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In Brazil, a 17-year-old girl named Ana received a mysterious cake delivered to her door with a note saying “A little gift for the most beautiful girl I’ve ever seen”

Overjoyed, Ana took one bite — but hours later she became violently ill.

36 hours later she was dead.

After investigation, it was discovered the “secret admirer” cake had been laced with arsenic.

The culprit? Her 17-year-old female friend who had become riddled with jealousy over Ana being a popular girl with boys, well-liked, and outgoing.

She wanted to make Ana suffer.

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Berlin police turn water cannons into cooling mist as heatwave hits 39.9°C

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A debate has erupted in Italy over the 18-year prison sentence handed down to 65-year-old Silvia Del Pino.

Silvia Del Pino was on her way somewhere when a Moroccan Muslim refugee attacked her, brandished a knife to rob her of her handbag, and attempted to flee.

Silvia then got into her Mercedes, drove after the fleeing Moroccan refugee, and ran him over, resulting in the robber's death.

An Italian judge has now stated that since the robber had already robbed her at knifepoint, it was merely a case of robbery; Silvia should not have killed the assailant.

This has sparked a nationwide debate in Italy; furthermore, when people investigated the judge's background, it was revealed that he had previously been a leftist activist.

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Meta: We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing.

By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language.

The results are promising:
- Avg word accuracy of 61% across participants
- 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant
- Performance scales log-linearly with data volume

To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, bcbl_, is releasing the v1 dataset.
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This is a magnetoencephalography (MEG) machine

Meta trained brain2qwerty v2 by having 9 volunteers wear one while typing for 10 hours each
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🧠⌨️ Decode language from brain activity without surgery. 🧠⌨️

Brain2Qwerty V1 is officially published in Nature Neuroscience. Today, we're releasing Brain2Qwerty V2.
We achieve unprecedented performance for a non-invasive MEG setup.

We’re happy to announce 2 releases today:

- 🧠Brain2qwerty v1 is published at @NatureNeuro

- 🚀 Brain2Qwerty v2 is now publicly released

Explore how we decode sentences from non-invasive brain recordings:

Website
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Daveigh Chase, star of the Ring and voice of Lilo in Lilo & Stitch, died from AIDS, medical examiner confirms

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BIG ‘EXPLOSION’ rocks Monaco

3 INJURED

Man seen dropping ‘BACKPACK’ before blast

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Meta says Brain2Qwerty v2 can decode natural sentences from non-invasive brain recordings in real time, reaching 61% word accuracy.

The system was trained on about 22,000 sentences from 9 volunteers, each recorded for 10 hours with MEG while typing.

Meta compares that with 8% word accuracy from prior non-invasive methods. Its best participant reached 78%, with more than half of sentences decoded with one word error or less.

This is still controlled lab research: small participant pool, MEG hardware, active typing data, and company-reported results. Not a clinical communication device yet.

Meta is releasing the training code, while BCBL is releasing the v1 dataset, pushing brain-to-text research further into open neuroscience infrastructure.

I am so hyped for the future.

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Git has taken another step towards making Rust mandatory.

“With this v2.55 release the Rust compiler is required unless you explicitly disable it in the build system.”

Specifically with the “NO_RUST=YesPlease” flag.

The stated goal is to eventually make the Rust compiler a hard requirement for building Git. Effectively rendering Git incompatible with systems for which Rust is either nonexistent or too buggy.

Don’t want the Rust tools on your system? Too bad. It’s being forced on you. With no significant new features, which require Rust, to justify the change.

The Git development team stated that, “Bringing Rust into Git has been an ongoing (and unfinished)” effort.

Interesting “Ongoing and unfinished” is also a good way to look at both the Rust language and Rust compiler.

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Japan lost to Brazil 2-1 and their fans STILL stayed behind to clean the stadium, and IShowSpeed grabbed a bag and joined them.

The most famous streamer alive picking up trash with heartbroken fans.

The class of Japanese supporters is contagious.

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Over the last 3 months, AI-affected sectors posted an average monthly decline of -11,000 jobs.

This includes roles in management consulting, graphic design, office administration, telephone call centers, computer systems, software publishers, and web search.

Since mid-2023, there have been only 2 months with a net increase in employment in these sectors.

By comparison, at its 2022 peak, employment in these industries increased by as much as +55,000 jobs per month.

Furthermore, employers cited AI as the reason for 38,579 job cuts in May, the highest monthly total on record, according to Challenger Gray data.

This marks the 3rd consecutive monthly increase since ~5,000 in February.

The impact of AI on the labor market is becoming increasingly visible.

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