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Showing posts from November, 2026

Writing as a thinking tool

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In middle school I could not get through long passages no matter how hard I tried. I would read an entire page and find that nothing had registered, so I started taking notes out of desperation. This was the late 80s, and Tony Buzan's mind maps had just reached Turkey. This led me to produce pages and pages of notes that looked like spiderwebs. Creating those notes have helped me tremendously. I made sense of what I was reading by figuring out which branch they would hang off in my mind maps. I realized my pages were not for storing the information as much as they were for processing it. It took me another ten years to understand why this works so well. During a stretch of PhD procrastination, I read Manuel Blum's advice to graduate students : You are all computer scientists. You know what FINITE AUTOMATA can do. You know what TURING MACHINES can do. For example, Finite Automata can add but not multiply. Turing Machines can compute any computable function. Turing machines are i...
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The Man from the Future: Why Johnny Can't Stop Abstracting

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I have come to suspect that to most people thinking is painful. Some of us are addicted to thinking. Some of us find it a necessity. Johnny enjoyed it. I even have the suspicion that he enjoyed almost nothing else. -- Edward Teller, eulogizing John von Neumann I listened to Ananyo Bhattacharya's " The Man from the Future: The Visionary Life of John von Neumann " in my car during the many commute hours Bay Area traffic afforded me. I thought I should share the Johnny phenomena here. I knew about von Neumann being a very influential mathematician/scientist/engineer but the full list still left me bedazzled. In his 53 years of life (1903-1957), von Neumann seeded so many fields. Every time there was an important discovery in physics, mathematics or computation, John von Neumann was there. Set theory and the foundations of mathematics. The mathematical formulation of quantum mechanics. Operator algebras (still called von Neumann algebras). Ergodic theory. Game theory. Expect...
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The Safest Job from AI may be Writing

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Today, tech folk are scrambling to change their workflows to meet newly inflated 5X productivity quotas, while getting pummeled under the cognitive debt of agent-generated code. With every new model release, the gap is widening and humans are becoming more of a bottleneck in the loop, approaching closer to obsolescence as "coders". While the programmer's job description is getting completely refactored, writing remains surprisingly unaffected. LLMs have gotten very good at generating code, but I am appalled at the absolute shit they spew as prose. They always follow the same robotic cadence and cliches, and sprinkle the same tired vocabulary all around. They take my broken yet soulful writing and transform it into a plastic soulless word slop in the name of improving prose. Their writing communicates no actual understanding and insight. I think we are all developing a visceral ick reaction to AI writing. It is trapped in the uncanny valley, and it may be stuck there for ...
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The Two Abstractions of System Design: Hide or Reduce

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When talking about TLA+, I keep referring to "abstraction" as the most important thing to learn . And it is about the hardest to learn as well. But a contradiction has been bugging me. Aren't CS people already supposed to be good at abstraction? Isn't abstraction supposed to be at the root of OS, networking, software engineering? Abstract Data Types (ADTs) are a staple of every in CS curriculum. So why do I (and every other formal methods/modeling person) see such a large skill gap in abstraction, and flag it as the core, make-or-break skill for modeling? I think I finally get to the root of this cognitive disonance. There are two kinds of "abstraction" conflated under the same umbrella term. Modularity abstraction: This is the traditional abstraction taught in CS curricula as ADTs, APIs, layered design, etc. It is all about encapsulation, drawing boundaries, and hiding internals. Modeling abstraction: This is what I talk about when I talk about abstracti...
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The Agentic Self: Parallels Between AI and Self-Improvement

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2025 was the year of the agent. The goalposts for AGI shifted; we stopped asking AI to merely "talk" and demanded that it "act". As an outsider looking at the architecture of these new agents and agentic system, I noticed something strange. The engineering tricks used to make AI smarter felt oddly familiar. They read less like computer science and more like … self-help advice . The secret to agentic intelligence seems to lie in three very human habits: writing things down, talking to yourself, and pretending to be someone else. They are almost too simple. The Unreasonable Effectiveness of Writing One of the most profound pieces of advice I ever read as a PhD student came from Prof. Manuel Blum, a Turing Award winner. In his essay "Advice to a Beginning Graduate Student", he wrote: "Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine." If you try to hold a complex argument enti...
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In Search of a Compositional Theory of Self-Stabilization

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My quest for a principled solution for metastable failures has taken me back to my roots on self-stabilization, as this recent paper related the problem to composition of self-stabilizing systems.  But, my literature search for recent work on composing self-stabilizing systems didn't yield anything useful. The layered stabilization idea was already in place by the early 2000s, and nothing fundamental seems to have been added since. Frustrating. So I decided to attack the problem using the concrete example I have. I had composed a rely-guarantee TLA+ model of a retry storm as two components with contracts . That model reproduces metastable failure because the composition that worked from good states failed to work when a large shock removes the base case that let the two conditions hold each other up. Searching for  rely-guarantee based composition from every state, turned up a 2017 control theory paper by Kim, Arcak and Seshia, "A Small Gain Theorem for Parametric Assume-Gua...
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Learning about distributed systems: where to start?

