Doug Simpson

Insanely curious, 3x founder, currently @amazon.
Personally investing in: previously not possible, non-consensus, market, compounding advantage, team.

[Doug Simpson @dougsimp]

Similar story. Recommend getting a CBCT scan if not already. The challenge with CPAP is if you have UARS you will need high pressure and that may cause wake-ups especially in someone that is likely sensitive to micro-arousals. The jaw advancement devices may or may not help depending on the CBCT but also risk cause bite changes and potentially TMJ. Depending on the CBCT some of the surgeries are not bad, palate expansion (EASE) from Dr. Kasey Li is not bad. One option for getting a CBCT is https://t.co/Ebd954R6qM

[Doug Simpson @dougsimp]

@simonlast Is it 1-way or 2-way sync? The docs only mention 1-way.

[Kevin Kwok @kevinakwok]

Cursor and SpaceX: In search of a complete loop

Quick notes on the deal:
- the new AI lab meta of needing to own product and model in coding
- why spacex and cursor need each other to close the loop
- beyond ipo timing why this structure is interesting

https://t.co/dZ2sBHqRXC

[Doug Simpson @dougsimp]

@Jason I'm in

[Doug Simpson @dougsimp]

@felixrieseberg Who do I harass to enable link sharing for Chats using advanced research? Annoying that you can share all chats except those that use research. Even private link sharing would be appreciated.

[Doug Simpson @dougsimp]

Seems like OpenAI will need to invest here to support their consumer HW roadmap, same for Google (and Apple), and Meta. Glasses, smart speakers, etc. Grok appears to be investing here. Also would think that the data on increasing use of voice by consumers will push them in this direction (devs as early adopters).

[Doug Simpson @dougsimp]

@NotionHQ @claudeai Are you planning to increase the rate limits of the API to support this use case?

[Lenny Rachitsky @lennysan]

My biggest takeaways from @simonw:

1. November 2025 was an inflection point for AI coding. GPT 5.1 and Claude Opus 4.5 crossed a threshold where coding agents went from “mostly works” to “almost always does what you want it to do.” Software engineers who tinkered over the holidays realized the technology had become genuinely reliable.

2. Mid-career engineers are the most vulnerable—not juniors, not seniors. AI amplifies experienced engineers by letting them leverage decades of pattern recognition. It also dramatically helps new engineers onboard. Cloudflare and Shopify each hired a thousand interns because AI cut ramp-up time from a month to a week. But mid-career engineers who haven’t accumulated deep expertise and have already captured the beginner boost are in the most precarious position.

3. AI exhaustion is real and underestimated. Simon runs four coding agents in parallel and is mentally wiped out by 11 a.m. He’s getting more time back, but his brain is exhausted from the intensity of directing multiple autonomous workers. Some engineers are losing sleep to keep agents running. This may just be a novelty issue, but the underlying dynamic—that managing AI amplifies cognitive load even as it reduces labor—is a real tension. Good companies will manage expectations rather than expecting 5x output indefinitely.

4. Code is cheap now. This simple idea has profound implications. The thing that used to take most of the time—writing code—now takes the least. The bottleneck has shifted to everything else: deciding what to build, proving ideas work, getting user feedback. Since prototyping is nearly free, Simon often builds three versions of every feature when he’s getting started.

5. The “dark factory” is the most radical experiment in AI-assisted development happening right now. A company called StrongDM established a policy: nobody writes code, nobody reads code. Instead, they run a swarm of AI-simulated end users 24/7—thousands of fake employees making requests like “give me access to Jira”—at $10,000 a day in token costs. They even had coding agents build simulated versions of Slack, Jira, and Okta from API documentation so they could test without rate limits.

6. "Red/green TDD" is the single highest-leverage agentic engineering pattern. Having coding agents write tests first, watch them fail, then write the implementation, then watch them pass produces materially better results. The five-word prompt “use red/green TDD” encodes this entire workflow because the agents recognize the jargon.

7. “Hoarding things you know how to do” is one of Simon's other favorite agentic engineering patterns. Simon maintains a GitHub repo of 193 small HTML/JavaScript tools and a separate research repo of coding-agent experiments. Each one captures a technique, a proof of concept, or a library he’s tested. When a new problem arrives, he can point Claude Code at past projects and say “combine these two approaches.”

