Jing Ge’s Post

Most people rush to write code. Very few pause to understand what code actually is. Python, at its core, is not just a programming language it’s a structured way of thinking. 🔹Take comments. They are ignored by the machine, yet essential for humans. That alone reveals something important not everything valuable in a system is meant for execution some things exist purely to create clarity and shared understanding. 🔹Variables may look simple, but they represent abstraction the ability to assign meaning to data. Naming rules are not arbitrary they enforce discipline. Clean names often reflect clean thinking, while messy names usually signal unclear logic. 🔹Then come data types integers, floats, strings, booleans. These are not just categories they are constraints. And constraints are what make systems predictable and reliable. A language that distinguishes between "12" and 12 is a language that demands precision in thought. 🔹Even string indexing carries a deeper idea any structure can be accessed, sliced, and interpreted differently depending on perspective forward or backward. It’s a reminder that how you look at something changes what you see. 🔹Type conversion introduces another subtle lesson. Sometimes transformation happens automatically (implicit), and sometimes it requires intent (explicit). Knowing when each occurs is the difference between control and assumption. 🔹And then there is truth in Python only a small set of values evaluate to false everything else is true. That’s not just syntax, it is a model of evaluation clear, minimal, and consistent. 🔹Finally, Python’s execution model bytecode and the Python Virtual Machine reminds us that what we write is never what the machine directly understands. There’s always a layer of translation. What feels simple at the surface is powered by deeper abstraction underneath. At this level, programming stops being about syntax. It becomes about systems, logic, constraints, and clarity of thought. #Python #PythonProgramming #Programming #Coding #SoftwareDevelopment #ComputerScience #Tech #TechThinking #LogicBuilding #ProblemSolving #Abstraction #DataTypes #Variables #LearnPython #CodingJourney #DevCommunity #SoftwareEngineering #BackendDevelopment #FullStackDevelopment #ComputerScienceStudents #DeveloperLife #CleanCode #CodeNewbie #TechEducation #ProgrammingFundamentals

The Soft Power of Python If Go is hard power—efficient, structured, built for control—then Python is soft power. It doesn’t force its way into systems. It invites itself in. “Need to analyze some data?” Python. “Automate that boring task?” Python. “Build an AI model that might accidentally write your resignation letter?” Also Python. It wins not by being the best at any one thing, but by being good enough at almost everything—which, in a country built on general-purpose solutions and multipurpose tools, is the closest thing to invincibility. Go is a language you respect. Python is a language you use. And there’s a quiet hierarchy in that distinction. Because respect is earned in conferences, benchmarks, and architecture diagrams. But usage? Usage happens at 2 a.m., when something is broken, and you reach for the tool you know will work without arguing with you. The Uncomfortable Truth (Delivered Calmly) “Python rules them all” isn’t a technical statement. It’s a behavioral one. It means: more people choose it more problems get solved with it and more industries quietly depend on it Not because it’s flawless—but because it’s frictionless enough. And in the long arc of American systems—economic, technological, cultural—the thing that reduces friction tends to win. Not the strongest. Not the fastest. The one you don’t have to think twice about.

Wrote code in Sublime, just regular old Python autocomplete and sytnax highlighting. Asked claude to challenge ME to write some code (JS world ~10 years, not much python since college). When I didn't know something, I looked at documenation. I'll tell ya what, my solution was 💩 But it worked. I had fun. I felt achievement. I was reminded in Python dict on a list of 2-item lists it pops them out as nice key-value pairs. Re-learned that line.split() works on any amount of whitespace in strings, nothing like that in JS. List comprehension after I submitted my dooky solution. Practicing string manipulation and data structures feels important.... How else do we learn to put better things in and get better things out? Maybe with future models -- it doesn't matter, the AI is just better than you at everything from database design to dev ops. For now, I think there's still a reason to hone your craft, and a reason they have a bunch of PHDs building these models.

