NEW: The CIA used a secret tool called "Ghost Murmur" that uses AI to find heartbeats to rescue the U.S. airman who was stranded in Iran, according to the New York Post.
The secret technology was allegedly used for the first time in the field, according to the Post.
"The secret technology uses long-range quantum magnetometry to find the electromagnetic fingerprint of a human heartbeat and pairs the data with artificial intelligence software to isolate the signature from background noise," the Post reported.
"It’s like hearing a voice in a stadium, except the stadium is a thousand square miles of desert," the source said.
"In the right conditions, if your heart is beating, we will find you."
"The name is deliberate. ‘Murmur’ is a clinical term for a heart rhythm. ‘Ghost’ refers to finding someone who, for all practical purposes, has disappeared..."
"Advances in a field known as quantum magnetometry, specifically sensors built around microscopic defects in synthetic diamonds, have apparently made it possible to detect these signals at dramatically greater distances."
CIA Director John Ratcliffe appeared to hint at this technology on Monday, saying the CIA possessed "unique capabilities" but said he couldn't "tell you everything that you want to know."
President Trump also revealed during the press conference that the CIA spotted the officer from about "40 miles away."
Insane.
If Claude is really doing so much of the coding for Anthropic, why haven't they used it to create a fucking ui for Claude Code?
It's 2025. Why the fuck am I forced to use a cli for everything as if it were 1995?
OpenAI o3-mini is now available in ChatGPT and the API.
Pro users will have unlimited access to o3-mini and Plus & Team users will have triple the rate limits (vs o1-mini).
Free users can try o3-mini in ChatGPT by selecting the Reason button under the message composer.
Developing (Alpha phase) a DDI & Drug-Food Interaction module for UNHIS with data integrated from OpenFDA, RxNorm and RxTerms using AI. After evaluating Llama 3.2, BioGPT & Grok, chose GPT-4o/mini for accuracy. OpenAI’s Batch API (1–1.5B tokens) sped processing & cut costs.
Worked on RAG using FAISS (search ready in-memory vectors), Sentence Transformers, and LLaMA-3.2B-Instruct—all running on NVIDIA RTX 4090.
Was surprised by the sub-second inference times and fairly accurate knowledge retrieval the system achieved. Open-source and works offline
🇰🇷 Presidents of South Korea
1. Syngman Rhee (1948–1960) – Overthrown.
2. Yun Bo-seon (1960–1962) – Overthrown.
3. Park Chung-hee (1962–1979) – Assassinated.
4. Choi Kyu-hah (1979–1980) – Removed by a military coup.
5. Chun Doo-hwan (1981–1988) – Sentenced to death after his presidency.
6. Roh Tae-woo (1988–1993) – Sentenced to 22 years in prison after his presidency.
7. Kim Young-sam (1993–1998) – Imprisoned during the term of President No. 3. As president, secured convictions against two of his predecessors.
8. Kim Dae-jung (1998–2003) – Imprisoned under President No. 3 and sentenced to death under President No. 5 (later pardoned). Nobel Peace Prize laureate.
9. Roh Moo-hyun (2003–2008) – Impeached (later overturned by the Constitutional Court). Investigated for corruption after his term and committed suicide.
10. Lee Myung-bak (2008–2013) – Arrested for corruption after his presidency; sentenced to 15 years in prison.
11. Park Geun-hye (2013–2016) – Impeached and arrested for corruption; sentenced to 24 years in prison.
12. Moon Jae-in – Recent president; no imprisonment.
13. Yoon Suk Yeol – Impeachment likely.
📸: Yoon Suk Yeol
Mil gracias a todos
Many thanks to all
Merci beaucoup à tous
Grazie mille à tutti
谢谢大家
شكرا لكم جميعا
תודה לכולכם
Obrigado a todos
Vielen Dank euch allen
Tack alla
Хвала свима
Gràcies a tots
The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Read more about their story: https://t.co/nWxcZs6wqC
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
Huge congrats to @AIatMeta on the Llama 3.1 release!
Few notes:
Today, with the 405B model release, is the first time that a frontier-capability LLM is available to everyone to work with and build on. The model appears to be GPT-4 / Claude 3.5 Sonnet grade and the weights are open and permissively licensed, including commercial use, synthetic data generation, distillation and finetuning. This is an actual, open, frontier-capability LLM release from Meta. The release includes a lot more, e.g. including a 92-page PDF with a lot of detail about the model:
https://t.co/48e3YJ8Sg9
The philosophy underlying this release is in this longread from Zuck, well worth reading as it nicely covers all the major points and arguments in favor of the open AI ecosystem worldview:
"Open Source AI is the Path Forward"
https://t.co/AdmpadCRM0
I like to say that it is still very early days, that we are back in the ~1980s of computing all over again, that LLMs are a next major computing paradigm, and Meta is clearly positioning itself to be the open ecosystem leader of it.
- People will prompt and RAG the models.
- People will finetune the models.
- People will distill them into smaller expert models for narrow tasks and applications.
- People will study, benchmark, optimize.
Open ecosystems also self-organize in modular ways into products apps and services, where each party can contribute their own unique expertise. One example from this morning is @GroqInc , who built a new chip that inferences LLMs *really fast*. They've already integrated Llama 3.1 models and appear to be able to inference the 8B model ~instantly:
https://t.co/b2kdSsz0fH
And (I can't seem to try it due to server pressure) the 405B running on Groq is probably the highest capability, fastest LLM today (?).
Early model evaluations look good:
https://t.co/RLR5YBpmks https://t.co/ipT4x4wCvy
Pending still is the "vibe check", look out for that on X / r/LocalLlama over the next few days (hours?).
I expect the closed model players (which imo have a role in the ecosystem too) to give chase soon, and I'm looking forward to that.
There's a lot to like on the technical side too, w.r.t. multilingual, context lengths, function calling, multimodal, etc. I'll post about some of the technical notes a bit later, once I make it through all the 92 pages of the paper :)