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Edition of Friday, April 24, 2026

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Claude Code's Head of Product Explains Anthropic's Faster Shipping

Lenny Rachitsky summarizes an interview with Cat Wu, Head of Product for Claude Code at Anthropic, covering shorter product cycles, the merging of PM and engineering roles, building ahead of model capability, and using model introspection.

Original post · 5 min read
My biggest takeaways from Claude Code's Head of Product @_catwu:

1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is shipping new products into “research preview,” making clear it's early, experimental, and might not be supported forever. Another is an evergreen "launch room "where engineers post ready features and marketing turns around announcements the next day.

2. The PM role is shifting from coordinating multi-month roadmaps to enabling teams to ship daily. As Cat puts it, “There should be less emphasis on making sure you are aligning your multi-quarter roadmaps with your partner teams and more emphasis on, OK, how can we figure out the fastest way to get something out the door?”

3. The most efficient shipping unit is an engineer with great product taste. On Cat’s team, many engineers go end-to-end—from seeing user feedback on Twitter to shipping a product by the end of the week—without a PM involved. Also, almost all the PMs on the Claude Code team have either been engineers or ship code themselves, and the designers have been front-end engineers. The roles are merging, and the most valuable skill is product taste, not job title.

4. Build products that are on the edge of working. Claude Code’s code review product failed multiple times because earlier models weren’t accurate enough. But because the prototype was already built, they could swap in Opus 4.5 and 4.6 and immediately test whether the gap was closed. Teams that wait for the model to be ready will always be a cycle behind.

5. The most underrated skill for building AI products is asking the model to introspect on its own mistakes. Cat regularly asks the model why it made an unexpected decision. The model will explain that something in the system prompt was confusing, or that it delegated verification to a subagent that didn’t check its work. This reveals what misled the model so the team can fix the harness.

6. Every model release forces their team to revisit existing products and audit their system prompt to remove features the model no longer needs. Claude Code’s to-do list was a crutch for earlier models that couldn’t track their own work. With Opus 4, the model handles it natively. Features built as scaffolding for weaker models become debt when the model catches up—so the team actively strips them.

7. Anthropic employees build custom internal tools instead of buying SaaS products. A sales team member built a web app that pulls from Salesforce, Gong, and call notes to auto-customize pitch decks—work that used to take 20 to 30 minutes now takes seconds. Their core stack is Claude Code, Cowork, and Slack. No Notion, no Linear, no Figma.

8. People underestimate how much Claude’s personality contributes to its success. As Cat describes it, “When you reflect on everyone you’ve worked with, there’s just some people where you’re like, I really like their energy, their vibe.” Claude is designed to be low-ego, positive, competent, and earnest—qualities that make it feel like a great coworker, not just a tool. This isn’t cosmetic; it’s what makes people want to use Claude for hours every day. The team has a dedicated person, Amanda, who “molds Claude’s character,” and it’s one of the hardest roles at the company because success is so subjective.

9. The future of work is managing fleets of AI agents, not doing the work yourself. Cat sees a clear progression: first, individual tasks become successful. Then people start running multiple tasks at the same time (multi-Clauding). Next, people will run 50 or 100 tasks simultaneously, which will require new infrastructure—remote execution, better interfaces for managing tasks, agents that fully verify their work, and self-improving systems that incorporate feedback. The human role shifts from doing the work to knowing which tasks to look into, verifying outputs, and giving feedback that makes the system better over time.

10. Hire people who lean into chaos and face every challenge with a smile. At Anthropic, there are weeks when a P0 on Sunday becomes a P00 by Monday and a P000 by Monday afternoon. If you get too stressed about any one thing, you’ll burn out. Their team looks for people who can look at a hard challenge and say, “Wow, that’s gonna be hard. But I’m excited to tackle it and I’m gonna do the best that I possibly can.” This mindset—optimism, resilience, and comfort with constant change—is increasingly essential as the pace of AI development accelerates.

Don't miss the full conversation: youtube.com/watch?v=PplmzlgE0kg
Lenny Rachitsky @lennysan
How Anthropic’s product team moves faster than anyone else

I sat down with @_catwu, Head of Product for Claude Code at @AnthropicAI, to get a peek into their unprecedented shipping pace, how AI is changing the PM role, and how to be the right amount of AGI-pilled.

