Minnedemo is a showcase for working tech products made in Minnesota. Demos are 7 minutes long and PowerPoint is not allowed, think of it as Geek Show & Tell!

I am a teacher, I teach coding to kids. I am trying to learn backend development. In my journey to learn FastAPI I got to make many APIs. But I'd never gotten a chance to use an external API. So I built a service that lives entirely on other people's APIs- weather, currency and news. Here's what it taught me. ## Chaining API calls- I used Open-meteo's weather API, which requires the latitudes and longitude of the city whose weather we want to know. I had to use another API of open-meteo to get the latitudes and longitude of any city in the world by using its name. So, we get the latitudes and longitude of city whose weather is requested by the client, from the first API and give that as input to the weather API. The output of one API became the input of the next. ## Whose failure is it- I handled the failures of the system in a way that the client knows whose fault it is. If the request fails because of the timeout error then the client should be informed about it so they don't keep trying, if our own server fails, then client can inform us. A 500 error if our own server fails, 502 error if the external API provided an invalid response and 504 error if the external API service timed out. I tested this by setting my timeout to 0.001 seconds, forcing every call to fail. Before: an ugly 500 traceback. After: a clean 504 weather service timed out. ## Graceful degradation- I made an aggregate endpoint which gets the responses from all three APIs and gives it to the client. Client enters the city name and we get the weather of the city, currency conversion and news. If any of these 3 APIs fail, we only get the responses from the successful APIs and also the name of the API that's causing the error and give it to the user. In case user enters an unknown city name, the request would be killed and client would get a 404 error. I could have followed the same way of handling the unknown city failure just like the ones we mentioned before but it just didn't make sense to me to give the currency conversion and news if the city is not recognised. ## Caching- I used a TTL cache for my project, every response is remembered for 5 minutes which saves a lot of resources. But, TTL cache has its own limits, one of which is that if the system is to be stopped and restarted for some reason we might get a lot of requests from the clients which is called cache stampede. Another big issue with TTL is that it lives in the process, if any other workers in the project want to use it, it wouldn't work. ## Testing with mocks- I set up tests with Pytest and tested all the endpoints, including the failure paths.. The project contains the code for what would happen if the external APIs timed out or gave an invalid response. That code was untestable because we can't actually wait for the API to be down to test that code. So I staged the disasters myself, during tests, I replaced httpx with a fake that returns whatever I script — a 500, a timeout. My real error-handling code runs against fake failures, and the tests verify it responds correctly, without any real service being down. I learned not only how to use external APIs but also pytest in this project. Here's the link for the GitHub repo: https://github.com/Gunjan-redu/api-aggregator

My journey starts pretty far from tech. After years in scientific research (Biotechnology at UFU, Master's at UNIFESP), I'm transitioning into back-end development and data. I built the LabTrack API to organize lab experiments, samples, and reagents, a problem I got to know up close during my time in scientific research. Python, FastAPI, and SQLAlchemy, with the API already live and documented via Swagger. What I noticed building this project is that the researcher's instinct to distrust data, look for outliers, check inconsistencies, and spot patterns helps a lot more in code than I expected. There's still ground to cover (tests, authentication, PostgreSQL), but that's how I'm learning: building and sharing along the way. API: https://labtrack-api-9byb.onrender.com/docs Feedback is always welcome.

At Cyclad, we work with top international IT companies to help them deliver cutting-edge technologies that shape the future. We are currently looking for an experienced Python Backend Developer to join an international team . In this role, you will develop and maintain backend services and REST APIs using Python and FastAPI while contributing to cloud-based, data-intensive, and AI-driven solutions. You will work on an application that digitalizes and automates procurement and sourcing processes using Generative AI and Large Language Models (LLMs), collaborating closely with Cloud Engineers, AI Engineers, Data Scientists, Product Owners, and Frontend Developers. Project information: Type of project: IT services Office locations: Gdańsk, Poznań, or Wrocław, Poland Work model: Hybrid – 2 days per week in the office, 3 days remotely Budget:95-120 PLN net/hour – B2B Project length: Long-term Start date: ASAP (depending on the candidate's availability) Only candidates with citizenship in the European Union and residence in Poland Your responsibilities: Design, develop, and maintain backend services and APIs using Python Build and evolve RESTful APIs supporting frontend functionality and system integrations Implement reliable, scalable, and maintainable backend business logic Contribute to the backend architecture and technical design decisions Integrate backend services with external systems and enterprise applications Support data-intensive processing and backend workflows Collaborate with Cloud Engineers on deployment setup, runtime architecture, environment configuration, and CI/CD Work closely with Frontend Developers on API design, contracts, and integration flows Contribute to AI-driven and data-intensive application workflows Ensure backend components meet requirements for security, observability, scalability, and operational supportability Participate in testing, troubleshooting, performance optimization, and continuous improvement Work with Git and follow Agile software development practices Collaborate with AI Engineers, Data Scientists, Product Owners, Cloud Engineers, and Frontend Developers Requirements: 3–5 years of professional experience in backend development with Python Strong hands-on experience with a modern Python web framework, ideally FastAPI Strong experience designing, building, and maintaining REST APIs Practical experience working with AWS and cloud-based application environments Good understanding of backend architecture and system integration patterns Good knowledge of interface and API design Experience with Git and Agile software development practices Understanding of secure, scalable, and maintainable backend development Excellent communication skills in English Ability to work effectively in a cross-functional international team Nice to have: Experience with data pipelines or data-intensive applications Exposure to AI/ML-oriented systems, including inference endpoints or model-serving components Experience with Snowflake or similar cloud data platforms Familiarity with SAP-related interfaces or enterprise integration technologies such as OData, APIs, IDoc, RFC, or BAPI Experience with Docker and containerized application delivery Experience with CI/CD pipelines and automated deployment practices Knowledge of secure API design, authentication, and authorization patterns Experience collaborating with Data Science teams and integrating ML models or inference endpoints We offer: Hybrid working model with 2 days per week in the office Full-time cooperation agreement based on B2B Private medical care with dental care (covering 70% of costs) Multisport card (also for an accompanying person) Life insurance Opportunity to work on innovative AI-powered solutions in an international IT environment Recruitment process: Introductory call Technical interview Final decision

Telescope pointing and coverage planner. Contribute to elacerda/jasytata development by creating an account on GitHub.