8. The "lethal trifecta" makes AI agent security fundamentally unsolved. Whenever an AI agent has access to private data, exposure to untrusted content (like incoming emails), and the ability to send data externally (like replying to email), you have a lethal trifecta. Prompt injection—where malicious instructions in untrusted text override the agent’s intended behavior—cannot be reliably prevented. Simon has predicted a “Challenger disaster” for AI security every six months for three years. It hasn’t happened yet, but he’s pretty sure it will.

9. Start every project from a thin template, not a long instructions file. Coding agents are phenomenally good at matching existing patterns. A single test file with your preferred indentation and style is more effective than paragraphs of written instructions. Simon starts every project with a template containing one test (literally testing that 1 + 1 = 2) laid out in his preferred style. The agent picks it up and follows the convention across the entire codebase. This is cheaper and more reliable than maintaining elaborate prompt files.

10. The pelican-on-a-bicycle benchmark accidentally became a real AI benchmark. Simon created it as a joke to mock numeric benchmarks—get each LLM to generate an SVG of a pelican riding a bicycle, and compare the drawings. Unexpectedly, there’s a strong correlation between how good the drawing is and how good the model is at everything else. Nobody can explain why. It’s become a meme: Gemini 3.1’s launch video featured a pelican riding a bicycle. The AI labs are aware of it and quietly competing on it.

Don't miss our full conversation: https://t.co/ghZZeyvWBZ

[Doug Simpson @dougsimp]

Claude confirmed that I have a problem: The user has 1,483 tabs across 164 Chrome windows — this is a serious scale problem.

[Doug Simpson @dougsimp]

@KyleSamani Agree. These are examples of: identify the bottleneck, vertically integrate to solve it, take on the hardest problems, and question everything. Is it necessary? Does it have to be done this way? What is possible within the limits of physics?

[Doug Simpson @dougsimp]

@KyleSamani I wrote about this in Jan 2023, part of personal context across device form factors (desk, mobile, glasses, home).

[sunny madra @sundeep]

“The token cost to build a production feature is now lower than the meeting cost to discuss building that feature.”

Don’t think, just do.

[Tomasz Tunguz @ttunguz]

“Since last November, 100% of my code has been written by Claude Code. I have not manually edited a single line, shipping 10 to 30 PRs per day.”
Boris Cherny, creator of Claude Code, ships 20-30 pull requests per day. Major code changes, not typo fixes. He runs five parallel AI instances, each on a separate branch.

Compare that to a traditional engineer : 3 PRs per week. Cherny isn’t 10% more productive. He’s 30x more productive.

That productivity gap compounds at the company level. Anthropic generates ~$5 million per employee. Cursor, $3.3 million. Midjourney, $2 million. Traditional SaaS considers $200-300k strong. A 10-20x difference.

One explanation : communication overhead. The math follows Metcalfe’s Law. Each new team member adds n-1 new connections. Coordination drag doesn’t grow linearly. It explodes.

Now consider what AI does to this equation.

A traditional 150-person organization runs four layers deep. The org chart creates 11,175 potential communication channels. Meetings multiply. Alignment decays.

An AI-enabled team producing equivalent output might need 30 people. Communication channels drop to 435. A 96% reduction.

[Doug Simpson @dougsimp]

@KyleSamani At the same time: 3 months from data center to laptop: https://t.co/xJKm0zvNHd

[Tomasz Tunguz @ttunguz]

This week, Alibaba released Qwen3.5-9B, an open-source model that matches Claude Opus 4.1 from December 2025. It runs locally on 12GB of RAM. Three months ago, this capability required a data center. Now it requires a power outlet.

[Doug Simpson @dougsimp]

@TheGregYang Might be histamine intolerance (higher histamine in dried fruit)

[Doug Simpson @dougsimp]

@SawyerMerritt Thanks! https://t.co/8SZDIorRC8

[Doug Simpson @dougsimp]

@TheGregYang Typically takes several days for ketone levels to rise enough to feel good when starting keto. Use Keto Mojo to check your ketone, glucose and GKI levels.

[Harry Stebbings @HarryStebbings]

90% of the funding going into robotics today will go to zero.

A lot slinging checks with little idea.

[Gavin Baker @GavinSBaker]

Why are value and macro investors so comfortable making highly confident prognostications about AI despite their manifest ignorance?

I don't recall many tech/growth investors penning long, bearish, highly confident and utterly ignorant analyses about energy in 2022/2023.

[Alex Krüger @krugermacro]

The Stablecoin Regulation Tracker

Pretty cool

Link to interactive site: https://t.co/qkdKtQVH8f