Do you actually understand what Python is… or do you just know its definition?🐍 Most people say: “Python is a high-level, interpreted language created by Guido van Rossum in 1991.” That’s not understanding. That’s memorization. Python is not just a language. Python is a layer of abstraction. ⚙️ When early languages like C were designed, they stayed very close to the machine. 💻 You had to think about memory, pointers, and low-level details. That’s why C is fast—because it sits close to hardware. But here’s the trade-off: Closer to hardware → more control, more complexity Higher abstraction → less control, more productivity Python was built to move you away from the machine and toward problem-solving. Someone already did the hard work: Memory management? Handled. Complex system interactions? Hidden. Syntax complexity? Reduced. So instead of thinking: “How does the computer execute this?” You think: “What logic solves this problem?” 🚀 That’s why Python is widely used in: Machine Learning Web Development Automation Data Analysis Not because it’s the fastest — it’s not. But, because it allows you to build faster and think more clearly. Final point: 🎯 Python didn’t become popular by accident. It became popular because it removes friction between your idea and implementation. #python #pythonprogramming #learnpython #coding #programming #machinelearning #deeplearning #datascience #artificialintelligence #ai #ml #softwareengineering #systemdesign #computerscience #codinglife #programminglogic

Two years ago, Sam Thach, Caleb Hart, Joshua Aguayo, and I took on a formidable project: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐡𝐚𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐬 𝐦𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥 𝐝𝐨𝐜𝐬𝐭𝐫𝐢𝐧𝐠𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐩𝐫𝐨𝐣𝐞𝐜𝐭? We explored several NLP-based ideas such as translation systems, auto-documentation, and text generation, but ultimately landed on a Python Code Commenter because it solved a problem we all had firsthand experience with. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐥𝐞𝐠𝐚𝐜𝐲, 𝐩𝐨𝐨𝐫𝐥𝐲 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐞𝐝 𝐜𝐨𝐝𝐞. 𝐓𝐡𝐞 𝐠𝐨𝐚𝐥 𝐰𝐚𝐬 𝐬𝐭𝐫𝐚𝐢𝐠𝐡𝐭𝐟𝐨𝐫𝐰𝐚𝐫𝐝: Take a file of uncommented Python functions and return the same code with autogenerated docstrings that explain what each function does. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭: • Fine-tuned a T5 transformer model using PyTorch • Trained on a non‑proprietary dataset from Hugging Face • Framed the problem correctly as a code-to-text translation task • Generated docstrings only for valid function definitions to ensure reliability • Designed and implemented a Tkinter GUI so the tool was usable by non-ML users By the end, we had a functional prototype that could process large volumes of uncommented code and meaningfully document function definitions, landing an average accuracy score of ~1.43/2 across independent evaluations (with 2 being amazing). 𝐒𝐜𝐨𝐩𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 & 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐜𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬: This project was a great lesson in adapting plans to reality. 𝘞𝘩𝘢𝘵 𝘴𝘵𝘢𝘳𝘵𝘦𝘥 𝘢𝘴: • Line-by-line comments • CodeBERT • Broad scope 𝘌𝘷𝘰𝘭𝘷𝘦𝘥 𝘪𝘯𝘵𝘰: • Function-level docstrings • Switching from CodeBERT to T5 • A narrower, more robust and defensible solution 𝘈𝘭𝘰𝘯𝘨 𝘵𝘩𝘦 𝘸𝘢𝘺, 𝘸𝘦 𝘥𝘦𝘢𝘭𝘵 𝘸𝘪𝘵𝘩: • School-imposed security restrictions • Insufficient hardware and delayed access to Data Science machines • Shared environment issues • Version control growing pains • Team availability constraints during a compressed timeline None of these stopped the project, but 𝐚𝐥𝐥 𝐨𝐟 𝐭𝐡𝐞𝐦 𝐟𝐨𝐫𝐜𝐞𝐝 𝐮𝐬 𝐭𝐨 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 𝐛𝐞𝐭𝐭𝐞𝐫, 𝐫𝐞𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐜𝐨𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲, 𝐚𝐧𝐝 𝐦𝐚𝐤𝐞 𝐩𝐫𝐚𝐠𝐦𝐚𝐭𝐢𝐜 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. 𝐖𝐡𝐚𝐭 𝐈 𝐭𝐨𝐨𝐤 𝐚𝐰𝐚𝐲: Beyond the technical skills, this project reinforced lessons I still apply today: 1. Scope management matters! 2. “Crunch” is real, and planning for it is essential 3. Many problems already have partial solutions; understanding them is half the job 4. Framing the problem correctly can unlock progress 𝐌𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭𝐥𝐲, 𝐢𝐭 𝐫𝐞𝐦𝐢𝐧𝐝𝐞𝐝 𝐦𝐞 𝐡𝐨𝐰 𝐦𝐮𝐜𝐡 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐚𝐫𝐞 𝐰𝐡𝐞𝐧 𝐚 𝐭𝐞𝐚𝐦 𝐬𝐭𝐢𝐜𝐤𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐮𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 𝐚𝐧𝐝 𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐚𝐛𝐚𝐧𝐝𝐨𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. If you’re curious, the full project can be found here: 🔗 https://lnkd.in/gPNaKjv3

