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🚨 THIS IS WHY BITCOIN KEEPS CRASHING BELOW $67,000 If you still think Bitcoin trades like an asset whose price is determined purely by spot supply and demand, you need to read this carefully. Because that market no longer exists. What you’re watching right now is NOT normal price action. This isn’t just an AI stock sell-off. This isn’t just hotter inflation. And this definitely isn’t retail suddenly deciding to dump Bitcoin at the exact same time. Most people have no idea what is actually happening. And by the time it becomes obvious, the damage is usually already done. This didn’t start today. It has been building underneath Bitcoin for YEARS. And now it’s accelerating. Here’s the truth: Bitcoin still has a hard cap of 21 MILLION coins. But Wall Street can create almost unlimited financial EXPOSURE around those coins. That distinction changes everything. Because once derivatives dominate short-term price discovery... Bitcoin stops trading purely on who wants to own the actual coins. It starts trading on: 1) LEVERAGE. 2) HEDGING. 3) MARGIN. 4) LIQUIDATIONS. 5) FORCED SELLING. And that is exactly what is happening right now. It’s the same mechanism that repeatedly drives violent moves in: → Gold → Silver → Oil → Stocks Once derivatives become large enough, spot buyers are no longer the only force setting the price. And this is the part almost nobody understands: Bitcoin’s scarcity didn’t disappear. But scarcity alone no longer controls the SCREEN PRICE. Wall Street built an entire financial layer on top of Bitcoin: → Cash-settled futures → Perpetual contracts → Options → ETFs → Broker lending → Swaps → Wrapped BTC The 21 million cap controls the COINS. It does NOT cap the amount of leveraged exposure that can be created around them. And once that leverage starts unwinding... The market doesn’t care what the long-term thesis is. It SELLS. Look at what just happened: Over $1.9 BILLION in leveraged crypto positions wiped out in 24 hours. More than $1.1 BILLION liquidated in Bitcoin alone. Spot Bitcoin ETFs have already seen roughly $1.9 BILLION in outflows this month. Once leveraged selling overwhelms real buyers, price stops responding only to long-term demand. It starts responding to positions. Hedging. Margin calls. And cascading liquidations. Wall Street does not need to own millions of physical Bitcoin to push the market around. It only needs enough derivatives exposure and enough leverage sitting in the wrong place. The playbook is brutally simple: 1st: Build enormous leveraged exposur 2nd: Push price through obvious liquidity 3rd: Trigger stops and liquidations 4th: Forced selling pushes price even lower 5th: Buy back into the panic 6th: Repeat THIS is why Bitcoin can lose thousands of dollars while the number of coins on-chain barely changes. The coins didn’t suddenly disappear. The PRICE DISCOVERY mechanism changed. That’s what people are missing. Bitcoin is still scarce. But the financial claims built around Bitcoin are NOT. And when leverage controls the marginal trade... 21 million coins can behave like a market with FAR more supply than 21 million coins. That’s not the Bitcoin market retail thinks it is trading. That’s a WALL STREET PRICING SYSTEM built on top of Bitcoin. You can choose to ignore it. But don’t pretend nobody warned you. Bitcoin is now trading around $66,600, its lowest level since August and roughly 48% below the $126,200 high. If $65k breaks, the next level I’m watching is $62k. Reminder: I called the Bitcoin top at $126,000 and told you to start buying again around $60,000. The next call will matter even more. When I EXIT the markets completely, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.

Si eres diseñador UI/UX, guárdate esto. 1. → más de 600 prompts con código completo para webs 3D y con scroll, además de componentes, shaders y pantallas de UI 2. → una biblioteca enorme de bloques de UI listos para meter en tu layout y lanzar antes 3. → fondos animados con shaders para hero sections, en código, vídeo o imagen Guárdatelo 🔖

Woman: "Hey, can you lower your laptop volume? The entire café can hear your Zoom meeting." Tech Bro: "Sorry, I’m leading a product sprint. It’ll only be another hour." Woman: "There are people trying to study and read here. Why not use earbuds?" Tech Bro: "Earbuds hurt my ears. Honestly, it's a public coffee shop, deal with it." He smirked, sipped his iced latte, and unmuted his mic to address his team. Tech Bro: "Alright team, let's review the Q4 lay-off list before HR finalizes it." The woman across the table calmly closed her book, walked over, and pulled up a chair right next to him.

Beta Updates Surge Engine • Added VLESS Encryption (mlkem768x25519plus), including share-link import and proxy editor controls. • Added VLESS XHTTP transport with stream-one, stream-up and packet-up modes over HTTP/1.1, HTTP/2 or HTTP/3, including VLESS Encryption and Vision. • Reduced memory retained after QUIC downloads on lossy networks. • Improved QUIC upload performance and reduced CPU usage when chained through a proxy's UDP relay. • Fixed established VLESS gRPC, HTTP/2 CONNECT and TrustTunnel connections being incorrectly counted as connection setup failures when they ended with an error. • Fixed WebSocket policies failing to connect when their ALPN list included h2 and the server selected it. • Fixed Tailscale connections staying on a relay after a peer restarted because updated peer information was not applied. Surge iOS • The proxy editor now imports share links for all supported proxy protocols, as well as proxy lines copied from Surge profiles; unsupported settings are reported instead of silently omitted. • Reorganized the proxy editor with an Advanced page that indicates settings in use, and added controls for more protocol, TLS, WireGuard, Tailscale and testing settings. • Fixed Always use CONNECT not being saved for HTTPS policies, and corrected when TLS and Block QUIC settings appear. • Updated list layouts and badges, and added member counts for policy groups in the proxy list. Surge Mac • The proxy editor now imports share links for all supported proxy protocols, as well as proxy lines copied from Surge profiles; unsupported settings are reported instead of silently omitted. • Reorganized the proxy editor with a remembered Show Advanced Settings option and controls for more protocol, TLS, WireGuard and Tailscale settings.

