API Developer Emotions: Handling Creative Breakage

If your Spring Boot app can call an API… it can now think, search, and act too. Can Java replace Python? This document breaks down the Spring AI ✅ what it is ✅ what it can build (Chat, RAG, Tool Calling, Structured output) ✅ what you need to set it up ✅ mini code examples + real enterprise adoption pattern ✅ the honest answer: “Will it replace Python?” If you’re a Java #dev #architect #manager planning #GenAI in #production, this is your quick roadmap. Which part are you most interested in? RAG or Tool Calling? #SpringAI #SpringBoot #Java #GenerativeAI #LLM

Python is like that high school ex you run into: you remember the good times, but five minutes in, you're reminded exactly why you moved on. 🐍 After being fully immersed in the TypeScript ecosystem, jumping back into a Python project felt... weird. Going from the safety of strict typing and seamless ESM imports back into the land of manual labor was a wake-up call. Here is the "Welcome Back" package I didn't ask for: The Venv Ritual: Why am I still manually creating and activating virtual environments in 2026? It feels like hand-cranking a car engine just to go to the grocery store. 🛠️ The "Invisible" Packages: That moment when uv list shows the package is there, the interpreter is set correctly, but Python still insists it doesn't exist. Ghosting at its finest. 👻 Manual Everything: Coming from a world where the tooling feels like it has your back, Python's setup feels like it's actively trying to trip you up over a stray .env or a path mismatch. Don't get me wrong, Python is a powerhouse for AI and Data Science, but the developer experience gap is becoming hard to ignore. When you're used to the speed of tools like Bun or the reliability of TS types, those "silly" import errors feel a lot less like a minor bug and a lot more like a tax on your productivity. Has anyone else felt this "syntax shock" lately, or have I just been spoiled by the TS ecosystem? #SoftwareEngineering #Python #TypeScript #WebDevelopment #FullStack #CodingLife

I built my first Claude Code skill to learn how skills worked. That experiment became Python Mastery, and today I'm releasing it publicly for the first time. It's an opinionated, practitioner-grade Python reference covering the language from A to Z with the judgment of a senior engineer baked in. Not a cheat sheet. Twenty focused modules covering the stuff that actually trips people up in production: - When to use a class vs. a function, and why it matters - Safe refactoring without regressions - Debugging methodology, not just debugging syntax - FastAPI, SQLAlchemy, asyncio, Pydantic v2, pytest, Docker, and more - Architecture decisions, ADRs, tech debt and the architect mindset Built from real experience, not just the docs. See Readme on how to Install in one line, i tried to make it simple since its currently not nativly. Or grab the .skill file and upload it straight to Claude.ai. This is v1 and I want to make it better, so if you try it I'd love to hear what's missing, what's useful, or what could be sharper. Comments, GitHub issues, and DMs all welcome. Repo Link: https://lnkd.in/eM-cRG8A #Python #ClaudeCode

One observability lesson that took me years to fully understand: Slow queries rarely appear suddenly. They usually start small. A feature is added. A queryset grows. A join becomes heavier. Indexes stop being enough. And one day a request that used to take 40ms suddenly takes 900ms. What changed how I build Django systems was starting to observe database behavior continuously, not only when something breaks. A few practices that helped a lot: Tracking query count per request Monitoring slow query logs Measuring endpoint latency over time Profiling ORM-heavy views Making database metrics visible to the team Django’s ORM is powerful, but it can hide the real cost of queries if you don’t measure them. Performance problems don’t usually come from a single bad line of code. They come from systems evolving without visibility. Observability is what lets you see those changes before users feel them. Hashtags #Django #Observability #BackendEngineering #DatabasePerformance #Python #SoftwareEngineering

Something I've noticed lately: Devs are describing their stack as Cursor, Claude Code, Copilot... not React, React Native, not Swift, not Python. The AI tool became the identity. The framework became the implementation detail. Anyone else noticing this?

I shipped PromptLedger v0.3.0 today. PromptLedger is my small local-first prompt version control project built around a simple idea: prompts should have proper history, diffs, labels, and review workflows just like code. In this release, I added: a new review workflow semantic summary diffs markdown export for reviews a better read-only comparison UI structured review support in the Python API I tried to keep the scope tight: no SaaS no telemetry no backend services SQLite stays the source of truth The goal was not to turn it into a huge framework, but to make prompt changes easier to inspect, review, and share. GitHub release: https://lnkd.in/e9nBkAiz PyPI: https://lnkd.in/ePTqsH6N Devto: https://lnkd.in/ebbFNq2H #OpenSource #Python #SQLite #DeveloperTools #BuildInPublic

Most people don't realise how far you can push n8n when you pair it with Python. 🐍 I've just finished building something I'm pretty excited about. A Python sidecar service running alongside our n8n automation platform. It sounds more complicated than it is, but the impact is massive. Here's what was bugging me: n8n is brilliant for connecting apps, moving data and building workflows. But the moment you need serious data processing, JavaScript hits a wall pretty fast. And I kept running into that wall. So I built a FastAPI service that lives in the same Docker network as n8n. When a workflow needs heavy lifting, it fires off a request to Python, gets the result back, and carries on. From n8n's side it's just another HTTP node call. Simple. The difference in what's now possible is honestly kind of wild. Where JavaScript would struggle, Python just gets on with it. Pandas for processing thousands of rows in milliseconds. NumPy for calculations that would crash a spreadsheet. Access to pretty much every ML and AI library out there. Data cleaning, enrichment, financial modelling, NLP — all available inside a workflow with a single HTTP node. The whole thing runs self-hosted on Hetzner, locked down behind API key auth and internal Docker networking, so nothing is exposed that shouldn't be. The thing I keep coming back to is this. You don't need to rip out your low-code tools when they hit their limits. You just need to know where to extend them. n8n handles the orchestration. Python handles the intelligence. If you're building automation pipelines and finding yourself fighting against the tool rather than with it, I'd genuinely recommend looking at this pattern. Happy to share more if anyone's curious 👇 #n8n #Python #Automation #FastAPI #Docker #LowCode #WorkflowAutomation

I’ve been working on refactoring a legacy Django project to a more modern Flask project, and I will tell you this… it really shows how much complexity can build up in a system over time. The original app worked, but it had grown pretty heavy and difficult to maintain. Moving it to Flask has been a good opportunity to simplify the architecture, separate business logic from routes, and make the code easier to understand and extend. It is a good reminder that sometimes improving a system is not about adding features, but about making the foundation cleaner for the developers who come next. #SoftwareEngineering #Python #Flask #Django #Refactoring #Backenddevelopment #Cleanarchitecture

agentpad is live. A multi-language runtime for AI agents that actually runs your code — bash, Python, JavaScript, SQL - against a real project directory. Not a sandbox pretending to be one. Most agent runtimes fake the environment. agentpad doesn't. You get full control over what the agent can touch, what it can write, and exactly what happened when it's done. > Overlay mode to stage changes before they stick. > Read-only mode when writes aren't allowed. > Glob allowlists and timeouts so nothing runs wild. > Structured output every time - stdout, stderr, exitCode, files written. > A full session run log so nothing is a black box. > OpenAI tool helpers so you're not rewriting the function-calling loop from scratch. Just run npm install agentpad Or pip install agentpad Website/Docs: https://slaps.dev/agentpad #openSource #aiAgents #developerTools #typescript #python

When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python