In this unique primer, an experienced civil engineer and instructor presents the physics and fundamentals that underlie the many fields of engineering. Far from a dry, nuts-and-bolts exposition, however, 101 THINGS I LEARNED® IN ENGINEERING SCHOOL probes real-world examples to show how the engineer's way of thinking can-and sometimes cannot-inform our understanding of how things work. Questions from the simple to the profound are illuminated throughout: Why shouldn't soldiers march across a bridge? Why do buildings want to float and cars want to fly? What is the difference between thinking systemically and thinking systematically? How can engineering solutions sympathize with the natural environment? Presented in the familiar, illustrated format of the popular 101 THINGS I LEARNED® series, 101 THINGS I LEARNED® IN ENGINEERING SCHOOL offers an informative resource for students, general readers, and even experienced engineers, who will discover within many provocative new insights into familiar principles.
My Thoughts on the Bun Rust Rewrite - Andrew Kelley
When Jarred joined the Zig community about 5 years ago, I described him as someone who had strong "beginner energy". That is, he moved fast and tried a lot of different stuff, jumping head first into problems that he was not yet equipped to solve, leading to mediocre outcomes in terms of engineering, but learning a whole heck of a lot in the process. I see it as quite a healthy attitude, particularly for young people and students. This is the best way to level up and learn new things.
Prompt Engineering Guide – Nextra
A Comprehensive Overview of Prompt Engineering
The Engineering Leadership Report 2026
Our survey of 600 engineering leaders exploring how the role is evolving, where the challenges lie, and what the future holds for the job.
Get Your Reps: product, design, and engineering lessons from building Sail & Muddy - Alejandro García Salas
product, design, and engineering lessons from building Sail & Muddy
How to Do AI-Assisted Engineering
15 experienced engineers and engineering leaders share their real-world experiences with AI-assisted engineering.
Cognitive engineering
Cognitive engineering is an interdisciplinary field that applies principles from cognitive psychology, cognitive neuroscience, and human factors to design and develop engineering systems that effectively support or enhance human cognitive processes.[1][2] The field emerged in the 1980s when Donald Norman and others recognized the need to better understand how humans interact with complex technological systems.[3]
What is First Principles Thinking?
First Principles thinking breaks down true understanding into building blocks we can reassemble into something that simplifies our problem.
Up and Down the Ladder of Abstraction
"In science, if you know what you are doing, you should not be doing it. In engineering, if you do not know what you are doing, you should not be doing it. Of course, you seldom, if ever, see either pure state."
The Interview Question Bank for Engineering Managers
Organised by theme, and every question comes with a short thinking prompt
Bridges & scruples
For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated. —Ursula Franklin
Resetting on engineering expectations in the age of AI
I’m accepting new sponsorships for The Modern Leader for 2026. If you’re interested (or you think your company may be interested), you can learn more here.
ADD / XOR / ROL
A blog about reverse engineering, mathematics, politics, economics and more ...
Build a Reasoning Model (From Scratch)
"An exceptional deep dive into the next frontier of AI.” —Aman Chadha, Google Build a Reasoning Model (From Scratch) is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation. The progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones. The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Readers see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs. It also discusses common failure modes, including cases where refinement can make answers worse. Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, and visual workflows make pipelines and scoring methods easier to follow. Reading the book feels like following a guided technical build rather than a loose survey of AI topics. Each concept is introduced because the project now needs it. Diagrams, roadmaps, code listings, exercises, and repeated workflow summaries help readers stay oriented through advanced material. This structure reflects Sebastian Raschka’s professional strength: explaining complex machine learning topics by making every detail concrete and showing exactly where each section fits in the larger story. He does not treat mechanisms like evaluation, log-probabilities, KL regularization, or distillation as isolated abstractions; he connects them to the goal of making reasoning models understandable and implementable. Physically and organizationally, the book has eight chapters and seven substantial appendixes. That design keeps the main narrative focused while moving supporting material like references, exercise solutions, model source code, larger models, batching, evaluation alternatives, and chat interfaces into ordered appendixes. The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build.
Design Engineering Knowledge
Great articles and other media on design engineering
The physics behind spring animations - The Blog of Maxime Heckel
Interfaces › Cheat Sheet
The Making of Cursor's Icons
Performance
React handbook
Teaching agents product design at Vercel