Alex Michael’s Post

📘 What I Learned Today: Error Handling & Debugging in Python Today’s focus was on writing code that doesn’t just work — but handles failures gracefully. 🔹 Key concepts: → Exceptions (try / except / finally) → Custom exceptions for better control → Common runtime errors (ValueError, TypeError, IndexError, etc.) 🔹 In simple terms: Error handling is about preparing your code for the unexpected and ensuring it doesn’t break silently. 🔹 Why it matters in AI: AI systems deal with messy, unpredictable data and external dependencies. Without proper error handling, small issues can lead to incorrect results or system failures. 🔹 My takeaway: Good developers write code that works. Great developers write code that handles when things don’t. #AI #Python #ErrorHandling #Debugging #LearningInPublic #BuildInPublic

I built an AI agent from scratch. No LangChain. No LangGraph. No CrewAI. Just Python, Gemini 2.5 Flash, and raw tool calling. Here's what I learned that no framework tutorial will teach you: 1. The agentic loop is embarrassingly simple Build messages → call LLM → if tool_call → execute → feed result back → repeat. That's it. Every framework is just a wrapper around this. Once you see it raw, you can never unsee it. 2. Frameworks hide your bugs from you When something breaks in LangChain, you're debugging the framework. When something breaks in raw Python, you're debugging your logic. Big difference. One makes you smarter. One makes you dependent. 3. Tool schema design is where agents actually fail The LLM doesn't call the wrong tool because it's dumb. It calls the wrong tool because your schema description was ambiguous. Write your tool descriptions like you're explaining them to a junior dev on their first day. Precise. No assumptions. 4. 50 lines of Python is enough to go to production My personal concierge agent — the one that lives on my portfolio, captures leads, and pings my phone instantly — is ~50 lines. No overhead. No magic. Just code I fully understand and can debug at 2am. 5. You should build one without a framework at least once Not because frameworks are bad. LangGraph is excellent. I'm using it next. But if you've never written the raw loop yourself, you're flying blind. You're trusting abstractions you don't understand. Build it raw first. Then use the framework. You'll use it 10x better. --- Full source code in the comments — ~50 lines, no magic, just the loop. Follow along if you're into agentic AI and building real things, not just demos. #AgenticAI #Python #BuildingInPublic #LLM #SoftwareEngineering

📘 What I Learned Today: Pythonic Thinking Today’s focus was not just writing Python code — but writing it the right way. 🔹 Key concepts: → Iterators & generators (memory-efficient data handling) → zip, enumerate, map, filter, reduce (clean transformations) → Shallow vs deep copy (avoiding hidden bugs) → Mutability vs immutability (understanding data behavior) → *args & **kwargs (flexible function design) 🔹 In simple terms: Pythonic thinking is about writing cleaner, smarter, and more efficient code instead of longer code. 🔹 Why it matters in AI: AI workflows involve large datasets and complex transformations — efficient and bug-free code makes a huge difference. 🔹 My takeaway: Good Python code is not just about “working” — it’s about being readable, efficient, and scalable. #AI #Python #LearningInPublic #CleanCode #TechJourney #BuildInPublic

Most Python frameworks were not built for AI. FastAPI was. Here is why every serious AI engineer uses it: 1. Async by default LLM calls take 2 to 10 seconds. FastAPI handles 50 requests while yours is still waiting. 2. Pydantic validation built in Bad input wastes expensive API tokens. FastAPI rejects it before your code even runs. 3. Auto-generated documentation Every endpoint is live and testable at /docs the second you write it. Zero extra work. 4. Clean error handling AI services fail constantly. FastAPI gives you the structure to handle that without messy code. 5. Scales with your stack LangChain, LlamaIndex, every major AI SDK integrates cleanly with FastAPI patterns. You could fight a synchronous framework to build AI products. Or you could just use the right tool. Are you building with FastAPI? Drop your stack below. #AIEngineering #FastAPI #Python #LLM #BuildingWithAI

Unpopular Opinion Your AI integration does not need a vector database, a fine-tuned model, a RAG pipeline, and six Python wrappers just to answer "What is my account balance?" That is not an AI product. That is a research paper with a login screen. Ship the boring call. Add the smart layer only when the boring call fails you. #BuildAINotABurden #MVPBeforeMLOps

📰 Getting Started with Smolagents: Build Your First Code Agent in 15 Minutes Build an AI weather agent in 40 lines of Python using Hugging Face's smolagents library. Learn to create tools, connect LLMs, and run autonomous tasks. 🔗 https://lnkd.in/d_XNugZr #أخبار_التقنية #ذكاء_اصطناعي #تكنولوجيا

Ever felt your AI/ML scripts dragging when performing the same computation multiple times? 🤔 Whether it's complex feature engineering, lookup tables, or even some model predictions, repeated calculations can seriously slow you down. Good news! Python has a neat trick up its sleeve: @functools.lru_cache. This isn't just a fancy decorator; it's like giving your functions a super-smart memory. It stores the results of expensive function calls and, if you call that function again with the *same inputs*, it instantly returns the cached result instead of re-running the whole thing. 🧠💨 Think about it for feature generation: if you compute `sentiment_score('positive review')` dozens or hundreds of times, `lru_cache` ensures that complex calculation only happens ONCE. The rest are instant lookups! This little gem can dramatically speed up your data preprocessing and model experimentation. Ready to give your ML workflows a serious boost? What's your go-to Python trick for optimizing ML pipelines? Share below! 👇 #Python #MachineLearning #AICoding #PythonTips #DataScience

forty lines of python to make an “agent.” cute. great for demos, terrible as a promise of reliability. neat way to learn how tools, LLMs, and wiring fit together – but expect brittle glue, silent failures, and one weird input that breaks the loop at 3am. play with it, then refactor with retries, types, and tests. shrug.

Source: KDnuggets https://search.app/Y3EbX Introduction If you have built AI agents that work perfectly in your notebook but collapse the moment they hit production, you are in good company. API calls timeout, large language model (LLM) responses come back malformed — and rate limits kick in at the worst possible moment. The reality of deploying agents is messy, and most of the pain comes from handling failure gracefully. Here is the thing — you do not need a massive framework to solve this. These five Python decorators have saved me from countless headaches, and they will probably save you, too.

Just published: "How to Build Your First AI Agent with Groq Using Python" https://lnkd.in/gysVQxV3 I see developers struggle with their first AI agent ALL the time. They try complex frameworks → get lost in abstractions → build nothing useful. Here's the simple truth: You can build a working AI agent with 100 lines of Python + Groq. No overengineering. Just clean code that works. What you'll get: ✅ Working agent code (copy-paste ready) ✅ Tool calling + decision loop ✅ Production guardrails ✅ Real business use cases ✅ Common mistakes to avoid https://lnkd.in/gysVQxV3 We also run corporate AI training to help teams build agents that actually deliver ROI (not just demos). Contact: supriyochatterjee@cseametry.co.in Visit: cseametry.co.in #AIAgents #Groq #Python #AIWorkflows #DeveloperTools