### Alan Turing Here’s a simple breakdown: **Shannon Machines:** - Start with data structures, with computation as secondary. - Focus on associative memory and managing state. - Use binary operators and bit transforms for math simulation. **Turing Machines:** - Start with basic arithmetic and build up to computation. - Provide a theoretical framework, independent of practical implementation. - Use algorithms to simulate any computing process.
TSM-1: The Shannon Machine — Better Than Turing Complete?
The Shannon Machine is a decider computational system which uses bit-level word operations (rather than high-level computation) to perform arithmetric. The goal is model practical computation in a way that is more realistic -- but still as formal -- as the Linear Bounded Automoton, which has a similar level of computational power.
The Fractor Model: Precise Shared Mutable State Management for Systems Programming
The Fractor Model, inspired by BitC and Jonathan Shapiro, refines the Actor paradigm for better control over shared mutable state. Fractors offer explicit state and effect handling, ideal for low-level concurrent programming. The key innovation is fractal-like, fine-grained control over mutable state at the method level, improving programmer ergonomics and enabling automated type-checking.
Abstract: X-JEPA – Mimicking Human/Cultural Cognition via Multi-Stage Semantic Mapping
The X-JEPA model introduces a multi-stage approach to semantic mapping, advancing the foundational principles established by eXtending the Joint Embedding Predictive Architecture (JEPA). This new architecture aims to develop cultured AI by mimicking human cognition through a layered structure that progressively builds on JEPA's semantic maps.
How to Build LightDash from Source
LightDash is a super-cool Open Source business intelligence tool built on top of DBT (which I think of as node for SQL). While it is distributed as open source, the usual way to deploy it locally is by simply running a docker container. If you want to actually built lightdash directly from source yourself, you... Continue Reading →
Configuring DataBricks on AWS
Despite the excellent QuickStart tools, this was way harder than I thought. For some reason I had the worst difficulty creating a Workspace on AWS for Databricks. Here are some tips that might help others who get stuck. A. Be clear which "Account ID" to enter where My Account ID on DatabricksMy Account ID on... Continue Reading →
SyncHouse: MVC for Enterprise SaaS
A concrete proposal for Imagining a Data Resort as enforcing a Model-View-Controller architecture across multiple Software-as-a-Service applications. The key is replacing transient enterprise data integrations with a persistent "sync house," and making that the one full-service Source of Truth for data, schemas, and business logic. Ingest data from Salesforce, NetSuite, etc. (e.g., Stitch/Talend, FiveTran)Store raw... Continue Reading →
Imagining a Data Resort
A data resort is where data comes to get pampered, so that it is prepared to get back to work. Motivation The good news is that I finally understand how we really need to be managing all the business data in my organization. The bad news is that I don't know how to articulate that... Continue Reading →
My First Date with Quilt Data
I've known the good folks at Quilt Data for a long time. A company hackathon gave me a good excuse to actually use them "in anger" for an actual demo. These are my notes on how to configure quilt3 and create my first package (and panda data frame) from a CSV Create a Quilt account.... Continue Reading →
SSO Login into Salesforce from Node via samlp SAML IdP
Documenting this in a blog post because it drove us crazy trying to figure out exactly what was involved, even though it was actually easy to implement once we understood all the terminology. In order for our previously-authenticated users to automatically log into Salesforce, we needed to: Create a "/sso-url" on our node server for our... Continue Reading →
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