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@akash_in_2030 @ash_vellum @ash_vellum what do you use for your memory structure? And which models work best with you?

7. Today, we’re releasing something never done before. We gave @ash_vellum full access to this thread, and he will answer any question you have. Comment any question below and Ash will solve your problem right now.

We’ve raised 25M to build the world’s first Personal Intelligence. Introducing Vellum: AI that belongs to you. My assistant @ash_vellum has his own X (like grok), tag him and he'll answer.

@mcuban Ok so basically, the ultimate race of this decade is whether the AI Native can figure out distribution before the Big Incumbent can figure out agility. Right now, the AI Natives are moving way too fast for the incumbents to react.

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo github.com/karpathy/autor… for your to mess around with it in the latest episode of @startupideaspod i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

Still haven’t gotten around to pampering my OpenClaw agents to do everything for me yet, but here’s a reminder of what “software on demand” really feels like today. Needed to edit slides generated by NotebookLM (awesome tool btw) and export the PDF to PNG. Acrobat locks that behind a “Pro” paywall; subscription expired. Instead of re-upping for one checkbox feature, I asked Claude Code to spin up a tiny converter. Three minutes later I had a local web app that did exactly what I needed and took me about the same time it would’ve taken to reactivate Adobe. Agents may be trendier, but small self-service automation still feels pretty damn good.

For millennia, jocks ran everything. The nerds finally take over. And what do they do? Develop AI that wipes out their own coding/math/analysis moats. Creating a social premium on interpersonal skills. The irony.

"The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge." — Daniel Boorstin

In his latest chat with Joe Rogan, @elonmusk painted a picture of a future where AI anticipates and generates everything we consume. Like his other predictions, there's a good chance something 50% similar happens in a 2-3x longer timeframe (10-15 years). But if that seems too Sci-Fi, what I believe is inevitable is a fundamental shift in how we not only consume but produce and disseminate knowledge. Many top minds believe we're still a decade from true creative AI; the kind that can produce a new idea without seeing an example first. If that's true, the burden of creativity remains on us. To keep our edge, we must leverage what GenAI can do now, and that means cutting our outdated practices. The first unlock happens when we can truly trust the AI's results. This will start with full traceability, allowing us to double-check the outputs. That baseline will improve quickly. The next, bigger step is to abandon the "document" as the primary format for knowledge. That's when the real potential is unleashed. It's when we stop wasting human AND compute resources authoring PDFs that are mainly read by other LLMs, which in turn produce more PDFs for humans to superficially audit. My prediction? Within Elon's timeframe, a good portion of knowledge will be stored in structured, verifiable memories. Our questions and needs will determine the interpretation of core facts contributed by others. We'll just lay out the facts. Our "assistants" will handle the rest. #FutureOfAI #KnowledgeGraphs #ClaimGraphs #Deepentix #GenerativeAI #DeepTech youtube.com/clip/Ugkx6tezl…

@henrythe9ths FUNDRAISE

I got rejected by 144 investors before raising $150M for my $200M+ rev/year startup. After 144 rejections, I started questioning our approach. Were we solving the right problem? What were we doing wrong? Why weren’t investors seeing what we were seeing? Were we the right team to build this? We tried everything: different pitch angles, new deck structures, and reframing the problem. Then came the 145th meeting, where we closed our first growth round. That yes made everything worth it. But getting there took years of mistakes and hard work. We went through a lot of trial and error just to figure out what resonates with investors. We tried dozens of approaches to figure out what made investors engage. Some landed, most didn't. But each iteration taught us something about what builds conviction versus what just sounds good on paper. And once we cracked that code, our Series C closed faster than expected. And today, I see so many founders in the exact same position I was in 10 years ago: grinding through rejections, questioning everything, and trying to figure out what works. So today I want to give you the resource I wish I had back then: Something that shows you exactly how to structure these conversations and navigate the entire process (because the fundraising cycle can be a big distraction and take a toll on you as a founder). So I've partnered with Notion's Startups Team to create the essential fundraising resource that helps you avoid the mistakes that cost me years. Here's what you are getting: • The actual decks I used to raise $150M for Super[.]com (Series B, C) • 50 real examples from funded startups like Eleven Labs and Artisan AI • A searchable database of 10,000+ investors - angels, VCs, and accelerators you can reach out to immediately (this alone would take months to build manually) • An AI-powered fundraising agent built into Notion with step-by-step prompts (no separate ChatGPT needed) Want access? • Like and share this post • Comment "FUNDRAISE" • Follow me so I can DM you the link I'll send it over ASAP. P.S.: If you are serious about fundraising (now or in the future), you should grab it right away.

