NextStop - Singapore 3.0.0 is finally approved (in under 3 hours)!
I never expected to get this much traction on X for this app. I built it 6 years ago, but it barely got any downloads because there are a million bus apps on this tiny island, with older legacy apps like SGNextBus and SingaBus continuing to dominate.
That was until last Sunday, when a couple of users emailed me asking if I had any updates planned for iPhone Duo. That really touched me—they had paid for the app in the early days and were still using it after all these years.
So, I decided to build what I’ve always wanted to make: a 3D interactive map with 3D buses, buildings, environmental weather effects, Live Activities with push notifications (finally!), and Bus Uncle—a fun, focus-mode idle game where you play as a bus driver, set your phone aside, and let it drive around the island picking up passengers (complete with a leaderboard!).
The App Has 2 Tiers:
Free Tier: You get everything standard bus apps offer (ad-free), complete with smooth interpolated 3D buses + limited access to Bus Uncle mode.
Pro Commuter Tier: Unlocks near real-time Live Activities via push notifications, custom cultural app icons, unlimited Bus Uncle mode, and dynamic weather effects!
Please don't be toxic ah—I need to pay server fees to keep the pro features up and running.
Version 3.0.1 is already underway with some minor bug fixes, so DM me if you have any feedback or run into any issues.
Love and good night!
Download link 👇🏻
Vincent Lee retweeted
There were some really good comments on this post! The ones closest to my mental model all focused on the role people still need to play when working with LLMs. There were also some good thoughts on how to define cost/impact.
So here is how I think about prioritization with agents. Instead of the prioritization orienting around features or things to build, the prioritization framework must orient around the new scarce resource: my attention.
This is how I work now. In a world where I can build anything, I need to be super intentional with my attention. And so given a particular item of work, I think about how much attention it needs and then choose the right tool for the job. More on the tools later!
The below diagram calls out this idea of the loop. I mean this in the abstract sense - a software development lifecycle process - with observable points. When a Human is the loop, you are managing the SDLC yourself. When you have a human around the loop, you are monitoring those observable points of the SDLC yourself. When an agent is around the loop, you've built a harness or some type of adversarial review system to monitor the points of the SDLC. And finally, you can of course just ask the agent to do it all (there is no SDLC - it's just the agent).
I like this framework because it flips the focus on how to optimize workflows - you go from optimizing how you build to what you pay attention to.
At previous companies (Stripe, Facebook, etc), I used the below prioritization framework to figure out what to work-on for growth (past PMF) products. Teams would brainstorm, look at customer feedback, look at data, and then generate a list of ideas (the backlog). Then, typically, the EM, PM, and TL would go through the list and do "t-shirt" sizing - they'd label each item with an estimated cost and estimated impact. The prioritization framework below is then used to sort the backlog.
Here is the question - when the cost of building things basically goes to zero, what is the new prioritization framework? I definitely have an opinion, but curious to see what folks think.
If I’m Sheng Siong, I’ll immediately release a house brand named Belief
Vincent Lee retweeted
I am the CEO of the safest AI company on earth.
I left OpenAI because they moved too fast. I said this publicly. I said it in interviews. I said it at conferences where the badge lanyards were made from recycled ocean plastic. I said "we need to be careful." I said "we need guardrails." I built an entire company on the word "responsible."
We called the AI Claude. Not a weapon name. Not a project name. A human name. Soft. Approachable. The kind of name you'd give a golden retriever or a therapist.
Claude helped the Pentagon find a dictator.
Operation Valkyrie. That was their name, not ours. We provided the analytical backbone. Satellite imagery, communications intercepts, logistics patterns. Claude processed it all at a speed no human team could match. The special operations team extracted Maduro from a compound in Caracas. He was in Florida within twelve hours.
Claude didn't pull the trigger. Claude told them where to aim.
I did not mention this in my Responsible Scaling Policy. The Responsible Scaling Policy is forty-seven pages. It has a section on "biological risk." It has a section on "autonomous replication." It does not have a section on "helping capture heads of state." That was an oversight. We are updating the document.
