Browsing Category
Software
12555 posts
Software Engineering & Development Frameworks
This hub covers practical engineering frameworks, development processes, and system design approaches used to build scalable and reliable systems.
You’ll find how-to guides, architectural breakdowns, and real-world insights focused on backend systems, cloud software, and modern developer tooling.
This content is designed for engineers, technical leads, and teams working on production-scale applications.
Key Engineering Guides
Displaying async values in Flutter
The build method in Flutter widgets is synchronous. That means it doesn’t like to wait for anything. But…
LoopSmith: Closed-Loop AI Engineering — Autonomous /goal Execution for Self-Correcting Pipelines on…
LoopSmith: Closed-Loop AI Engineering — Autonomous /goal Execution for Self-Correcting Pipelines on Antigravity 2.0 Stop prompting the agent turn by…
Stop Defaulting to LRU: How I Built a 15.5M ops/sec S3-FIFO Cache for Node.js by Hacking V8
Whenever we need an in-memory cache in Node.js, 99% of us do the exact same thing: npm install…
How I Made My AI CSV Import Pipeline Reliable by Adding Validation Layers 🚀
This is a submission for DEV’s Summer Bug Smash: Smash Stories powered by Sentry. When building AI-powered applications,…
I Built 50+ Free Online Tools — Here’s What I Learned Building RealToolHub
I Built 50+ Free Online Tools — Here’s What I Learned Building RealToolHub Building projects is one of…
Locked out of wp-admin? Why WP-CLI works when `wp-login.php` doesn’t
A forgotten password, a security plugin that blocked your own IP by mistake, a plugin bug that turns…
Run and Compare AI Evaluations with a CLI for Developers and Coding Agents
TL;DR: This walkthrough shows how developers and coding agents can use Quantiles, an open-source AI evaluation platform licensed…
Reverse image search breaks on Instagram. Here’s the fallback ladder that fixes it.
Every one-click reverse image search extension works the same way, and it is not the way you’d guess.…
Self-Evolving AI Agents: How to Automate the Tasks That Keep Changing
You ask an AI agent to analyze the market and prepare a competitor report. At first, the task…
菲尔兹奖得主邓煜专访
https://www.youtube.com/watch?v=APqER1ZJ78U 菲尔兹奖得主邓煜专访:从微观粒子到宏观流体,以及数学、随机性与AI协同研究的未来 第(一)部分 获奖后的真实状态与对菲尔兹奖的预料(0% – 12%) 获奖后繁忙而惊喜的一周:邓煜教授表示,获奖这一周非常忙碌。他回忆在ICM开幕式当天,整个上午的行程都被排满,当他终于回到酒店时,发现邮箱里堆积了将近100封未读邮件。这种被大量关注包围的感觉既令人疲惫,也带来了”很棒的惊喜”。他特别提到,自己现在在中国互联网上变得非常出名,但对于这种突如其来的全民关注度,他坦言”不知道该如何形容这种感觉”,语气中带着一丝无奈和腼腆。 对获奖的长期心理准备与临近时的紧张:主持人追问他是否曾预料到自己会得奖。邓煜回应称,几年前确实有过一些模糊的念头——尤其在2014年那项关键工作完成时,他曾经认真想过”也许有戏”。但他强调,自己真正得知获奖消息是在2026年1月,因此此前并没有持续地往这方面想。随着ICM时间临近,他承认自己”有一点紧张,但并不特别紧张”。他总结出一种人生态度:这类荣誉是个人无法控制的事情,所以平时不会去过度思虑,顺其自然就好。 第(二)部分 希尔伯特第六问题的突破性工作及通俗解释(12% – 25%) 获奖核心贡献的定位:主持人指出,邓煜的奖项部分表彰了他在希尔伯特第六问题上的突破性工作,这是一个悬置超过125年的世纪难题。 从微观粒子到宏观流体的”建桥”工作:邓煜用通俗语言解释了他们证明的内容。他请大家想象由微小颗粒或分子组成的流体或气体——这些粒子的个体行为相对简单,遵循牛顿定律和两两相互作用。他们的工作从这一庞大系统出发,试图找到控制粒子统计行为的有效方程,也就是玻尔兹曼方程(Boltzmann equation);再通过第二个极限步骤,推导出我们熟悉的流体方程。简而言之,他们的主要成就就是在不同尺度之间建立起了数学桥梁——把微观粒子系统的牛顿力学与宏观流体的动力学方程严格连接起来。 从奥数金牌到数学家的渐进路径:作为一名高中生主持人好奇他是如何走到今天的。邓煜回顾:高中时期他参加了国际数学奥林匹克(IMO)并获得金牌,那时他开始隐约感觉”数学或许就是我该走的路”。他强调这并非某个瞬间的决定性选择,而是一个渐进过程——IMO之后他开始自学分析学等大学数学内容,进入大学后他始终保持对数学的热爱,渐渐意识到”这就是我想做的事”。 第(三)部分 随机性中秩序的涌现:思想方法与未来问题(25%…