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Applications for the latest Standard Capital Series A funding cycle are now open!
Apply by July 21, hear back by July 31.
Apply by July 21, hear back by July 31.
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Dalton Caldwell retweeted
Our most requested feature just shipped 🪝
Easily build agents with Slack, Linear, Stripe, Supabase… basically any service you’re using
Easily build agents with Slack, Linear, Stripe, Supabase… basically any service you’re using
Introducing Webhooks to Trigger: now you can build AI agents on top of the services your customers already use, like Stripe, Supabase and Slack.
tgr.dev/webhooks-x
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Dalton Caldwell retweeted
Our number 1 feature request is out now: Webhooks is now in public beta. Your webhooks, now even more durabler (durablish?)
Introducing Webhooks to Trigger: now you can build AI agents on top of the services your customers already use, like Stripe, Supabase and Slack.
tgr.dev/webhooks-x
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airbnb for data
We're announcing the public beta of datavendor.ai
We spent the last two years building the best solution for people buying and selling data, whether you're a researcher in a lab or a company trying to sell data.
Data is the fundamental bottleneck for AI progress, and infra that solves it.
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Dalton Caldwell retweeted
Revenue is the worst number for telling whether you have product-market fit.
I’ve heard founders claim PMF at under $500K in ARR, and others at $7M say they’re nowhere close. Same question, opposite answers, and revenue explained none of it.
Then I heard a definition of PMF evenue threshold at all. It came down to two things: low churn and strong NRR.
Revenue tells you people bought once. Churn tells you whether they’d buy again. NRR tells you whether they want more. Strong revenue with high churn means you’ve built a good acquisition motion, on top of a product people leave.
So at Artie, revenue is a target, not proof. We still set the goal and hold ourselves to it. The proof however is two numbers: churn under 3% and NRR above 170%.
Now when I ask whether Artie has PMF, the question is how much of our revenue we’d keep if we stopped selling tomorrow.
I’ve heard founders claim PMF at under $500K in ARR, and others at $7M say they’re nowhere close. Same question, opposite answers, and revenue explained none of it.
Then I heard a definition of PMF evenue threshold at all. It came down to two things: low churn and strong NRR.
Revenue tells you people bought once. Churn tells you whether they’d buy again. NRR tells you whether they want more. Strong revenue with high churn means you’ve built a good acquisition motion, on top of a product people leave.
So at Artie, revenue is a target, not proof. We still set the goal and hold ourselves to it. The proof however is two numbers: churn under 3% and NRR above 170%.
Now when I ask whether Artie has PMF, the question is how much of our revenue we’d keep if we stopped selling tomorrow.
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Dalton Caldwell retweeted
Magic Patterns (W23) is one of the fastest-growing design tools in AI, helping product teams turn ideas into interactive, code-backed prototypes in minutes.
@alexdanilowicz and his co-founder, both front-end engineers, built @magicpatterns to solve a problem they had etter way to turn ideas into something visual. They were among the first teams building prompt-to-design tools back in 2023.
In this episode of Design Review, Alex joins YC’s @aaron_epstein to demo Magic Patterns and its new MagicOS canvas, where teams can bring together prompts, images, PRDs, and other context to explore ideas visually.
Then they review a handful of startup websites to break down what makes a site feel “vibe coded,” where founders go wrong, and how to make AI-generated design feel more intentional.
02:11 — From YC Pivot to Product-Market Fit
03:44 — Staying Ahead as AI Design Tools Improve
04:37 — Demo: Designing With Magic Patterns
09:52 — MagicOS
11:23 — Designing With Context on a Canvas
14:50 — How to Get Better Results From AI Design Tools
15:35 — How Far Can AI-Generated Design Go?
18:17 — Website Review 1: Ego
23:59 — Website Review 2: Sentient OS
32:01 — Website Review 3: Cerenovus
36:59 — Website Review 4: Slashy
43:33 — How to Develop Better Design Taste
44:20 — Why Everyone at Magic Patterns Talks to Users
45:07 — Balancing Speed and Design Quality
46:07 — The Future of Design and Magic Patterns
@alexdanilowicz and his co-founder, both front-end engineers, built @magicpatterns to solve a problem they had etter way to turn ideas into something visual. They were among the first teams building prompt-to-design tools back in 2023.
In this episode of Design Review, Alex joins YC’s @aaron_epstein to demo Magic Patterns and its new MagicOS canvas, where teams can bring together prompts, images, PRDs, and other context to explore ideas visually.
Then they review a handful of startup websites to break down what makes a site feel “vibe coded,” where founders go wrong, and how to make AI-generated design feel more intentional.
