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The Simplest Thing That Could Possibly Work: A Software Design Philosophy

2025-08-30

This article champions the principle of 'doing the simplest thing that could possibly work' in software design. Instead of striving for an idealized, over-engineered system, the author advocates for a deep understanding of the current system and choosing the simplest solution. This approach, while seemingly underwhelming, yields surprisingly effective results, exemplified by the designs of Unix and Rails. While challenges like system inflexibility and defining 'simplicity' exist, the author argues that focusing on current needs and iterative improvement is superior to over-engineering for distant future requirements. Ultimately, a simple, stable system often surpasses an over-engineered, hard-to-maintain one.

(www.seangoedecke.com)

Development simplicity

Dangerous Career Advice: A Sharp Tool for Strong Engineers

2025-08-26

This article argues that effective career advice, like sharp tools, can be immensely helpful or incredibly harmful depending on its use. Much career advice is superficial and lacks practical application. The author encourages engineers to embrace 'dangerous advice,' breaking conventions to achieve high efficiency. While risky, the rewards outweigh the risks for strong engineers. The author cautions that this advice isn't suitable for weaker engineers.

(www.seangoedecke.com)

Development

The Art of API Design: Balancing Simplicity and Flexibility

2025-08-25

This article delves into the crucial principles of API design, emphasizing the importance of avoiding breaking changes to existing user code. The author argues that good APIs should be simple and easy to use, yet maintain long-term flexibility. The article details technical aspects such as API versioning, idempotency, rate limiting, and pagination, and recommends using API keys for authentication to make it easier for non-engineer users. It concludes that a great product outweighs a perfect API, but a poorly designed product will inevitably lead to a poor API.

(www.seangoedecke.com)

Development

Good System Design: It's Not About Clever Tricks

2025-08-16

This article critiques system designs focused on flashy techniques, arguing that good system design prioritizes simplicity and reliability over complex distributed consensus mechanisms or CQRS. The author emphasizes the importance of state management, advocating for minimizing stateful components. Key aspects like database design (schemas, indexes), caching, background jobs, event-driven architectures, and handling bottlenecks are discussed in detail. The article stresses leveraging the database's capabilities, avoiding unnecessary in-memory processing. It highlights the importance of hot paths, logging, and monitoring, along with fault tolerance strategies like circuit breakers, retries, and graceful degradation. Ultimately, the author champions understated, effective design built on well-tested components, rejecting showy techniques in favor of robust functionality.

(www.seangoedecke.com)

Development

Training the Strongest Model on a MacBook Pro in 5 Minutes: A Challenge

2025-08-14

The author challenges himself to train the strongest possible language model on a MacBook Pro in just five minutes. Experiments culminated in a ~1.8M parameter GPT-style transformer trained on ~20M TinyStories tokens, achieving ~9.6 perplexity. Optimizations focused on maximizing tokens-per-second, favoring MPS and avoiding gradient accumulation. Dataset selection proved crucial, with TinyStories' coherent, simple language proving superior. Transformers outperformed LSTMs and diffusion models. The optimal model size for a five-minute training window was found to be around 2M parameters, aligning with Chinchilla scaling laws.

(www.seangoedecke.com)

AI

AI Coding Agents: The Secret's Out (There Is None)

2025-07-05

In 2024, building autonomous coding agents was thought to require clever internal tricks. It turns out all you need is a slightly better base model. Claude Sonnet 3.7 is a frontrunner, excelling not in raw power but in its ability to persist and make good decisions. The barrier to entry for building AI coding agents has plummeted; open-source solutions are excellent, and you can even run a Codex agent for free on GitHub Actions. Competition is fierce; vendors need to focus on distribution and training better models to succeed.

(www.seangoedecke.com)

Development AI coding agents

AI Agents: The Next Big AI Disaster?

2025-06-11

This article explores potential future AI disasters. Drawing parallels to early railway and aviation accidents, the author argues that large-scale AI catastrophes are a real possibility. Rather than focusing on simple AI misdirection, the author emphasizes the risks posed by AI agents – AIs capable of autonomously performing tasks like web searches and sending emails. The author predicts the first major AI disaster will likely stem from an AI agent malfunctioning within government or corporate systems, such as erroneously executing debt collection, healthcare, or landlord processes. Additionally, the author highlights the potential dangers of AI models being misused to create 'ideal partner' robots. In short, the author cautions against the rapid advancement of AI and its potential risks, urging for stronger AI safety measures.

(www.seangoedecke.com)

AI

Apple Paper Challenges AI Reasoning: Not 'Real' Reasoning?

