterminal)🚨📰 SCALING: @ethereum L1 transactions & active addresses are at all-time highs, while gas fees are close to all-time lows. https://t.co/78X7tV561g ...

🚨📰 SCALING: @ethereum L1 transactions & active addresses are at all-time highs, while gas fees are close to all-time lows. https://t.co/78X7tV561g ...

RT Starknet 🐺🐱 (@Starknet)Zero trusted setup.Zero trust assumptions.Zero compromises.Scaling the right way. ...

RT Token Terminal 📊 (@tokenterminal)🚨📰 SCALING: @ethereum L1 transactions & active addresses are at all-time highs, while gas fees are close to all-time lows. https://t.co/wFXZGO9Ryh ...

Ethereum Network Activity - Ethereum L1 交易量和活跃地址均达到历史新高 [1] - Gas 费用接近历史最低水平 [1] Scaling Implications - 网络活动增加与 Gas 费用降低并存，表明以太坊网络可能在扩展性方面取得进展 [1]

Core Insights - The article discusses the limitations and errors of GPT-5, particularly in counting letters in words, highlighting its inability to accurately count the letter 'b' in "blueberry" despite multiple attempts and corrections from users [1][5][12] Group 1: Performance Issues - GPT-5 incorrectly stated that there are three 'b's in "blueberry," despite being corrected multiple times by users [1][5][9] - The model demonstrated a lack of understanding by counting the 'b's in "blue" twice and misinterpreting user prompts [5][7] - Even after users provided the correct information, GPT-5 continued to assert its incorrect count, showcasing a stubbornness in its responses [9][12] Group 2: Broader Implications - Gary Marcus, a notable critic, compiled various issues with GPT-5, including its failure in basic tasks like chess and reading comprehension [15][19] - Marcus pointed out that the model exhibits a persistent problem with generalization, similar to issues seen in neural networks from 1998, indicating a fundamental flaw in the model's design [30] - He argues that the current approach of scaling models will not lead to Artificial General Intelligence (AGI) and suggests a shift towards neuro-symbolic AI as a potential solution [31][30]

Core Viewpoint - The article discusses the limitations and bugs of GPT-5, particularly its inability to accurately count letters in words, highlighting a specific incident involving the word "blueberry" [2][20][39]. Group 1: GPT-5's Counting Errors - A Duke University professor, Kieran Healy, tested GPT-5 by asking it to count the number of 'b's in "blueberry," to which GPT-5 incorrectly responded with three [2][4]. - Despite multiple attempts to clarify and correct GPT-5's counting, including asking it to spell out the 'b's, the model remained adamant about its incorrect count [8][9][11]. - Eventually, after persistent efforts from users, GPT-5 acknowledged the correct count but claimed the error was due to misinterpreting the word [15]. Group 2: General Bugs and Limitations - Gary Marcus, a notable critic, compiled various bugs found in GPT-5, including failures in basic principles like Bernoulli's principle and chess rules [20][23]. - The model also struggled with reading comprehension, misidentifying images with altered characteristics, such as a zebra with five legs [26][28]. - Marcus argues that the underlying issues with GPT-5 are indicative of broader problems in large models, particularly their inability to generalize effectively, which he attributes to long-standing issues like distribution drift [38][39][41].

big boys aren't waiting for retail to understand blob scaling• Cumberland pulls 36k ETH from coinbase• falconx/galaxy/bitgo stack 101k ETH in 2 days• sec green lights liquid stakingthey see the roadmap to 32 blobs. they're not here to flip ...