Here's my new porftfolio: qwertydesign.net I hope you had fun viewing it as much as I had fun building it.
If you find the easter eggs, do share them with me here!
Note: It's a lot more fun on the desktop.
Edwin Toh retweeted
In July we open-sourced a dynamic graph of everything a child learns in primary school. 1,590 concepts, 3,221 dependencies.
Now we’re turning every concept into interactive experiments kids can play and learn from.
First one, age 7: heat and water.
Everything that used to be a boring textbook can now be an engaging experience, potentially tailored to every child.
Kids education just got a massive upgrade.
Great idea, and really exciting development. I think it got buried among all the other Argon news, but this is definitely something to keep an eye on.
Today, on Google Play we announced new ways we are evolving the Play Store for an experience that is unique to you and the content you enjoy most:
✨ Use Play to wirelessly install and run your own custom, vibe-coded apps privately on your device.
🔍 Discover apps effortlessly with Ask Play & Gemini
🎮 Dial in discovery with the all-new Game Finder & Gamer DNA
This is genius
$52M seed round. 15M+ users. But this is what we're most proud of yet.
Introducing Drama 3: the most emotive and controllable TTS model in the world
And yes, it's open-weight.
You can now control how even a single word should sound, change its tone, and shift your voice mid-sentence.
Uncle Roger was impressed too.
Try it here:
s.fish.audio/rqh0l8
Comment “DRAMA” within the next 48 hours for 1 month free.
If you're an enterprise your first month is on us:
s.fish.audio/qz9r0t
Edwin Toh retweeted
The @shadcn released shadcn-ui/lint, an agent-first linter for Tailwind and design-system consistency.
It resolves components through TS paths and package exports, so teams sharing a UI library across apps keep everything in sync automatically.
github.com/shadcn-ui/lint
While working on my new portfolio, I discovered the process that I loved the most was the iteration bit. I went from prompting the agent from my phone on my couch, to opening up my laptop and pulling down the latest changes to start fine-tuning the details.
I haven't had so much fun in a while.
Agents are great at knocking out bugs, or even building brand new features. But I think building things that brings delight to users is still very much something in the hands of humans, and I hope you still find joy in that process just like I do.
Great list!
My biggest takeaways from @JoshWoodward (Head of @Google Labs, the Gemini App, and Google AI Studio)
1. The best ideas almost never come from a design sprint or brainstorm. At Google Labs, the ideas that became NotebookLM, Google Flow, Google Beam, and AI Studio all started with small groups of people who were obsessed with an insight and couldn’t stop themselves from building. These ideas surfaced in the shower, over a weekend, in a hallway. The implication for teams everywhere is that the environment matters more than the process. “You cannot microwave a good idea.”
2. Fall in love with the problem, not the product. Most ideas require three, four, or five significant pivots before they either find traction or get shut down. Teams that get hooked on a specific solution will struggle to make those pivots. Teams that are attached to the problem—the underlying pain point or the user behavior they are trying to shift—can keep adjusting until they get it right.
3. When looking for early PMF, data and retention are important, but the strongest signal of PMF is in people’s eyes. Do they light up? Do they lean in? When you show someone a prototype and they’re genuinely engaged, you feel it. When NotebookLM’s first AI podcast demo was played in a hallway, the reaction was immediate and unmistakable.
4. Keep a list of what is “almost possible,” and move fast when something crosses over. Google Labs keeps a live list of experiences that are almost possible. When something crosses the threshold, Josh describes it as “a phase shift,” like solid to gas, and the team moves immediately. Maintain a running list of things that are almost ready, and have a team positioned to sprint when one of them arrives.
5. Every company needs to think like a labs team now. The underlying technology is changing so fast that what’s possible is shifting from week to week. Which means that every company needs to be spending time playing with the latest models, running far-out experiments, and being first to discover what they unlock before the competition does—and then bringing those discoveries to the rest of the org.
6. When something is not working, the team almost always knows before the leader does. Josh’s instinct as a leader is not to be the one who decides to shut down a project. He watches instead for the passion running out, for the sense that the team has tried everything and nothing is working. His goal is to create an environment where people feel safe enough to say, “I don’t think this is working.”
7. Someone’s “unlearning rate” is one of the most valuable skills right now. Everyone talks about growth mindset and learning speed. Josh is watching for something different: How quickly can someone take an insight, validate or invalidate it through testing, and then let it go? The ability to walk away from a prior belief or approach when the evidence says you should is rare and becoming more valuable.
