Rewriting Python code with Rust seems easy. Especially with AI. But the devil is always in details.
To confidently rewrite Python code to Rust with AI, you should follow these four steps 🧵
The AI ponzi scheme goes like this:
Everyone is generating all these long ass docs and then passing them off for others to read
Then the person receiving is like, wtf this is way too long, and hands that into an AI to read and summarize
Then they are generating a long ass response back
and this cycle goes like that forever. and we call this work now 😅
The token lords watch this from their towers nodding and grinning.
Most engineers change teams way too often.
If you chop and change a lot you never learn how to build a codebase that can be supported. You never see the consequences of the choices you made.
Growth comes from breadth and depth.
Google 把内部工程师的代码审查(Code Review)规范公开啦
这几乎是目前业界最顶级的标准
很多程序员只会写代码,但不知道怎么审代码,可以看看 Google 是怎么做的
1.双向指南:不仅教审查者怎么挑毛病,还教作者怎么写出容易通过的代码
2.术语科普:解释了 Google 内部常用的 LGTM(看起来不错)和 CL(变更列表)到底意味着什么
3.实战价值:这套规范不是理论,而是 Google 每一位工程师都在用的实际操作准则
如果你想提升团队的代码质量,或者想知道顶级大厂的开发门槛,这份文档必读!
https://t.co/OdaozRkMYn
The first thing I do on every project is setting up CI/CD with code quality, tests, deployment and e2e tests.
This way, you eliminate “works on my machine” situations and set up things for success and calm Friday deployments.
Check the course below👇
Are you a #python package maintainer and looking for funding? Read the Docs allows you to collect funds via EthicalAds. And now if you'd like, you can route that directly to the #Django Software Foundation
https://t.co/mUh1amp8Bl
Really cool way to find out which models you can run on your computer:
1. Install llm-checker
$ npm install -g llm-checker
2. Detect your hardware
$ llm-checker hw-detect
3. Get a recommendation
$ llm-checker recommend --category coding
Here are some of the recommendations I got:
7 Python tips that will level up your code 🐍
1. Use f-strings instead of .format() or % for string formatting - cleaner and faster
2. List comprehensions are more Pythonic than for loops for creating lists
3. Use enumerate() instead of range(len()) when you need both index and value
4. Context managers (with statements) automatically handle resource cleanup
5. Default dict values with .get() to avoid KeyError exceptions
6. Use pathlib instead of os.path for file operations - more readable and cross-platform
7. Generator expressions save memory for large datasets compared to list comprehensions
Which tip do you use most often?
“Celebrated are the minimal dependencies, the humble function that just quietly does the job, the code that doesn't need to be touched for years because it was done right once.” https://t.co/EmT4KleCx2
Claude Code is using inline imports so much that I wrote my own extensible linter where it's easy to implement your own rules like “No inline imports” or “No IFs in tests”.
You can find it here: https://t.co/4dSZRGgFld