Product @ideogram_ai | 2x Exited Founder | Prev. @ComfyUI & Google | Sharing AI Ideas

Joined August 2022
What's the point of Dot on ChatGPT? Been doing all the advertised use-cases (and a lot more) using Codex for months 🤔 Love the TUI redesign 😍 @OpenAIDevs. One feedback! One thing I like with Claude Code is the concept of "shells" when there are background tasks. Would be cool if you could add something similar.
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Mac Derival retweeted
design engineering as a discipline has popped off, but there are limited resources on how to define the role, find great talent, and evaluate them finally started putting together a guide on design eng hiring, focused on part 1: defining the role
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Mac Derival retweeted
bittersweet to share, but I left Netflix. I couldn’t have imagined a better place to start my career. it was a dream to work on a consumer product that reaches millions of people and shapes conversation and culture. I got to build prototypes exploring new modes of discovery and interaction across mobile, growth, search, voice on TV, and more, bringing a design-thinking lens to engineering, while also building an internal ai tool from zero to one. one of the biggest reasons I took this role was because Netflix was one of the few places that let me do both design and engineering. I worked closely with designers to bring their visions to life, often exploring things that weren’t on any roadmap yet. it almost felt like a hackathon as a job, constantly asking each other “what if we could do x?” and pushing beyond what we thought was possible, while being surrounded by such a prolific engineering culture. I’m so grateful to have learned from people who are truly best in class at what they do. Netflix’s culture constantly pushed me to raise the bar for myself, and I appreciate the team for taking a bet on me and molding me into the builder I am today. I’ll always look back on my time here fondly. I almost feel like I grew up at Netflix, joining during such a pivotal part of my early 20s as the first early-career hire in the design org. it truly was the coolest job ever, which made it incredibly hard to leave. but I’m excited for what’s next, which I’ll share soon :)
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Mac Derival retweeted
A startup that raised more than $50M is shutting down, and most are asking "Why didn't they just get acquired instead?" Speaking from experience, it's really, really hard to have this outcome. There's a perception that it's super easy, and people just buy companies. You see the headlines of 18-month-old AI startups getting acquired for $1 billion, and think acquisitions grow on trees. Every week there seems to be a new life-changing outcome. Reality is just so different. I've tried. I have a ton of friends who've tried. "Just sell" to a founder feels like saying "Just play in the NFL." Companies simply aren't out hunting for startups to buy, because it's a risky game. So many purchases go sideways for the acquirer. Even if the company is doing well. And if they're not? No one wants to buy a company not doing well, nor are there tons of companies with cash / stock looking to do the rollup game. Even if you do get an LOI, it's not guaranteed. Paul Graham, the founder of Y Combinator, puts it well: "Birds fly. Fish swim. Deals fall through." Companies at the $100b+ scale move slowly, so you need a product leader / champion to buy into what you're building, and THEN 50-100 people need to independently sign off. There's so much surface area for a 3-month due diligence process to end in a "No thanks." Pulley did the right thing by providing a soft-ish landing for their customers, transitioning them over to Carta. This alone probably took weeks of back-and-forths with the Carta team. Exactly why "just sell" is harder than it looks. Thoughts going out to the Pulley team.
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It's funny how with AI everyone is an Engineer. Now we have GTM Engineers, Talent Engineers, Sales Engineers ... The actual engineers got renamed Member of Technical Staff.
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two rooms just opened at mission control, the oldest hacker house in SF scale ai, hightouch, pylon among other unicorns started here residents throughout the past decade have raised more than $2B in funding rent is $1.8k/month, one year min stay apply missioncontrol.house
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If you disagree with this, you don't have the mindset it takes to win.
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We’ve raised $6M to turn sad employees into happy ones. One pizza at a time. Led by Atomik.vc sliceline.ai/ Now live in Slack, Gmail & more.
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OpenClaw for the win 🙌🏾
Today we’re introducing the OpenClaw Foundation: a nonprofit home for open, independent personal AI. A full-time team. Great partners. One mission: bring personal AI to everyone. Welcome to the age of the lobster.🦞 openclaw.ai/blog/introducing…
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Mac Derival retweeted
Existing image models can't generate on-brand visuals. Introducing Ideogram Custom Models. Train your model, on your images, not the internet. Every new generation will follow your art direction, typography, and visual identity. Available now on Team, Pro, and Enterprise plans, and the API.
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I will always be mind-blown by the @ComfyUI ecosystem 😌
A true Blender running inside ComfyUI. Viewport preview, rendering — not sure how far this’ll go, but I’ll take it one step at a time.
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Friend: Those 21 years old founders are super cracked. They dropped out of [fancy school] and won [fancy competition] at age 15. Me: If they are so cracked, why is the AI not working.. They have competitors with AI that works. Please don't tell me you only hire cracked engineers if the product is not working 😪
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Paid marketing is now a contrarian 🤔
Crude, un creative, non generational companies I ran paid for. (Fire me!) > TikTok - $300B > Airbnb - $113B > DraftKings - $28B > FanDuel - $20B > Robinhood - $40B > Blue Apron - $2B > HelloFresh - $20B > Wish - $17B And let’s not forget: > Uber - $200B > DoorDash - $72B > Booking.com - $130B > Expedia - $28B > Wayfair - $45B > Chewy - $43B > SoFi - $20B > Credit Karma - $8B > Chime - $25B > Snap - $130B > Lyft - $25B > Instacart - $39B > LendingTree - $5B > NerdWallet - $4B > Lemonade - $9B > Dollar Shave Club - $1B > Hopper - $5B
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It's interesting to see the negative feedback on @midjourney's new model and the debate between pure diffusion vs hybrid autoregressive (AR) & diffusion. The frustration is from V8 issues with hands, text, and coherence. Many are pointing to models like Nano Banana that have shifted toward AR approaches. The Seoul World Model paper shows what strong AR grounding can achieve when anchored to reality. → Retrieval-augmented conditioning on millions of real Seoul street-view panoramas → Virtual Lookahead Sink stabilizes long-horizon generation → Cross-temporal pairing fixes data sparsity and misalignment Why this matters: The hybrid wave is accelerating for both image and video generation. This paper proves AR models grounded in real data can deliver spatially faithful city-scale world simulation which is a big step for robotics and agents. Paper: arxiv.org/abs/2603.15583
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Congratulations @contextkingceo for his seed round 🤩. Keep killing it Nishkarsh!
We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️
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Text-to-video generates pixels. But spatial intelligence needs physics. Code2Worlds turns coding LLMs into physics-grounded 4D world simulators. → Dual-stream architecture separates object detail + environment structure → VLM-Motion Critic provides closed-loop feedback to fix physical hallucinations → Code4D benchmark covers fluids, particles, rigid & soft bodies Text-to-video needs world simulation, not just pixels. This is the agent pipeline approach. arxiv.org/abs/2602.11757
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