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This is definitely not a "learn distributed systems in 21 days" post. I recommend a principled, from the foundations-up, studying of distributed systems, which will take a good three months in the first pass, and many more months to build competence after that. If you are practical and coding oriented you may not like my advice much. You may object saying, "Shouldn't I learn distributed systems with coding and hands on? Why can I not get started by deploying a Hadoop cluster, or studying the Raft code." I think that is the wrong way to go about learning distributed systems, because seeing similar code and programming language constructs will make you think this is familiar territory, and will give you a false sense of security. But, nothing can be further from the truth. Distributed systems need radically different software than centralized systems do.  --A. Tannenbaum This quotation is literally the first sentence in my distributed systems syllabus. Inst...
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Building a Database on S3

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Hold your horses, though. I'm not unveiling a new S3-native database. This paper is from 2008. Many of its protocols feel clunky today. Yet it nails the core idea that defines modern cloud-native databases: separate storage from compute. The authors propose a shared-disk design over Amazon S3, with stateless clients executing transactions. The paper provides a blueprint for serverless before the term existed. SQS as WAL and S3 as Pagestore The 2008 S3 was painfully slow, and 100 ms reads weren't unusual. To hide that latency, the database separates "commit" from "apply". Clients write small, idempotent redo logs to Amazon Simple Queue Service (SQS) instead of touching S3 directly. An asynchronous checkpoint by a client applies those logs to B-tree pages on S3 later. This design shows strong parallels to modern disaggregated architectures . SQS becomes the write-ahead log (WAL) and logstore. S3 becomes the pagestore. Modern Aurora follows a similar logic : t...
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Foundational distributed systems papers

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I talked about the importance of reading foundational papers last week. To followup, here is my compilation of foundational papers in the distributed systems area. (I focused on the core distributed systems area, and did not cover networking, security, distributed ledgers, verification work etc. I even left out distributed transactions, I hope to cover them at a later date.)  I classified the papers by subject, and listed them in chronological order. I also listed expository papers and blog posts at the end of each section. Time and State in Distributed Systems Time, Clocks, and the Ordering of Events in a Distributed System. Leslie Lamport, Commn. of the ACM,  1978. Distributed Snapshots: Determining Global States of a Distributed System. K. Mani Chandy Leslie Lamport, ACM Transactions on Computer Systems, 1985. Virtual Time and Global States of Distributed Systems.  Mattern, F. 1988. Practical uses of synchronized clocks in distributed systems. B. Liskov, 1991. Exp...
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What I'd do as a College Freshman in 2025

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Do Computer Science Absolutely. Still would. Many are spooked by LLMs. Some, like Jensen Huang, argue that "nobody has to learn how to program."   I argue the opposite . And I double down.  Being supported by AI tools is not a substitute for mastery . You can’t borrow skills. You have to earn them.   Computer science builds vital skills : hacking, debugging, abstract thinking, and quick adaptation. These don’t go out of style.   Do STEM. It’s LLM-resistant. LLMs can retrieve and remix information, but do you know what to do with them? Like the dog chasing the car, what now? STEM teaches you that. It teaches you to think, to reason, to act. It gets you from information to wisdom. But only after you've mastered the foundations. We're heading into the age of π-shaped people: depth in two areas, and generalist across . Building depth first, and then ranging is good strategy .   So yes, I would learn the foundations of both CS and AI. And then do AI + X, where X is s...
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Cloudspecs: Cloud Hardware Evolution Through the Looking Glass

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This paper (CIDR'26) presents a comprehensive analysis of cloud hardware trends from 2015 to 2025, focusing on AWS and comparing it with other clouds and on-premise hardware. TL;DR: While network bandwidth per dollar improved by one order of magnitude (10x), CPU and DRAM gains (again in performance per dollar terms) have been much more modest. Most surprisingly, NVMe storage performance in the cloud has stagnated since 2016. Check out the NVMe SSD discussion below for data on this anomaly. CPU Trends Multi-core parallelism has skyrocketed in the cloud. Maximum core counts have increased by an order of magnitude over the last decade. The largest AWS instance u7in now boasts 448 cores. However, simply adding cores hasn't translated linearly into value. To measure real evolution, the authors normalized benchmarks (SPECint, TPC-H, TPC-C) by instance cost. SPECint benchmarking shows that cost-performance improved roughly 3x over ten years. A huge chunk of that gain comes from AWS G...
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