Financial fraud in the modern digital age is complex, fast, and highly networked. Traditional relational (SQL) databases struggle when analyzing multi-hop connections—such as shared IP addresses, linked device IDs, and rapid money transfers across accounts—because deeply nested JOIN operations introduce severe latency. To tackle this, we built Agentic Fraud Sentinel: an autonomous, real-time fraud detection system powered by TigerGraph’s GraphRAG engine and a FastAPI backend. It analyzes transaction streams, traverses deep graph networks in milliseconds, and provides actionable decisions with explainable Chain-of-Thought (CoT) reasoning. 🏗️ High-Level System Architecture Our solution follows a decoupled full-stack architecture to ensure low latency and high scalability: ┌──────────────────────────┐ ┌──────────────────────────┐ │ Frontend (Vercel) │ HTTP │ Backend (Render) │ │ - Single-page Dashboard │ ───────> │ - FastAPI Application │ │ - Tailwind CSS / JS │ <─────── │ - Python 3.x │ └──────────────────────────┘ └────────────┬─────────────┘ │ RESTPP APIs ▼ ┌──────────────────────────┐ │ TigerGraph Database │ │ - Graph Analytics │ │ - GraphRAG Traversal │ └──────────────────────────┘ Frontend (Vercel): Responsive dashboard providing live simulation triggers, risk score meters, and step-by-step reasoning views. Backend (Render): FastAPI service handling REST endpoints, orchestration, and business logic. Graph Engine (TigerGraph): Core graph engine running RESTPP endpoints to execute deep multi-hop queries and power GraphRAG evidence extraction. ⚡ Core Features Walkthrough 1. Real-Time Simulation & Event Triggering The system accepts live transaction payloads via REST API endpoints. Through an interactive simulation modal, security analysts can trigger test transactions and monitor instant system responses. 2. Multi-Hop Graph Traversal via GraphRAG When a transaction is flagged, TigerGraph performs rapid graph traversal across connected entity nodes (cards, devices, IPs, merchants). The system returns structured Chain-of-Thought (CoT) steps explaining why a transaction is risky. 3. Historical Case Matching By querying historical fraud benchmark cases stored in the graph database, the system calculates similarity scores (e.g., 0.94 similarity to prior proxy fraud patterns) to validate new threats instantly. 4. Dynamic Scoring & Next Best Action (NBA) The sentinel dynamically adjusts confidence scores based on graph evidence: Pre-NBA Score: 78% (Requires Step-Up Auth) Post-NBA Score: 95% (Automated Action: BLOCK CARD & FILE SAR) 🛠️ Key Technical Challenge: Troubleshooting Vercel Deployment Deploying a repository containing both static frontend files (index.html) and backend Python files (main.py, requirements.txt) to Vercel presented a unique engineering roadblock. The Error Upon deployment, Vercel threw a 500 FUNCTION_INVOCATION_FAILED error. Root Cause Vercel automatically detected Python files in the root folder and attempted to build the app as a Serverless Python Function. Because no serverless wrapper was present, function execution failed. The Solution We decoupled the execution layer by instructing Vercel to treat the repository strictly as a static web application: Created vercel.json for URL Rewrites: JSON { "rewrites": [ { "source": "/(.*)", "destination": "/index.html" } ] } Updated Vercel Project Settings: Set Framework Preset to Other. Overrode Build Command and Install Command to remain empty. Redeployed without build cache. This successfully rendered our static dashboard on Vercel while our FastAPI backend remained independently hosted on Render. 🔥 Why TigerGraph? Choosing TigerGraph as our graph engine was pivotal to achieving enterprise-level fraud detection performance: Unmatched Query Speed: TigerGraph’s RESTPP endpoints execute multi-hop graph traversals with sub-second response times. GraphRAG Power: Integrating Graph-based Retrieval-Augmented Generation provides rich contextual evidence directly to AI decision workflows. Scalability: Handles massive dataset connections without degradation in query latency. 🚀 Conclusion & Future Roadmap The Agentic Fraud Sentinel demonstrates how combining graph databases with modern API-first architectures enables real-time, explainable threat detection. 🔗 Live Frontend Demo: https://tiger-graph-gilt.vercel.app 📂 GitHub Repository: github.com/das09power/tiger-graph

Deploying LoRA Optimised BERT as a FastApi service on GKE

🐍 Déployer FastAPI ou Django sans jamais toucher à un serveur web ni configurer Uvicorn ? Cloudflare passe Python Workers en disponibilité générale. Fini la glu JavaScript, place au natif à l'échelle mondiale avec connecteurs ASGI et agents IA 👇

AI-powered virtual executive team — a single coherent executive persona backed by 8 specialist agents (FastAPI + Next.js). - SenteLabsAI/OpenExecutive

A modern experimental framework built on FastAPI. 100% FLOSS. - mrpg/uproot

Python Workers allow developers to run Python web frameworks and AI orchestration libraries natively in the Cloudflare Workers runtime. You can seamlessly integrate with Cloudflare's ecosystem includi...