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Originally published on TechSaaS Cloud Originally published on TechSaaS Cloud An API gateway sits between clients and your backend services. It handles cross-cutting concerns so your services do not have to: authentication, rate limiting, request routing, load balancing, caching, and observability. WebMobileIoTGatewayRate LimitAuthLoad BalanceTransformCacheService AService BService CDB / Cache API
A few months ago I was reviewing my SaaS metrics and everything looked normal on the surface Retention was stable churn was not alarming usage charts looked fine But revenue was still not growing the way it should have been That contradiction stayed in my head So I started looking deeper What I realized was uncomfortable Users were not really leaving in a clear moment There was no sudden drop no o
A deep, opinionated, practical guide for the engineer who has crossed the mid-level threshold — or is about to. The mental models, technical habits, ownership patterns, communication skills, and career mechanics that separate "solid senior" from "engineer the whole team builds around." Grounded in 2026 reality — AI-augmented coding, distributed async teams, post-ZIRP efficiency pressure, and a mar
One thread. Multiple AIs. Deliberation, not polling. Most people use AI like this: 🤦 Ask one model → get one answer Ask multiple models → compare results That’s not thinking. That’s polling. Not side by side. Not isolated. But in sequence — where each one reads what the previous one said before responding. Manual Council is the simplest form of that idea. No backend. No orchestration. No
If you've ever built a form backend or an automation workflow, I built MultiValidator to fix that. One API call. Up to 50 fields. Send a batch of fields, get back validation results for all of them: import requests payload = { "fields": [ {"type": "email", "value": "[email protected]", "field_name": "email"}, {"type": "phone", "value": "+447911123456", "field_name": "mobile"}
Table of Contents Introduction Environment Requirements Core Features Core Design and Code Analysis Actual Execution Demo Architecture Overview How You Can Expand Future Plans & Conclusion What is this It is a basic debugger, running on Linux and implemented in C++, aiming to create a debugger that is easy to read and expand. In addition, Lavender's main function is to help users analyze the logic
I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions. It clicks quickly, which is exactly why it’s easy to walk away thinking you’ve “done RAG.” What bothered me was that the demo corpus is usually tiny and artificial. I write on DEV.to about things like NLP routing and CNN image classification. If I