Why Python is Still Winning in 2026 🐍 People keep saying “Python will die”… But Python is still winning in 2026 😳 Content: Every year new languages come… But Python still stays on top 👇 Here’s why Python is still dominating: 🔥 Simple & easy to learn → Perfect for beginners and pros 🔥 Huge ecosystem → Libraries for AI, Web, Data, Automation 🔥 Used in AI & ML → Most AI tools are built with Python 🔥 Fast development → Build projects quickly 🔥 Strong community → Millions of developers support it What people think: ❌ Python is slow ❌ Python will be replaced Reality: Python is not the fastest… But it is the most practical language 🚀 Why this matters: Choosing the right language can save you years Big advantage: With Python, you can build: • APIs (FastAPI / Django) • AI apps • Automation tools • Data systems Pro Tip: Don’t chase trends… Learn tools that actually solve problems 💯 CTA: Follow me for real dev insights 🚀 Save this post if you’re learning Python 💾 Comment "PYTHON" if you believe in it 👇 #Python #Programming #Developer #Coding #Tech #SoftwareEngineer #Developers #AI #LearnPython #FutureTech

🚨 If performance is everything, then how come Python continues to dominate in AI/ML/DL? This is one of the most common questions that arise when one talks about programming languages like C, C++ and Java whose performance is way higher compared to Python.  Developers consistently choose Python. Here's why: 1️⃣ Ecosystem over raw speed: Python's rich libraries like TensorFlow, PyTorch, scikit-learn removes the need to build complex algorithms from scratch, accelerating innovation. 2️⃣ Simplicity that scales productivity: Python code is easy to understand and maintain which allows faster experiments, iterations, and implementations, something very important when developing intelligent systems. 3️⃣ Strong community and continuous evolution: A global community actively contributes to frameworks, tutorials, and tools, making problem-solving faster and more collaborative. 4️⃣ Integration with optimized backend engines: There's no big difference since Python often serves as a frontend layer over the optimized C/C++ codebase. 5️⃣ Focus on results not optimization: In AI, time-to-solution and experimentation matter more than micro-level performance gains. The reality is: Python isn't replacing faster languages, it's orchestrating them. #Python #MachineLearning #ArtificialIntelligence #DeepLearning #SoftwareEngineering #TechInsights #Innovation

I heard a tip to use Rust instead of Python whenever you are coding with AI due to the speed and more importantly the validation. The code wont compile if there are errors. Unlike writing in the Python where you have to do the validation for AI and go back and forth with prompts to fix it. I'm finding it way faster to generate to code. Even though I dont know Rust that well it will be a great learning experience. Right now I'm using Claude Code but I might switch back to OpenCode's models again to see if that works. https://lnkd.in/gaDkaHXu

Everyone's generating code with LLMs. Almost nobody is systematically checking it. I ran into this while translating Python to C++. The translation part was easy. Knowing whether the output was correct? That was the real problem. I haven't written C++ for a while, so I might not even recognize a wrong answer. So instead of hoping the output is correct, I added a verification layer. 3 agents, each with a different job: → Agent 1 (Gemini 2.5 Flash): translates Python to C++ → Agent 2 (GPT-5 Mini): reads the original Python and generates test expectations → Agent 3 (GPT-5 Mini): evaluates the C++ against those expectations and flags issues If the evaluation fails, the issues get fed back to the translator. It retries (up to 3 rounds) until it passes or returns its best effort. One deliberate choice: the translator and the evaluator use different LLMs. If the same model translates and evaluates, it tends to confirm its own mistakes. Gemini translates, GPT evaluates. A genuine second opinion. The whole verification is static analysis. No compiler, no execution. The evaluator reads the C++ and reasons about correctness. For deterministic math code, this works surprisingly well. For anything more complex, the lack of execution is a real gap. And the obvious next step. It started as a course exercise where you translate Python to C++ and manually compile to verify. I wanted to automate the part where you stare at the output and hope. Repo: https://lnkd.in/g3tWFUPZ