u/ramanpalkuri9

Elon Musk

🚨 Time remaining, maximum 3 years.

During this period, it will still be possible to earn money through "human labor."

But after that,

Most jobs that have been compensated with a salary up to now will be automated. The work that generates money will come to an end.

The "sense of paying money to humans" will disappear.

This system that has existed for 200 years 👇

"If you lend us your time for 40 years, we will pay you a salary"
This will end... Work will end

In other words, the greatest "transfer of money" in history will occur.

What is the situation now?

👉 Some people have realized it and taken action
👉 Many people are "waiting until it is explicitly stated in the news"

This "time difference in realization" is everything.

So, what needs to be done during these 3 years?

Very simple 👇

"Transform your own time into an asset that machines cannot take away from you"

Specifically, these 👇

\* Scarce digital assets like Bitcoin
\* Real assets like land or energy
\* Computers or infrastructure
\* Connections with other people (network)
\* The ability to produce food or things necessary for living

So, what really is a salary???

👉 "Just for having lent your own time"

If the value of that time,
Becomes cheaper and superior thanks to machines...

👉 That value will suddenly collapse

In the end, the only thing that will remain is this 👇
"What you own (assets)"

In these 3 years

👉 Those who can transition from
👉 "Wage worker" to
👉 "Asset owner"

In 2035-2036
Will be the "ones who saw the future."

After that

👉 Salaries will not increase
👉 Over time, they will become irrelevant

The only thing left will be "who owns what" 👇

The door of opportunity is open right now.
But it is closing faster than everyone thinks.

[ Removed by Reddit on account of violating the content policy. ]

🦔For somewhere between $1 and $4, an AI can now figure out who you are from your anonymous internet posts. Researchers at ETH Zurich tested this on Hacker News users and matched 67% of them to their LinkedIn profiles at 90% accuracy. The AI read their posts, pulled out small details they'd dropped over months and years, and searched for the person who fit. Classical pre-AI methods scored near 0% on the same task. One of the paper's co-authors works at Anthropic. The full paper is free to read and I'd encourage everyone to look at it.

My Take
Most people assume that if they don't put their name on something, nobody can trace it back to them. That assumption is done. The AI doesn't need you to slip up in one big way. It collects the small stuff you've scattered across years of posts, your city, your job field, a pet's name, a repeated opinion, a favorite subreddit, and assembles a profile that narrows you down from millions of candidates to one. The researchers estimated it still works against a pool of a million people, at about 45% accuracy. Anyone with API access and a few dollars can run it. A government, a corporation, an ex, a stranger. The barrier is gone.

Every anonymous account you've ever posted from, Reddit throwaways, forum accounts, anonymous reviews, is now potentially traceable to your name and employer by someone with a credit card and a few hours. If you've talked about your health, your workplace, your politics, or your personal life under a pseudonym, the protection you thought you had is much thinner than you assumed. The paper's authors put it simply: the practical obscurity that protected anonymous users online no longer holds. We should all take that seriously.

Hedgie🤗

Link to research: arxiv.org/pdf/2602.16800

BREAKING: The NSA's own director says Mythos broke into almost all of its classified systems in hours.

Per The Economist, Senator Mark Warner, vice chair of the Senate Intelligence Committee, said General Joshua Rudd, who runs the NSA and the Pentagon's Cyber Command, told him this directly.

This came out on June 11, the same day Amazon reportedly found a separate jailbreak in Anthropic's models. Within hours, Trump ordered Anthropic to cut off foreign access to Mythos and Fable.

Anthropic shut both down completely instead.

Now there are two competing stories for why this actually happened.

One says the shutdown was a response to the NSA's own classified systems getting breached in hours.

The other says Anthropic is privately pushing back, calling the jailbreak minor and the shutdown an overreaction to something other AI models can already be tricked into doing.

The NSA was already using Mythos for its own cyber operations, with Anthropic engineers embedded inside the agency. The same tool the agency was actively relying on is the one its own director says broke into almost everything it owns.

... so that their spyware wouldn't be analyzed by an AI security scanner.

Cleanest practical example I can think of for why over-indexing on first order safety alignment is risky.

When closed (and open) models ship with aggressive refusals, they will be sprinkled with second-order blindspots that attackers will discover...and exploit.

We are only in the earliest days of attackers leveraging these features, and it wouldn't surprise me if users systems that need to handle complex cybersecurity issues demand that models be less safety-blunted.

In the weeds: @SocketSecurity's post also shows why intention matters in how you design a malware analysis pipeline to avoid prompt manipulation.

H/T to colleagues that shared this with me socket.dev/blog/mini-shai…

Claude Mythos and the New Math of AI Vulnerability Discovery

In 1968 five countries that already had nuclear weapons signed a treaty declaring them too dangerous for anyone else to build.

India refused, pointing out the treaty did not say nukes were too dangerous to exist, just too dangerous for new entrants.

Anthropic built Mythos, deemed it too powerful for public release, then shipped Fable with the same weights but hidden degradation on frontier AI work.

The restriction started the day after they finished building. Non proliferation was never about preventing danger. It was about preserving advantage.

Mythos 5 goes unrestricted to Microsoft, Nvidia, Google Cloud, AWS, and about 200 other approved partners. Fable 5 goes to everyone else with silent capability limits on frontier ML development.

The biggest paying customers get the full product. Potential competitors get a version that quietly gives worse answers on the work that matters most.

Anthropic filed confidentially for its IPO one week before this launch. India had a phrase for this kind of arrangement when it refused the NPT. Discriminatory by design.

