LLMs on Aneesh Sathe

The Deep Dark Terroir of the Soul

Fri, 26 Dec 2025 02:17:37 +0000

This is the third and final part of the Thicket Series:
Part 1: Logic of the Thicket and the Unsearchable Web
Part 2: The Architecture of Resistance

The history of the working subject might be best understood not as a ledger of wages or a sequence of industrial breakthroughs, but as a study in the migration of the Master. In the eighteenth century, the Master was a concrete presence, a figure residing in the castle or the cathedral, distinct from the worker by a physical and social chasm. One knew where the authority lived because one could see the smoke from its chimneys. By the nineteenth century, this figure had moved into the factory office, closer to the rhythm of the machine but still identifiable by the suit and the watch. The twentieth century saw a further dissolution; the Master became atmospheric, blending into the very walls of the institutions that housed us—the schools, the hospitals, the barracks.

The Architecture of Resistance

Tue, 23 Dec 2025 05:38:32 +0000

The seventeenth-century Hague, the mid-twentieth-century Levant, and the digital terraforming of 2025 have a shared preoccupation with the “Average.” Whether it is the theologian’s way or predictive stats, control begins by smoothing out the landscape. The project of power is a project of cartography and illumination—an attempt to banish the dark corners where the unmapped might grow. Thus, the history of resistance, of being “against the world”, is less a history of rebellion than a history of seeking cover.

The Shelter as Epistemic Engine

Wed, 17 Dec 2025 06:35:42 +0000

This is a continuation of my ongoing exploration of places and spaces. Previously: We need homes in the delta quadrant, Thinking with places, Problems are places questions are spaces.

Introduction: The Terror of the Open Field #

We tend to think of “Space” as a vacuum—an emptiness waiting to be filled. But geographically and philosophically, Space is actually a condition of high-entropy potential. As Yi-Fu Tuan famously articulated, space is “freedom,” but it is also “possibility without orientation.” It is the open field where everything is possible, which means nothing is yet distinct.

Four Early-Modern Tempers for a World That Can Summon Itself

Sat, 06 Dec 2025 10:12:37 +0000

This is a partial synthesis of the books read through 2025 in the Contraptions Book Club.

We live in a moment when the whole of human culture has become strangely available, no longer just an archive but something that behaves like a responding presence. A sentence typed into a search bar or messaging window returns citations and, more strikingly, continuations: pastiche, commentary, new variations of ideas that never existed until the instant we requested them. The canon now behaves more like a voice than a library. It is easy to treat this as convenience, yet summoning culture alters our relation to meaning in ways we are only beginning to see. The question is no longer whether we can find the relevant text, but what it means to think in a world that can generate its own echoes.

Work or Play? Ludic Feedback Loops

Mon, 28 Jul 2025 05:57:58 +0000

In his substack post today, Venkatesh Rao wrote about reading and writing in the age of LLMs as playing and making toys respectively. In one part he writes about how the dopamine feedback loop from writing drove his switch from engineering to writing. For him, writing has ludic, play-like, qualities.

I have made almost all my “career” decisions as a function of play. I originally started off with a deep love of plants, how to grow them and their impact on the world. I was convinced I was going to have a lot of fun. I did have some. My wonderful undergrad professor literally hand held me through my first experiments growing tobacco plants from seeds. But that was about it. My next experiment was with woody plants and growing the seeds alone took 6 months, and by the end I had 4 measly leaves to experiment with. I quickly switched to cell biology.

AI: Explainable Enough

Wed, 23 Jul 2025 05:50:31 +0000

They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem.

Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my angle. The issue was that in 2015 no biologist would accept any kind of deep learning analysis and definitely not if you couldn’t explain the details.

The secret flag of content

Sun, 20 Jul 2025 06:36:28 +0000

I don’t have any fun when I use LLMs to write. It may have perceived utility: popping out a LinkedIn article or two everyday. But I bet no one is actually reading. It’s a strip mall for a thumb stroll.

LLMs suck at writing. The summaries that LLMs give with the “Deep Research” are so poor in quality that I start to skim it. Yes, I skim the thing that is already a summary.

Chatbots, Bats & Broken Oracles

Sat, 05 Jul 2025 05:54:05 +0000

I had the strangest conversation with my son today. There used to be a time when computers never made a mistake. It was always the user that was in error. The computer did exactly what you asked it to do. If something went wrong it was you, the user, that didn’t know what you wanted. After decades of that being etched in today I found myself telling him that computers make mistakes, you have to check if the computer has done the right thing and that is actually ok. A computer that hallucinates also provides a surface for exploration and seeking answers to questions.

Domain Ontologies: Indispensable for Knowledge Graph Construction

Wed, 15 Jan 2025 08:06:00 +0000

AI slop is all around and increasingly extraction of useful information will face difficulties as we start to feed more noise into the already noisy world of knowledge. We are in an era of unprecedented data abundance, yet this deluge of information often lacks the structure necessary to derive meaningful insights. Knowledge graphs (KGs), with their ability to represent entities and their relationships as interconnected nodes and edges, have emerged as a powerful tool for managing and leveraging complex data. However, the efficacy of a KG is critically dependent on the underlying structure provided by domain ontologies. These ontologies, which are formal, machine-readable conceptualizations of a specific field of knowledge, are not merely useful, but essential for the creation of robust and insightful KGs. Let’s explore the role that domain ontologies play in scaffolding KG construction, drawing on various fields such as AI, healthcare, and cultural heritage, to illuminate their importance.

The Universal Library in the River of Noise

Sun, 12 Jan 2025 08:06:00 +0000

Few ideas capture the collective human imagination more powerfully than the notion of a “universal library”—a singular repository of all recorded knowledge. From the grandeur of the Library of Alexandria to modern digital initiatives, this concept has persisted as both a philosophical ideal and a practical challenge. Miroslav Kruk’s 1999 paper, “The Internet and the Revival of the Myth of the Universal Library,” revitalizes this conversation by highlighting the historical roots of the universal library myth and cautioning against uncritical technological utopianism. Today, as Wikipedia and Large Language Models (LLMs) like ChatGPT emerge as potential heirs to this legacy, Kruk’s insights—and broader reflections on language, noise, and the very nature of truth—resonate more than ever.

Dancing on the Shoulders of Giants

Fri, 10 May 2024 18:31:09 +0000

In Newton’s era it was rare to say things like “if I have seen further, it is by standing on the shoulders of giants” and actually mean it. Now it’s trivial. With education, training, and experience, professionals always stand “on shoulders of giants” (OSOG). Experts readily solve complex problems but the truly difficult ones aren’t solved through training. Instead, a combination of muddling through and the dancer style of curiosity is deployed, more on this later. We have industries like semiconductors, solar, and gene sequencing with such high learning rates that the whole field seems to ascend OSOG levels daily.

Reimagining AI in Healthcare: Beyond Basic RAG with FHIR, Knowledge Graphs, and AI Agents

Fri, 03 May 2024 21:15:05 +0000

Introduction #

While exploring the application of AI agents in healthcare we see that standard Retrieval-Augmented Generation (RAG) and fine-tuning methods often fall short in the interconnected realms of healthcare and research. These traditional methods struggle to leverage the structured knowledge available, such as knowledge graphs. Data approaches like Fast Healthcare Interoperability Resources (FHIR) used alongside advanced knowledge graphs can significantly enhance AI agents, providing more effective and context-aware solutions.