Upgrade Your Python App with AI in Hours, Not Months

A Python script answers questions. Nobody else can use it. A FastAPI endpoint answers questions. Everyone can. That gap is 10 lines of code. I closed it on Day 17 — here is everything I measured. —— I spent 20 days building an AI system from scratch. No LangChain. No frameworks. Pure Python. Phase 5 was wrapping it in FastAPI and measuring everything honestly. —— Day 17 — two endpoints, full pipeline behind HTTP POST /ask runs the full multi-agent pipeline. GET /health reports server status and tool count. Swagger UI at /docs — interactive docs, zero extra code. First real response: 60,329ms. Day 18 — one log file changed everything Per-stage timing showed this: mcp_init: 31,121ms planner: 748ms orchestrator: 3,127ms synthesizer: 1,331ms 31 of 60 seconds was initialization. Not the model. Not retrieval. The setup — running fresh every request. Two fixes. No model change. Fix 1: direct Python calls instead of subprocess per tool. Fix 2: MCP init moved to server startup — paid once, never again. Result: 60s → 5.7s. 83% faster. Day 19 — RAGAS on the live API Same 6 questions from Phase 2. Real HTTP calls. Honest numbers. Faithfulness: 0.638 → 1.000 Answer relevancy: 0.638 → 0.959 Context recall: went down — keeping that in. Explained in the post. —— The number that reframes the whole journey: 54 seconds saved by initializing in the right place. Not a faster model. Not more compute. Just knowing what to load at startup and what to create per request. Expensive + stateless → load once at startup. Stateful or cheap → create fresh per request. That one decision is the difference between a demo and a production system. —— The full score progression — all 20 days: Phase 2 baseline: 0.638 Phase 2 hybrid retrieval: 0.807 Phase 2 selective expansion: 0.827 Phase 5 answer relevancy: 0.959 Phase 5 faithfulness: 1.000 —— 20 days. Pure Python. No frameworks. Every number real. Every failure documented. Full writeup with code, RAGAS setup, and the FastAPI tutorial: https://lnkd.in/eBDdAMiY GitHub — everything is open source: https://lnkd.in/es7ShuJr If you have built something with FastAPI — what was the first thing you wished someone had told you? #AIEngineering #FastAPI #Python #BuildInPublic #LearningInPublic

There’s a small change coming to Python that looks simple on the surface — but has real impact once you think in terms of systems. PEP 810 introduces explicit lazy imports - modules don’t load at startup - they load only when actually used At first glance, this sounds like a minor optimization. It’s not. Every engineer has seen this pattern: You run a CLI with -help - and it still takes seconds to respond Why? Because the runtime eagerly loads everything - even code paths you’ll never touch in that execution That startup cost adds up - especially in services, scripts, and short-lived jobs Lazy imports change that behavior. Instead of front-loading everything at startup - the runtime defers work until it’s actually needed So now: - unused dependencies don’t slow you down - cold starts improve - CLI tools feel instant again It’s a small shift in syntax - but a meaningful shift in execution model What’s interesting is not the idea itself. Lazy loading has existed for years - across languages, frameworks, and runtimes But Python never had a standard way to do it - teams built custom wrappers - some even forked the runtime That fragmentation was the real problem. PEP 810 fixes that - by making it opt-in - preserving backward compatibility - while finally standardizing the pattern That decision matters more than the feature. Earlier attempts tried to make lazy imports the default - and ran straight into compatibility risks This time, the approach is pragmatic: - no breaking changes - no surprises in existing systems - but a clear path for teams that need performance gains That’s how ecosystem-level changes actually stick. From a systems perspective, this connects to a broader principle: Startup time is part of user experience. Whether it’s: - a CLI tool - a containerized service - a serverless function Cold start latency directly impacts usability and cost And most of that latency isn’t business logic - it’s initialization overhead Lazy imports attack that overhead at the root. Not by optimizing logic - but by avoiding unnecessary work entirely Which is often the highest-leverage optimization you can make. The bigger takeaway isn’t just about Python. It’s this: Modern systems are moving toward just-in-time execution - load less upfront - execute only what’s needed - keep everything else deferred You see it in: - class loading strategies - dependency injection frameworks - container startup tuning Now it’s becoming part of the language itself. It’ll take time before this shows up in everyday workflows. But once it does, expect a shift in how people structure imports - especially in performance-sensitive paths Explore more : https://lnkd.in/gP-SeCMD #SoftwareEngineering #Python #Java #Backend #Data #DevOps #AWS #C2C #W2 #Azure #Hiring #BackendEngineering Boston Consulting Group (BCG) Kforce Inc Motion Recruitment Huxley Randstad Digital UST CyberCoders Insight Global