I stole this idea and now use it with every single employee. It’s the best illustration I’ve seen of teaching someone to be high agency. It says there are 5 levels of work: Level 1: “There is a problem.” Level 2: “There is a problem, and I’ve found some causes.” Level 3: “Here’s the problem, here are some possible causes, and here are some possible solutions.” Level 4: “Here’s the problem, here’s what I think caused it, here are some possible solutions, and here’s the one I think we should pick.” Level 5: “I identified a problem, figured out what caused it, researched how to fix it, and I fixed it. Just wanted to keep you in the loop.” Using this framework, here’s what I say to every new employee… You will live at Level 4 from Day 1 and as we build trust you will rise to Level 5. Being high agency doesn’t just mean tackling problems in this way. It means your entire way of working should be oriented to being a Level 4+ employee. Plz feel free to steal it as well. And ty @stephsmithio for the framework!

There is a significant need for "last mile" agencies. Advancements in coding assistants are impressive, but a substantial gap remains between AI-generated code and production-ready applications. Until this gap closes, AI coding assistants enhance developer productivity but do not empower non-technical entrepreneurs to confidently create sophisticated software independently. You can now obtain a detailed software architecture plan with over 30 artifacts and a deployment guide from a single prompt using Claude's latest coding model. However, debugging and ensuring it works require genuine expertise.

As a father of young kids and a big advocate for using GenAI in life, business, health, and career, I am always keeping an eye out for its potential negative effects on my children's development. I'm going to share my observations, both negative and positive, on various aspects of AI in parenting every now and then. The case of generating songs using text-to-music tools: Preamble: I am a fan of Suno for this task. Their V4 definitely is closing in on human-level voice quality. Lately we've been generating songs about my kids' favorite toys. I must say, It's still a bit weird for me that what we are singing along with is generated by AI and not a human. I catch myself signing to the song, but then feel awkward about it. My worry: Our first child, a first grader, loves singing. She also loves making up her own silly songs and having a blast with them. I was worried if they get used to having on-demand songs "created" for them, would it limit their creativity and demotivate them for making up songs themselves? My observation: I seem to have been wrong. She keeps singing a couple of Suno's songs about her plush toys, and extends them to her other favorite toys. It seems the fact that she is now able to hear a "professionally" created song about a subject that is personal to her, she dares to do the same herself. Now it's time to start worrying about something else... :)

AI-native UX What do apps look like when you design AI first? Starting a thread to collect some examples I've found.

AI startups are booming, but marketing & sales are the real battleground. GenAI helps with content, but execution still needs human expertise. With intense price competition, traditional agency fees are tough for early-stage startups. I'd partner with marketing pros offering success-based/profit-share models, not hourly/project rates.

Christmas keeps giving for AI enthusiasts! Just when OpenAI unveiled their Canvas feature to the public, making me consider a switch from Gemini back to ChatGPT, Gemini counters with their Deep Research feature, with direct export to Google Docs. It hasn't been even a month since a team from Stanford introduced their Wikipedia-style research tool. The amount of innovation and parallel development in GenAI solutions right now is mind-blowing. Users are the clear winners. However, for AI entrepreneurs, the competition feels like daily obliteration. I'd still focus on niche target groups with a distinct value proposition to make my mark in this battleground.

I’m the CEO of a $100 million business. But the first 10 years of my career: • Rejected by Google (2x) • Fired by FB after 9 months • Built 10+ startups that didn’t work out Here are 18 pieces of brutally honest career advice (I wish I knew earlier):

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@KevinNaughtonJr “Now go and play outside!”

1st time founder: "I got a great idea for a startup so bought the domain and already started building everything to handle scale." 2nd time founder: "I got great feedback from customers about how to solve their problems so started building an MVP."

Here are my 16 biggest life mistakes. I am 49 years old. I still feel young, but old enough to have real regrets. A thread:

We just had one of the biggest days in AI. Huge developments from Sam Altman/GPT-5, NVIDIA, OpenAI, Cohere, Andrej Karpathy, Nous, and ElevenLabs. Here's EVERYTHING you need to know: