Context Engineering: Mastering 4 Pillars for AI Success

we wrote a paper with a provocative title: 'the difference between you and god is throughput.' the thesis: every intelligence architecture runs the same loop. observe, process, act, learn. bacteria do it. humans do it. GPT-4 does it. the only variable is bandwidth - how much reality flows through the loop per unit time. we tested this empirically. same lifecycle loop runs everything in our system - 10-line bash scripts through multi-agent harnesses. expanded from 1 learning channel to 3 (operational, behavioral, epistemic) without changing the loop structure. just wider pipes. implication: AGI doesn't need a new architecture. it needs the same architecture with more bandwidth. human consciousness processes roughly 40 bits per second. same loop as a thermostat. just more throughput. this reframes the entire scaling debate. it's not 'we need bigger models.' it's 'we need wider channels through the same pattern.' every system expansion we've done - new learning channel, federated network, knowledge cells - is a bandwidth increase. more reality flowing through the same loop. paper: 'The Difference Between You and God Is Throughput: Bandwidth as the Only Variable in Intelligence'

A must read book for data professionals at this moment, Claude Code From Source is a deep-dive into the internal architecture of Claude Code, Anthropic's agentic coding assistant. Reading it, you learn how a production-grade AI agent is actually built under the hood — not just how to use it, but how it works. https://lnkd.in/dBQ-mQf6

AI hangover: When you vibed vast chunks of code and realise the architecture and actual function is not right and you have to fix it by hand.

Pleased to announce that I will be speaking at the upcoming Knowledge Graph Conference in NYC on May 5th. I will be discussing reasoning with knowledge graphs - specifically what to do with graph-level sets of inference where reification and RDF* are insufficient because they treat annotations at the individual triple level (causing scalability problems in large graphs). Using a layered ontological approach, we can add a new layer to manage these things - The Super Domain. This is a domain that handles epistemic facts which are the product of hypothesis, conjecture or if-then statements. Super Domains keep base models (Domains & Sub-domains) about brute facts that are taken as T. Instead Super Domains contain transient information only T under certain conditions, at certain times, or from certain perspectives. This does not replace reification or RDF* - it adds another engineering approach to deal with these items as larger graph patterns and gives them a home.

We reduced graph diffing complexity from O(N²) to O(1) in Go — here's how. Rafal Kokoszka from the IFS Tech team breaks down the full optimization journey in a new Medium post: the problem, the bottleneck, and the solution that made it scale. Whether you're deep into graph theory or just care about writing efficient Go, this is a solid read on what thoughtful algorithmic design looks like in practice. Click Here 👉 https://ifs.link/urmjdA #PerformanceEngineering #TechBlog #IndustrialAI

Breaking: Anthropic's Claude source code leaked again — March 31, 2026. This time: full Opus 4.6 architecture, autonomous agent features, performance benchmarks. Root cause: unobfuscated TypeScript in npm source maps. The irony: A safety-first AI lab that sells governance frameworks just handed competitors their entire unreleased roadmap. This is the 2nd leak in 14 months. Enterprise teams evaluating Anthropic for regulated deployments should ask one question: If they can't secure their own IP, how will they secure mine? 📰 Source: CNBC — https://lnkd.in/dbtyZX9U

It’s interesting how much (and how little) we know about modern LLM architectures. For example, some GPT-3 variants use up to 96 attention heads in their transformer layers. But when it comes to GPT-4, key architectural details like the exact number of attention heads haven’t been publicly disclosed. As models get more advanced, we’re seeing a shift: less focus on raw architecture transparency, and more on capabilities and outcomes. Still… part of me really wants to know what’s under the hood....

This is a great way to view and grasp the advances in LLM architectures, and to better understand what is going on behind the scenes in the large foundation models. It's not just a model that you are interacting with. --- Visual reference lets you trace every major LLM architecture decision from GPT-2 to today https://ow.ly/AVYv50YCPI4

We reduced graph diffing complexity from O(N²) to O(1) in Go — here's how. Rafal Kokoszka from the IFS Tech team breaks down the full optimization journey in a new Medium post: the problem, the bottleneck, and the solution that made it scale. Whether you're deep into graph theory or just care about writing efficient Go, this is a solid read on what thoughtful algorithmic design looks like in practice. Click Here 👉 https://ifs.link/G4EFCH #PerformanceEngineering #TechBlog #IndustrialAI

🚀 Building machine learning models is only the beginning — deploying them into production is the real challenge. In 2026, the focus is shifting from experimentation to building scalable, reliable ML production systems that deliver real-world value. I’ve just published a comprehensive guide on: 👉 Machine Learning Production Systems Architecture https://lnkd.in/d46VqnsR In this article, you’ll explore: • How to design ML systems for production environments • Key components of scalable ML architectures • Monitoring, performance, and reliability strategies • Common pitfalls developers face in production Developers who understand ML production systems will be far more valuable than those who only focus on model training. 💡 Real impact happens when models are deployed and used at scale.