👨💻 程序员TL,也是经济学爱好者 🤖 深入研究 AI Agent 架构、Harness 实践与工程落地踩坑 📊 用工程师的数据视角,从新闻、研报和生活里琢磨经济怎么影响普通人 📌 用图表与长文,深度拆解经济学与 AI 前沿
Health is a trend reference from public data only. It says nothing about the account's standing on X; X's own status is authoritative.
| Metric | Today | Yesterday | 7 days | 14 days | 30 days |
|---|---|---|---|---|---|
| Tweet views | ≥ 308 | ≥ 67.6K | ≥ 333K | ≥ 522K | ≥ 523K |
| Posts | 3 | 11 | 87 | 339 | 366 |
| Replies sent | 43 | 483 | 2,875 | 3,998 | 4,350 |
| Growth | +21 | +183 | +1,303 | +2,762 | — |
| Avg views / post | 6,921 | 6,921 | 6,921 | 6,921 | 6,921 |
| Top post | ≥ 58 | ≥ 22.0K | ≥ 93.4K | ≥ 93.4K | ≥ 93.4K |
This page counts only the account's own tweets; retweets are excluded. Avg views / post uses tweets past 24h only. "≥" means the window still holds tweets gathering views / replies, or only part of it was updated (earliest update: 10-01 23:36), so the real value is at least this; "—" means data is incomplete, not zero.
X doesn't publish creator payouts. Paid cycles only show collected figures: "Verified" = checked and entered by us, "Tweet" = the amount the account posted itself (verified), "Reported" = a reviewed user report; nothing collected means "—". The amber bar beside each cycle is the estimate kept from before it was paid, to show how close it came. "This cycle so far" is an estimate, always $ per million views × this cycle's views (average daily tweet views over the last 14 days, replies included, retweets not, × days elapsed). Accounts with a known payout use their own rate (payout ÷ that cycle's views, with 14 days of the account's average daily views standing in where that cycle wasn't fully collected); others use the site-wide average, currently $131.62. The average comes from 333 accounts with a known payout. A ballpark only. Know the real figure? Use "Report revenue" above.
最近在团队代码 Review 时发现了一个特别危险的苗头: 大家用 AI 助手写代码越来越上头,编辑器里 Tab 键敲得飞快,一整段完美的逻辑几秒钟就吐出来了。 但只要稍微追问一句:“这个高并发场景下的事务隔离级别为什么选这个?” 对方瞬间卡壳。 AI 让写代码的“手速”提高了十倍,但也彻底剥夺了程序员对底层工程细节的“警惕性”。 大家都把 AI 当成了不报错的黑盒,直到生产环境突发内存泄漏,才发现自己根本不懂 AI 到底写了啥。 把…
很多人觉得 AI 发展起来后,最先失业的是初级码农(CRUD 仔)。 但作为一个带团队的 TL,在实测过几十种 AI 辅助开发工具后,我得到了一个完全相反的结论: AI 最先淘汰的,恰恰是那些“不上不上”的中层架构师。 为什么? 初级码农成本低,现在加上 AI 赋能,一个人能干过去三个人的活,产出性价比极高; 而部分中层架构师,天天开会做 PPT、画流程图,自己不写代码,技术细节落后于最前沿的 AI 工具,管理成本还极高。 当基础程序…
深夜跟几个大厂的架构师聊天,大家不约而同达成了一个细思极恐的共识: 以前,写一手优雅、规范、高并发的业务代码是程序员安身立命的护城河,能换取高额的薪资溢价。 而现在,当 AI 能够一秒钟吐出标准模板时,“写代码”这件事彻底沦为了毫无壁垒的通货。 当技术门槛被大模型夷为平地,程序员如果还把核心竞争力局限在“熟练工种”上,离被降维优化真的只有一步之遥。 在 AI 已经把代码“平权”的今天,你觉得程序员的核心溢价到底在转移到哪里?
很多创业公司老板以为接入大模型后,就能把年前积攒的历史“屎山代码”一口气重构掉。 直到上周我们用 Agent 尝试去跑一个沉淀了 5 年的遗留业务模块: 大模型不仅没有理解复杂的隐式依赖,反而用极其优雅的代码规范,把原有的业务逻辑漏洞重写了一遍,测试环境当场崩溃。 AI 根本不懂什么叫业务历史妥协,它只会给你生成外观精美、底层剧毒的“新屎山”。 在你们公司,有人真的敢把旧系统的核心重构彻底交给 AI 吗?
By UTC day: the growth title goes to #1 on that day's board, the posts / replies sent / views titles to #1 among the tweets collected that day. Today's entries can still change.