📘 #𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗕𝗮𝘀𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 | 𝗥𝗲𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 | 𝗚𝗼𝗼𝗴𝗹𝗲 | 𝗔𝗺𝗮𝘇𝗼𝗻 | 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁-𝗣𝗮𝗿𝘁 𝗜 Python interviews don’t test syntax alone. They test how you reason through real‑world code. Here are 10 real Python scenarios that interviewers love to ask 👇 👉 The pass Statement — An empty function and an empty class both contain pass. Why is it necessary, and what happens if you omit it? 👉 List Comprehension One‑Liner — Given [2, 33, 222, 14, 25], subtract 1 from every element in a single line. How would you write it? 👉 Flask vs Django — Your team is building a lightweight microservice. Why would you choose Flask over Django? 👉 Callable Objects — What does it mean for an object to be “callable”? Give examples beyond just functions. 👉 List Deduplication Preserving Order — [1,2,3,4,4,6,7,3,4,5,2,7] → produce unique values in order. One‑liner? 👉 Function Attributes — Attach a custom attribute to a function and access it later. Why would this be useful? 👉 Bitwise XOR on Strings — Perform XOR on two binary strings of equal length (without using ^ directly on strings). Write the logic. 👉 Statements vs Expressions — Is if a statement or an expression? Can you assign it to a variable? Explain with examples. 👉 Python Introspection — How can you inspect an object’s attributes and methods at runtime? Name at least three built‑in tools. 👉 List Comprehension with Condition — Generate all odd numbers between 0 and 100 inclusive in one line. 😥 “I knew the syntax… but I couldn’t explain why it works that way” — sound familiar? 𝗧𝗵𝗮𝘁 𝗴𝗮𝗽 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗛𝗮𝗰𝗸𝗡𝗼𝘄 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗮𝗳𝗲 𝗳𝗼𝗰𝘂𝘀𝗲𝘀. We train scenario thinking, not memorization. 💬 𝗪𝗵𝗶𝗰𝗵 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲𝗱 𝘆𝗼𝘂 𝗺𝗼𝘀𝘁 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗲𝗻𝗰𝗼𝘂𝗻𝘁𝗲𝗿𝗲𝗱 𝗶𝘁? --------------------------------------------------------------------------------- 𝗙𝗿𝗼𝗺 𝗡𝗼𝘁𝗵𝗶𝗻𝗴 ▶️ 𝗧𝗼 𝗡𝗼𝘄 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗝𝗼𝗯 𝗿𝗲𝗮𝗱𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 ...✈️ ---------------------------------------------------------------------------------

