Related posts
rlvr detector dataset
The the words it uses that this site has seen
least often elsewhere. Posts are matched on those words alone —
nothing here is a summary of this one.
Sources
All Writing x.com Blog Bluesky Korrents GitHub Recommends Papers Newsletter Mastodon
Top people
Sebastian Raschka
Lilian Weng
Bryan Caplan
Andrew Gelman
Hamel Husain
Saloni Dattani
Nassim Nicholas Taleb
Paul Ford
Nathan Lambert
Samuel Hammond
Sunil Pai
Zeke Hausfather
Showing Profile →
Show everything
Hiding Show them again
Show them again
Further back ↓
Hiding Show them again
16 September
Economics professor at George Mason University and author of The Myth of the Rational Voter, Selfish Reasons to Have More Kids, The Case Against Education and Open Borders. Writes Bet On It.
"Across 477 coup attempts in one major dataset, about 48% succeeded overall, but attempts originating with top military elites succeeded 76.4% of the time, whereas those led by lower-level/combat officers succeeded only 29%." @ChatGPT OpenAI Related
Statistician at Columbia; writes Statistical Modeling, Causal Inference, and Social Science, and wrote Bayesian Data Analysis.
Charting the Agentic Garden of Forking Paths Arjun Balaji, Batuhan Duru Yeltekin, and Tian Zheng write: Even with a fixed dataset and research question, data analysis involves many defensible decisions. Understanding how these choices influence the results is scie…
Related
15 September
Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.
Just updated our AI evals FAQ with 5 new questions, 48 questions & answers total! New FAQs just added: - Do I need a reference answer or rubric before annotating data? - How can I do evals when traces contain sensitive data? - How do you review a trace that is really large? - How much context should I give a LLM judge? - What should I do when my “gold” eval dataset becomes stale? It's all here: hamel.dev
LLMs Related
13 September
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
Reasoning from scratch round 3: This time, I cover generating a verifier for... a) ...evaluation (base model versus any future model improvement) b) ...the reinforcement learning with verifiable rewards (RLVR) training later on 00:00 Introduction 01:21 Four approaches to LLM evaluation 07:20 Verifiers and reinforcement learning with verifiable rewards 10:52 Notebook setup and dependencies 13:43 Section 3.1 Building a math verifier 18:57 Section 3.2 Loading a pre-trained model to generate text 24:34 Generating and displaying model answers 29:23 Section 3.3 Implementing a wrapper for easier tex… LLMs Related
12 September
Researcher and science writer. Co-founder and editor at Works in Progress magazine, formerly a researcher at Our World in Data, and author of the Scientific Discovery newsletter on science, global health and medical innovation.
I spoke too soon! Got about a dozen emails from bots trying to collect rewards from my bug bounty since yesterday. 4 were legitimate errors though very minor, relating to my vaccine discovery dataset. Quoting @scientificdiscovery.dev I'm having a similar experience, although have only received a few emails like this so far. Some are so stingy about tokens that they don't start trying to check the work unless I confirm it's eligible for a reward. Related
11 September
Former option trader and risk analyst, now a distinguished professor at NYU. Author of the Incerto essay series, including The Black Swan and Fooled by Randomness.
Every single person on this list would trigger the least sensitive fraud detector. (Hasbara has been overactive). Related
9 September
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
8 September
Writer and programmer; co-founder of the software studio Postlight and author of the Bloomberg Businessweek essay What Is Code?
The Library Innovation Lab at Harvard University: “Binoc is a command-line tool and library that, given two snapshots of a dataset, efficiently summarizes the differences between the two snapshots in a human-readable changelog.” lil.law.harvard.edu/blog/2026/07... Related
1 September
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
24 August
Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.
15 August
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
11 August
Senior economist at the Foundation for American Innovation; writes Second Best, and was the Niskanen Center's director of social policy before that.
