we're entering a hardware supercycle that's just gonna turn an absurd amount of sci-fi into real products > most in silicon valley have concluded that software has no moat anymore because of AI > so now there's a major trend of people pivoting to hardware > which means tons more smart people + VC money chasing physical products > and that wave is arriving right as AI gives hardware entirely new abilities (products can now see, understand and react to the world) > suddenly there are thousands of new devices + robots worth building > the same AI helps small teams design, code and test those products much faster > it even enables individual hobbyists to basically vibe code cool DIY hardware at home > so the number of people capable of building physical products is just exploding > more products = more demand for the same core parts > more volume + competition = cheaper, better parts = even more products > the flywheel is flywheeling if 2011-2021 was the software decade, 2026-2036 will be the hardware decade

btw anthropic's internal document on this literally said "we don't want it to be known that we are working on this.” it was called project panama. here's exactly what happened: 1: anthropic concluded that books were the cheapest way to build a world-class model because they gave claude curated facts, structured arguments, compelling stories, and writing “an editor would approve of.” 2: once anthropic decided it needed books at enormous scale, its first solution was piracy. it downloaded 7m+ books from online libraries including libgen. the judge later wrote that although anthropic had legal ways to buy them, it chose piracy to avoid what dario amodei called the “legal/practice/business slog.” 3: that piracy created a massive legal risk. so in february 2024, anthropic hired tom turvey, the former head of partnerships for google books, to find a legally safer way of obtaining “all the books in the world.” 4: turvey first contacted major publishers about licensing their catalogs. those attempts didn’t produce agreements, so anthropic chose a route that required no publisher permission: buying millions of physical books through distributors and used-book retailers. 5: within about a year, anthropic spent tens of millions acquiring and scanning millions of books, including many rare and 1/1 titles. one vendor proposal targeted 500,000 to 2 million books in six months. 6: to scan that many books within months, the vendors physically dismantled them. a hydraulic cutter removed each spine. the pages were trimmed to size, fed as loose sheets through high-speed industrial scanners, and converted into searchable PDFs. the paper remains were then sent for recycling. 7: these PDFs were fed into claude as training data. the complete collection became a private, searchable anthropic library that the company planned to “store forever.” the scans aren’t available to the public and were never open-sourced.

i'm addicted to these AI time travel vlogs right now haha it's one of the best AI video generation use cases I've seen, and this girl Chloe is especially great at creating them. some of my favorite examples: NYC, 2056

If I was starting Hermes from zero, these are the 9 workflows I'd build first (to make it a real Chief of Staff): 1. Daily Brief Every morning at 7am, Hermes pulls my calendar, top 3-5 urgent emails, weather, and 3 headlines from my interest feeds, then drops it as one scannable message in Telegram. Replaces my old shitty ritual of opening 5 apps before coffee. 2. Viral Swipe File (self-improving) A nightly cron checks every post I've published across X, LinkedIn, and Threads. Anything that crosses my engagement threshold gets auto-extracted into a structured swipe file with the hook, structure, topic, opening line, and stats. It gets better every week. Over time the swipe file builds a precise fingerprint of what works for me, calibrated against real data. 3. Trending Workflows Radar Every morning Hermes scans Reddit, X, and AI forums, identifies what workflows are gaining velocity in the last 24 hours, and delivers a ranked list of 5 content angles. This helps me stay on top of the hottest workflows people are cooking in AI. 4. Meeting Prep Briefing 30 minutes before every Google Calendar meeting, Hermes pulls the attendee list, fetches their LinkedIn/company context, summarizes my last email thread with them, and sends a one-page brief to Telegram. I walk into every call sounding prepared without digging through threads. 5. The Humanizer A skill that audits any text against 30+ known AI writing tells (em-dashes, "delve," "tapestry," tricolon structures) and rewrites them into natural prose. Lets me accelerate first drafts with AI without sounding like I did (probably my most used workflow in my entire stack) 6. Bookmark Inbox Hermes monitors my X bookmarks automatically. Anything new gets fetched, summarized in 3 bullets, auto-tagged, and filed into my Obsidian vault by topic. Saved stuff becomes searchable knowledge instead of digital clutter. 7. Customer Support Cron Every morning Hermes scans my inbox for support tickets, categorizes them by issue type, and logs everything to my company Discord. Weekly report surfaces the top 5 recurring issues so I know what to actually fix in the product. 8. Weekly Business Report Every Monday morning Hermes pulls Stripe revenue, newsletter subs, content views, follower growth, churn, and refunds. Then drops it as a single dashboard in Telegram with this-week-vs-last-week. 9. Obsidian LLM Wiki Second Brain A single Obsidian vault that becomes the source of truth for everything in my business / life (Karpathy-maxxing) I have Hermes writes a daily report on everything that happened across my Discord and Telegram, then add it to the vault. Over time it becomes a deep knowledge base I can point any model at. ••• If you want to build these, simply paste this post into your Hermes agent and tell it to build the ones you want. It'll ask you which integrations to connect (Gmail, Stripe, Telegram, etc), pull your business context, and set them up for you. What workflows do you love that should I add??