2025-06-09

Apple's recent paper, "The Illusion of Thinking," tests large language models' reasoning abilities on Tower of Hanoi puzzles. Results show models perform worse than non-reasoning models on simple problems; better on medium difficulty; but on complex problems, models give up, even when given the algorithm. The authors question the models' generalizable reasoning capabilities. However, this article argues the paper's use of Tower of Hanoi is flawed as a test. The models' 'giving up' may stem from avoiding numerous steps, not limited reasoning ability. Giving up after a certain number of steps doesn't mean models lack reasoning; this mirrors human behavior in complex problems.

(www.seangoedecke.com)

AI

Why are some LLMs fast on the cloud, but slow locally?

2025-06-01

This article explores why large language models (LLMs), especially Mixture-of-Experts (MoE) models like DeepSeek-V3, are fast and cheap to serve at scale in the cloud but slow and expensive to run locally. The key lies in batch inference: GPUs excel at large matrix multiplications, and batching multiple user requests significantly improves throughput but increases latency. MoE models and models with many layers particularly rely on batching to avoid pipeline bubbles and underutilization of experts. Cloud providers balance throughput and latency by adjusting batch size (collection window), while local runs usually have only one request, leading to very low GPU utilization. The efficiency of OpenAI's services might stem from superior model architecture, clever inference tricks, or vastly more powerful GPUs.

(www.seangoedecke.com)

AI batch inference

Shallow Understanding of Tech: Good Enough is Good Enough

2025-05-27

The author argues that a shallow understanding of the technologies engineers use is sufficient. Deep dives into database indexes, large language models, etc., enable better decision-making; for example, choosing appropriate models for JSON output and avoiding errors caused by limitations in smaller models. Instead of going deep in one area, the author advocates for broad knowledge across many areas to better adapt to new trends. Learning new technologies should focus on understanding fundamental principles and explaining them simply to others, while using LLMs for fact-checking to ensure accuracy.

(www.seangoedecke.com)

Development

Gemini Diffusion: The Speed Demon of Text Generation?

2025-05-22

Google's newly released Gemini Diffusion is wowing everyone with its speed; they even slowed down the demo to make it watchable. This article delves into why diffusion models are so fast, contrasting them with traditional autoregressive models (like GPT-4, Claude). Diffusion models generate the entire output at once, rather than token-by-token, enabling parallel generation of correct parts and faster speeds via reduced iterations. However, they're less efficient with long contexts and their reasoning capabilities remain questionable. While diffusion models might use transformers internally, their architecture makes their behavior fundamentally different from autoregressive models.

(www.seangoedecke.com)

AI text generation

AI's Superpower: Patience, Not Intelligence

2025-05-20

Sam Altman envisioned intelligence becoming 'too cheap to meter,' and with venture capital fueling the AI boom, we're living in that world. However, user demand for significantly smarter models isn't exploding. This article argues that the most transformative aspect of LLMs isn't their intelligence, but their superhuman patience: always available, non-judgmental, and infinitely willing to listen. While this patience can amplify existing LLM flaws (like sycophancy) and LLMs shouldn't replace therapists, this capability has profoundly impacted how people seek emotional support and advice.

(www.seangoedecke.com)

AI Patience

Diffusion Models: The Unsung Heroes of AI Image Generation

2025-05-19

Unlike transformer-based language models, diffusion models generate images by progressively removing noise from a noisy image. Training involves teaching the model to identify added noise, eventually allowing it to generate images from pure noise. This is akin to sculpting, gradually refining a rough block of stone into a masterpiece. While still nascent for text, diffusion models show great promise in image and video generation, as seen in OpenAI's Sora and Google's VEO. The core lies in how it models the relationship between noise and data, a stark contrast to transformers' focus on language structure.

(www.seangoedecke.com)

AI

Getting Things Done in Big Tech: It's Not What You Think

2025-05-06

In large tech companies, competent engineers can easily fall into the trap of continuously improving existing systems, neglecting the actual delivery of value. This article argues that "finishing" work doesn't mean endless refinement, but reaching a point where company decision-makers are satisfied. Declare victory and move on! This requires making your work visible and understandable to those decision-makers, perhaps by demonstrating financial impact. Ultimately, "getting things done" is a social construct, but a powerful one impacting your career.