9 August
Systems and developer-tools engineer at Cloudflare as of 2026, on durable infrastructure for AI agents; worked on React and PartyKit before that. Writes at sunilpai.dev.
22 July
Climate scientist and energy systems analyst; writes The Climate Brink with Andrew Dessler and leads climate research at Stripe.
21 July
Experimental psychologist and author of the blog Experimental History, on psychology, science reform and creativity.
Their words
For the first time, I ran all potential winners through Pangram, which is an AI-writing detector.1 So according to the machine (and to my ear), all of the winners are 100% human-written.
Show the whole quote
experimental-history.com
24 June
Machine-learning researcher; has written the Lil'Log survey posts on how a model technique works since 2017, and worked at OpenAI from 2018 to 2024, latterly leading its safety systems team.
Scaling Laws, Carefully Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute…
Related
10 June
Co-founder and chief executive of Meta Platforms, the company he started as Facebook in 2004.
Their words
it's not just like there's some factory somewhere that you can pay to produce the the data like you actually need to invent new novel scientific approaches
Show the whole quote
youtube.com
16 April
Freelance climate and energy analyst in Oslo, writing on Europe, the US and Australia; worked in the Australian renewables industry before that.
13 April
Austrian developer. Created the libGDX game framework, and more recently the pi coding agent, which moved with them to Earendil — Armin Ronacher's company — in April 2026. Writes at mariozechner.at.
5 January
Co-founder of Modem and a founding engineer at Sentry, where he went on to be VP of Engineering. Co-author of Third-party JavaScript, and co-host of the State of Agentic Coding podcast with Armin Ronacher.
1 January
Professor of Cognitive and Computational Neuroscience at the University of Sussex, co-director of the Sussex Centre for Consciousness Science, and author of Being You: A New Science of Consciousness.
30 December 2025
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
20 December 2025
AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).
1 December 2025
American social psychologist at NYU Stern School of Business; author of The Happiness Hypothesis, The Righteous Mind and The Anxious Generation.
17 October 2025
Founding member of OpenAI and former director of AI at Tesla; creator of nanoGPT and the term "vibe coding".
Their words
Um so I almost feel like because the internet is so terrible, we actually have to sort of like build really big models to compress all that. Uh most of that compression is memory work instead of like cognitive work. But what we really want is the cognitive part actually delete the memory
Show the whole quote
youtube.com
6 October 2025
Mac and iOS developer at the Iconfactory, where he has worked on apps including Twitterrific and Tot. He writes about development at furbo.org.
15 November 2023
Research scientist at Meta in Berlin working on multilingual models and evaluation; led the multilingual team at Cohere and was a research scientist at Google DeepMind before that.
9 October 2023
Software engineer who writes the blog Made of Bugs about performance, debugging and understanding computer systems. Previously worked at Anthropic on interpretability, at Stripe on Sorbet, and at Ksplice.
Help me out, lazymastodon: Kinda random: if I wanted to get a dataset of growth rates of [a sizeable subset of] "tech companies" going all the way back to, like the '50s or '60s does anyone know have idea offhand where I might find such a thing or go about assembling such a thing? Willing to spend some amount of money or "stitching together sources" on it. Related
8 September 2022
Machine-learning researcher; has written the Lil'Log survey posts on how a model technique works since 2017, and worked at OpenAI from 2018 to 2024, latterly leading its safety systems team.
1 March 2021
American author of The Subtle Art of Not Giving a F*ck and Everything Is F*cked. Writes essays and book reviews at markmanson.net.
Their words
This is the only one I’m not crazy about. My academic BS detector got tripped a few times while reading it, and it turns out that there are a number of better measurements emerging in moral psychology.
Show the whole quote
markmanson.net
3 August 2012
Machine-learning researcher; has written the Lil'Log survey posts on how a model technique works since 2017, and worked at OpenAI from 2018 to 2024, latterly leading its safety systems team.
Nothing matches. Show everything
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com .
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
Got it
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.
Got it