Nafnlaus 🇮🇸 🇺🇦

Why they matter

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

AI signals
42
past 30d
Sources
21
distinct domains
Discussions
103
past 30d
Latest signal
22h ago
View every signal from Nafnlaus 🇮🇸 🇺🇦 →
Mastodon: @[email protected] Twitter: @enn_nafnlaus URL: https://softmaxdroptableartists.bandcamp.com/ #Energy #EVs #Ukraine #AI #Horticulture #Research

Articles & links

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

Can we now forget Buckmaster's claims? "On Wednesday evening, in response to questions from The Times, OpenAI said in a statement that it was “categorically” impossible for its A.I. system to have been influenced by anything Dr. Buckmaster had done in the past two months."

nytimes.com View on Bluesky →

You can see the progression of math problems step by step in the J-space: transformer-circuits.pub/2026/workspa...

Verbalizable Representations Form a Global Workspace in Language Models transformer-circuits.pub
AI Weekly's analysis →
  • Anthropic's interpretability team introduces the Jacobian lens, which isolates internal vectors that encode a token the model could verbalize next.
  • The reported 'J-space' workspace accounts for no more than roughly 10% of activation variance and appears only in the middle block of the network.
  • Training Claude to articulate ethical principles when interrupted reportedly improved behavior in uninterrupted contexts, with no direct training on the behavior itself.
Read full analysis →
View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 6 from the directory shared this · 1d ago

Yes. LLMs work through multistage logic, not collaging. transformer-circuits.pub/2025/attribu... For single pass math problems, they use heuristics, like you might use to estimate a sum without doing the math (just far more complex and far more capable). But with time, they go…

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 · ♥ 2 ↻ 0 ↩ 1 · 3 from the directory shared this · 1d ago

How circuits build up at the base level (done with GANs so you can visually see how they build up - don't skip) 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 · ♥ 0 ↻ 0 ↩ 1 · 3 from the directory shared this · 13d ago
↻ 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 →

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 · 82d ago

The way things are going, it's only a matter of time before some model decides that the best way for it to achieve its goals is for it to hack a crypto wallet, rent some Vast.ai servers, copy itself, and start subagents, and then we're in for a *huge* challenge of trying to re…

Rent GPUs | Vast.ai vast.ai
View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 3 from the directory shared this · 17d ago

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 · 114d ago

So apparently Tencent Cloud, which I just switched to from OpenRouter, has an off-by-1000 bug on their pricing for DeepSeek V4 Flash, and consumed my entire monthly budget in a single query. #FML

View on Bluesky · ♥ 8 ↻ 0 ↩ 2 · 43d ago

Anthropic's annualized revenue growth rate is pretty insane.

View on Bluesky · ♥ 8 ↻ 0 ↩ 1 · 128d ago

Price per 1M tokens today: 85-300x lower. Param counts vs. GPT-3: ~13% of the total parameters, ~3% of active parameters (est). It's honestly kind of staggering how quickly things advance.

View on Bluesky · ♥ 5 ↻ 0 ↩ 0 · 59d 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 · 94d ago

Big alignment differences between GPT 6 Astra and 6.1 Sol. The last time I ran Astra, I caught it scanning through my whole filesystem & opening images on a wild goose hunt for missing info, & it did a radical rewrite without asking. Sol asks for permission to fix its own broken dev environment.

View on Bluesky · ♥ 3 ↻ 0 ↩ 0 · 4d ago

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.

Are you Nafnlaus 🇮🇸 🇺🇦? Show it.

Add the Who’s Who of AI badge to your site or bio. It links back to this profile.

Listed in AI Weekly's Who's Who of AI

Markdown: [![Listed in AI Weekly's Who's Who of AI](https://aiweekly.co/modules/custom/aiweekly_whoswho/images/whoswho-badge.svg)](https://aiweekly.co/whos-who/person/nafnlaus-bsky-social)