What I learned today

  LLM Research Table of Contents PART I — FOUNDATIONS: WHAT OBJECT ARE WE STUDYING? 1.1 Artificial intelligence, machine learning, deep learning, transformers, and LLMs 1.2 Model versus inference process versus deployed system 1.3 LLM versus agent versus research system 1.4 Model weights versus runtime state 1.5 Token sequence versus latent computation 1.6 Output versus process that generated the output 1.7 Representation versus represented object 1.8 Readout versus mechanism 1.9 Local behavior versus global capability 1.10 Capability versus intelligence 1.11 Intelligence versus learning 1.12 Memory versus history 1.13 Knowledge versus accessibility 1.14 Generation versus discrimination 1.15 Certification versus truth 1.16 The central non-collapse laws 1.16.1 SEMANTIC_CLOUD ≠ INTELLIGENCE 1.16.2 SEMANTIC_CLOUD ≠ MEMORY 1.16.3 SEMANTIC_CLOUD ≠ LEARNING 1.16.4 FLUENCY ≠ SUPPORT 1.16.5 DENSITY ≠ TRUTH 1.16.6 CONSENSUS ≠ WARRANT 1.16.7 PROJECTION ≠ SOURCE 1.16.8 REPRESENTATION ≠ OBJECT...

Intelligence and the Geometry of Choice Part I — The Primitive Architecture Exposure Geometry 1.1 Gradients, asymmetries, constraints 1.2 Accessible versus inaccessible trajectories 1.3 Why possibility precedes choice Distinction 2.1 THIS | NOT-THIS 2.2 Consequential versus irrelevant difference 2.3 Distinction without representation The Semantic Cloud 3.1 Distributed possibility space 3.2 Latent alternatives 3.3 Context-dependent activation 3.4 Semantic cloud ≠ explicit model Generating Alternatives 4.1 Candidate continuations 4.2 Suppressed possibilities 4.3 Novel combinations 4.4 When no useful alternative appears Better and Worse 5.1 Preference without language 5.2 Local ordering 5.3 Partial ordering and incomparability 5.4 Context changes the ordering Intelligence 6.1 Intelligence as evaluation 6.2 A = {a1…an} -> ordering ≻ 6.3 Intelligence ≠ candidate generation 6.4 Intelligence ≠ choice 6.5 Intelligence ≠ success Choice 7.1 Ordering → selection 7.2 Choice under uncertainty 7....

  Why LLM Research Ignores the Actual Structure of Semantic Clouds Table of Contents The Missing Object 1.1 The strange success of LLMs without a theory of what they learned 1.2 Objective, architecture, computation, representation, and object are different things 1.3 NEXT-TOKEN PREDICTION != INTELLIGENCE 1.4 TRAINING OBJECTIVE != LEARNED STRUCTURE 1.5 Why describing input-output behavior does not identify the generative object 1.6 The semantic cloud as the missing level of description 1.7 MODEL = learned possibility field , not database, lookup table, or bag of facts 1.8 Why semantic clouds are invisible to research organized around measurable projections What Is a Semantic Cloud? 2.1 From stored items to relational possibility fields 2.2 Alternatives as the primitive of decision 2.3 CURRENT STATE + POSSIBILITY FIELD + CONSTRAINT -> RESPONSE 2.4 Relations, transformations, generators, accessibility, and constraint propagation 2.5 Semantic possibility versus explicit symbolic rep...

   Europe’s Megalithic Masons Local Stone, Waterborne Networks, and the First Stone Engineers TOC Prologue — The Stone Survives; the System Disappears Why the monument is the wrong starting object Quarry, mason, transport, labor, route, and monument as separate carriers What survives: stone quarry scars sockets tool marks What largely disappears: boats rope sledges timber food provisioning seasonal labor spoken instruction apprenticeship route knowledge Europe’s megalithic record as a preservation-filtered residue of a much larger technical system The core question: not “Who were the megalithic people?” but “How were large-stone technologies locally generated, transmitted, and reproduced?” Part I — What Is a Megalithic Mason? 1. Monolith Builder, Quarryman, or Mason? Why “mason” cannot be used loosely Selecting a naturally detached boulder Quarrying a block Dressing a block Moving a block Erecting a monolith Building a wall Corbelling a chamber Cutting a doorway Constructing a...

  Europe’s Megalithic Masons Stone, Skill, Mobility, and the Making of Monumental Landscapes TOC Introduction — The Wrong Question: “Who Built the Megaliths?” The problem with treating “megalithic” as a people Monument type ≠ population Monument similarity ≠ common ethnicity Stone transport ≠ migration Shared construction knowledge ≠ demographic replacement Why archaeology over-observes stone and under-observes labor, routes, boats, timber, rope, food supply, and technical instruction The monument as surviving endpoint of a largely vanished production system The central question: how did European communities acquire, reproduce, and transmit large-stone engineering? Part I — Before the Mason 1. Monumentality Before Monumental Stone Recurrent gathering before permanent construction Territorial memory without villages Burial grounds, ancestor places, route markers, and aggregation nodes Cerny and Passy as a useful pre-megalithic comparison Monument ≠ permanent settlement Monument ≠ ag...