I was going through the Python 3.15 release notes recently, and it’s interesting how this version focuses less on hype and more on fixing real-world developer pain points. Full details here: https://lnkd.in/gSvcuvWg Here’s what stood out to me, with practical examples: --- Explicit lazy imports (PEP 810) Problem: Your app takes forever to start because it imports everything upfront. Example: A CLI tool importing pandas, numpy, etc. even when not needed. With lazy imports: import pandas as pd # only loaded when actually used Result: Faster startup time, especially for large apps and microservices. --- "frozendict" (immutable dictionary) Problem: Configs get accidentally modified somewhere deep in your code. Example: from collections import frozendict config = frozendict({"env": "prod"}) config["env"] = "dev" # error Result: Safer configs, better caching keys, fewer “who changed this?” moments. --- High-frequency sampling profiler (PEP 799) Problem: Profiling slows your app so much that results feel unreliable. Example: You’re debugging a slow API in production. Result: You can profile real workloads without significantly impacting performance. --- Typing improvements Problem: Type hints get messy in large codebases. Example: from typing import TypedDict class User(TypedDict): id: int name: str Result: Cleaner type definitions, better maintainability, stronger IDE support. --- Unpacking in comprehensions Problem: Transforming nested data gets verbose. Example: data = [{"a": 1}, {"b": 2}] merged = {k: v for d in data for k, v in d.items()} Result: More concise and readable transformations. --- UTF-8 as default encoding (PEP 686) Problem: Code behaves differently across environments. Result: More predictable behavior across systems, fewer encoding-related bugs. --- Performance improvements Real world impact: Faster APIs, quicker scripts, and better resource utilization. --- Big takeaway: Python 3.15 is all about practical improvements: - Faster startup - Safer data handling - Better debugging - More predictable behavior Still in alpha, so not production-ready. But it clearly shows where Python is heading. #Python #Backend #SoftwareEngineering #Developers #DataEngineering

Day 10: Python Code Tools — When Language Fails, Logic Wins 🐍 Welcome to Day 10 of the CXAS 30-Day Challenge! 🚀 We’ve connected our agents to external APIs (Day 9), but what happens when you need to perform complex calculations or multi-step logic that doesn't require a database call? The Problem: The "Calculator" Hallucination LLMs are incredible at understanding context, but they are not calculators. They are probabilistic next-token predictors. If you ask an LLM to calculate a 15% discount on a $123.45 cart total with a weight-based shipping surcharge, it might give you an answer that looks right but is mathematically wrong. In an enterprise environment, "close enough" isn't good enough for billing. The Solution: Python Code Tools In CX Agent Studio, you can empower your agent with deterministic logic by writing custom Python functions directly in the console. How it works: You define a function in a secure, server-side sandbox. The LLM's Role: The model shifts from calculator to orchestrator. It extracts the variables from the conversation (e.g., weight, location, loyalty tier), calls your Python tool, and receives an exact, guaranteed result. Safety First: The code runs in a secure, isolated sandbox, ensuring enterprise-grade security while giving your agent "mathematical superpowers." 🚀 The Day 10 Challenge: The EcoShop Shipping Calculator EcoShop needs a reliable way to quote shipping fees. The rules are too complex for a prompt: Base fee: $5.00 Weight surcharge: +$2.00 per lb for every pound above 5 lbs. International: Flat +$15.00 surcharge. Loyalty: Gold (20% off), Silver (10% off). Your Task: Write the Python function for this logic. Focus on handling the weight surcharge correctly (including fractions of a pound) and applying the loyalty discount to the final total. Stop asking your LLM to do math. Give it a tool instead. 🔗 Day 10 Resources 📖 Full Day 10 Lesson: https://lnkd.in/gGtfY2Au ✅ Day 9 Milestone Solution (OpenAPI): https://lnkd.in/g6hZbtGX 📩 Day 10 Challenge Deep Dive (Substack): https://lnkd.in/g6BM8ESp Coming up tomorrow: We wrap up the week by looking at Advanced Tool Orchestration—how to manage multiple tools without confusing the model. See you on Day 10! #AI #AgenticAI #GenerativeAI #GoogleCloud #Python #LLM #SoftwareEngineering #30DayChallenge #AIArchitect #DataScience #CXAS