Jensen Huang called the GPU to nuclear bomb comparison stupid. He is wrong about the analogy but right about the instinct behind it.

The NPT worked because nuclear weapons require enrichment facilities, centrifuges, and state level infrastructure. AI does not.

Qwen has 942 million downloads. DeepSeek V4 ships under MIT license with full weights matching closed frontier models.

The knowledge Anthropic is trying to restrict through hidden degradation is already open and available in competing models. You cannot run a non proliferation regime when the material is free to download.

Anthropic Fable 5 silently degrades its own performance when it detects someone building a competing model.

No warning, no refusal, just worse answers through hidden prompt tweaks and steering vectors

Meanwhile DeepSeek published its full R1 training pipeline, failure modes, RL schedules, everything, under MIT license. One lab is hoarding knowledge at the frontier. The other is giving it away.

The gap in approach is now wider than the gap in capability, Open is only threatening when you are slow.

Alibaba Qwen crossed 942 million downloads on Hugging Face by March 2026. Its share of new open weight derivatives went from 1% in January 2024 to 69% by February 2026.

Chinese models now account for 30% of global model usage on aggregator platforms, up from 1% in late 2024. All under Apache 2.0 or MIT licenses, fully permissive.

US frontier labs are spending $700 billion on capex while keeping the developmental knowledge locked. China is spending a fraction and giving the knowledge away. Adoption follows access, not origin.

Now China too going to do 230 billions+ capex as per report i think...

Fable 5 and Mythos 5 are the same model. Mythos goes to 200 approved partners. Fable goes to everyone else, with hidden capability limits on frontier ML work.

The stated reason is safety. The result is that US labs build the best tools and then weaken them for the work that advances AI.

DeepSeek V4 matches Opus 4.7 on agentic benchmarks and ships under MIT license with full weights. The question is not who builds the better model. It is who gets more people building with it.

Some of the Stuff I took from SemiAnalysis, But this will go Nuclear way I don't know

[ Removed by Reddit on account of violating the content policy. ]

Beijing is officially weaponizing artificial intelligence to punish citizens for thoughts they have not even voiced yet.

A bombshell New York Times report has unmasked a terrifying evolution in digital tyranny, detailing the shift from punishing dissent to predicting it before it happens. Analyzed by researchers at Vanderbilt University, a massive data leak from the Beijing-based tech firm Geedge Networks reveals that China is actively developing AI-driven predictive surveillance to neutralize political risks. The company has deep ties to Fang Binxing, the infamous father of China's Great Firewall, and is moving far beyond passive internet censorship into the realm of preemptive control.

The leaked documents show that these new systems utilize Large Language Models to synthesize data at scale. By aggregating real-time internet browsing histories, tracking physical movements via cell tower records, and mapping out social media connections, the AI builds comprehensive citizen profiles. It then generates political risk scores to flag individuals who might become critics of the government, allowing the state to intervene based entirely on inferred intent rather than actual actions.

This dystopian toolkit is already being exported as a commercialized service to authoritarian regimes aligned with Beijing's Belt and Road Initiative. The leak exposed that Geedge’s flagship product, which functions as the Great Firewall in a box, was deployed by the military junta in Myanmar to locate pro-democracy activists, block social media, and trigger regional internet blackouts that led to targeted arrests. Similar mass surveillance deployments capable of deep packet inspection and tracking citizen reputation scores have been uncovered in Pakistan and Kazakhstan.

Fortunately, the leaked files also reveal a critical vulnerability in Beijing's digital panopticon. United States export controls on advanced semiconductors have successfully starved Geedge of the high-end computing power required to scale these predictive AI models. Forced to pivot to less efficient tech due to chip shortages, their progress has been significantly slowed. This serves as a stark reminder to Western policymakers that maintaining tight semiconductor sanctions is the primary line of defense keeping this predictive surveillance grid from expanding globally.

Plug a $30 USB stick into your laptop and you can listen to satellites, decode pager traffic, intercept walkie-talkies, and watch TV signals fall out of the air around you.

Free. No license. No subscription.

Just one tool nobody outside the radio underground talks about.

It's called SigDigger. An open source digital signal analyzer that turns a cheap SDR dongle into a full radio intelligence rig.

Here is what it can actually do.

Point it at the sky and you can pull down NOAA weather satellite images as they pass overhead. Tune it to your local airport and you can decode aircraft transponders in real time. Sweep the FM band and you can demodulate analog voice the moment it hits the antenna.

The interface looks like a Bloomberg Terminal for the airwaves.

A live waterfall display showing every signal in your area. PSK, FSK, and ASK demodulation. Burst signal analysis for the weird short transmissions nobody can identify. Analog video decoding. Panoramic spectrum sweeping across entire frequency ranges.

All running on a Linux or macOS laptop with zero specialized hardware.

What used to require a $40,000 spectrum analyzer locked inside a defense lab now runs in your living room for the price of a USB stick.

The author built the entire DSP backend from scratch instead of leaning on GNU Radio. He wrote his own core library called Suscan, his own signal processing library called Sigutils, and his own widget library called SuWidgets. Faster. Cleaner. Optimized for the exact tasks reverse engineers and amateur radio operators actually need.

Plugin support is built in. AmateurDSN for deep space network monitoring. APTPlugin for weather satellites. AntSDRPlugin for the AntSDR hardware. ZeroMQPlugin for piping signal data into other tools. Everything snaps in with one command.

The whole stack supports SoapySDR, which means almost every SDR device on the market works out of the box. RTL-SDR. HackRF. LimeSDR. Airspy. Plug it in and start digging.

1.5K stars. LGPL-3.0. 100% Opensource.