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If you've tried building an AI agent in the last six months, you've hit the same wall: there are half a dozen frameworks, each with a different philosophy, a different API surface, and a different definition of what an "agent" even is. I spent a weekend writing the same simple agent — "read a GitHub issue, classify it as bug/feature/question, and post a comment" — in six different frameworks. This
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 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
I recently had a requirement where, if a user opens the app in multiple browser tabs, only one tab can be active at a time, and the rest are locked. Here are the core requirements as I see them: Uniquely identify each tabs Store the active tab somewhere If a user opens a new tab, lock it if there is already an active tab If the active tab is closed, detect that and inform the other tabs Sounds sim
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