02:11 — From YC Pivot to Product-Market Fit
03:44 — Staying Ahead as AI Design Tools Improve
04:37 — Demo: Designing With Magic Patterns
09:52 — MagicOS
11:23 — Designing With Context on a Canvas
14:50 — How to Get Better Results From AI Design Tools
15:35 — How Far Can AI-Generated Design Go?
18:17 — Website Review 1: Ego
23:59 — Website Review 2: Sentient OS
32:01 — Website Review 3: Cerenovus
36:59 — Website Review 4: Slashy
43:33 — How to Develop Better Design Taste
44:20 — Why Everyone at Magic Patterns Talks to Users
45:07 — Balancing Speed and Design Quality
46:07 — The Future of Design and Magic Patterns
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Way to go Podium!
We built a $150M ARR business twice.
The first time took 8 years. The second took 2.5.
Today, Podium crossed $150M in AI annual recurring revenue.
Three years ago, we already had a great software business. Well over $100M in revenue, tens of thousands of customers, and a ly a decade building.
And we decided to start over.
The business was performing well. But we believed AI was going to fundamentally change the software industry and make much of traditional software obsolete.
As a founder, you pour your life into what you're building. An insane amount of time, sacrifice, and effort. The hard part is not getting too attached to it.
When we started building our first AI agents, we had to confront the possibility that much of what we'd built might not be that valuable in the future. That's a tough pill to swallow.
But we also realized those same 10 years had put us in a unique position to take advantage of what AI could do for our customers.
Instead of protecting what we'd spent a decade building, we decided to bet the future of the company on AI employees that actually do the work for our customers.
We massively underestimated what we'd signed up for.
We ended up building entirely new operating systems for the industries we serve, from the AI agents all the way down to the systems of record. Now customers run their entire businesses on Podium.
Reinventing our product wasn't even the hardest part. We had to reinvent how the entire company operated.
When you've been running a company for over a decade, the operational inertia is incredibly powerful. We had to rethink how we build products, how we sell, how we serve customers, and how we run the business.
We effectively started a second startup inside an already successful company. And we're still at the very beginning.
Today, we have 20,000 AI agents working for our customers. They've helped generate more than $13 billion in revenue for those businesses. And we're barely scratching the surface of what AI will be able to do for them.
And we've done all of this while becoming profitable and generating free cash flow. We're building Podium to be around for decades.
Huge credit to our team for having the guts to make this bet and the ability to actually pull it off. We still have a long way to go and tons of problems to solve, but it's so damn fun to be winning when the opportunity ahead is this big.
In the age of AI, you can either disrupt your own company or wait for somebody else to do it.
I'd rather do it ourselves.
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Modern problems: trying to figure out if someone emailed you something that seems a bit strange or their agent did.
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Dalton Caldwell retweeted
there's really no limit to what agent swarms are able to do if they share context. we tasked 128 agents with access to the same shared file system to self-organize and build their own version of factorio.
they broke up the problem, claimed tasks, and did their own testing.
less tool calling, the agents were able to get access to exactly the amount of CPU they needed on a per-tool basis, spiking to over 100 cores simultaneously, but because they were only charged for what was used, the total amount of compute required was on the order of ~$0.50
archil implements leasing internally, which allows the agents to write to the same directories and files without stomping over each other.
they broke up the problem, claimed tasks, and did their own testing.
less tool calling, the agents were able to get access to exactly the amount of CPU they needed on a per-tool basis, spiking to over 100 cores simultaneously, but because they were only charged for what was used, the total amount of compute required was on the order of ~$0.50
archil implements leasing internally, which allows the agents to write to the same directories and files without stomping over each other.
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Dalton Caldwell retweeted
the newest frontier is agent swarms. watch us launch 700 sandboxes -- all connected to the same file system -- in 10s, then have them collaborate and build a city on that file system
i used sandboxes here, but if i used serverless tool calling? the total cost of this compute ure is packing agents 40x tighter on each CPU and giving them access to the same shared file system context
i used sandboxes here, but if i used serverless tool calling? the total cost of this compute ure is packing agents 40x tighter on each CPU and giving them access to the same shared file system context
@jhleath @yourclouddude could it run like this but kind of on the 'same' file system? x.com/AniC_dev/statu…
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Dalton Caldwell retweeted
we're about to witness a rapid reshuffling of the "ai infrastructure" as people realize that the tools required to run a single agent are very different than the tools required to run thousands of agents that need to coordinate
the bedrock of what that future looks like? real, distributed file systems that can be mounted from many locations simultaneously in milliseconds.
for the past year, most agent data has just lived on the local disk of the sandbox (persistent or otherwise). this will not scale as agents work with larger data sets and coordinate more amongst each other, you need *shared storage* which allows multiple sandboxes to access the same data
luckily, we've been building for this future for years at Archil, with the worlds fastest shared file system (approaching local disk) that can also reduce the amount of vcpu required for agents by 40x with serverless tool calling
the bedrock of what that future looks like? real, distributed file systems that can be mounted from many locations simultaneously in milliseconds.
for the past year, most agent data has just lived on the local disk of the sandbox (persistent or otherwise). this will not scale as agents work with larger data sets and coordinate more amongst each other, you need *shared storage* which allows multiple sandboxes to access the same data
luckily, we've been building for this future for years at Archil, with the worlds fastest shared file system (approaching local disk) that can also reduce the amount of vcpu required for agents by 40x with serverless tool calling
Do any of the sandbox providers give me the ability to:
- have N sandboxes running with disks that...
