What every SaaS developer should read on vacation - 2024 Edition
The annual round-up of long-ish papers and videos. Start the new year with new knowledge and inspiration.
Happy holidays to all SaaS hackers! The earth, once again, completed its annual tour around the sun. The days are the shortest they’ll be for the next 365 days. Time to curl around the fire with a good paper or a substantial blog post, dive into meaty tech content and perhaps gain some inspiration for the coming year. I divided the reading recommendations to a technical and a non-technical sections, so you can pick the type of reading you are in the mood for. And toward the end of the blog I also share some thoughts on trending technologies for 2025.
Technical Deep Dives
I’m not sure if this is a classic that I missed or the world’s most under-rated paper, but Out of the Tar Pit is one of the best software engineering papers I’ve read, and I’m surprised it took 18 years for me to find it. The paper builds of classics such as No Silver Bullet. It expresses the (well supported) opinion that complexity stems from managing State and that object-oriented programming is the wrong approach, and then proposes a combined Functional-Relational model as a way to reduce complexity in software systems. If you read it and say “this is just common sense and how most people build software these days”, I’ll happily agree. This paper is a great read about the reasons and thinking behind modern software design.
Marc Brooks is one of my favorite bloggers. His blogs are always technically grounded, deep and inspiring. He wrote about 20 blogs this year (how???) and it is hard for me to recommend just one. They are all amazing. The series on Aurora DSQL is obviously interesting, but so is the one on Transaction Coordination, TCP NO_DELAY and the one about leaving CAP behind. Its the holiday, so I suggest just reading everything.
If you prefer long content in video format, I highly recommend this Youtube collection of SaaS talks from re:invent. It should keep you busy for few days and keep you up to date on SaaS-related tech content - lots of AuthN, AuthZ and multi-tenancy. If you only have time for one talk, I recommend Bill Tar’s Next Generation SaaS. Another great video for SaaS developers, this time from KubeCon, is Daniel Bryant’s Platform engineering for developers and architects
My own best writing in 2024 is pg_karnak: Transactional schema migrations across tenant databases - where I dive deep into an important part of Nile’s architecture and teach you quite a lot about Postgres extensions, hooks, locks, transactions and two-phase commits.
I tried to keep this blog light on AI, because I figured everyone else is already covering that. But you may be interested in reading On the Measure of Intelligence - this is the paper behind the now famous ARC benchmark, which tries to measure the “common sense” intelligence of AI. This paper and benchmark drove a lot of the buzz around OpenAI O3 model, so its a good read in order to understand what the buzz is about and move beyond it.
Everything that Joe Hellerstein writes is great, and Looking Back on Postgres is especially great. It talks about the evolution of Postgres, and the design choices that made it not just an awesome database but a flexible data platform in its own right. If you believe that learning history helps in building the future, you should read this one.
To complement the look into the past, Modern Hardware for Future Databases goes over recent advancement in networking and storage, and how it will change the design of databases.
And of course, I can’t write an annual summary without highlighting my favorite VLDB papers of the year:
The Clickhouse architecture paper
Resource Management in Aurora Serverless - which won the best industry paper aware
Petabyte Scale Row Operations in Data Lakehouse - I enjoyed it because I know and admire many of the authors, but if you are working on modern data analysis systems, this paper is both interesting and important.
Soft Skills
Writing is a key skill for developers, and blogging is a fun way to improve this skill while sharing your ideas with a wider audience and advancing your career. I wish more developers blogged. If you are even thinking about blogging, Writing for Developers, is a must-read. It is basically “design patterns” for blogs, with a lot of references and ideas. I really like the short-snippet structure, since you can get a lot of the book without committing to weeks of head-down reading. I really hope this book will inspire more of you to blog - and if it does - please comment and link to your blog!
Technical leadership is hard, and perhaps even harder if it involves people management. Mistakes you’ll make as a new manager covers the most common mistakes new managers made (I made most of them and invented some new ones in my first 6 month as a manager). Decision-making Pitfalls of Technical Leaders by Chelsea Troy (possibly the best engineering blogger) adds few more traps. After reading all the mistakes, come back and read Chelsea’s advice on How not to flub technical decision making as a group.
About a decade ago, Sam Altman turned 30 and shared some life advice. It is a bit shallow, but always good to read a reminder to drink water, hang around smart people and do work that matters to you.
One of the blogs that inspired me the most this year is Lego vs Woodworking mindset. It talks about the ways software engineering and startups can be hard and can feel very inefficient - and how the inefficiency is a feature rather than a bug. The main thesis is that you can’t explore and you can’t be creative efficiently. And that efficiency isn’t the appropriate approach to every problem.
2025 Trends and Predictions
I will start by observing that, in my experience, pronouncements that a technology is “dead” rarely pan out. While Jensen Huang may be right that kids these days shouldn’t learn to code, the demise of software engineering profession is unlikely to arrive in 2025. Along the same lines, I think this time next year, LLMs will still be going strong, SaaS will do fine, cloud computing and Serverless will still be in use, RAG will continue to supplament LLMs for many business use-cases, and everyone will still complain that data engineering is a pain.
The trends that I’m most interested in, going into 2025, are:
LLM validation and observability
The essential non-determinism of LLMs make this both a serious challenge and a must-have. Without solid data collection and analysis, it is easy to fool yourself into believing your model / product is much better than it really is. On the other hand, it is also hard (and scary) to take advantage of optimizations if you don’t have a trusted system that makes sure the quality bar remains high. I believe that this time next year, we’ll look at AI validation and observability the same way we look at unit tests today - an essential tool for continuous improvement process.
LLMs are scaling down and out, not up
There is increasing talk that the ability to build larger and larger models is hitting a wall - OpenAI’s missing GPT-5 is often cited as evidence, as well as the question of whether we ran out of training data and the exploding cost of harder (and energy). With a big question mark around bigger LLMs, we are seeing fast improvements in few critical areas:
- Inference is becoming faster and cheaper. This trend is driven both by the ability to train small models with good performance and the ability to shrink trained large models without reducing quality. Either way, models are trained once and then endlessly used for inference, so anything that makes inference faster, cheaper, less energy-demanding is going to accelerate the adoption of AI with fewer negative externalities.
- LLMs are being used as part of complex systems of inference. Whether it is multi-agent system, response validation workflows, multi-step reasoning, etc - we are looking at using LLMs in more complex architecture that can make-up for some of the inherent limitations.
Relational Databases at Scale
This one isn’t a surprise - I’m interested in this every year. This space just never stops advancing. The cycle keeps shifting between scaling out, with distributed architectures, and scaling up with compute/storage decoupling and improved containers and hardware. It looks like we are firmly back in the distributed architectures side of the cycle. But this time around there is renewed effort in making distributed tradeoffs legible for application engineers.
And, thats a 2024 wrap! I’ll see you in the new year! Don’t forget to send me interesting things to read, and share what you inspires you for 2025.