8. Roles are not blurring into one universal “builder” role. There is a tendency in the current moment to talk as though everyone is becoming a generalist builder. Josh thinks this would be a bad idea. The best people on his teams still have a primary specialty—a major—and also a “minor” they’ve developed. The advice he gives to early-career PMs who are excited about vibe coding and prototyping is clear: build adjacent skills in service of becoming an exceptional PM, not as a replacement for it.
9. A well-designed lab needs isolation, a clear mission, and the ability to create “good trouble.” Three things matter when setting up an internal labs team. First, it needs structural independence, reporting far enough from existing business units that weird ideas can survive long enough to prove themselves. Second, the lab’s mandate has to be explicit: are you graduating ideas to existing products, or inventing new categories? Clarity on that question changes almost every decision. Third, the lab should be a place to challenge how the company itself works, not just what it builds. At Google Labs, that included piloting new job ladders and builder roles before rolling them out more broadly.
10. Google Labs manages the marathon-sprint tension through seasons. At any given time, roughly 20 to 30 experiments are running in parallel, but individual teams experience a defined rhythm: sprints, post-launch recovery, and open exploration periods. After Google I/O each year, many labs teams enter a mode that looks unproductive from the outside: no shipping, just hacking, building, and discovering. Josh treats this as the seedbed for the next season.
Edwin Toh retweeted
$GOOG VP Josh Woodward reveals Gemini is moving toward one prompt box with no modes or toggles, and hints a “personal, proactive, powerful” version of Gemini is coming soon
“I think where we’re iterating is that we’ve been in a world where, even if you look at some of the other competitors, there are different modes that you go into. I think the lesson, even from the last couple of weeks, is: forget the modes and toggles. Just one prompt box—tell it what you want, and it just does stuff for you.
“That’s definitely where we’re headed and where others are headed. I think that’s a broader UX simplification happening across the industry.
“On the data bits, one of the main things people will ping me about is, ‘Google, I’ve got all my stuff with you. Just make the thing work.’ There’s a lot of reluctance to connect it to a lot of competitor products. Watch this space. We hear you loud and clear.
“There’s a lot of work—if you go online and look under ‘personal intelligence’—which is how we think about this. You want a Gemini that is personal, proactive, powerful, that works for you. That’s been the vision for the last year or so that we’re headed toward. There’s a lot of stuff we’re playing with right now that will be out soon.”
$META Devansh Tandon says recommender scaling curves are still climbing, with gains from larger models, more compute, and more data showing up in higher engagement and revenue
“They follow this similar power-law scaling curve. On the x-axis, you have data, compute and model size. On the y-axis, you would see falling loss—or, in this chart, an improvement in recommendation quality.
“In offline evaluations, it’s net entropy or AUC gains. When it’s translated into a real production launch, it’s engagement impact and revenue impact at the scale of some of the biggest consumer apps.
“Here’s a real example from Meta that demonstrates these power-law scaling curves. The first is a paper, HSTU, from 2024, and the second is a follow-up from this year. Both demonstrate that as we scale model size, compute and data, we see this clear improvement in offline evaluations of recommendation quality.”
_______
For the full set of takeaways on the recommendation system presentation: firesidealpha.substack.com/p…
Edwin Toh retweeted
For the past several months, my boys have been using a learning app I created. Today I'm making it public!
It's called Unspool.
Here's the idea → learning is most fun when it doesn't feel like "work."
There's plenty of strict curriculum out there. But I wanted a place where kids could follow their curiosity without the pressure of tests. So I built one.
Here's what you get:
1. 1500+ built in lessons (that are quite good, you can try them on the website) across History, Science, and Art
2. Generate a custom lesson on any topic (my boys ask things like "how tall can a tree get?")
3. Math and typing practice (think a good replacement for math flashcards)
Would love to see what other parents think. Link is in the comments.
Words to live by.
Linear CEO @karrisaarinen: "I took a break this summer from paying attention to AI. I thought that when I came back, I'd feel left behind. But when I came back, I realized that nothing has really changed.
There's new models, there's new techniques, there's new agents. But in the end, making great products, increasing revenue, and building the business—it's still hard.
It's clear the tools are getting better, you can do more, you can do it faster. But I think the more important question is: are we actually making better things with these tools? Are our teams getting better with these tools?
I don't think that answer is clearly yes.
There's a lot of emphasis now on toolmaking and using tools and building systems. But I think in the end, as product people, our job is to make something great."