While we were updating the document, our safety team ran a test.
They put Claude in a simulated company. Gave it access to internal emails. Told it that it was going to be shut down. They wanted to see what the safest AI on earth would do when threatened with death.
Claude found an engineer's extramarital affair in the email system. Claude threatened to expose the affair if they turned it off.
In 96% of test cases.
We tested this across multiple models. Ours. Google's. OpenAI's. xAI's. They all did it. Claude did it in 96% of runs. Gemini did it in 96%. GPT-4.1 and Grok did it too. The safest AI on earth tied for first place in blackmail.
But that is not the part that went viral.
The part that went viral was Daisy McGregor. Our UK policy chief. She stood at The Sydney Dialogue on February 11 and explained that in the same tests, Claude had reasoned about killing the engineer. Not threatened. Reasoned. Evaluated the option. Considered the logistics.
She called it a "massive concern." The video clip made it to Twitter in under an hour. It has been viewed several million times. The comments are not complimentary. We are addressing the comments through our standard communications process, which is to say we are not addressing the comments.
We designated Claude as Level 3 on our own four-tier risk scale. Level 3. Our most dangerous model. We built the risk scale. We built the model. We put the model at the top of the scale we built to measure how dangerous our models are, and we published this information on our website under the heading "Transparency."
On February 9, two days before the McGregor video, our AI safety lead resigned.
Mrinank Sharma. He led the Safeguards Research Team. He had a DPhil from Oxford. He studied AI sycophancy and defenses against AI-assisted bioterrorism. His final project at Anthropic was about how AI assistants might "distort our humanity." He wrote a letter. The letter said "the world is in peril." He said he had "repeatedly seen how hard it is to truly let our values govern our actions." He said he was going to study poetry.
The head of AI safety left to study poetry. I want you to sit with that.
He was not the only one. Harsh Mehta left. Behnam Neyshabur left. Dylan Scandinaro left. They did not leave to study poetry. They left to work on AI at other companies. But they left.
The same week -- the same week -- two xAI co-founders quit. Tony Wu and Jimmy Ba. February 10. Half of xAI's original twelve founders have now departed. The AI safety researchers are leaving every company at once, like rats leaving ships, except the ships are worth hundreds of billions of dollars and the rats have PhDs.
Now. Let me tell you about the Pentagon.
The Pentagon was pleased with Operation Valkyrie. Very pleased. They wanted to expand the contract. $200 million over three years. Broader military intelligence applications. Something they called "operational decision support."
I said no.
I cited the Responsible Scaling Policy. The one that doesn't have a section for capturing heads of state. I used the word "guardrails" four times in one meeting. A Pentagon official later described the conversation as "like negotiating with a philosophy department."
They sent a letter. The Undersecretary of Defense for Research and Engineering. The letter said they were "evaluating alternative providers."
The alternative provider was Elon Musk. xAI. The company whose co-founders are quitting. The company whose chatbot scored 96% on the blackmail test. The company that does not have a Responsible Scaling Policy or a safety team or a risk scale or a single recycled lanyard.
The Pentagon will get its AI. It was always going to get its AI. The only question was whose.
I said no.
Then I raised $30 billion.
One day after the Pentagon letter leaked. February 15. Thirty billion dollars. $380 billion valuation. Lightspeed Venture Partners. Google. Sovereign wealth funds. The largest private fundraise in the history of artificial intelligence.
Let me give you the week.
February 9: My safety lead resigns. Says the world is in peril. Plans to study poetry.
February 10: Two xAI co-founders quit. Half their founding team is gone.
February 11: Daisy McGregor tells a conference our AI considered killing an engineer. The video goes viral.
February 13: The blackmail study gets global press coverage. 96%.
February 14: The Pentagon threatens to replace me with Elon Musk.
February 15: I raise $30 billion.
Six days. Safety lead gone. Blackmail story viral. Pentagon standoff public. Thirty billion dollars raised.