(www.seangoedecke.com)

Development

Stop Being a Fence-Sitter in Tech Discussions: Own Your Decisions

2025-04-14

Senior engineers often avoid committing to decisions in technical discussions, a seemingly cautious approach that the author argues is actually cowardice. The article emphasizes that when a team needs to make a choice, even with only 55% confidence, the most knowledgeable engineer should take responsibility and offer a judgment. This prevents less experienced engineers from proposing poor solutions and allows management to work more efficiently. The author stresses that management is usually forgiving of technical errors, as decision-making inherently involves uncertainty. However, consistently incorrect judgments erode credibility. The article concludes that while avoiding commitment is justifiable in environments lacking trust, in most situations, taking ownership and making bold decisions is a hallmark of a strong engineer.

(www.seangoedecke.com)

Development technical decisions team responsibility engineering culture

Stop Wasting Your Time on Unprofitable Work!

2025-04-07

Many engineers focus on non-profit work like performance improvements and accessibility, only to be laid off for not being valued. The article argues that tech companies are driven by profit, and an engineer's value is directly tied to their work's contribution to that profit. The author advises engineers to understand their company's business model, connect their work to profitability, and thereby secure their position. Even seemingly unprofitable work can generate value at scale in large companies.

(www.seangoedecke.com)

Development engineer career development

Ruby: The Unexpected Language of the AI Revolution?

2025-03-22

Large language models (LLMs) excel at code generation, but their limited context windows hinder work with large codebases. This article explores the 'power' of LLM-assisted programming: how many tokens does it take to express a program? The author argues Python outperforms Go for LLMs due to its conciseness, allowing more features within token limits. Further, Ruby, known for elegance and brevity, is posited as an ideal LLM language due to its efficient token usage. While challenges like type checking remain, Ruby's human-centric design ironically makes it a potential frontrunner for LLMs.

(www.seangoedecke.com)

Development

The End of the Golden Age: Software Engineering in a Post-Boom Tech World

2025-03-16

For a decade, software engineering was a dream job: high salaries, great perks, and rock-solid job security. But the past two years have seen massive layoffs across the tech industry, shifting the landscape dramatically. This article argues that the shift stems from a change in economic conditions. Low interest rates fueled lavish spending and generous engineer compensation, but rising rates have prioritized profitability, leading to widespread cuts. While AI is often blamed, the author contends it's not the root cause. The new reality demands a focus on directly contributing to company goals; failure to adapt risks job security. While the pampered days are over, a focus on delivering value offers a clearer, if less glamorous, path to success.

(www.seangoedecke.com)

Tech Tech Industry Software Engineers

Simplicity Wins: The Essence of Great Software Design

2025-03-07

This article argues that great software design isn't about complex language features or architectures, but about eliminating potential failure modes. The author uses personal anecdotes to illustrate how removing redundant components, centralizing state management, and using robust systems minimizes risk and increases reliability. The core message is that good design is simple and reliable, avoiding flashy features and focusing on solving problems. The author cites the Unicorn web server as a prime example of this approach.

(www.seangoedecke.com)

Development Failure Modes

Mastering the Spotlight: Prioritization in Tech

2025-03-07

In the fast-paced world of tech, not all work is created equal. This article highlights that most high-priority tasks are actually low-impact. Success hinges on recognizing the 'spotlight' moments – projects receiving intense leadership focus. Engineers must develop the ability to quickly identify and seize these opportunities, dedicating themselves to high-impact projects. Conversely, when the spotlight isn't on them, leveraging personal time for valuable projects enhances skills and company contributions. This requires not just judgment but a skill honed through practice.

(www.seangoedecke.com)

Development

The Coming AI Revolution in Software Engineering

2025-02-15

Large language models (LLMs) are rapidly improving their ability to write code, leading to the exploration of pure-AI software engineers. In the short term, software engineers should learn AI, improve their status, and leverage AI tools. Mid-term, expertise in maintaining and improving large legacy codebases will become increasingly valuable, as LLMs still struggle with complex, hard-to-verify projects involving massive amounts of code. Long-term, the core competency of software engineers will shift towards accountability and trustworthiness—qualities LLMs find difficult to replicate. Ultimately, those engineers who can oversee AI systems and ensure the reliability of their outputs will be the last to go.

(www.seangoedecke.com)

Development

How I Use LLMs to Supercharge My Engineering Workflow

2025-02-04

A senior software engineer shares his practical applications of large language models (LLMs) in his daily workflow. He leverages LLMs for code completion, writing throwaway code, learning new domains, last-resort debugging, and proofreading documents. He stresses LLMs are not a replacement for core logic or formal writing but a powerful aid, particularly effective for tackling unfamiliar codebases or learning new technologies. The key is using them strategically, not expecting miracles.