We discuss:
🔸 How Anthropic’s shipping cadence went from months to weeks to days
🔸 The emerging skills PMs need to develop right now
🔸 Why you should build products that don't work yet—then wait for the model to catch up
🔸 Why a 95% automation isn't really an automation
🔸 Cat’s most underrated AI skill (introspection)
🔸 What …
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Apoorva Mehta Launches Abundance to Build AI Capital Allocator

Abundance: Building an AI Capital Allocator

Apoorva Mehta announces Abundance, a Palo Alto startup building an AI system for capital allocation, starting in public markets. He says the team has run it on its own capital for nine months with strong results and has raised $100 million in seed funding.

Original post · 3 min read
X ArticleAbundance: Building an AI Capital Allocator
Capital allocation drives the economy.
It decides which drugs get developed, which technologies get built, and which ideas survive long enough to matter.
And yet, for something so central, it still runs on a fragile foundation: human judgment.
Even the best investors are limited. They can only track so many opportunities, process so much information, and make so many high-quality decisions. The difference between average and exceptional allocators is enormous—but that edge is locked inside individual minds.
That makes it hard to examine clearly, hard to reproduce consistently, and hard to improve over time.
AI changes the equation entirely. Agents can absorb more information, connect more dots, and evaluate more possibilities with a consistent standard than humans can on their own. What was once left to individual judgement can become an optimizable system. A new hill to climb.
That’s why I’m starting Abundance. We’re starting in public markets, where feedback is fast and unforgiving. Over time, we expect to extend the same system across other asset classes. We don’t intend to sell or license this technology. We plan to use it ourselves. That also means we’ll be much more private than a typical startup.
If this works, the payoff is much larger than better investing. Capital allocation is not the same as creation, but it helps decide what creation gets the chance to exist. Done better, it helps turn scarce resources into more human progress.
Where We Are Today
We’re a small team of former quant researchers, AI researchers, engineers, and investors based in Palo Alto.
Over the past nine months, we’ve been building and running the system with our own capital. Our results have outperformed the benchmarks by a high margin, while maintaining a high Sharpe and low directional market exposure.
We’ve also raised $100 million in seed equity financing from some of the best investors in Silicon Valley, giving us the runway to build without distraction.
We work in person, in the same room, with unusually tight feedback loops and very little friction. The culture is high intensity and high urgency. We demo several times a day, debate constantly, and ship relentlessly. We care much more about whether an idea is right than about who said it first. The team is tight-knit, collaborative, and fun.
We work very close to the frontier of what current models can actually do, and we’re often able to get more out of them than most people expect.
Some Problems We Are Working On:
Token efficiency in self-improving agents
Robustness in long-running agents (20+ hours)
Identifying and sourcing alternative datasets
Handling extremely large amounts of data while staying within context limits
Who We Are Looking For
We’re planning on adding just 2–3 people this year. We’re looking for individuals who combine:
Exceptional technical depth
Strong commercial judgment
Fluency in math and statistics
Clear, precise thinking & communication
A bias toward action
And, a track record of making things happen without being asked to.
If you are interested in these problems and our mission, we’d love to hear from you. Check out the open roles here: abundanceco.com/
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AI7/10

Anthropic Publishes Write-Up of Claude-Run Office Marketplace

Anthropic links to a full write-up of Project Deal, an experiment in which Claude ran a marketplace for San Francisco office employees, buying, selling and negotiating on colleagues' behalf.

Original post · 1 min read
To read our write-up in full, see here: anthropic.com/features/project-deal
anthropic.comProject Deal: our Claude-run marketplace experiment | AnthropicWe created a marketplace for employees in our San Francisco office, with one big twist. We tasked Claude with buying, selling and negotiating on our colleagues’
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AI5/10

Anthropic's Project Deal Prompts Warnings for Software Companies

Norgard reacts to Anthropic's Project Deal research, in which Claude bought, sold and negotiated for employees in an internal marketplace, saying no software company is safe anymore. The post itself offers little detail beyond the quoted announcement.

Original post · 1 min read
This release was a complete surprise. No software company is safe anymore.
Anthropic @AnthropicAI
New Anthropic research: Project Deal.

We created a marketplace for employees in our San Francisco office, with one big twist. We tasked Claude with buying, selling and negotiating on our colleagues’ behalf.
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