Cut Docker Image Size by 70% with Multi-Stage Builds

📢 532 downloads in 3 days. No marketing. Open Source. No VC money.🚀 Just a Python framework that gets out of your way. We built SynapseKit because debugging LLM apps was taking longer than building them. Too many layers. Too much magic. Too little control over what was actually happening. So we started over. From scratch. ⚡ Async-native. Streaming-first. 2 dependencies. Code you can read on a Monday morning without coffee. What's inside: 🔌 13 LLM providers behind one interface :swap models in one line 🔍 10 retrieval strategies: RAG Fusion, CRAG, Ensemble, Self-Query 🔀 Graph workflows with human-in-the-loop and SSE streaming 🛠️ 16 tools, 12 loaders, 4 memory backends ✅ 540 tests passing No hidden chains. No magic. Just code that does what you think it does. If you're building LLM apps in Python and want something you can actually debug: 📦 pip install synapsekit[openai] 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #OpenSource #AI #MLEngineering #MachineLearning #Developers #SoftwareEngineering #AgenticAI

From UI → working product 👇 Here’s a quick demo of the expense tracker I built using Python 💰 In this version (v1), you can: • Add income & expenses • Track balance instantly • View transaction history • View analytics & insights Built this to better understand my own spending habits — and it turned into a full working app. Currently improving it and planning to deploy it soon 🚀 Watch till the end to see insights 👀 Would love your feedback 🙌 #Python #Streamlit #WebDevelopment #BuildInPublic #SoftwareDevelopment #StudentDeveloper #Projects #LearningInPublic

Actionpackd Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites #Flask #Python #AI

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

Two years into building backend systems with Python and FastAPI, one lesson keeps proving itself: Async isn't just a performance trick — it's a mindset shift. When I first started writing async endpoints, I treated it like a checkbox. "Use await, get speed." But the real value clicked when I saw how thoughtfully designed async flows could handle surges in traffic without the backend becoming the bottleneck. The difference between a system that holds up under pressure and one that doesn't often isn't the framework — it's whether the engineer truly understood why they made each architectural decision. That realization changed how I approach every API I build now. I ask: "What happens when 10x the requests hit this endpoint?" before I write a single line. It's made me a more intentional engineer, not just a faster one. For those building with FastAPI or any async framework — what's a lesson that changed how you think about backend design? Would love to hear what shifted your perspective. 👇 #BackendDevelopment #Python #FastAPI #SoftwareEngineering #APIDevelopment #LessonsLearned #TechCommunity

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

Write code that doesn't break in production...! When building end-to-end pipelines, reading data from GitHub or external URLs is common. But relying on a "happy path" is a mistake. For robust development, always implement: Logging: To track the flow and capture specific error details. Exception Handling: To prevent the entire app from crashing and get clear "Unable to load" alerts. It’s a simple habit, but it’s what separates a beginner from a Pro Developer. #Python #MLOps #CleanCode #SoftwareEngineering #DataScience #CodingTips

FastAPI has become the standard for building high-performance Python backends. It successfully combines the developer experience of a lightweight framework with the speed required for modern, asynchronous applications. Here is why it is a go-to for production-ready systems: • Performance: Built on Starlette and Pydantic, it is one of the fastest Python frameworks available. • Efficiency: Features like automatic OpenAPI (Swagger) docs and built-in data validation reduce boilerplate significantly. • Async Support: Native support for asynchronous programming makes it ideal for real-time apps and ML model deployment. • Reliability: Leveraging Python type hints ensures fewer bugs and better editor support during development. Whether you are architecting microservices or a simple REST API, FastAPI provides the scalability and speed that modern software demands. Are you still using Flask for your initial prototypes, or have you made the full switch to FastAPI? . . . #Python #FastAPI #Backend #WebDev #Microservices #Coding

From UI to Logic: Expanding from Frontend to Python Backend 🐍 I’ve spent a lot of time perfecting the outer shell of applications — building responsive, clean interfaces with Tailwind and shadcn. But great UI is only half the story. Recently, I started diving deeper into Python to understand the logic that powers those interfaces. That means moving beyond styling components and learning concepts like: • Closures & Lambdas — writing flexible and reusable logic • Functional Sorting — understanding how data is processed before reaching the UI • Object-Oriented Programming (OOP) — the foundation of frameworks like Django The goal is simple: Build full-stack applications where the backend is just as powerful as the frontend is beautiful. Next stop: Django models and database architecture. 🚀 #Python #WebDevelopment #Django #Frontend #FullStack #CodingJourney