⚡️ CDR on now disarms larger files in milliseconds. Macros, OLE objects and scripts come out. Text, tables, images and layout stay where they were. Users get a clean copy that is safe to open, without the wait.

I talked my friends who are in Seattle, Washington working in Microsoft. They have been in the office for the last 12 hrs and couldn't find the Root cause of the Outlook issue. They are suspecting that some AI scripts went out of scope and did some patching without any logs and traces. The engineer team that actually coded legacy outlook is not in the Organization and got impacted due recent layoff in the Microsoft. With AI managing almost everything in the Microsoft, so higher management thought of reducing the workforce of Core engineering team. The code of Outlook is so vague that only core engineer (no more in the org) knows about it and earlier they fixed these kinds of issues within 30 mins but now it has been more than 12 hrs and the new team is unable to FIX it.

I am actually disgusted by the timing of this drop. And this will get a bit more technical. Let’s do a real technical teardown of this "1,385-sigma Blackwell weight fingerprint" report that OpenAI and Anthropic are breathlessly promoting today, because banking on nobody reading past the headline is how DC narratives get manufactured. ​First, look at the actual CUDA maths: when PyTorch initialises model weights (torch.nn.init) with a fixed PRNG seed, curand/Philox parallelises random number generation across the GPU’s Streaming Multiprocessors (SMs). Because Hopper has 132 SMs and Blackwell has 148 or 152 SMs, the exact same seed lays out initial weights in a different spatial pattern depending on the GPU’s SM count. In huge MoE models, certain low-gradient null-spaces or unrouted slices preserve faint directional traces of those step-0 weights even after training. ​So yes, brute-forcing the seed and SM grid can show that a model’s step_0 weight initialisation script ran on a 148/152-SM Blackwell GPU. Now read what the summary explicitly admits in the fine print: 1. Initialisation is not full training: The report literally states that these signatures show how the weights were created, and "support (but do not prove) the inference that Blackwell also participated in model training". Initialising a model checkpoint takes seconds on a single rented dev node before loading the safetensors onto a standard Hopper cluster. The "2,000 to 3,000 Blackwell GPUs" and "$500 million" cluster numbers do not come from the weight fingerprint and Depue admits in his own thread that he simply "estimated" cluster sizes from release schedules and model details. ​2. Zero geolocation and zero proof of smuggling: The final sentence of the executive summary explicitly admits that the findings "do not establish the physical location of the chips, how they were acquired, or whether export control regulations were violated". Depue himself rented a cloud GB200 instance just to run his verification test. Commercial labs that are not on the Entity List renting bare-metal Blackwell nodes in offshore clouds (Singapore, Japan, or the Middle East) is completely legal under BIS rules and leaves the exact same SM fingerprint. Now ask yourself why a former OpenAI researcher dropped this on November 12, the exact day Jensen Huang was pushed out, with OpenAI and Anthropic immediately stepping in to "independently verify" it. US closed-source labs are watching their paid API moats get eaten alive by free Chinese open-weights models. Conflating a step-0 PRNG initialisation seed with a "$500M smuggled Blackwell cluster" hands DC hawks the exact pretext OpenAI and Anthropic want to regulate global cloud compute and kneecap open-source AI.

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

Google Search is getting its biggest model update yet with Gemini 4 Hydrogen — rolling out globally for all users today. Here’s what’s new in Search: 🔍 Better understands intent: Ask nuanced, multi-part questions in your natural flow of thought — even with specific requests or typos — and get relevant responses across all supported AI Mode languages. 👀 More visual responses: Get adaptive, easy-to-scan layouts with images, comparison tables, and custom tools. 🌐 Upgraded query fan-out: Hydrogen’s reasoning upgrades how Search runs simultaneous subtopic queries to connect you to a broader range of helpful web links.

Multi-agent workflows have a scaling problem! The agents inside the same workflow rarely need the same amount of capacity at the same time. Say you have a planner, multiple research agents, and a final synthesis agent. The planner might finish quickly while requests start piling up behind one of the research agents. Scaling the entire application would add capacity everywhere, even though only one part of the workflow actually needs it. CanyonOS is an open-source, workflow-aware platform for multi-agent Python applications and the harness between your agents. Each agent runs as a separate service with its own queue and replica limits, so the busy research agent can get additional capacity while the planner and synthesis agents stay where they are. It also handles communication between the agents and gives you visibility into what happens across the workflow after a request comes in. CanyonOS records the path a request took, including the agents it called, parallel branches, model usage, execution time, and estimated model costs. And you don't need to add tracing decorators or a separate instrumentation SDK throughout your agent code. You still integrate the workflow with CanyonOS, but tracing is handled at the platform layer. I also ran a multi-agent portfolio analysis workflow through it with separate agents working across three stocks. A single request produced 10 traceable steps across the workflow, and I could see which agents ran, where the LLM calls happened, how long each stage took, and the estimated model cost. For multi-agent applications, being able to operate each agent independently while still seeing the complete execution path becomes much more useful as the workflow grows. CanyonOS is open source. Link to the GitHub repo in the comments!