Week 23 Summary

Blogs
Machine Learning, Large Language Models, Webassembly, Mathematics, Systems Engineering, Technical Interviews, Hiring, Talent Assessment, Engineering Management, Attention Mechanisms, Transformers, Javascript, Npm, Supply Chain Attacks, Cybersecurity, Artificial Intelligence, Software Engineering, Technical Debt, Information Retrieval, Go, Ipv6, S3, Object Storage, Open-Source, Mental Health, Self-Hosting, Infrastructure

Engineering Reads — Week of 2026-05-28 to 2026-06-05#

Week in Review#

This week’s reading reflects an industry furiously negotiating the boundaries of abstraction, complexity, and human attention. As the cost of generating software artifacts drops to near zero via AI, engineers are confronting the reality that our bottlenecks have shifted entirely away from writing code and squarely onto system verification, security boundaries, and organizational discipline.

Must-Read Posts#

The Last Technical Interview · Steve Yegge Yegge argues that standard tech interview loops are statistically bankrupt pseudosciences that function primarily as unconscious bias filters rather than predictors of job performance. To fix this, he proposes a “campfire” model of paid, provisional work where candidates tackle real tickets alongside the team, walking away with a portable, verified reputation stamp regardless of the final hiring outcome.

2026-05-30

Blogs
Large Language Models, Attention Mechanisms, Transformers, Machine Learning

Engineering Reads — 2026-05-30#

The Big Idea#

The evolution of attention mechanisms reflects the industry’s ruthless drive to optimize foundational ML primitives, trading raw representational granularity for the memory and compute efficiency required to serve massive context windows. Understanding this shift requires tracing the arc from raw multi-head attention to the highly compressed, shared-state architectures powering today’s state-of-the-art open models.

Deep Reads#

Understanding and Coding Self-Attention, Multi-Head Attention, Causal Attention, and Cross-Attention in LLMs · Sebastian Raschka To reason effectively about modern language models, you have to strip away the high-level framework abstractions and implement the core mechanics from scratch. This piece provides a code-first deep dive into the foundational attention primitives: self, multi-head, causal, and cross-attention. By forcing you to confront the raw tensor operations and masking logic, it builds the structural intuition necessary to understand why these mechanisms eventually become bottlenecks at scale. While this covers foundational designs rather than cutting-edge optimizations, it is essential scaffolding. Any engineer looking to demystify the inner workings of transformer architectures should read this to ground their mental models in actual code.