Plutonic Rainbows

I built Guardrail Gateway as an AI safety platform to make interactions with Large Language Models more secure and transparent. It adds a layer of content filtering, policy enforcement, and audit logging between applications and providers such as OpenAI. The system runs on a FastAPI backend with a React frontend, acting as an intelligent proxy that checks every request and response against a set of customizable safety policies before it reaches the model.

The core of the platform is a policy engine that uses regex-based rules with adjustable severity levels and actions like blocking, warning, or redacting content. Right now, I’ve implemented two main policy sets: one for detecting and redacting personally identifiable information, and another for identifying prompt injections or attempts to extract system prompts. Every event is logged for traceability and compliance.

Developers (including myself) can test and tune policies through a web interface, which includes tools for validating configurations, managing policies, and reviewing audit logs. The system uses SQLite for development and PostgreSQL for production, with JWT authentication for secure access and UUID support across databases. Typical requests — from scanning to response logging — complete in about two seconds, with most scans finishing in under 50 ms.

I designed Guardrail Gateway to run quietly in the background, using Python’s asyncio loop on a high port (58001) to minimize interference with other services. It’s written for Python 3.13 and built to scale horizontally thanks to its stateless API design. The frontend, built in React with TypeScript and Vite, includes full documentation for both developers and AI agents.

I've finally added a search functionality to my blog, after many months of deliberating over styling and performance impact. After considering various options, I implemented a lightweight client-side search that lets readers quickly find posts by typing keywords into the search box now positioned in the header. The search looks through post titles and content excerpts, highlighting matching terms and displaying up to 10 results in a dropdown. It's nothing revolutionary, but it works well — searches execute in under 10 milliseconds once the index is loaded, and the whole implementation adds just 5KB of JavaScript and CSS to the initial page load.

Search uses a lazy-loading mechanism. Rather than forcing every visitor to download the 143KB search index (containing data for all 629 posts), the index only loads when someone actually clicks or tabs into the search box. This means most visitors who come to read a specific post aren't penalized with extra download time they'll never use. When someone does focus the search input, the index loads in the background while they're typing their query — if they've already entered text by the time it loads, the search runs automatically. It's a simple optimization, but it keeps the blog fast for everyone while still providing instant search for those who need it. The entire search feature added less than half a second to my build time, which felt like a reasonable trade-off for the functionality gained.

Anthropic release the new model. Pricing remains the same as Claude Sonnet 4, at $3/$15 per million tokens.

Apologies for the oversight in my previous post — I should have included Google Gemini in the discussion. Gemini is a key player in the current AI landscape, offering a versatile suite of models that combine strong reasoning, coding, and multimodal capabilities. Leaving it out may have given the impression that it isn’t relevant alongside GPT, Claude, Grok, and Qwen, but in reality, it deserves recognition as one of the most significant entrants shaping the competitive field.