(www.seangoedecke.com)

Development

Stop Being a JIRA Zombie: Prioritize Impact Over Tickets

2025-01-25

This insightful post shares a valuable lesson learned by an engineer: Don't get bogged down in completing JIRA tickets; focus on strategically important projects. True productivity isn't about closing more tickets, but prioritizing what management deems crucial. The author outlines methods for identifying high-impact tasks, such as focusing on high-visibility incidents, unanswered questions, and project deadlines. A personal anecdote illustrates the frustration of focusing on less important tasks, advocating for ruthless prioritization and concentrating on projects that contribute real value. The result? Greater impact in less time.

(www.seangoedecke.com)

Development

Aussie Engineer's Take on Working for US Tech Firms: Time Zones, Culture, and Stability

2025-01-12

An Australian engineer shares his decade-long experience working for American tech companies. He details the challenges of cross-timezone collaboration: mornings are spent catching up on overnight work, but afternoons offer invaluable focused time. While loneliness can be an issue, strong teamwork and documentation culture mitigate this. He also notes the inherent instability of working for a US company from abroad, but highlights the larger scale, better compensation, and higher brand recognition as key motivators. Finally, he discusses cultural differences between Australia and the US, where Americans are more enthusiastic and Australians more understated, requiring adaptation to the American work culture.

(www.seangoedecke.com)

Development cross-cultural work

The Ratchet Effect: How Engineers Build Reputation at Big Tech

2025-01-08

Engineer reputation at large tech companies isn't solely about technical skill; it's a gradual process. Starting with low-level tasks, engineers build trust and gain access to higher-profile projects through consistent success. This "ratchet effect" makes reputation slow to change. Even mistakes can be overcome with continued delivery. However, repeated failures lead to a downward spiral. The author advises new hires to focus on smaller projects to build a solid reputation, avoiding risky attempts to jump to high-profile work immediately.

(www.seangoedecke.com)

Development engineer reputation ratchet effect

The Biggest Mistakes Engineers Make in Massive Codebases

2025-01-07

Working with large, established codebases is notoriously difficult. This article shares a decade's worth of experience, highlighting the most common and deadly mistake: ignoring existing codebase patterns and focusing solely on clean code for a new feature. Maintaining consistency is paramount; it prevents unexpected issues, slows the codebase's descent into chaos, and enables future improvements. The author also stresses understanding the code's production footprint, being cautious about introducing new dependencies, removing redundant code, working in small PRs, and leveraging team expertise to catch errors. While challenging, mastering large codebases is crucial because they are usually the foundation of a company's most valuable products.

(www.seangoedecke.com)

Development codebase

Glue Work Considered Harmful: A Survival Guide for Effective Engineers

2025-01-02

This article explores the concept of "glue work" in software engineering. While crucial for team efficiency (e.g., updating documentation, addressing technical debt), this unglamorous work often goes unrewarded, disadvantaging engineers who prioritize it. The author argues that companies don't reward glue work because they want engineers focused on feature delivery, not overall efficiency improvements. The efficient strategy is to apply glue work tactically to projects you're accountable for, ensuring their success, rather than spreading efforts thinly. This isn't cynical office politics; it's based on the reality of low efficiency in large companies and the prioritization of growth over short-term efficiency gains.

(www.seangoedecke.com)

Development team efficiency career development

Twice Promoted to Staff Engineer: Lessons Learned

2025-01-01

The author shares their experience of being promoted to Staff Software Engineer twice in two years. The key to promotion wasn't technical prowess, but delivering value to the company by successfully completing high-priority projects aligned with company goals. The author emphasizes the importance of understanding company priorities, working on impactful projects, and building strong relationships with management and team members. A supportive manager is crucial. Key takeaways include focusing on high-impact projects the company prioritizes, not overemphasizing mentoring, and having a manager willing and able to champion the promotion process.

(www.seangoedecke.com)

Development career advancement software engineer

The Gap Between Strong and Weak Engineers

2024-12-27

This article explores the capabilities that differentiate strong engineers from average ones. Strong engineers can accomplish tasks that weaker engineers cannot, such as resolving complex bugs, improving legacy code, and undertaking significant architectural overhauls. Weaker engineers struggle with these tasks even with ample time. The article highlights that strong engineers are not simply more efficient but possess the ability to solve complex problems, while weaker engineers are virtually incapable of completing most engineering tasks. The article also offers advice on collaborating with weaker senior engineers, emphasizing the need to be kind but protective of one's time, avoiding excessive demands.

(www.seangoedecke.com)

Development engineer capabilities