Agentics: Memorizing Session Transcripts Isn't Useful

Keep track of artifacts, not scratch. Alt title: Claude, please stop trying to memorize random crap

The cognitive impact of coding agents

A fun thing about recording a podcast with a professional like Lenny Rachitsky is that his team know how to slice the resulting video up into TikTok-sized short form vertical …

Book Review: The Math Academy Way

Memorization is just knowing things

bluesky-social/workshop-codespace

Workshops for learning atproto. Works as a Github Codespace or locally.

bluesky-social/workshop-codespace

Workshops for learning atproto. Works as a Github Codespace or locally.

The Fragility Of Moral Judgment In Large Language Models

People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems cannot interrogate missing context and judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January-March 2025), we generate three families of content perturbations: surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (self-positioning, social proof, pattern admissions, victim framing). We also vary the evaluation protocol (output ordering, instruction placement, and unstructured prompting). We evaluated all variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N=129,156 judgments). Surface perturbations produce low flip rates (7.5%), largely within the self-consistency noise floor (4-13%), whereas point-of-view shifts induce substantially higher instability (24.3%). A large subset of dilemmas (37.9%) is robust to surface noise yet flips under perspective changes, indicating that models condition on narrative voice as a pragmatic cue. Instability concentrates in morally ambiguous cases; scenarios where no party is assigned blame are most susceptible. Persuasion perturbations yield systematic directional shifts. Protocol choices dominate all other factors: agreement between structured protocols is only 67.6% (kappa=0.55), and only 35.7% of model-scenario units match across all three protocols. These results show that LLM moral judgments are co-produced by narrative form and task scaffolding, raising reproducibility and equity concerns when outcomes depend on presentation skill rather than moral substance.

Memory Efficient RL | Unsloth Documentation

We're excited to introduce more efficient reinforcement learning (RL) in Unsloth with multiple algorithmic advancements:

Vibe coding and agentic engineering are getting closer than I’d like

I recently talked with Joseph Ruscio about AI coding tools for Heavybit’s High Leverage podcast: Ep. #9, The AI Coding Paradigm Shift with Simon Willison. Here are some of my …

AI for Me, Not (Yet) for Thee? Desirable Difficulties and Deliberate Friction with LLMs

In December 2025, I was grading final projects for my course on data visualization with R. On one of my screens, I had a browser open to my university’s learning management system and came across a now-familiar phenomenon: a student had used verbatim ChatGPT output for their assignment. The code they included was flawless, but used function arguments and code syntax that we never covered in class and that weren’t actually necessary. More concerning, the original chat prompt was inadvertently still included, along with fill-in-the-blank sections that the large language model (LLM) had included to help tailor the response to the course (e.g. “[insert an example from your professor’s class here]”). Following my course’s AI policy, I gave a sizable point reduction, sighed heavily, and moved on to the next assignment.

Flashcards as Notes - Wesley's notes

Effortful reading habits and resisting the urge to defer attention

A taxonomy for next-generation reasoning models

Where we've been and where we're going with RLVR.

Reward is not the optimization target — LessWrong

TurnTrout discusses a common misconception in reinforcement learning: that reward is the optimization target of trained agents. He argues reward is b…

Prime Agent: A self-improving RLM agent

Prime Agent is our open-source, self-improving coding harness built around two abstractions: the Recursive Language Model (RLM) and the Continual Harness. With Opus 5, it achieves 95.5% on ARC-AGI-3, surpassing the reported human expert baseline.