In the previous article, I combined Paxos and PaxosLease into a small master-based replicated state machine. The obvious inefficiency was that every command still ran a full two-phase Paxos round. In this article, I add the simplest possible optimization: once a node has become master and completed one full Paxos round, later fresh log slots can skip the prepare phase and go directly to propose.

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The article walks through the integration of durable Paxos with PaxosLease to arrive at a small master-based replicated state machine: Paxos decides the command log, PaxosLease decides who is allowed to append to it, and clients must discover the current master before submitting writes.

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A follow-up to the async MQ series: building the same small message queue server in Rust using Tokio, channels, Arc, and Mutex, while learning Rust’s ownership and async model along the way.

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This post extends the simple Multi-Paxos implementation with disk persistence, ensuring acceptor promises and the replicated log survive restarts, and showing how log replay restores state after crashes.

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This article contrasts how Ilya Sutskever, Satya Nadella, and Sam Altman think about AI progress, from research and generalization, through infrastructure and economics, to agents, scaling, and societal impact.

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I asked ChatGPT to summarize the top 10 positives and negatives of Orban Viktor's 15 years as Hungary's leader.

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This article extends the basic PaxosLease algorithm with practical lease renewal and explicit release semantics, showing a full working Python/Flask implementation and explaining why the protocol remains safe without disks or synchronized clocks.

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This article introduces PaxosLease, a simplified Paxos variant that uses expiring leases instead of persistent values to enable fast, reliable master election in distributed systems.

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This post builds a minimal Multi-Paxos system in Python with Flask HTTP endpoints, showing how repeated Paxos rounds form a fault-tolerant replicated log.

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A straightforward explanation of Paxos using a simple Python Flask HTTP implementation to show how distributed systems achieve consensus in the presence of faults.

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A deep dive into how to structure a marketing measurement and experimentation framework — from universal control groups and campaign holdouts to attribution windows, outlier handling, and statistical rigor. Based on lessons from building a real-world policy for large-scale marketing analytics.

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I explore several points that are rarely explained in Modern Monetary Theory narratives: from taxation in non-tax economies to central bank independence, inflation targets, and the many forms of money that exist beyond government spending. These are areas where the story of MMT could be told more completely.

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I argue that widely used digital infrastructure like operating systems, compilers and browsers deserve public investment, just like roads, hospitals, and schools.

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This article explains how Practical Byzantine Fault Tolerance (PBFT) builds on Lamport’s theoretical consensus algorithms by introducing leaders, quorums, and cryptographic optimizations to make Byzantine consensus practical, contrasts it with Paxos and crash-fault tolerance, and explores its influence on modern blockchain protocols.

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This article, following Lamport's work, extends the original Byzantine Generals algorithm from “oral-messages” to signed messages, showing how digital signatures drastically simplify the problem and reduce message complexity, with an implementation given in Python using Flask HTTP nodes.

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This article examines where Lamport's original oral-messages Byzantine Generals algorithm does and does not apply in practice, contrasting unsuitable cases like distributed databases and rocket launches with a suitable case in redundant flight control systems.

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This follow-up factors Lamport’s Byzantine Generals algorithm into a generalized class that's independent of I/O and payloads. Any node can initiate a round (be "king"), values can be arbitrary strings, and multiple rounds can run concurrently.

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I discuss Lamport's Byzantine Generals problem and why it requires a total of N≥3M+1 nodes with M malicious nodes, and then implement the solution using Python with HTTP Flask servers.

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This post shows how to implement the Ricart–Agrawala Distributed Mutual Exclusion algorithm with Lamport's logical clocks using HTTP Flask servers.

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This post shows how to implement Lamport’s Bakery algorithm with Python HTTP Flask servers. The Python code is short and readable, helping newcomers grasp the algorithm’s logic without the syntactic overhead of C++. The asynchronous HTTP flow also generates plenty of overlap between workers, so the need for mutual exclusion is obvious. Exposing the variables through REST endpoints enables observation of ticket picking and critical section entry in real time, potentially making the flow of the algorithm easier to understand.

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