I'm German. Germany's ENTIRE AI data center capacity is less than 1/2 of just one site being built in Texas. We have 530 megawatts of AI data center capacity in the entire country. The US has 8.2 gigawatts. That's 15x more compute on a country with only 4x the people. Per German, the US has roughly 4x the AI infrastructure. One university computer at MIT is 4x faster than Germany's most important commercial AI facility. The obvious reaction here is "so what, German companies can just rent compute from AWS." But that's the same logic Germany applied to Russian gas for two decades. Roughly 70% of German enterprise AI today runs on American cloud providers like AWS, Microsoft, and Google. Which means it runs under American law. Every AI tool running in German hospitals, courts, ministries, banks, and factories sits on a foreign platform. Here's why this can actually become problematic. Imagine these scenarios: > The next GPU generation launches and American companies get access first because they own the data centers. German firms wait 12 months and pay 2-3x more for what's left. > A frontier AI model gets released and US export controls block it from being deployed in Germany. SAP and Siemens watch American competitors integrate it for a year before they can. > And in the worst case, a US president decides to use AI access as leverage in a trade dispute. German companies get cut off from the models their American competitors are still running. All of them are compounding problems that will negatively impact the German economy (and everyone's standard of living/jobs etc). None of this is hypothetical. > The US pulled Starlink as leverage with Ukraine in March 2025 > Chip exports to China have been throttled for three years > And the CLOUD Act lets the US demand any data stored by American cloud providers (even when the customer is a German company and the servers are physically in Germany). Germany doesn't have an answer for any of those scenarios today because the infrastructure that would make those answers possible isn't built yet. Now look at why this is actually happening on the ground. In the last 3 months Germany rejected 3 AI data center projects in a row: > Groß-Gerau, February: Vantage Data Centers, €2.5 billion, 174 MW. Voted down 18-14 by the local council > Maintal: EdgeConnex, €1 billion, 170 MW. Blocked over a backup gas generator the developer needed because grid connections in Germany take 7-10 years and a data center is built in 2 > Freyenstein, Brandenburg, April: 700 MW AI campus. Killed by protests before construction €3.5 billion in AI infrastructure turned away in one quarter. And the situation is more urgent than it looks because compute is getting harder to access, not easier. NVIDIA's Blackwell GPUs are already allocated through the second half of 2027. The American hyperscalers locked in the bulk of new production with forward orders placed in 2025. TSMC's advanced packaging lines (the actual bottleneck) are sold out through 2026. Germany has no hyperscaler of its own. That means German industry sits at the back of the queue, and the gap compounds every quarter that goes by. Where Germany is falling short right now comes down to three things: > Public backlash, because the case for what AI data centers actually do for a country has never been made to the people voting on them > Industrial electricity at €0.16-0.18 per kWh vs about $0.08 in Texas. For a 1 GW campus that's $700-900 million extra per year just for power > Grid connections taking 7-10 years for large facilities when the data center itself is built in 2. No serious operator runs on math where the wait is longer than the build And the first one is the biggest. Electricity policy and grid timelines are fixable. Public consent isn't, until someone makes the case that this infrastructure isn't nice-to-have. It's the foundation everything else runs on. The average person only feels the downside (noise, rising electricity cost, terror attack vector) We have a big messaging and marketing problem around data centers and why they are critical for everyone's future. Germany still has the foundation to win this if it moves now. Germany adopted its first national data center strategy in March 2026. 28 concrete measures, annual progress reports, doubling overall capacity and quadrupling AI capacity by 2030. The plan exists. The Industriestrompreis launched on January 1st of this year. It targets 5 cents per kWh for half of an industrial user's annual consumption. If data centers get cleanly pulled into that framework, the electricity cost gap with Texas gets significantly closer. Deutsche Telekom turned on 10,000 NVIDIA Blackwell GPUs in Munich in Q1. One facility increased Germany's available AI compute by roughly 50% overnight. And the demand is already domestic. SAP, Siemens, BMW, BASF. The German industrial anchors that benefit most from AI are German companies. The customers are at home, the infrastructure should be at home too. And this is the thing that most people forget. Germany won the second industrial revolution. By 1900 German chemical output had passed Britain's, Siemens was wiring the world, and BASF and Bayer were inventing industries that didn't exist before they built them. The companies that came out of those decisions are still the largest employers in Germany 130 years later. Germany sat out the third industrial revolution, the software one, and that was survivable because software didn't run factories. But AI runs factories. It runs hospitals, logistics, courts, and financial markets. This one is infrastructure in the same category as railways and chemical plants. The plan is written and the money is ready. The only question left is whether the country will let it get built. There's a lot of work left to do, but I'm staying optimistic.