diogo mafra

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.

you pity the moth confusing a lamp for the moon, yet here you are confusing a screen for the world

Alibaba figured out how to get 90% of frontier coding performance from 1% of the active compute. That sentence should restructure how you think about AI development costs. Qwen3-Coder-Next sits at 80B total parameters with 3B active. It scores 44.3% on SWE-Bench Pro. To its right on this chart: DeepSeek-V3.2 at 671B, Kimi K2.5 at 1000B+, Claude Opus 4.5 at “we don’t disclose.” They all score within a few points of each other. At 46GB RAM, a single M4 MacBook Pro runs this model locally. A Thoughtworks developer reported that one coding task on Sonnet 4 costs $10 to $20 via API. The same task on Qwen3-Coder-Next costs electricity. MIT research from last month found that open model inference runs 87% cheaper than proprietary APIs, and the optimal reallocation could save the industry $25 billion annually. The future architecture is becoming obvious: frontier models for planning, open-weight models for execution. You pay for intelligence at the top of the stack and run commodity inference everywhere else. Alibaba gives the model away for free because adoption is the product. Every developer who builds muscle memory on Qwen’s architecture is a developer who defaults to Alibaba’s ecosystem. The switching costs accumulate silently while everyone celebrates the open weights.

Excited to launch Pencil INFINITE DESIGN CANVAS for Claude Code > Superfast WebGL canvas, fully editable, running parallel design agents > Runs locally with Claude Code → turn designs into code > Design files live in your git repo → Open json-based .pen format