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week 3 of building my couples app and realizing i need to target the woman of the relationship because they enjoy taking care of the pet pig a lot more and can pressure their man into buying the app for them
trust me , it works 😂
set a wedding date and now i’m realizing maybe it’s time i start paying myself more then $5,000 a month i have a ton of cash laying in my business account cause ive essentially been paying myself minimum wage for the last 2 years
$5,000 CAD. thinking about bumping it up double
RT @LotusDecoder: 李博杰的 10 月文章。 《Agent 持续学习的困境：为什么 Reasoner 不是真正的 Agent？》 喜欢的一些观点摘录： （训练模型）这些成本远低于大多数人的想象。更重要的是，现在的训练框架已经非常成熟了：trl、verl、A…
Believing is seeing
This is an extremely exciting initiative on Saturday. I'm especially excited about the fact that they're creating evals for their task! Bummer it's only in SF. Folks who are there should give this a shot!! I was asked to share the event, but I wanted to find a moment to write some thoughts: I get that the caricature of the tools below is just a caricature, but one must add that building a good system and building a hackathon system call for opposite tradeoffs. A good system is maintainable and portable into the future, even as the underlying technology shifts. It revolves around separation of concerns. A good tool thus prevents you from premature hand-engineering, even though the bitter lesson tells you that hand-fitting *will* in fact help you in the short term. In other words, if my goal is to build a throwaway artifact in a few hours, I will shamelessly consider low-level tricks and duct tape. The only way they can fail me is if I'm not a very good prompt engineer for some reason or if there are just too many settings and models to handle at once by hand. All that said, I'm super excited about the resources that this will produce. We need better public evals and my understanding is that this could produce one.
Asst professor @MIT EECS & CSAIL (@nlp_mit). Author of https://t.co/VgyLxl0oa1 and https://t.co/ZZaSzaRaZ7 (@DSPyOSS). Prev: CS PhD @StanfordNLP. Research @Databricks.
Good writeup. The West uses plenty of methods and money to subsidize supply though. They just Have Worse Outcomes. Imo the main difference is that the CPC is teleological, while the USG is homeostatic. You need to cultivate tech trees, not just pass politically viable budgets.
re the original post. Chynese are too poor and low-productivity to compete with Mississippi. They just ain't got any money to spend, so it stands to reason that GDP is so low. Kimi with search gives a breakdown of annual spending of a typical student in Chongqing (urban pop. 22M).
I'm afraid these who believe AGI is very far away must probably be thinking in terms of "will LLMs scale to AGI?" rather than "is humanity closer to AGI?" Like they completely forget to account for upcoming breakthroughs, and they certainly don't think about how existing tools accelerate the pace of these breakthroughs. They see GPT-3, GPT-4, GPT-5, and picture in their heads: "will GPT-7 be AGI?". Then they realize that, no, it wouldn't, obviously. And they then project AGI as being many years away. If I'm not mistaken, it took Karpathy about 1 year to implement NanoGPT. Now, take a moment to imagine a model capable of passing this prompt: "write a working clone of GPT-2 in plain C, except with..." As soon as such a thing exists and is broadly available, LLMs will be nearing their end. We'll instantly enter a transition era between this thing and the next thing, because labs all around the world will be doing ultra fast research and experimentation, trying new systems, reasoning about the very nature of intelligence. And the result of that will be a truly general intelligence system. I honestly think this will catch many off guard, in particular these working on major AI labs, because they're getting comfortable with the LLM curve. They think the LLM curve is THE intelligence curve. But it isn't. The intelligence exponential was driven by step breakthroughs. It started with life, passed through bacteria, fish, dinosaurs, humans, fire, agriculture, writing, mathematics, the printing press, steam engines, electronics, computers, the internet, and now LLMs. Each thing accelerated progress towards the next thing. LLMs aren't the last thing, but they're the thing before the last. When I say "AGI around end of 2026" I'm not talking about GPT-7. I'm talking about XYZ-1, which will be implemented by a team with access to GPT-6...
Kind / Bend / HVM / INets / λCalculus