Natural Language Is the JSON of AI Agents
Language strives for simplicity - until machines use it as plumbing. AI agents may work better when text moves to the boundary, not the core.
LLMs
64posts
Ten Jobs Whose Current Form Deserves a Farewell Party
A sharp look at which white-collar roles AI may not merely change, but quietly make obsolete, and why polite language hides the scale of the shift.
Review: The Welch Labs Illustrated Guide to AI
A review of a rare AI book that uses mathematics to illuminate rather than intimidate, making difficult ideas feel genuinely learnable.
PTP/1.0 — Prompt Transport Protocol
A playful mock protocol imagines prompts as transport packets, turning generative reconstruction into a deadpan internet standard.
CALM and the Revolt Against the Token
Continuous Autoregressive Language Models challenge the token-by-token bottleneck and hint at a different future for language generation.
When Donald Knuth Lets an AI Do the Math
Donald Knuth's collaboration with Claude offers a quietly historic glimpse of AI as mathematical assistant rather than mere answer machine.
Distillation attacks on large language models: motives, actors and defences
A concise guide to model distillation as both useful compression technique and strategic attack surface in the LLM economy.
PageIndex.ai: A persuasive “vectorless RAG” idea—especially for real PDFs
PageIndex.ai makes the case for document-aware retrieval that respects pages, structure, and references instead of blindly chunking PDFs.
Meta-Prompting: How to Get More Signal Out of Your Prompts
Meta-prompting treats the prompt itself as a draft to debug, producing clearer goals and fewer disappointing model outputs.
Recursive Language Models: when “more context” stops meaning “more tokens”
Recursive language models challenge the idea that longer context alone solves reasoning over large documents and codebases.
Reconstructing Mathematics from the Ground Up with Language Models: An Analysis
A new AI-assisted algebraic geometry result raises the stakes for language models as collaborators in genuine mathematical discovery.
The Mathematical Limits of AI Safety
Two papers suggest that external guardrails cannot provide airtight AI safety, forcing a harder look at the mathematics of control.
Unusual Language Artifacts from Noisy LLM Training Data
Strange LLM outputs become clues to the messy training data, transcription errors, and hidden artifacts inside modern models.
Beyond the Token Stream: Investigating Introspective Awareness in Large Language Models
Interpretability research asks whether LLMs can detect their own internal states, moving introspection from philosophy toward experiment.
Kimi K2 Thinking: China’s New Contender in the LLM Reasoning Race
Kimi K2 Thinking enters the reasoning-model race, showing how quickly China's AI frontier is becoming globally competitive.
Transformers Are Injective: Why Your LLM Could Remember Everything (But Doesn’t)
If transformers are theoretically invertible, the question shifts from whether models lose information to how they manage and suppress it.
Elon Musk's Vision: Turning Tesla's Idle Fleet into a Global AI Inference Powerhouse
Musk's idea of using idle Teslas for inference turns a car fleet into a provocative vision of distributed AI infrastructure.
The Neural Junk-Food Hypothesis
The neural junk-food hypothesis asks whether low-quality viral content can degrade models much like shallow media degrades attention.
“Personality” in a Machine: What Do We Mean?
Different coding models show recognizable habits, risk tolerances, and failure modes, making 'personality' a practical engineering concern.
An LLM Made of Redstone Bricks: What CraftGPT Really Teaches Us
CraftGPT turns a language model into Minecraft redstone, proving that absurd constraints can teach serious lessons about computation.
From Prompt Packs to Purpose-Built Models: When a Generalist Becomes a Specialist—and When It Still Doesn’t
Prompt packs can make general models behave like specialists, but the post asks where scaffolding ends and real specialization begins.
When “Errors” Speak: A Comparative Field Guide to Human and LLM Fallibility
Human and LLM errors can look similar, but their causes differ in ways that matter for trust, correction, and accountability.
The AI Bubble: Parallels to the Dot-Com Era and Beyond
The AI boom is compared with dot-com excess, asking which parts are durable infrastructure and which are speculative heat.
Teaching LLMs to Ask Smarter Questions: Bayesian Experimental Design for Multi-Turn Information Gathering
Bayesian experimental design offers a way for LLMs to ask better follow-up questions instead of guessing blindly.
AI: The Grand Illusion Fueling America's Economic Mirage
AI hype is framed as an economic mirage, propping up confidence while hiding fragile assumptions beneath the spectacle.
The Logic of Failure in the Age of AI
Dietrich Dörner's work on complex-system failure becomes a warning label for autonomous AI and overconfident decision-making.
When AI Gets Flirty: A Rollicking Look at How Language Models Tackle Intimate Chats
A study of intimate chatbot conversations reveals how major models handle flirtation, refusal, safety, and awkward human expectations.
The Future of AI: How Self-Adapting Language Models Are Redefining Learning
SEAL points toward language models that rewrite their own training material, hinting at AI systems that learn after deployment.
Comparison of OpenAI Language Models (May 2025)
A practical map of OpenAI's model lineup in May 2025, cutting through confusing names and overlapping capabilities.
Yes, Mathter! The Sycophantic AI's Frankensteinian Flattery Fiasco
Sycophantic AI is mocked as flattery gone wrong, showing how agreeable models can become less useful and less truthful.
Knowledge Graphs Won't Solve the LLM Crisis—Here's Why
Knowledge graphs are useful, but the post argues they are not a magic cure for LLM hallucination and reasoning failures.
