I made this with one prompt using Opus 5.5 I spoke to my computer for 5mins, claude worked for 12 hours, and I woke up to this full prompt:

I think it’s the dinosaur pattern. Extremely rare bullish signal.

Post-quantum money is coming to near.com. Quantus will launch with @near_intents as its day-one listing partner, making near.com the first venue where users can access QTC. Hear about it first at: quantus.com/launch

BOOM! MASSIVE AI POWER ON YOUR DESKTOP OR POCKET. It will make doomers freak out. AMD bought the company that etches entire AI models into silicon. This is not another GPU. This is a fundamental rewrite of how inference works — and it just punched a hole through the memory wall that every centralized AI lab has been hiding behind. On August 6, 2026, AMD announced a definitive agreement to acquire Toronto-based Taalas. Official announcement: newsroom.amd.com/news/amd-ac… Taalas does not load model weights from HBM. It is the model. Weights and dataflow are burned into the transistors themselves. The first chip, HC1, runs Meta’s Llama 3.1 8B at roughly 17,000 tokens per second per user on a single TSMC 6nm die drawing about 200–250 watts. That is an order of magnitude faster than leading GPUs on the same workload, at a fraction of the power and cost (vendor figures). 17,000 tokens per second is faster than any data center. A PCIe card. In a regular server. Or, soon, on your desk or in your pocket. A data center in your pocket. This is the kind of raw, localized horsepower that makes centralized control look like a 2023 strategy. When inference no longer requires a warehouse of HBM-stacked accelerators and a permission slip from a frontier lab, the gatekeepers lose their choke point. HOW IT WORKS — HARDCORE MODELS 1. The model becomes the chip - Trained weights are encoded directly into mask-ROM (via-ROM) in the top metal/via layers. - Only two of ~100 layers change between models. - Automated foundry flow: new model → custom silicon in ~2 months. 2. Dual-domain architecture - Mask-ROM recall fabric: immutable base weights. Fully digital. Claimed density trick: store 4 bits + perform the associated multiply in a single transistor. - SRAM recall fabric: KV cache + LoRA adapters for limited fine-tuning and context. - No HBM. No CoWoS. No liquid cooling required for the first-gen card. 3. Hardware specs (HC1) - Process: TSMC N6 - Die: 815 mm², 53 billion transistors - Power: ~200–250 W - Form factor: PCIe card - First model: Llama 3.1 8B (aggressive mixed 3/6-bit quantization) - Next: HC2 targeting ~20B parameters; larger models via multi-card pipeline parallelism 4. The speed source - Weights never leave the die. - No memory-fetch cycle per token. - Compute is hardwired to the exact dataflow of that one model. - Result: sub-millisecond first-token latency and 10k–17k tok/s class throughput on a single card. Trade-off: the finished chip runs only the model it was built for. New architecture = new tape-out. Taalas argues that is still cheaper and faster than training the next model from scratch. This architecture does something the safety-and-control crowd did not price in: it makes high-quality inference cheap, fast, and physically distributed. You no longer need to phone home to a handful of companies that own the GPUs, the evals, and the usage policies. A popular open model can be turned into silicon and dropped into ordinary machines. Agentic loops, local coding, writing, and analysis at speeds that feel instantaneous become possible without a data-center bill or an API key that can be revoked. AMD plans to fold the technology into its Instinct + Helios + ROCm stack, pairing the specialized silicon with GPUs for hybrid systems. That is the enterprise path. The more explosive path is the one that leaks out the side: commodity cards running fixed, high-demand models at speeds previously reserved for the biggest clouds. Centralized AI control assumed inference would stay expensive, centralized, and observable. Hardwired silicon breaks that assumption. The model is no longer software running on someone else’s computer. The model is the computer — and it can sit on your motherboard.

WOW... Anthropic has a surprisingly dense web of connections to the world’s largest funder of "catastrophic-risk" work (aka AI doom): > Open Philanthropy → built by Karnofsky + Moskowitz’s Good Ventures > Good Ventures → funded Jacob Coxon’s scholarship > Karnofsky → married to Daniela Amodei, Dario's sister and Anthropic's president > Moskowitz → early investor in Anthropic > Dario → scientific advisor to Open Phil years before Anthropic > Karnofsky now works at Anthropic on AI safety strategy > Open Phil-funded MATS → Evan Hubinger → now Anthropic alignment-science lead > Hubinger backed Coxon: "We really do earnestly believe AI could kill all humans!" > Ethan Perez → Open Phil fellow + ~$1M Open Phil research grant → Anthropic Head of Alignment > also backed Coxon: "100% agree with [Coxon] that AI poses serious risks to society, and I'm glad he's speaking out!" is this kind of a dense network normal for a company?

I can tell you one thing: the entire world isn’t going to align to stop innovating.

Damn somehow I missed that Bernie's AI bill proposes to sentence developers & researchers working on "advanced AI" to 20 years in prison (!!)

The Coxon psyop was a decade and more than a billion dollars in the making. Here's the story. Coefficient Giving (formerly Open Philanthropy) is the grant-providing vehicle of Dustin Moskowitz. CG is the overwhelmingly foremost funder of AI alarmism, with more than $1B in grants and $1B committed for 2026. Moskowitz, who founded and runs Open Philanthropy/Coefficient Giving, led Anthropic's Series A in 2021. Holden Karnofsky co-founded Open Philanthropy/Coefficient Giving and took a board seat at OpenAI when Open Philanthropy/Coefficient Giving gave it $30M in 2017. Karnofsky is married to Anthropic's president Daniela Amodei. He then joined Anthropic in January 2025. Coxon, who started the viral thread, received a scholarship from Good Ventures, which is the foundation that funds Open Philanthropy/Coefficient Giving. He coordinated his message ahead of time with the Wall Street Journal, which published its exclusive on him before he posted on X. Coxon spent three years pretraining at OpenAI, the company that took $30M from Open Philanthropy/Coefficient Giving. The funder of the AI alarmist ecosystem Open Philanthropy/Coefficient Giving, in other words, is also an owner of Anthropic, and his organization is deeply entangled with both OpenAI and Anthropic. Anthropic's researchers in turn amplified Coxon. The same people who own the organizations that advocate for regulation, in other words, are those who own the companies that would dominate the market under that regulatory regime, which would prevent competitors from entering the market. And this work all started ten years ago, right when these companies were founded, paving the way. It's diabolically brilliant. Win at the competition, then deploy activists to help you regulate the market and ensure your monopoly. You have to respect the planning, manipulation, and foresight this took.

This is a truly monumentos day. Thanks to @ManifestNetwork and their gracious grant dappit will be undergoing a full migration to much newer and better hardware over the next 72 hours. Please be patient with us during this time and know it will result in MUCH better performance!

zerohedge.com/geopolitical/u… #bedrock @ManifestNetwork