This is bigger than it looks. First, Understand the Problem. You buy a powerful server with 10 CPU cores. You build a Python API. You deploy it. Python uses 1 core. The other 9 sit there. Idle. Doing nothing. You just paid for 10, got 1. This wasn't a bug. It was a design decision from the 1990s called the GIL — Global Interpreter Lock. A rule that said: only ONE thread runs at a time, no matter how many cores you have. Why did it exist? It made Python safer and simpler to build back then. Memory management was easier when only one thing ran at a time. It was a smart tradeoff — for 1991. For 2025? Not so much. Since Python couldn't use multiple cores in one process, the solution was: → Run 10 separate Python processes instead of 10 threads → Each process gets its own RAM, its own startup time, its own everything → 10 processes × 500MB RAM = 5GB just to use the machine you already paid for It worked. But it was expensive, wasteful, and messy. Teams switched to Go or Node.js specifically because of this. What Actually Changed ? 🔹 Python 3.13 (October 2024) → Free-threaded build introduced. Experimental. 🔹 Python 3.14 (2025) → Free-threaded officially supported. No longer experimental. Still optional. Note: The GIL hasn't been deleted forever. It's been made OPTIONAL. You choose to disable it. This was a deliberate, careful decision — the Python team didn't want to break the entire ecosystem overnight. FastAPI 0.136.0 now officially supports running on this free-threaded Python. So What Does This Actually Mean? Remember that 10-core machine? With free-threaded Python, FastAPI can now actually use those 10 cores — inside a single process — running threads in true parallel. Real benchmark numbers: → 5 threads on standard Python (with GIL): same speed as 1 thread. No improvement. → 5 threads on free-threaded Python (no GIL): 4.8x faster. In practical terms for your API: → Same traffic, fewer servers needed → Fewer servers = less RAM, less cost, less complexity → Response times improve under heavy load → Scaling becomes a choice, not a survival requirement ━━━ Who Should Pay Attention? ━━━ If you're building: 🔹 ML inference APIs — running a model on every request 🔹 Data processing endpoints — transforming, aggregating, scoring 🔹 Real-time pipelines — processing events as they come 🔹 Document parsing — PDFs, contracts, files at volume 🔹 Any API that actually computes something, not just fetches from a DB The GIL was also acting as an invisible safety net — it prevented two threads from touching the same data at the same time accidentally. Without it, if two threads modify the same variable simultaneously — you can get corrupted data or crashes. These bugs are hard to reproduce and painful to debug. The gains are real. But they require intentional adoption. If you're building Python APIs, this release deserves more than a scroll. Read the changelog. Test it. The ceiling just got raised. Thank you FastAPI

7,250 downloads. 1,880 clones in 14 days. 404 developers using it . When we started building SynapseKit, we made one rule: Don't ship the framework without shipping the documentation. Because I've used too many "promising" Python libraries that had great internals and zero explanation of how to actually use them. You'd clone it, stare at the source code for 20 minutes, and give up. SynapseKit was built to be the opposite of that. What is SynapseKit? An async-native Python framework for building LLM applications — RAG pipelines, AI agents, and graph workflows — across 27 providers with one interface. Swap OpenAI for Anthropic[Anthropic]. Swap Anthropic for Ollama[Ollama]. Zero rewrites. Streaming-first. Async by default. Two hard dependencies. But here's what actually makes me proud: The 7,250 downloads aren't from a viral post or a Product Hunt launch. They came from developers finding it on GitHub, engineers discovering it on PyPI while searching for tools, and people landing on the docs and actually understanding what they found. That last one is everything. Good documentation doesn't just explain your code. It builds trust. It tells engineers — "this project is maintained, this project respects your time, this project will still work six months from now." 105 open issues. 30 pull requests in March alone. People aren't just downloading SynapseKit — they're contributing to it. What's inside: → RAG Pipelines — streaming, BM25 reranking, memory, token tracing → Agents — ReAct loop, native function calling for OpenAI / Anthropic / Gemini / Mistral → Graph Workflows — DAG async, parallel routing, human-in-the-loop → Observability — CostTracker, BudgetGuard, OpenTelemetry — no SaaS required → Vector Stores — ChromaDB, FAISS, Qdrant, Pinecone behind one interface All of it documented. All of it referenced. All of it open source. If you're building LLM applications in Python, I'd genuinely love for you to take it for a spin. 📖 https://lnkd.in/dvr6Nyhx ⭐ https://lnkd.in/d2fGSPkX And if you find something broken, missing, or confusing - open an issue. That's exactly how 105 conversations started. No framework survives bad documentation. We're building both. #Python #OpenSource #LLMFramework #SynapseKit #AIEngineering #RAG #AIAgents #BuildInPublic #MachineLearning #LLM

