Van Ngo @vanngo97

V1 is approved today btw 👌

Fable 5 is old news, but I'm still stoked that it helps me to build this immersive and interactive app to learn about the solar system 🤓

What should I do to improve this?

How many submissions did it take for your 1st app? Mine got rejected 3 times before being finally live today 😭 #appstore #ios

Here's the NY Times article everyone is talking about. A gift from me to you. 👀 nytimes.com/2026/06/10/mag…

i hooked my whoop to my work calendar to find which coworker gives me the most stress 🚨 thanks to fable, I reverse engineered whoop to pull per minute heart rate. nd matched spikes with cal events and attendees I now have a leaderboard and I think about it daily. few info masked for obvious reasons ;)

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@GoogleAIStudio Day 5 going strong 🥹

@briannekimmel A social macro tracker I guess 😁

There are no words.

Colours of the sky

books are so cool because there are NO FUCKING ADS IN THEM

@build_canada The 1st biosensor to capture multi-omic data from saliva and the molecular intelligence layer

world models are a sexy misnomer. @ylecun , @nvidia , and @drfeifei are all building world models. they are not building the same thing. lecun is working on cognitive architecture: a system that builds causal models of reality and plans inside them. nvidia is building simulation infrastructure: physics-based environments that train, evaluate, and run physical AI systems at scale. fei-fei li is building spatial intelligence: systems that understand and reason about physical space. same term. three different bets. three different timelines. bundling them into one category inflates the hype. it also hides where value actually accumulates. the simulation moat is the most obvious. native physical interaction data was scarce. synthetic environments filled the gap. but as robotics companies accumulate real interaction data at scale, that scarcity could end. the world model framing was a data poverty artifact. the spatial intelligence bet is the most grounded. fei-fei li is trying to give machines a persistent, accurate model of physical space: where things are, how they move, what they afford. narrow, reliable, deployable. the timeline is shorter. the cognitive architecture bet is the longest. what lecun is actually building requires three components: a causal model of how the world works, a forward simulator that imagines possible futures, and a pruning mechanism. a prior over which futures are worth simulating at all. that third component does not exist in any current system. it is the difference between prediction and genuine planning. three definitions. three timelines. three completely different implications for where value accumulates.

Silicon Valley is quietly running on Chinese open source AI models. Here are the receipts: → Cursor confirmed last month that Composer 2 is built on Moonshot's Kimi K2.5 → Cognition's SWE-1.6 model is likely post-trained on Zhipu's GLM → Shopify saved $5M a year by switching to Alibaba’s Qwen model. Airbnb CEO Brian Chesky has also said: "We rely a lot on Qwen. It's very good, fast, and cheap." And now Zhipu dropped GLM-5.1, an open source model that performs almost as well as Opus on coding benchmarks. 📌 More on the Anthropic + OpenClaw drama and what I'm learning about AI on the ground in China in my new post: creatoreconomy.so/p/the-all-you-…

Peter Yang @petergyang

3 months ago

As much as I love using Claude Max and ChatGPT Pro, I don't think these all-you-can-use AI subscriptions will last forever. Here's my new deep dive that covers: → Why Anthropic cut off OpenClaw access → How to run local models on your Mac → What I'm seeing on the ground in

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@sid_srk Sounds intriguing 🤔 would like to follow along to see what you're up to 👀

@jnananaa Can't agree with this approach more!! More often than not, I just want simple apps that do one thing well and I'm happy to pay one-time fee. For lightweight multilingual LLMs, Cohere Labs have Tiny Aya which might work well for your case

"I'm at my limit" emotional or claude?

@kathytzhou So cool and cute 🥰

Cursor is raising at a $50 billion valuation on the claim that its “in-house models generate more code than almost any other LLMs in the world.” Less than 24 hours after launching Composer 2, a developer found the model ID in the API response: kimi-k2p5-rl-0317-s515-fast. That’s Moonshot AI’s Kimi K2.5 with reinforcement learning appended. A developer named Fynn was testing Cursor’s OpenAI-compatible base URL when the identifier leaked through the response headers. Moonshot’s head of pretraining, Yulun Du, confirmed on X that the tokenizer is identical to Kimi’s and questioned Cursor’s license compliance. Two other Moonshot employees posted confirmations. All three posts have since been deleted. This is the second time. When Cursor launched Composer 1 in October 2025, users across multiple countries reported the model spontaneously switching its inner monologue to Chinese mid-session. Kenneth Auchenberg, a partner at Alley Corp, posted a screenshot calling it a smoking gun. KR-Asia and 36Kr confirmed both Cursor and Windsurf were running fine-tuned Chinese open-weight models underneath. Cursor never disclosed what Composer 1 was built on. They shipped Composer 1.5 in February and moved on. The pattern: take a Chinese open-weight model, run RL on coding tasks, ship it as a proprietary breakthrough, publish a cost-performance chart comparing yourself against Opus 4.6 and GPT-5.4 without disclosing that your base model was free, then raise another round. That chart from the Composer 2 announcement deserves its own paragraph. Cursor plotted Composer 2 against frontier models on a price-vs-quality axis to argue they’d hit a superior tradeoff. What the chart doesn’t show is that Anthropic and OpenAI trained their models from scratch. Cursor took an open-weight model that Moonshot spent hundreds of millions developing, ran RL on top, and presented the output as evidence of in-house research. That’s margin arbitrage on someone else’s R&D dressed up as a benchmark slide. The license makes this more than an attribution oversight. Kimi K2.5 ships under a Modified MIT License with one clause designed for exactly this scenario: if your product exceeds $20 million in monthly revenue, you must prominently display “Kimi K2.5” on the user interface. Cursor’s ARR crossed $2 billion in February. That’s roughly $167 million per month, 8x the threshold. The clause covers derivative works explicitly. Cursor is valued at $29.3 billion and raising at $50 billion. Moonshot’s last reported valuation was $4.3 billion. The company worth 12x more took the smaller company’s model and shipped it as proprietary technology to justify a valuation built on the frontier lab narrative. Three Composer releases in five months. Composer 1 caught speaking Chinese. Composer 2 caught with a Kimi model ID in the API. A P0 incident this year. And a benchmark chart that compares an RL fine-tune against models requiring billions in training compute without disclosing the base was free. The question for investors in the $50 billion round: what exactly are you buying? A VS Code fork with strong distribution, or a frontier research lab? The model ID in the API answers that. If Moonshot doesn’t enforce this license against a company generating $2 billion annually from a derivative of their model, the attribution clause becomes decoration for every future open-weight release. Every AI lab watching this is running the same math: why open-source your model if companies with better distribution can strip attribution, call it proprietary, and raise at 12x your valuation? kimi-k2p5-rl-0317-s515-fast is the most expensive model ID leak in the history of AI licensing.

Harveen Singh Chadha @HarveenChadha

4 months ago

things are about to get interesting from here on

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