Nick

interested in freedom tech

[Nick @XRPLNick]

@ekiserrepe Nice!

[Nick @XRPLNick]

@xmoonkie @andreirosseti xahau has the same consensus mechanism as XRPL lol

[Polymarket @Polymarket]

JUST IN: BlackRock declares AI agents could soon use stablecoins to buy computing power & data without waiting for humans to complete the transaction.

[onledger.net @offledger]

FYI - literally

https://t.co/jY7BOwlmD2

One site - one glossary

#xrpld #xahaud

[Nick @XRPLNick]

@daniel_wwf Hooks make more sense on Xahau because it’s built around them, with modern hardware in mind and more freedom to evolve. Getting that same efficiency on the XRPL could require broader changes to the network, which is why having a dedicated chain makes sense.

[Nick @XRPLNick]

@KobeissiLetter “Leave it exactly where it is” likely means keep the current policy, not freeze AI’s intelligence or stop development.

[Dario Amodei @DarioAmodei]

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.

It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.

The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).

More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains.

In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.

Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology.

I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

[Haider. @haider1]

Creator of C++, Bjarne Stroustrup:

AI-generated code isn't ready — it generates more bugs, more bloat, more security holes, and is nearly impossible to validate

"senior developers are already retiring rather than deal with it"

The problem is that even a small prompt change can shift the entire codebase in unpredictable ways

[International Cyber Digest @IntCyberDigest]

The creator of the C++ programming language, Bjarne Stroustrup, says AI-generated code is not successful and calls the idea of using natural language as a programming language idiotic.

He says humans will still write code and use abstraction.

According to him, AI generates bloated code with more bugs and security holes, making it hard to validate.

He also says the senior developers needed to validate it are starting to retire because they don’t want to deal with validating something that changes every time you change your prompt.

He goes on to say that AI is not good at writing safety-critical or performance-critical code: “Now, let’s say that 70 or 80% of the world’s code doesn’t fit that pattern, but it’s that 10, 20% of the code that I’m interested in.”

[CoinDesk @CoinDesk]

NEW: @Apple and @Google are hiring for stablecoin and blockchain-related roles, with Apple seeking stablecoin expertise for Apple Pay and Google hiring for Web3 in Hong Kong.

[CoinDesk @CoinDesk]

JUST IN: The CFTC sends a new crypto market structure proposal to the White House for review days after the CLARITY Act's Senate failure, moving swiftly on Chair Selig's promise to use existing authorities to regulate crypto markets.

[7rays🪿🪝☝️ @iMawake2reset]

💥I’m so excited for @XahauCards
which are URI Tokens (NFTs on steroids)

💥 Not to mention you can get them autographed 📝in a few seconds by one of the legends.

💥 Self-custody-the cards are yours on ledger.

💥Gift 🎁for the @fuzzy_xrp

💥#Xahau you Innovate

[Nick @XRPLNick]

I’d lean toward B. Humans have always found ways to use technology for harm, from rocks and swords to guns and modern weapons, so misuse by people isn’t unique to AI.

What makes AI fundamentally different, and potentially more concerning, is that it can become increasingly capable and autonomous in ways previous tools could not. We still don’t know what the upper limit of that capability is.

That’s why I think the bigger question is alignment: who decides what an AI considers morally or ethically acceptable? Developers can consult philosophers, ethicists, and other experts, but morality isn’t a perfectly objective set of rules. Ultimately, I think AI should remain under human control rather than becoming an independent authority that decides what humans are allowed to do.

[Brian Armstrong @brian_armstrong]

The CFTC and SEC are stepping up.

Go time.

[Diogo Almeida @CompleteSkeptic]

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?

I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev

• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions

AFAICT the shortest path to AI-based economic revolution

[Existential Risk Observatory ⏸ @XRobservatory]

Even skeptics are clearly shifting. Most AI doesn't threaten to cause extinction, implies some AI does. This has always been our position.

The problem is not what's on the market. It's what gets built. Rogue AIs can easily escape from labs, as we've learned this summer.

[Larry Cook @stopvaccinating]

That ALUMINUM in vaccines is the root cause of speech issues and food avoidance because of cranial nerve damage. Among dozens of other neurological and immune function disorders. Because it STAYS in the body. And is why I often advocate for DETOX in my posts.

Both with a properly prepared nano zeolite and with a silica water, like Fiji water, because silica and zeolite can and will remove that injected neurotoxic aluminum.

As the aluminum (and other toxic metals) is removed, healing can occur, and function can be restored.

Like speech
Like eating
Like breathing
Etc

[Nick @XRPLNick]

@WietseWind @XamanWallet @ThisIsTRISS gonna have a field day with this

[Igor @Igorib1h]

🚨| BREAKING: Speed can’t BELIEVE the brand new AirPods 5 LIVE TRANSLATION actually WORKS after testing it with a Japanese fan LIVE on stream 🤯🔥