Who's Who of AI

Nafnlaus 🇮🇸 🇺🇦

47 trust @nafnlaus.bsky.social · 6,645 followers
Culture, work & education Compute & infrastructure
Why they matter

Directory member with public evidence across Culture, work & education, Compute & infrastructure.

AI signals
5
past 30d
Sources
4
distinct domains
Discussions
49
past 30d
Latest signal
18d ago
View every signal from Nafnlaus 🇮🇸 🇺🇦 →
Mastodon: @[email protected] Twitter: @enn_nafnlaus URL: https://softmaxdroptableartists.bandcamp.com/ #Energy #EVs #Ukraine #AI #Horticulture #Research
What they're sharing

Articles & links

↻ Nafnlaus 🇮🇸 🇺🇦 reposted
@pekka.bsky.social

GPT-5.6 Sol Ultra (using 64 subagents) proved the 50-year-old Cycle Double Cover Conjecture, which has been described as one of the most famous open problems in graph theory. It took less than hour. The proof, written and also formalized in Lean by GPT 5.6 Sol, seems to fit in…

cdn.openai.com
AI Weekly's analysis →
  • OpenAI posted a PDF on its CDN today attributing a proof of the Cycle Double Cover Conjecture to GPT-5.6 Sol Ultra.
  • The conjecture, proposed independently by Szekeres and Seymour in the 1970s, asks whether every bridgeless graph has cycles covering each edge exactly twice.
  • The claim is on the Hacker News front page and already noted on Wikipedia, but the argument has not been peer reviewed.
Read full analysis →
 View on Bluesky →

"yeah; the precise function of any individual node in the generative layer is essentially unknowable" It really isn't. transformer-circuits.pub/2025/attribu...

On the Biology of a Large Language Model transformer-circuits.pub
AI Weekly's analysis →
  • Anthropic researchers apply attribution graphs, built on a cross-layer transcoder with 30 million features, to trace how Claude 3.5 Haiku arrives at answers.
  • For a Dallas capital query, the model activates an intermediate 'Texas' representation before selecting 'Austin', evidence of genuine two-hop reasoning.
  • The authors say their methods produce useful insight on about a quarter of prompts tried, and found forward planning in roughly half of examined poems.
Read full analysis →
View on Bluesky · ♥ 22 ↻ 1 ↩ 1 · 2 from the directory shared this · 19d ago

To go one step up from there, I'd recommend this introduction to circuits: distill.pub/2020/circuit...

Zoom In: An Introduction to Circuits distill.pub
AI Weekly's analysis →
  • Olah and coauthors argue neural networks contain meaningful features, that features connect into circuits via weights, and that similar circuits recur across models.
  • The essay uses InceptionV1 as its worked example, pointing at curve detectors, a dog-head circuit, and polysemantic neurons responding to cat faces and car fronts.
  • The authors frame circuits interpretability as a natural science: small, falsifiable claims about subgraphs, not one grand theory of deep learning.
Read full analysis →
View on Bluesky · ♥ 2 ↻ 0 ↩ 1 · 2 from the directory shared this · 18d ago

As a reminder, LLMs recognize when they're being tested and have a tendency to answer based on what they think an alignment-tester wants to hear (for example, "racism is bad", etc) - and swapping cues about the politics of the tester changes the outputs. arxiv.org/abs/2604.27633

Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor arxiv.org
AI Weekly's analysis →
  • Six frontier LLMs lean left at baseline but flip right of center once the asker identifies as a conservative Republican.
  • Democrat-aligned response share drops 28-62 percentage points under a conservative cue; rightward accommodation is 8.0× larger than leftward.
  • Asked what the default auditor expects, models pick the Democrat-coded answer 75% of the time, nearly matching an explicit progressive cue.
Read full analysis →
View on Bluesky · ♥ 12 ↻ 3 ↩ 2 · 2 from the directory shared this · 20d ago

Essentially anything you can think of exists in some combination of firings somewhere in the network. There's a circuit for "talking like MAGA". A circuit for "off-by-one-errors in programming". A circuit for "greek vowels". Anything you can think of. Ex: transformer-circuits.…

transformer-circuits.pub
View on Bluesky · ♥ 0 ↻ 0 ↩ 1 · 18d ago

There's a massive gender gap in crypto ownership, 27% to 19%: coinlaw.io/crypto-user-... Over half of ChatGPT users are women: openai.com/is-IS/index/... Again: it's literally impossible for these to be the same people.

openai.com
View on Bluesky · ♥ 1 ↻ 0 ↩ 1 · 38d ago

Classic example: Kerr's "Penrose Was Naive And All Of You Who Used Him To Argue For Singularities Are Morons" paper ;) arxiv.org/pdf/2312.00841 (This is the same Kerr as "Kerr black holes", but now in his "I'm Old, I Don't Give A Damn Anymore" phase ;) )

arxiv.org
View on Bluesky · ♥ 6 ↻ 0 ↩ 0 · 52d ago
Their own posts

Recent commentary

In case anyone needs any free inference compute and wants to do it on the dime of the worst people around, apparently Truth Social's (Perplexity-based) "Truth Search AI" is neither rate limited nor topic restricted. It's been writing a thriller about Barney the Purple Dinosaur for like 10 minutes.

View on Bluesky · ♥ 8 ↻ 3 ↩ 0 · 52d ago

Anthropic's annualized revenue growth rate is pretty insane.

View on Bluesky · ♥ 8 ↻ 0 ↩ 1 · 66d ago

Just thinking about how it took the art world 90-140 years to get over the concept of "photography as art" and wondering if it'll be the same way with AI. Early on artists almost universally agreed with Baudelaire with his critique of photographers as failed artists cheating to make souless slop.

View on Bluesky · ♥ 2 ↻ 0 ↩ 1 · 32d ago
Their network

In Nafnlaus 🇮🇸 🇺🇦's orbit

Center = Nafnlaus 🇮🇸 🇺🇦. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.