If the last decade of AI has taught us one lesson, it's that scaling compute builds better models.

Programming agents might work, but there are a lot of ways they can go wrong. The amount of guardrails necessary to keep the model in check doesn't scale. The more steps of a process rely on generative output, the higher the potential for error and catastrophic failure. Any process that involves these models must strive to maximize determinism and minimize model variance.

The "works" in "it works" is scoped strictly to coding tasks. I have no evidence, and seemingly no one else does, that the same kind of success is available outside the world of highly structured language with deterministic outputs.

Methodology To maximize determinism, each step of the build used test-driven development (TDD). Using the Markdown planning file as a starting point, the model generated tests for functions that would define the features, then implemented each in turn. After each coding round, cargo check and cargo test were run to confirm compilation and test passing. I reviewed every line of code the model generated. For initial drafts, very little had to change. Now to be fair, this is not a particularly complex app. It's basic CRUD app with some specialized requirements. Still, getting it all right, including auth and data handling, really mattered. After the initial drafting phase, I went through the entire app and made a list of tasks for improvement/change in the codebase. This TODO.md became the new starting point for model context in plan creation. Unexpectedly, as items were addressed in the document, the model updated the file with checkmarks and details of implementations. This was not an instruction I gave the model, but it was behavior I liked, since it created a trail of accountability. After all the features I wanted were functional, context was cleared entirely and new instructions were provided to the model. Instead of acting as a software developer, I instructed the model to perform as a security auditor and secure code expert, finding vulnerabilities in the code and recommending remediations. The findings would be written to a FINDINGS.md file, keeping with our established "Plan, Document, Execute, Log" pattern established in earlier rounds.

Why This Matters: From Probabilistic Guesses to Deterministic AnswersGrounding your AI in a Knowledge Graph delivers three non-negotiable enterprise advantages:1. Accuracy: Answers are derived from an explicit model of your business, not from statistical correlations in text. You eliminate both factual and relational hallucinations.2. Explainability: Every answer comes with a query that shows exactly how it was derived, which entities were connected and which rules were applied. This turns the AI from a black box into a transparent tool.3. Architectural Stability: The semantic layer, the ontology, remains stable even as the underlying systems change. When you migrate your CRM, you simply update the mapping to the ontology. Your AI, analytics, and dashboards continue to work without interruption. This is agility where it counts.

The dominant story is machine-centric. It’stechnological determinism.

Today the Magnum Theatrum is digitized and on the Web, but Zwinger’s original Theatrum is not.

we've spent 20 years now sequencing as many genomes as we can the output as 00:08:46 promised simply hasn't appeared

Another science-fiction writer and vicar among the Dictionary Peoplewas the Revd Edwin Abbott Abbott, who came by his repeated last namebecause his parents were first cousins.

In The Agony and the Ecstasy, Irving Stone’s biographical novel ofMichelangelo, Stone described vividly how Michelangelo released thestatues from the stone, “breaking the marble spell,” carving from theimages in his mind.

Richard Carter says: November 16, 2023 at 5:38 am   (Edit) Mortimer Adler read books more than once? I guess that made sense from someone whose name was an anagram of ‘Mr Read-More-Lit’!

the Prison Notebooks, contain Gramsci's tracing of Italian history and nationalism, as well as some ideas in Marxist theory, critical theory and educational theory associated with his name, such as: Cultural hegemony as a means of maintaining and legitimising the capitalist state The need for popular workers' education to encourage development of intellectuals from the working-class An analysis of the modern capitalist state that distinguishes between political society, which dominates directly and coercively, and civil society, where leadership is constituted through consent Absolute historicism A critique of economic determinism that opposes fatalistic interpretations of Marxism A critique of philosophical materialism

War eine Dame häufig an der Bar anzutreffen, nannte man sie hinter vorgehaltener Hand «Miss Martini».

The hypothesis of linguistic relativity, also known as the Sapir–Whorf hypothesis /səˌpɪər ˈwɔːrf/, the Whorf hypothesis, or Whorfianism, is a principle suggesting that the structure of a language influences its speakers' worldview or cognition, and thus people's perceptions are relative to their spoken language.

does the universe have a way of pushing people towards careers that reflect their names

nominative determinism

Have I heard of nominative determinism?

only partly self-determined

aggrandizing

One is to imagine that culture is a self-contained "super-organic" reality with forces and purposes of its own; that is, to reify it. Another is to claim that it consists in the brute pattern of behavioral events we observe in fact to occur in some identifiable community or other; that is, to reduce it.

As Heilbroner (1994) and other researchers have argued, technological tra-jectories are responsive to social direction. I make the case that they may alsobe responsive to intellectual direction.1Indeed, a central premise of HCI isthat we should not force users to adapt.

The motivation in writing this paper is to examine some of these ideas about time and technology. The notion that digi-tal technologies in themselves have a temporal quality that is problematic is questionable.

Things stayed civil because the system aligned incentives correctly.

We are on the threshold of sweeping change that will make it easier for teachers to teach and students to learn faster and more effectively