I’ve spent 10 years building bots that bypass anti-fraud systems. Now I fight them by building anti-bot detection systems - and most defenses don’t work. In this article, I’ll break down how human-like bot traffic actually works - and show a simple way to make bots click on hidden links. Almost every website receives large volumes of “direct” and “referral” visits that are not real users. These vi
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"}
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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
If you are running production workloads, this is for you. Not side projects. Not early-stage experiments. Not a single-service app with low traffic. This is for teams shipping real systems. Systems with users, uptime expectations, and release pressure. Because at that stage, your deploy process is no longer a convenience. It is part of your product. And right now, for most teams, it is the weakest
I just shipped v1.1.0 of oh-my-kimi — a multi-agent orchestration harness that wraps the Kimi Code CLI (K2.6) into parallel coding teams. One prompt → planned, parallelized, reviewed project: npm install -g @oh-my-kimi/cli omk chat — Interactive Kimi session with resumable context, tmux support omk cockpit — Real-time dashboard with parallel TODO/agent rendering omk hud — Full terminal dashboard
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
Most candidates overthink "Tell me about a time you failed." They assume the safest move is to soften the story, pick a harmless mistake, or package a "failure" that is secretly a strength. That usually backfires. In software interviews, especially for experienced engineers, a real failure is often better than a polished non-answer. Hiring managers are trying to figure out whether you can own mist