@PaceCap
Account based inUnited Kingdom
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Joined January 2021
- Tweets128
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Pace Capital retweeted
Today we're announcing a $60M Series B at a $1B valuation, led by @FirstMarkCap to build the agent cloud.
The round also saw participation from some top-tier founders, including @patrickc (Stripe), @tobi (Shopify), @AravSrinivas (Perplexity), @BrendanFoody (Mercor), @oliveur (Datadog), @adamdangelo (Quora) and @ericsyuan (Zoom).
Full announcement👇
Pace Capital retweeted
Glad to have the continued backing of @PaceCap, @upfrontvc and @e2mindset
This round also saw participation from @QualcommVenture, @SamsungNext, @okta ventures, @EverGreen_Ntwrk, @alumniventures and @torch
Read more: daytona.io/dotfiles/daytona-…
Pace Capital retweeted
We just raised a $60M Series B at a $1B valuation to build @daytonaio into the Agent Cloud.
@FirstMarkCap led the round, with all key existing investors and angels doubling down.
We're also thrilled to welcome the founders of some generational companies aboard, including @patrickc (Stripe), @tobi (Shopify), @AravSrinivas (Perplexity), @BrendanFoody (Mercor), @oliveur (Datadog), @adamdangelo (Quora) and @ericsyuan (Zoom)
We're a 35-person team. But that's about to change; we're hiring across the board, and we'd love for you to join us.
Pace Capital retweeted
Many curves are actually just straight lines; you can't even tell where a paradigm shift happened: the compute buildout, Moore's Law, etc.
So how fast are other curves compounding? And which markets are on the precipice of something happening?
I tried to answer that by building this, inspired by Ray Kurzweil’s “The Law of Accelerating Returns.”
accelerating-returns.vercel.…
What happens when you put 16 leaders across tech, defense, media, and public health in a room, give them competing incentives, and ask them to navigate an AI crisis?
Can a game expose the decisions we’d actually make when our interests collide?
To explore these questions, Peggy designed a three-round simulation game: Trust Fall.
In the room: a NPR host, a NYU economist, a public health leader, a frontier AI researcher, and those coming from defense, education, and VC are all fighting limited resources: money, trust, and attention.
The game felt socratic -- who’s accountable for a thought an agent produces? Who has an incentive to *actually* slow down?
It's also predictive. From first principles, the room was able to get to an outcome that necessitates a neutral 3rd party audit firm the same way that things are playing out in real life!
Pace Capital retweeted
Comfy API is live
Point it at a workflow JSON and it handles the rest:
→ Resolves your custom nodes, models, LoRAs, and Python deps
→ Pins it all, ComfyUI version included, into a versioned Build
→ Cuts an immutable release, so what you test is what ships
→ Deploys as an autoscaling endpoint on RTX PRO 6000, H100, H200, or B200
Click the link below to deploy your first workflow and learn more with our blog ⬇️
Pace Capital retweeted
Ollama now supports Jev-like decision models all locally.
Use decision models like Nimble for tasks like ticket triaging, model routing, and content moderation.
ollama pull nimble
Here’s Nimble playing Ollama racer through the new local /v1/systemone API by making decisions in real-time. 🏎️
Pace Capital retweeted
Comfy Router is live
One API for frontier image, video, 3D, and audio models.
Same model string. Same arguments. No new SDK, no new key, no redeploy.
What Comfy Router gives you:
→ Explicit routing. You name the provider, we call that provider. It's down? The request fails there. No silent fallback.
→ Every job returns the provider that ran it. Log it, bill it, debug it.
→ Async. submit() returns a request ID immediately. The queue retries 429s and transient errors until a slot opens. subscribe() submits and polls to completion.
→ Batch-friendly. Queue a few hundred jobs, hold the IDs, pull results as they land. Nothing blocking on a 5-min video render.
→ 24h retention on inputs and outputs, then deleted.
→ Comfy credits. No sub, no Router fee.
Providers at launch: Comfy. Runware, Wavespeed, Fal, Higgsfield. Multi-provider where the model supports it.
Get Your API Key with the link below. ⬇️
Pace Capital retweeted
Getting the size of the market right doesn’t tell you who captures the most value.
Introducing AI Perez: our attempt to make sense of shifting bottlenecks, and who benefits, through Carlota Perez’s framework.
ai-perez-curve.vercel.app
Pace Capital retweeted
Periodically, I try to share my favorite corners of the internet.
