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Anthropic leaped to a $47 billion revenue run rate by May, compared to $9 billion in 2025. It’s the kind of growth that Menlo Ventures’ Matt Murphy says he’s never seen in 25 years of investing, not in the internet wave, not in mobile, not in the first cloud boom. Menlo led Anthropic’s $500M Series D, and Murphy has had a front-row seat as the company went from a pre-revenue, […]

There's a romantic idea about Bitcoin's chain: that it's a wall of human messages. Proposals, memorials, "Vahe was here," pizza jokes, the occasional protest note pinned into the world's most expensive append-only log. I wanted to know if that was actually true. So I counted. Every OP_RETURN output, from the genesis block to block 958,893, no sampling. The answer is no, and it's not close. The one number All human-readable OP_RETURN text ever mined into Bitcoin: 3,827,227 outputs. Runes, one token protocol, in its own era: 171,114,058 OP_RETURN outputs. That's a ratio of 44.7 to 1 . One machine protocol, in a single two-year stretch, wrote about 45 times more to the chain than every human-readable message in Bitcoin's entire history combined. The evidence, per era I split the chain into four eras by block height, not by any label stored in my database. Height boundaries are canonical and anyone can check them against a node, so the result doesn't depend on trusting my extractor's tags. era height range boundary event pre-ordinals 0 – 767,429 before the first inscription ordinals 767,430 – 779,831 inscription #0 to BRC-20 deploy boom-brc20 779,832 – 839,999 BRC-20 ordi deploy to Runes runes 840,000 – 958,893 Runes launch at the halving Then I counted the full population of OP_RETURN outputs in each era. Human-readable text, Runes token messages, and binary blobs (Veriblock and OMNI proof-of-proof timestamping, mostly). era total OP_RETURN human text human % Runes Runes % binary binary % pre-ordinals 51,965,944 861,532 1.66% 7 0.00% 51,103,723 98.34% ordinals 261,767 32,189 12.30% 3 0.00% 229,544 87.69% boom-brc20 2,980,954 402,161 13.49% 40,251 1.35% 2,538,248 85.15% runes 177,474,762 2,531,345 1.43% 171,114,058 96.42% 3,828,604 2.16% Here's the honest twist When I started, I expected to find a fall. A golden human era that machines later ate. That's the clean story, and it's wrong. Look at the human % column again. Human text was never the majority of OP_RETURN. Not

Hey HN, Henry & Roman here from Cactus. A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast. So, we post-trained Gemma 4 E2B post-trained to know when it's wrong. Every response comes with a confidence score between 0 and 1. Developers can accept the on-device when it's high, hand off to a bigger cloud model when it's low. By routing only 15-35% of queries to Gemini 3.1 Flash-Lite, Gemma-4-E2B matches Gemini 3.1 Flash-Lite on m

The company said it is reducing its headcount by 20%, or about 630 staff, to "support a leaner, more focused operating model" as it focuses on its AI Work Platform.

Out of Dyson’s two new affordable stick vacuums, this one is easily my favorite.

A platform that teaches how to navigate real-world code. Discussion | Link

Hey all! Is anyone else going to be at KDD in Jeju? Would love to connect with fellow attendees. I work on interpretability, fairness, and editing of text-to-image models, so I'd especially love to meet people working in these areas. But honestly, we can chat about anything: research, the conference, life, or just grab a coffee/drink. I land in Jeju on the night of the 8th, so hmu if you're around and want to link up! submitted by /u/Deep-Inevitable-1977 [link] [留言]

It turns out some of Benjamin Franklin's opinions from 240 years ago are still relevant today.

"This is day one for cybersecurity in the age of agents," Hugging Face CEO says.

Explain what you know to AI and discover what you don't Discussion | Link