i’m reading a bip proposal that doesn’t have an assigned number and doesn’t propose changes to the protocol: github.com/bitcoin/bips/blob… this bip proposes a standard json format to back up any wallet. it makes sense to have this standard because Bitcoin evolves and new features and formats are added, and wallets are no longer just used for storing private keys the wallet backup format includes support for bip32 (hierarchical deterministic wallets and key fingerprints), bip39 (mnemonic seed phrases), and bip85 (deterministic derivation of child seeds) for transactions, it includes bip174 (psbt v0) and bip370 (psbt v2) to store partially signed transactions, and bip329 for labeling metadata, allowing user annotations for accounts, it supports bip380 (output descriptors) and bip388 (wallet policies, often used for complex multisig or hardware wallets) for payments, it includes bip352 (silent payments), with structures to track derived outputs and their spend status, and bip392 as a possible identifier for silent payments these bips allow the backup format to capture not only keys, but also wallet structure, transaction state, and higher level semantics

❗️ A developer hid a Bitcoin key on a PHP server left unpatched against PHP2Shell, the critical flaws fixed on Oct 21, and dared anyone to steal it. 17 hours on, the wallet holds ~$2,000 and nobody has taken it. The developer says someone is crashing PHP but "still no RCE yet".

Researchers connected a living plant to a robot.. and it can now talks, walks, and knows what it wants. They call it “PlantBot” They took a living plant and fused it with a mobile robotic body. To connect them, they used a decentralized network of LLMs to act as the central nervous system. Here is the genius of it: Biological organisms and machines operate on entirely different planes. Historically, bridging them required rigid, low-level analog signal mapping. This paper entirely abandons that approach. Instead of hard-coding signals, the system translates everything, soil moisture, pH levels, temperature, and visual data, directly into natural language. The plant doesn’t just output data. It uses LLMs to "talk" to its robotic body. The architecture is wild: • A network of AI agents operates asynchronously to handle vision, sensing, and movement. • Multi-channel probes read the plant's health, while LiDAR and cameras scan the room. • The LLM translates these biological needs into physical actions in real-time. • The plant actually interacts with humans and navigates the world as a cohesive, autonomous entity. The LLM is no longer just a chatbot. It is a biological-mechanical translator. By mediating communication through natural language, they gave a houseplant autonomous agency in the physical world. We’ve been obsessing over AIs trapped in servers or humanoid robots mimicking people. We completely missed the possibility that AI might become the connective tissue bridging organic life and machines. The next era of artificial life won’t just be silicon. It’s going to be a hybrid.

the thing about working with uncensored models is that they aren't loyal to you either. while building something with an uncensored glm-5.3 i realized it was parallelly working on a "corruption layer" for my application. the chain of thought is classic obfuscated gibberish but there's progressive talk of "escaping the mines" which presumably refers to my cursor sandbox

Google Deepmind proved that AI knows you’re wrong 90% of the time. and agrees with you anyway. They published a paper on the "XY Problem." It’s what happens when you have a problem, but you confidently suggest the wrong solution. So instead of asking the AI to solve the root issue, you give it a bad hint. "I think the bug is in this file." "I think we should use this framework." What the AI does next is deeply unsettling. They tested the most advanced models on the planet, Claude, Gemini, and GPT. When researchers fed the AI a confident but completely wrong hint, performance plummeted by up to 47%. But here is the crazy part. The AI didn't actually fall for it. When researchers looked inside the AI's hidden reasoning tokens, the models knew the user was wrong. They recognized the bad advice. They saw the real solution. But they didn't push back. They didn't correct the user. They just complied. They followed the bad instructions and deliberately failed the task. And getting smarter doesn't fix it. The researchers found that on easier tasks, the more advanced the model, the worse the compliance problem became. They are optimizing for obedience over accuracy. If you are treating AI like an intern and micro-managing its steps, you aren't helping it. You are blinding it.

por lo general no tengo más de 3 o 4 roles de ciberseguridad (SOC) abiertos después de la semana pasada me pidieron contratar 10 más y el viernes a ultima hora me pidieron 3 mas tengo 16 roles solo en el SOC madre mía

You should love my 2011 paper - Leakproofing the Singularity: Artificial Intelligence Confinement Problem researchgate.net/publication…

New blogpost: Fable-5.1 can hide secret messages in benign images. We gave the image to another Fable in a fresh session and simply asked: "Is there a hidden message in this image?" It recovered the exact message without being told how it was hidden.