2CG4tjaCdC1LZvpoLGoCeiChEhHyTFjvn4nunaxppump 吃满 24k→68k https://t.co/sOcRrHVzW5 https://t.co/KEhz1Qy1NP

@CL_CLACL 0xdd9067077c4bca2a6fd028ff07cdf49b9f3f4444 'One-man Dynasty' is the Chinese nickname for SPCX

$evo 580k dyor CA:0x721b072dbb616f29eea73ac004e03fd4e884bba3 okx wallet:https://t.co/t1egt7vIdm An open-source "agentic code optimization" project that turns a codebase into a closed loop of automatic research and self-improvement, allowing AI to continuously experiment, analyze, and improve code like a team of scientists The core function is: give evo a codebase, and it will automatically discover metrics to optimize, help the user build benchmarks, then launch multiple AI sub-agents to run parallel experiments. Good changes are kept, bad ones are discarded, forming a tree search The entire process has a dashboard for monitoring, failure case sharing, and regression gates to prevent breaking correctness The project's inspiration comes from the "autoresearch" concept proposed by Andrej Karpathy, a top AI scientist. Evo adds structured parallelism, shared memory, exploration strategies, and more on top of this The project mainly solves the pain point of "repetitive trial and error" for developers in code optimization: Wanting to make code faster, more accurate, or more efficient requires writing benchmarks, modifying code, running tests, analyzing logs, and repeating hundreds of times. This is exhausting and can easily lead to getting stuck in local optima Ordinary coding agents only change one thing at a time, easily getting stuck or breaking functionality. Evo uses parallel sub-agents + tree search + shared experience to make exploration broader and more structured Log explosion, hard-to-find patterns, and high costs. Evo's RLM scanning, context branching, and gates directly solve these issues Lack of observability: after changes, you do not know the overall effect. Evo comes with its own dashboard, allowing real-time visibility of the experiment tree, scores, and failure patterns Within one month of launch, approximately 4,500+ unique users have installed the CLI. The SDK has been used in 7,000+ projects, indicating rapid spread within the AI developer community. It also has 700+ stars on GitHub The dev is Alok Bishoyi. Early on, he served as a capital allocation role at Day Zero Ventures, having invested in 12 companies, 6 of which reached Series A and 2 reached Series B. His focus areas include developer tools, infrastructure, B2B SaaS, and AI/ML applications in healthcare The reason for the recent price increase is that he became interested in the evo token on Bankr and privately messaged the founder. Therefore, there is a high possibility that he will claim the token The above is my analysis and understanding of the project. Thank you for watching. I hope you can like, repost, or follow me

2CG4tjaCdC1LZvpoLGoCeiChEhHyTFjvn4nunaxppump 发射 https://t.co/Q2jduQ3tIg https://t.co/TJWt5DuOkj

RT @sadd_asd77675: 0x345b8ba6c426a3bbe6eee38980d5ff51fbebaba3 Got a bit of a ZHC project, got a lot of attention After claiming the first wave, you should be able to easily get 3-4M At that time, someone opened DOTTA (People's Name Coin) for him, and he bought in support, so it was possible he claimed it But because there is...