Python vs Go: Choosing the Right Language for the Task

Andrej Karpathy published microgpt.py — a GPT in 200 lines of pure Python. No PyTorch. No dependencies. Just raw autograd and attention built from scratch. It's a useful way to see exactly what's happening inside a language model: the matrix multiplications, the gradient flow, the attention mechanism in actual code. We walked through it for developers — what each function does, why it works, and when you'd build from scratch vs call an API. Full breakdown: https://lnkd.in/gmNbi5kQ

Building a deep research agent with Ivan Leo right now. Python, Pydantic, and APIs are all you need. Thinking of this structure: 1.⁠ ⁠User prompt 2.⁠ ⁠Agent asks clarifying questions if needed 3.⁠ ⁠Planner writes research plan 4.⁠ ⁠Main agent orchestrates subagents with individual questions/topics 5.⁠ ⁠Subagents return answers + sources (the context eng bit) 6.⁠ ⁠Main agent synthesizes into final report Would you add/delete anything? Come build this with us in a few days. Register to join live or get the recording afterwards: https://luma.com/u9osjjbn

Yesterdays full day class for Silicon Dojo at American Underground in #Durham is now on #YouTube. "Intro to #SQL and #Bottle for Building #AI Apps in #Python (Full Day)" It's a good class to really start to see how "AI" actually fits into a tech stack. https://lnkd.in/e36ZW8j9

🗓️Day 199 of 365 Days I went through a Python cheat sheet to quickly revise the most important concepts. Reviewed: 🔹 Basic syntax and data types 🔹 Loops and conditional statements 🔹 Functions and list/dictionary operations 🔹 String handling and common built-in methods This quick recap helped refresh key concepts and improve recall speed. Strengthening the fundamentals before moving into deeper AI and ML topics 🚀 #365DaysOfCode #Day199 #Python #Revision #CheatSheet #LearningJourney #Consistency

Building AI apps with LangChain ❓ Then understanding its core components isn’t optional — it’s foundational. I’ve just published a hands-on guide explaining LangChain with practical Python examples, covering: → Models → Messages → Tools → Agents → Memory → Streaming → Structured Output → Middleware I break down each of these in a practical LangChain guide — with working Python code. This is implementation focused, not theory. Full article in the first comment 👇 #LangChain #AIEngineering #Python #LLM #GenerativeAI

This simple Python function pushes all zeros to the end of an array in-place, preserving the order of non-zero elements — all in O(n) time and O(1) extra space. A great example of: ✅ Two-pointer technique ✅ Space optimization ✅ Writing readable, interview-ready code Small problems like this sharpen big problem-solving skills. 🚀 #Python #DataStructures #Algorithms #CodingInterview #CleanCode #SoftwareEngineering

This problem demonstrates how dictionary comprehensions can create key–value pairs dynamically. It highlights how values can be generated from existing data while building a dictionary in a compact and readable way. A very useful technique when transforming datasets. THE ANSWER IS: B #Python #DictionaryComprehension #PythonChallenge #DataStructures #BuildInPublic

Weekend project: turning Lego chaos into a searchable database. Read the full article: Antigravity and Lego Inventory ▸ https://lttr.ai/Aoe3H #ai #dev #lego #python #antigravity

🤖 AI Journey #8: We Saw Attention mechanism helping understand meaning as a whole to get next Token. But HOW Does an LLM Actually Generate Code? Let us every step of "Write a Python palindrome function" — from your prompt to working code. In Post #6, we explored how the Attention Mechanism helps an LLM understand the full meaning of a sentence — resolving context, coreference, and relationships between words. But that raises the obvious next question: Understanding a sentence is one thing. But how does the model actually generate an answer — especially working code or detailed answer in as in post#7? Let's trace exactly what happens when you type: "Write a Python function to check if a number is a palindrome" I'll walk through every single step in Document attached

We have done presentations on LLMs for nearly 3 yrs. Taking a break from it and going back to the basics. Benjamin E. is doing a talk on visualizing Algos. This should be fun. Come check it out.