MylifeBits is a lifetime store of everything. It is the fulfillment of Vannevar Bush’s 1945 Memex vision including full-text search, text & audio annotations, and hyperlinks. The book Total Recall (paperback title Your Life, Uploaded) is the culmination of our thoughts regarding MyLifebits and the larger CARPE research agenda. There are two parts to MyLifeBits: an experiment […]

In this blog, we go on an interactive journey to understand common queueing strategies for handling HTTP requests.

The Unicode block Variation Selectors contains the codepoints from U+FE00 to U+FE0F.

Mint defines workflows as a directed acyclic graph (DAG) of tasks, rather than as scripts on VMs. This difference is the key to Mint’s unmatched performance and developer experience.

What we cover here is what the “Deepfake maker” can make and obscure whether the content is original or AI-made.

As discussed in Components of engineering strategy, a complete engineering strategy has five components: explore, diagnose, refine (map & model), policy, and operation. However, it’s actually quite challenging to read a strategy document written that way. That’s an effective sequence for creating a strategy, but it’s a challenging sequence for those trying to quickly read and apply a strategy without necessarily wanting to understand the complete thinking behind each decision.

As cloud computing continues to evolve, businesses must adapt to harness the full potential of these developments.

With platform engineering at the helm, the future of cloud native development is poised for unprecedented growth and transformation.

Applied to the API landscape, a platform provides consistency for technology and workflows, which simplifies the developer experience for API consumers.

You might have heard of TLA+, but how do you use it for debugging? FizzBee is a new formal methods system that you can grasp in just a weekend.

DSNP establishes a shared social graph no longer dependent on a specific app or centralized platform.

wingolog: article: on hoot, on boot

Recall uses AI features "to take images of your active screen every few seconds."

A dive deep into the design of Amazon MemoryDB, an in-memory database.

Learn about Merkle Directed Acyclic Graphs (DAGs) and why they're important to IPFS.

Explore the ins and outs of CIDs (Content Identifiers), the unique labels used to point to data stored on distributed information systems including IPFS, IPLD, libp2p, and Filecoin.

Learn how we can use CIDs to create content-addressable data structures for the distributed web!

Learn how hashing and content addressing enable verifiable data sharing with peers on the decentralized web.

It is all about integers

Base58 is a character encoding system developed by Satoshi Nakamoto. It was first released on the earliest Bitcoin source code tree. Satoshi felt that a new encoding was necessary for Bitcoin’s addresses and transactions, since he thought the existing ones, like Base64, would cause confusion when writing down Bitcoin addresses and TX hashes. In essence, […]

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).[1] While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

Learn about the Specification Design Pattern in C# and its benefits for your code. See how this pattern can improve code quality and how to implement it!

Although external caches are a great companion for reducing latencies, they often introduce more problems than benefits. Here’s what to do instead.

By considering this advice, IT leaders can move AI initiatives forward despite the obstacles and pressures they’re up against.

This week I found myself digging through the code of c4, an implementation of C “in four functions”, by Robert Swierczek. I remember coming across c4 when it was released ten years ago. It got me excited: hey, C in four functions, that means it’s easy to understand right?

Our setting is logic programming, a field which attempts to design programming languages whose semantics have a close relationship to formal logic. The reason we might want to do this is that it suits our application domain more precisely than an implementation in a traditional programming language. Thus, using a logic programming language allows us to write more obviously-correct code, and perhaps even code that can be extracted cleanly from a certified implementation. Alternatively, if we did it ourselves, we’d have to do what our compiler (interpreter, …) would do anyway, so there’s no sense in doing it manually. Unfortunately, when we see a powerful tool, we are tempted to use it for everything: if our application is not ultimately-suited to the operationalization strategy of the logic programming engine we’re using, we simply obfuscate the issue in a veneer of formalism and end up with leaky abstractions. This is, I speculate, why logic programming languages have never caught on broadly for general-purpose programming. In this blog, I will detail the various trade-offs and implementation paradigms for modern logic programming engines, starting from Datalog and with a focus on program analysis.