Seventeen frontier open-weight LLMs, surveyed in English and Chinese on the Inglehart-Welzel cultural map, all land in the same secular, self-expressive corner - and asking in Chinese moves them toward self-expression, not tradition.
SaaS sold interfaces, encoded expertise, and coordination overhead disguised as software. Agents make all three free. Which moats survive the great software repricing, and which businesses die with the rest?
The next systemic AI failure won't be a hallucination - it'll be an authorisation failure. Whoever holds the control-and-provenance layer when it happens wins the market the incident creates.
A multi-agent web exploration system that uses Monte Carlo Tree Search and shared graph memory to autonomously discover, map, and document complex web applications.
How we built a secrets pipeline with AWS Secrets Manager and Terraform that distributes credentials to ECS services, frontend apps, and local dev environments automatically.
We replaced our Loggly with OpenTelemetry sidecars on ECS Fargate so all our services ended up with one trace. Dual export to SigNoz and S3 for debugging and SOC 2 compliance.
We are shifting from prompt engineering - crafting the perfect string - to Context Engineering: treating the information environment around language models as a proper engineering problem. This post is how to do exactly that.
How we achieved 2x faster vector search with identical recall using Gemini embeddings, task-optimized retrieval, and pgvector's half-precision quantization.
Every major LLM loses 39% of its performance in multi-turn conversation, so how do you build agents that are nothing but multi-turn conversation with tools?
How I built an automated multi-agent system that transforms AI news into weekly podcasts, newsletters, and social media content using LangGraph, topic modeling, and TTS - all running autonomously on ECS.
Traditional Vector based RAG has too many shortcomings. GraphRAG instead uses a Knowledge Graph for richly understanding text datasets, making RAG great again.
An experiment into cultural biases in LLMs using the Inglehart-Welzel Cultural Map method - revealing unexpected value alignment across both Western and Chinese LLMs
When selecting a cloud compute region for your ML workloads you will often need to account for latency, cost, services and features, compliance, etc. But in this article I explore how you can also consider your carbon footprint as part of your selection criteria.
As developers, we often just focus on the technical aspects of AI without ever considering the broader geopolitical implications. From Leopold Aschenbrenner's 'Situational Awareness', I summarise his insights on the rapid development of AI and the path to AGI & ASI, as well as the geopolitical and security challenges we face with AI.
I recently upgraded to Ubuntu 20.04 LTS, but my experience wasn't as smooth as I'd hoped. Here's why I decided to switch back to Windows and my setup process for an optimal development environment.
Exploring how collaborative autonomous agents improve complex problem-solving through behavior simulation, data construction, and enhanced performance.
Explore how embedding models and vector databases can enhance retrieval systems in AI, with practical tips and insights for optimizing retrieval-augmented generation (RAG) systems.
Create an automated system that transforms newsletters into AI-generated summaries using Zapier and Telegram, then rebuild it with AWS SES & Lambda for a serverless solution.
When developing applications that require high-concurrency, efficiency, and robust error-handling, the Async Worker Pool pattern in Python can be a valuable pattern.
Browser fingerprinting with a zero-dependency NPM package that offers a swift, synchronous function for browser fingerprint computation without user permissions or cookies.
In this article, we explore the challenge of reading data from @socket.io/redis-emitter without a socket.io client and present a clean and effective solution by decoding using the notepack.io.
Making the switch from React to Next.js? This article explores how Next.js 13 revolutionizes the scene with upgraded development, performance, and user experience functionalities.