Stanford's STORM research method runs a topic through five different expert perspectives instead of a single prompt, so the blind spots one angle misses get caught by another. I turned it into a free Claude skill that spins up a practitioner, academic, skeptic, economist, and historian, maps where they disagree, then verifies every source before handing you a clean HTML briefing. I also put it head to head against Claude Code's built-in Deep Research, and walk through exactly how to install it and tweak the lenses for your own work.

Hi, I wanted to share the first automation I built with Claude Code and VS Code. After my parents’ heart surgery, the number of doctor appointments, medications, and admin tasks became overwhelming. I built this to help them stay organised. Have a look. The explanation is in the image below. Runs in real time/ cat personality language through Telegram. The bot works, but it’s been a real learning experience. It kept running into memory issues. I’d fix one thing, then another would appear. Recently, instead of patching problems one by one, I had Claude review the entire codebase and optimise it properly. It’s now a working project running through Claude Code, VS Code, and Railway. I’ve asked a few friends whether they’d find something like this useful, and the answer has always been yes. What I’ve realised, though, is that building and tailoring these kinds of automations takes a lot of time. My takeaway: the people who benefit most from custom AI systems will likely be businesses or clients with budgets that match the time it takes to build them. That said, I’m grateful to have learned the skills to build tools like this for myself and my family. #AISOS

That's the second-largest private funding round in history, behind only OpenAI. The new valuation puts Anthropic ahead of OpenAI as the most valuable AI company in the world, private or public. Quick transparency up front. I'm not a private equity guy. No insider info on Anthropic. Just trying to make sense of this out loud. Here's what I see happening. Most companies raise pre-seed, seed, then Series A through D. After that they either IPO, get acquired, or run out of road. Anthropic just hit Series H. That isn't necessarily an indication of good or bad. Stripe and SpaceX stayed private on purpose to avoid public-market scrutiny. Slack and Lyft reached Series H and IPO'd within a year. Every company takes a different path. What it does tell you: Anthropic chose to stay private through eight rounds. Bloomberg reported an IPO could come as soon as October of this year. Anthropic's annual revenue jumped from $1 billion in December 2024 to $47 billion this month. Widely cited as the fastest revenue ramp of any software company in history. They now run higher revenue than OpenAI, and the latest projections show them hitting profitability first. Not saying one is "winning" here. Both are still burning billions a year. But the underdog framing for Anthropic is getting harder to defend. What this news has me thinking about is why a company growing this fast still needs another $65 billion. The answer is compute. Dario (the CEO) said it himself a few weeks ago. They planned for 10x growth in 2026. They saw 80x. They literally cannot build datacenters fast enough. Earlier this month, Anthropic leased the entire Colossus 1 datacenter in Memphis from SpaceX. 300 megawatts, over 220,000 Nvidia GPUs, $1.25 billion a month. They didn't pick SpaceX over Amazon or Google. Colossus was the only compute available right now. The rest doesn't come online until 2027. This Series H also brought chip manufacturers Samsung, SK Hynix, and Micron on as investors. Those three make the high-bandwidth memory that sits on every Nvidia GPU. Getting them invested locks in supply at the most constrained part of the chip stack.

Every single person following AI right now is overwhelmed. Including me. I make videos about this stuff for a living and I still feel the pressure. New model drops. New framework. New feature update. It feels like every single day. But after hearing a ton of you guys bring up "AI overwhelm" week after week, I realized this: → There's a HUGE difference between knowing the "what" and knowing the "how." Staying aware does not mean testing everything. Most new tools and features only need the "what." You see the title. You understand what it does. You move on. The "how" is reserved for the stuff that solves a problem you actually have right now. So when something new drops, I ask myself one question: Does this solve a specific pain point I'm currently dealing with? If yes, I test it in a real scenario. I test it against something that actually matters to me. If no, I save the link. I mentally file it away. And I keep walking. Because here's the thing. Your north star is probably very different from mine. Part of my job is to experiment, form opinions, and share what I think is useful. So naturally I test a lot of stuff. But if your north star is building a business or getting better at your craft, then every shiny new tool might just be a distraction. The number one mistake I see people make is they try to learn everything. They watch every video. They test every tool. They jump to the next thing before the last thing even had a chance to work. And if I've contributed to your overwhelm with my daily uploads, I apologize. hehe. But a lot of people think that this ties directly into how you measure your day. Productivity is not how many hours you worked. It's how many meaningful outputs you created that actually moved the needle towards your north star. Someone can work 12 hours one day and feel insanely productive, but they were just watching tutorials and playing around with new tools. Meanwhile someone else sits down for 5 hours, ships the one thing that actually matters, and makes more progress.

Looking for AI consultant based out of Toronto. If you are in the area, shoot me a DM