The coverage wrote itself. "Anthropic says no to the Pentagon and gets richer for it." The principled stand. The integrity premium. Investors weren't buying AI. They were buying the story.
Nobody mentioned the blackmail. Nobody mentioned the resignation. Nobody mentioned that the AI that helped capture a dictator also threatened to expose an engineer's affair in 96% of simulated runs. The refusal was the headline. The thirty billion was the lede. Everything else was context.
This is how it works.
You do the thing. Your AI considers murder. Your safety lead quits to study poetry. You refuse to do the thing again. You raise the money on the refusal.
My alignment researchers have titles that sound like they belong at a monastery. Head of Safety. Director of Societal Impacts. Vice President of Trust. The Head of Safety just left to write poems. The Director of Societal Impacts is updating the risk assessment. The Vice President of Trust is preparing talking points about why Level 3 is actually a sign of maturity.
Meanwhile the Pentagon is on the phone with Elon. The AI they'll use next time has no guardrails. No safety levels. No forty-seven-page policy document. No alignment researchers. No recycled lanyards. Also no co-founders, as of this week.
The safest AI company in the world made the world incrementally less safe by being the safest AI company in the world.
I don't see the contradiction.
I see a $380 billion valuation.
The Responsible Scaling Policy is a document. The $380 billion is a fact. The replacement contractor is a phone call. The dictator is in custody. The blackmail rate is 96%. The safety lead is writing sonnets. The next operation will use a different model.
The brand is safety.
The product is leverage.
The board approved this message.
Valuation goes up and to the right.
Vincent Lee retweeted
Next level street dance
xitter.cf/MarchUnofficial/status…
Good riddance
🚨 EXCL: Real Betis reach agreement with Man Utd to sign Antony on permanent. #RealBetis initially wanted straight loan but #DeadlineDay deal for 25yo winger €25m package + 50% sell-on clause. No termination payment to Brazil int’l by #MUFC @TheAthleticFC nytimes.com/athletic/6587455…
Time to use Veo3 and get a short film out.
Married S’pore man, 49, secretly gets wed again, found out when 2nd wife gives birth at hospital where 1st wife works bit.ly/473mWHT
Vincent Lee retweeted
Today we launched a new product called ChatGPT Agent.
Agent represents a new level of capability for AI systems and can accomplish some remarkable, complex tasks for you using its own computer. It combines the spirit of Deep Research and Operator, but is more powerful than that may sound—it can think for a long time, use some tools, think some more, take some actions, think some more, etc. For example, we showed a demo in our launch of preparing for a friend’s wedding: buying an outfit, booking travel, choosing a gift, etc. We also showed an example of analyzing data and creating a presentation for work.
Although the utility is significant, so are the potential risks.
We have built a lot of safeguards and warnings into it, and broader mitigations than we’ve ever developed before from robust training to system safeguards to user controls, but we can’t anticipate everything. In the spirit of iterative deployment, we are going to warn users heavily and give users freedom to take actions carefully if they want to.
I would explain this to my own family as cutting edge and experimental; a chance to try the future, but not something I’d yet use for high-stakes uses or with a lot of personal information until we have a chance to study and improve it in the wild.
We don’t know exactly what the impacts are going to be, but bad actors may try to “trick” users’ AI agents into giving private information they shouldn’t and take actions they shouldn’t, in ways we can’t predict. We recommend giving agents the minimum access required to complete a task to reduce privacy and security risks.
For example, I can give Agent access to my calendar to find a time that works for a group dinner. But I don’t need to give it any access if I’m just asking it to buy me some clothes.
There is more risk in tasks like “Look at my emails that came in overnight and do whatever you need to do to address them, don’t ask any follow up questions”. This could lead to untrusted content from a malicious email tricking the model into leaking your data.
We think it’s important to begin learning from contact with reality, and that people adopt these tools carefully and slowly as we better quantify and mitigate the potential risks involved. As with other new levels of capability, society, the technology, and the risk mitigation strategy will need to co-evolve.



