- can be mounted on another sandbox (at least 32 at a time)
- without knowing those mount points before the sandbox has started
- and being able to vary them throughout the lifecycle of that one sandbox?
im basically looking for a way to have a parent sandbox, N children sandboxes (where N varies, and has max X concurrency, which would be lower than max of N), and have those children write to a point on the disk that the parent could read from. N readers 1 writer semi-persistent disks.
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Dalton Caldwell retweeted
Today, we're announcing Archil's new $10/mo plan for individual developers -- making it more accessible for builders to use the world's most advanced AI infrastructure.
To celebrate, we're hosting our inaugural [super fast] hackathon in a couple of weeks at Casco's offices in n prizes, and will be a great way to see how others on the frontier are building.
RSVP here luma.com/t9hsthtk and we'll see you soon!
To celebrate, we're hosting our inaugural [super fast] hackathon in a couple of weeks at Casco's offices in n prizes, and will be a great way to see how others on the frontier are building.
RSVP here luma.com/t9hsthtk and we'll see you soon!
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Dalton Caldwell retweeted
My chat with @daltonc on Claude trying to kill our startup and the launch of MagicOS:
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Dalton Caldwell retweeted
a convo about a founder's worst nightmare: dealing with competition from the frontier labs.
just stay focused. just keep winning.
p.s. we are hiring
x.com/daltonc/status…
just stay focused. just keep winning.
p.s. we are hiring
x.com/daltonc/status…
The question on a lot of founders minds: What do you do when Claude enters your market?
I sat down with @alexdanilowicz from @magicpatterns to provide some thoughts and advice for founders imagining how they would react and rise to the occasion.
In addition, we talk about Magic unch, a design agent built for the future.
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The question on a lot of founders minds: What do you do when Claude enters your market?
I sat down with @alexdanilowicz from @magicpatterns to provide some thoughts and advice for founders imagining how they would react and rise to the occasion.
In addition, we talk about Magic unch, a design agent built for the future.
I sat down with @alexdanilowicz from @magicpatterns to provide some thoughts and advice for founders imagining how they would react and rise to the occasion.
In addition, we talk about Magic unch, a design agent built for the future.
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Dalton Caldwell retweeted
The consistent advice is, "don't hire big tech execs"
The fundamental problem is that they've been RL'ed by an environment in which success is measured by headcount. They will make your company bloated and ineffective because in their experience, that's what success looks like
The fundamental problem is that they've been RL'ed by an environment in which success is measured by headcount. They will make your company bloated and ineffective because in their experience, that's what success looks like
When you talk to successful founders, they always wish they'd hired fewer top managers from outside. I don't think I've ever met a founder who wished he or she had promoted fewer people from within.
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Voice-first is an interesting approach, it's like pair programming with a world class designer (that also happens to be infinitely patient)
Today we're launching MagicOS. And we've come to the conclusion that designers are NOT getting replaced.
most of the industry moved to the same answer this year: give the agent its own canvas, let it run, hand you the output.
we made a different bet: the canvas should be yours ollaborator. that’s MagicOS.
most AI design tools are a linear conversation: prompt, generate, iterate, repeat. a hundred prompts in, you might have a great final design, but the reasoning, rejected directions, and inspiration that got you there are buried in a chat log nobody will ever scroll back through.
MagicOS is a persistent, spatial canvas. Your PRD, inspiration images, design system, variations, rejected directions, final picks - all of it lives together, all in one place, instead of stacked on top of itself and lost.
it’s voice-first. talk to it in real time and watch work land directly on the canvas as you speak.
it’s also multiplayer. your whole team works on the same canvas together.
and it still runs on your design system and components because none of this matters if the output doesn’t look like your actual product.
three months ago, this was internal only at Magic Patterns, my co-founder's weekend project. it’s now live for everyone.
Design everything, everywhere, all at once.
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Dalton Caldwell retweeted
"Great founders turn being uncomfortable into motivation, and it fuels them." - @daltonc
youtu.be/Bsxa_Yg1Xcw?is…
youtu.be/Bsxa_Yg1Xcw?is…
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