Engineering Insights &amp; AI

Sep 8, 20266 min read

Building a Multi‑Agent LLM Framework for Automated Financial Trading

Financial trading systems must reconcile low‑latency market data with sophisticated decision logic, a challenge that grows when you add large language models. I show how a modular multi‑agent architecture—leveraging the open‑source TradingAgents repo—turns LLM‑driven strategies into production‑grade trade execution.

#Financial Trading

Sep 4, 20266 min read

From 68000 Assembly to Godot: How I Ported a 1993 Amiga Game with LLM Assistance

I tackled the challenge of translating a 1993 Amiga game written in 68000 assembly into a modern Godot project, leveraging an LLM to auto‑translate low‑level code into high‑level logic. The result was a playable, maintainable remake that preserved the original feel while embracing Godot’s scene system and GDScript.

#Game Development

#Godot

Sep 4, 20266 min read

Building Scalable LLM Pipelines with GPT‑6 Astra

GPT‑6 Astra introduces a modular, multi‑stage inference architecture that tackles latency and cost at scale. I show how to integrate Astra’s hybrid token routing and dynamic quantization into a Next.js 15 + Node.js backend to deliver real‑time AI experiences.

#GPT-6

The Reasoning Ledger: Persisting Decisions for Trustworthy Systems

Modern applications store raw data but often lose the why behind each change, making audits and debugging painful. I introduce the Reasoning Ledger pattern, which captures the decision context alongside data, enabling transparent, reproducible, and AI‑friendly workflows.

#Architecture

#Observability

Building an AI-Powered Content Pipeline with Next.js, Node, and Python

I faced the challenge of orchestrating LLM‑driven content generation, storage, and preview within a single Next.js 15 app. By combining a Python microservice, a Node.js API layer, and Sanity CMS, I created a reproducible, production‑ready workflow.

#Next.js

Greatness Is Forged by Limitation: How Constraints Drive Better Software

When engineers face hard limits—memory, bandwidth, team size—they’re forced to rethink assumptions, strip away waste, and innovate. By embracing these constraints, we build more robust, maintainable, and scalable systems.

#Software Engineering

#Architecture

Detecting Watermarked LLM Outputs: Techniques and Implementation

LLM providers increasingly embed invisible watermarks to trace generated text, but detecting them in the wild remains a challenge. In this post I walk through the underlying watermark algorithm, practical detection strategies, and a production‑ready TypeScript/Node implementation.

#Watermark Detection

Evaluating Anthropic’s Claude Code Effort Reduction: A/B Testing Insights

Anthropic’s recent A/B tests suggest a new "reduced effort" mode in Claude Code that trades off response latency for lower token usage. By integrating this mode into our CI pipelines, we can cut inference costs while maintaining developer productivity.

#Anthropic

#Claude

Local LLMs: The Hidden Gap Between Model Size and Real-World Performance

Local LLMs often feel less capable than their cloud‑hosted counterparts because the inference pipeline, prompt design, and resource constraints are rarely optimized. By tightening tokenization, applying mixed‑precision quantization, and integrating retrieval augmentation, you can unlock the true potential of a locally hosted model.

#Local Models