🚀 This Python Roadmap Isn’t Just for 2025… It’s Timeless (2026, 2027 & Beyond!) One of the best things about Python? The core learning path doesn’t change which makes the python learning roadmap incredibly valuable no matter when you start 💡 Here’s a clearer, more detailed breakdown you can follow step-by-step 👇 🔹 1. Python Basics Start with the foundation: • Operators → Arithmetic (+, -, *, /), Comparison (==, !=, >, <), Logical (and, or, not) • Control Structures → if-elif-else, loops (for, while) • Functions & Error Handling → writing reusable code and handling exceptions 🔹 2. Data Structures Build strong problem-solving skills: • Basic → Arrays, Lists, Tuples, Sets • Advanced → Stacks, Queues, Linked Lists, Dictionaries 🔹 3. Algorithms Learn how to think efficiently: • Sorting → Bubble Sort, Merge Sort, Quick Sort • Searching → Linear Search, Binary Search 🔹 4. Advanced Python Topics Level up your coding: • Recursion • Modules & Packages • Iterators & Generators • List Comprehensions • Context Managers • Dunder (Magic) Methods • Regular Expressions • Lambda Functions 🔹 5. Object-Oriented Programming (OOP) Write scalable and clean code: • Classes & Objects • Inheritance • Polymorphism 🔹 6. Frameworks (Choose Your Path) • Async → Gevent, Aiohttp, Tornado • Web (Sync) → Flask, Pyramid • Modern → FastAPI, Django (supports both Sync & Async) 🔹 7. Design Patterns Improve code structure: • Singleton, Factory, Observer • Decorator, Builder, Strategy • Adapter, Command 🔹 8. Package Management Manage dependencies like a pro: • pip, PyPI • Conda • UV (modern tool) 🔹 9. Testing Your Applications Make your code reliable: • unittest • pytest • nose Why this roadmap works always Because it focuses on fundamentals + real world practices. Technologies will evolve. Tools will change. But these concepts will always stay relevant. Image Credits : Deepak Bhardwaj Whether its 2025, 2026, or 2027 - this roadmap will guide you the right way. That’s how you truly master Python 🐍 ♻️ I share cloud , data analysis/data engineering tips, real world project breakdowns, and interview insights through my free newsletter. 🤝 Subscribe for free here → https://lnkd.in/ebGPbru9 ♻️ Repost to help others grow 🔔 Follow Abhisek Sahu for more #python #programming #coding #softwaredeveloper

Python for AI Systems: Why Python + FastAPI is my default for AI backend services in 2025. I've built backends in Java (Spring Boot), PHP (Laravel), Node.js, and Python. Here's when I reach for each: For AI/LLM workloads → Python + FastAPI. Always. Here's why: FastAPI is genuinely fast-: Async by default, built on Starlette. Handles concurrent LLM calls without thread management headaches. AI ecosystem lives in Python: LangChain, LangGraph, OpenAI SDK, HuggingFace — all Python first. No wrappers, no translation layers. Pydantic = free input validation: Define your schema once, get validation + docs + serialization. Critical when LLM outputs need strict structure. Background tasks built-in: Streaming LLM responses + async background processing without a separate worker framework. Easy integration with data tools: Pandas, Airflow, SQLAlchemy — your AI service can talk to your data layer without impedance mismatch. Java Spring Boot is still my go-to for transactional enterprise systems. But for AI services? FastAPI + Python + Docker on AWS ECS = fastest path to production-ready AI endpoints. What's your preferred stack for AI backend services? #Python #FastAPI #LLM #AIEngineering #BackendDevelopment #AWS

An expert comparison of Flask and FastAPI for Python backends. Learn architectural trade-offs, deployment patterns with Docker and Kubernetes, performance tuning, and business impact for New Zealand projects.