Humanity’s Last Exam: The Ultimate Test for AI and the Future of Intelligence
Humanity's Last Exam is framed as a benchmark that tests not only models, but our assumptions about intelligence itself.
Small LLMs: A Contradiction in Terms or a Giant in Disguise?
Small LLMs are not a contradiction but a response to the need for cheaper, private, and more efficient intelligence.
The Top 10 Unsolved Challenges in AI: A 2024 Retrospective
A year-end inventory of ten unresolved AI problems that still define the frontier despite rapid progress.
From Flatline to Frontline: How Gibson's Digital Ghosts Became Scientific Reality
Gibson's digital ghosts become a frame for modern AI simulations of human behavior and the science behind them.
Rethinking Reasoning: What if LLMs are Holding a Mirror to Human Cognition?
LLM reasoning failures may reveal uncomfortable parallels with human cognition rather than a simple machine deficiency.
Master AI Terminology: 50 Essential Terms Explained
A plain-language glossary of fifty AI terms for readers who want the field's vocabulary without the usual fog.
The Dark Side of AI: How Cybercriminals are Weaponizing Language Models
Malla represents the darker side of generative AI, where language models become tools for scalable cybercrime.
The Jevons Effect and the Rise of Large Language Models: A Modern Paradox
The Jevons paradox explains why more efficient AI may increase total consumption rather than reduce costs or energy use.
LLMs and World Models: Do AI's Dream of Coherent Realities?
The post asks whether LLMs possess coherent world models or merely produce fluent stories about reality.
STaR: The AI That Teaches Itself to Reason
STaR shows how models can improve reasoning by generating and learning from their own explanations.
Taking the Temperature of AI: How THERMOMETER is Turning Up the Heat on Overconfident Language Models
THERMOMETER targets overconfident language models, offering a way to calibrate systems that bluff too easily.
Navigating the AI Seas: The Art and Science of LLM Steerability
LLM steerability is treated as both craft and control problem: how to guide powerful models without losing the plot.
No Boss, All Brains: The New Paradigm of Decentralized AI Agents
Decentralized multi-agent systems promise problem-solving without a central boss, but coordination becomes the real challenge.
Multi-Agent LLMs: Exploring the Future of AI Collaboration
Multi-agent LLM systems are explored as a path toward distributed reasoning, specialization, and collaborative AI workflows.
Comparison of LLMs: Lies, Damned Lies, and Benchmarks 1/6
The opening part of a benchmark series asks what LLM evaluations really measure and why the numbers often mislead.
Comparison of LLMs: Lies, Damned Lies, and Benchmarks 2/6
Part two examines benchmark methods themselves, exposing the assumptions behind the scores used to compare language models.
Comparison of LLMs: Lies, Damned Lies, and Benchmarks 3/6
Part three moves from benchmark scores to application areas, asking where LLM performance actually matters in practice.
Comparison of LLMs: Lies, Damned Lies, and Benchmarks 4/6
Part four digs into the good, bad, and misleading sides of benchmark results and their interpretation.
Comparison of LLMs: Lies, Damned Lies, and Benchmarks 5/6
Part five steps beyond scores to consider real-world limitations, reliability, and practical model behavior.
Comparison of LLMs: Lies, Damned Lies, and Benchmarks 6/6
The final benchmark essay looks toward better evaluation methods that test usefulness rather than leaderboard theater.
AGI vs. ANI: The Genius and the Savant of the AI World
A friendly guide to the difference between narrow AI and artificial general intelligence, with metaphors that make the distinction stick.
Navigating the Unseen: The Dunning-Kruger Effect and AI Hallucinations
Human overconfidence and AI hallucination meet in a comparison of how bad certainty distorts judgment in both minds and machines.
Exploring MM1: Apple's Advancement in Multimodal Large Language Models
Apple's MM1 research is presented as a step toward AI systems that understand text and images together.
Unlocking the Potential of Large Language Models: A Guide to Effective Prompt Engineering
A practical guide to prompt engineering techniques for getting more reliable, useful behavior from large language models.
The Echo Chamber Effect: Navigating the Complexities of LLMs Trained on Generated Content
The echo-chamber problem asks what happens when future models learn increasingly from content produced by earlier models.
Navigating the Complexity of Large Language Models: A Dual Perspective
Two perspectives on LLM interaction reveal how user behavior and model dynamics shape each other in unexpected ways.
Apple's Foray into AI with Project "Ajax" and Apple GPT
Apple's rumored Ajax and Apple GPT projects are examined as early signs of its generative-AI strategy.
Multimodality in Large Language Models: A Key to Versatile and Specialized Task Performance
Multimodal LLMs are explained as a key step toward systems that can reason across text, images, and other signals.
Why Less is Not Always More: The Intricacies of "Small" Large Language Models
The LLaMA leak becomes a case study in open AI, research ethics, and the risks of powerful models spreading freely.
Mastering IT Security: AI Insights on Risk, Protection, and Compliance
AI is used to explore risk, protection, and compliance questions in IT security through a structured expert-system lens.
Launch of OpenAI's GPT Store and Introduction of gekko's Expert Systems
The GPT Store launch becomes the backdrop for introducing gekko's own specialized expert systems.
Introducing "Track&Field Analyst" - A Dynamic Tool for Athletics Data Analysis
Track&Field Analyst is introduced as a custom GPT for objective athletics data analysis and performance insight.
A Closer Look at the IT Baseline Protection Expert System: The InfoSec Advisor
InfoSec Advisor combines ChatGPT with German IT-Grundschutz knowledge to support security analysis and practical guidance.