This round, I’ve learned how North America went from 144 time zones to 4, LEGO games' realistic environments, and what if every election went the other way.
Pace Capital retweeted
Josh's insight here is unique because he's had the best seat in the house for years. The browser is both a utility and a leisure tool, and the team at @browsercompany has seen exactly how people use it.
Consumers want their leisure amplified. They don't want productivity! Productivity has to be found, and finding it is *work*. Education is work. DIY is work. And an agent asking you a question is really the agent handing you a microtask. Very few people want to manage and even fewer want to be interrupted in order to do it. It's the same reason notifications have such a narrow band where they help before they annoy.
Meanwhile the For You page is the most successful consumer agent ever built and yet no one calls it one. It runs inference on your behalf constantly, makes thousands of decisions a session, and asks you for absolutely nothing.
That's where consumer is headed. Companies using AI to spoonfeed people more of the leisure they already want, not doling out microtasks.
Hot take… isn’t it kinda crazy that nobody is really using AI Agents? I don’t mean software engineers or AI early adopters. I mean “college friends talking about it in group chat,” the feeling you got when everyone started using Instagram or TikTok.
These frontier AI models are *insane* (as are the harnesses & tool calls & the like). And every large tech co has an AI agents platform, not to mention all the YC startups doing vertical agents. Yet all of your friends and family outside of tech — who spend all day staring at their iPhones and get paid to work in browser tabs — don’t really care or find themselves using any AI agents yet.
Yes ChatGPT, Claude, etc. are extremely popular… but if you look at the engagement data the vast majority of people are still using these aI chat tools like a glorified Google + Grammarly. That’s why the AGI labs are all pushing desktop apps for Codex, Cowork, etc. so hard to non-technical ppl. And yes exceptions for lawyers and customer service but even those have some asterisks and exceptions to rule.
Look I’m not saying the ChatGPT moment for AI Agents is not coming… it most definitely is! Remember we pivoted from Arc to Dia precisely because we believe computing is going to be radically reimagined around these AI primitives. No doubt. But that’s my point: it’s just so surprising it hasn’t happened yet because all of the tech you’d need is there.
Again if you stop for a second and think about it… for all the press and money and hype and models and crazy ARR numbers… this “AI Agent” moment does not *feel* like the other breakthrough tech moments we’ve lived through (e.g. think the shift to Stories via Snapchat & Instagram, or shift to on-demand via Uber/Airbnb/Doordash).
Which is a long way of saying: if you can figure out the answer to “why” most people don’t care about AI agents yet (and have no enduring interest in using them) — especially since the models and harnesses are here and ready — the answer to that question will allow you to capture a lot of marketshare and make a lot of money in 2027.
Theoretically, the tech is ready for AI Agents to totally transform how we work and live our lives… but alas the general public dgaf… that’s the generational puzzle to solve for the next 12 months for anyone not working on the models themselves.
Pace Capital retweeted
We've been asked why Cline works better with open weights models like DeepSeek than other harnesses and want to share some detail (more in our blog below).
Our internal evals show that many open weights models like DeepSeek, GLM, and Kimi are RL trained to spend more tokens verifying their work: run tests, check the build, re-read diffs before completing. We believe this is how they were able to close the gap with closed model performance.
Meanwhile closed models and their harnesses tend to optimize for efficiency and fewer tokens (which makes sense when massively subsidizing subscription plans).
So we've been intentional in building the Cline harness to let the model work however it was trained to work. At open weights pricing, the baked-in paranoia burning extra tokens verifying work gets significantly better results for less money.
In our benchmark runs, we've seen ~20% gains over competing harnesses just from changes that let the model lean into its RL training.
Last month, after burning through 61B DeepSeek tokens hunting bugs, here's where I've landed on CLI agents:
@cline - reasoning and tool use are on another level.
codex - solid tool use, decent reasoning.
@claude_code - great overall, but API calls randomly die in the middle of a hunt.
@opencode - good reasoning, but memory management needs work.
CodeWhale - good reasoning and tool use, but API reliability is hit or miss.
@Wrong_Stack - excellent orchestration, though API calls can be flaky sometimes.
Pace Capital retweeted
We are making the updated DeepSeek V4-Flash 0731 free in Cline.
This is the first flash model we've found performs at SOTA levels, and are excited for you to feel the new frontier.
1. npm i -g cline
2. Open /settings > Cline provider
3. Select deepseek-v4-flash
