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"}
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
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 spent long hours debugging why Google couldn't index my React app. Lighthouse showed green scores. The app felt fast. But Search Console kept flagging LCP failures and CLS shifts I couldn't reproduce locally. The fix? Four lines of metadata and one misunderstood render strategy. If you've ever shipped a "fast" SPA and watched it flatline in search rankings, this Core Web Vitals SEO guide is for
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
Most teams treat cloud cost as a finance problem. But the root cause is usually engineering. Bills spike, dashboards grow, alerts fire — but the underlying issue rarely gets fixed. That idea stood out to me while reading about an approach where AWS cost was handled like an SRE problem — using the same mindset applied to reliability and performance. Instead of asking “why is the bill high?”, the fo
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
The more I use AI, the more convincing it feels. Clear answers. Whether it’s: strategy code writing decision support AI rarely hesitates. And over time, I noticed something subtle. I stopped questioning it as much. Breaking the Expectation We assume better tools reduce errors. Smarter systems. And in many cases, that’s true. But there’s a hidden shift happening: As AI improves, our skepticism decr