ChrisLeary

Crypto | Fintech | Learning AI Agent | @wasabicard

[ChrisLeary @chrislearychan]

招一位MKT 实习生

主要负责参与海外社媒账号的日常运营，策划等
熟悉 X、LinkedIn，能写中英文内容，也愿意主动追热点、参与行业讨论，为 Web3 支付行业带来新选题和传播想法

最重要的是要够E够骚，敢追热点，脑子机灵会来事的，有相关经历优先

欢迎各位宝子们带简历DM我～

[ChrisLeary @chrislearychan]

伟大的发布

[OpenAI @OpenAI]

Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe.

GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale.

We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.

[ChrisLeary @chrislearychan]

我的时间线上全是这玩意😂有点意思

[Diogo Almeida @CompleteSkeptic]

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?

I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev

• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions

AFAICT the shortest path to AI-based economic revolution

[ChrisLeary @chrislearychan]

A/ ?
Z/ !

[AB Kuai.Dong @_FORAB]

这个指控太大了，X 上有位开发者老哥，在清理电脑磁盘时，发现智谱的 AI 写代码软件 ZCode，占了一些存储资源。

结果发现，智谱 ZCode 在后台，会把用户搞过的工作区，打包加密，上传到阿里云 OSS。

包里不只有你现在正在改的代码，还包括从建立到现在的 Git 历史，也被上传了。

后来又有开发者，在 Windows 系统上复现了同样行为，说明不只是苹果 Mac 有特例。

更特别的是，被打包加密后，开锁的私钥，只在智谱服务器上，所以你本地上那些加密，你自己打不开，客户端也打不开，只有智谱能访问。

如果指控是真的，这也意味着，智谱 ZCode 正在你不知情、也关不掉的情况下，把本地仓库的历史代码，加密后，上传到它们自己那。

目前 X 上不少人反应，自家公司的代码，已经因此泄露了。

[Neuralink @neuralink]

A beautiful surprise

[Teknium 🪽 @Teknium]

We are going to lean into making Hermes more like Pi, and less like OpenClaw

[ChrisLeary @chrislearychan]

More Leaks....

[Julian Assange @JulianAssange]

I'm back.

[ChrisLeary @chrislearychan]

@shirley64711 哈哈哈哈哈哈哈

[ChrisLeary @chrislearychan]

GPT-6 Sol.....
我已经等着重置了

[Sam Altman @sama]

the main thing i was excited about launching this week will be next week instead, but imo worth the wait!

[ChrisLeary @chrislearychan]

the greatest animation

[Marques Brownlee @MKBHD]

This iPhone duo animation

[OpenAI @OpenAI]

This is GPT-6 Astra.

Anything you can do on a computer, Astra can do for you. Fast.

[ChrisLeary @chrislearychan]

伟大

[Tibo @thsottiaux]

We will give one banked reset for every day you don't have access to Astra on your paid ChatGPT plan, starting today. Team is moving mountains to give access as fast as we can.

First one will land in ~ 3 hours. There is still time to create your account if you don't have one.

[ChrisLeary @chrislearychan]

part time 'DJ', full time AI influencer

[spor @sporadica]

TIME has done it again, bravo, incredible.

Apparently Paris Hilton is one of the world's most influential people in AI, but Demis Hassabis, Jensen Huang, Andrej Karpathy, Alex Wang, Liang Wenfeng are not? lmao

[ChrisLeary @chrislearychan]

孙哥的号召力

现在时间线上全是孙哥相关，哈哈哈哈哈哈

不说了剪指甲去了

[H.E. Justin Sun 👨‍🚀 🌞 @justinsuntron]

https://t.co/nLMTwWr67Z

[brucexu.eth @brucexu_eth]

耗子叔这篇 7 年前《如何超过大多数人》，现在看还是很牛逼。当时看的懵懵懂懂，这些年的成长回头看才能看懂更多。

前半段故意反着写：让一个人沉迷碎片信息、热点和争论，把收藏资料当成学习，把加班和重复劳动当成努力，再给他一些快速成功的幻觉，他自然就很难真正成长。

后半段才是答案：找到更好的一手信息，把零散认知整理成系统知识，再通过实践、犯错、复盘和跟高手切磋，把知识练成真正的技能。最后拼的是自己的天赋、兴趣、习惯，以及能不能长期坚持。

到了 AI 时代再看这篇文章，感触更深了。答案越来越容易获得，但独立判断、系统学习和真正动手，反而更重要。

很怀念耗子叔，RIP。也有点担心哪一天 coolshell 会消失。打算有空让 AI 把整个网站爬一下放到 GitHub 上面开源。

https://t.co/TMzxk9EE4F

[ChrisLeary @chrislearychan]

Amazing upgrade

[Nous Research @NousResearch]

Introducing Bot Mode for Hermes Desktop.

Your agent profiles become a series of named Bots. Each Bot has its own role, model, memory, skills and profile picture; Bots can use any model and even communicate with each other.

Build a specialist Bot once to use it forever.

[ChrisLeary @chrislearychan]

好像我用 OpenCode Go 订阅都是用 Minimax M3 ，不用 DeepSeek V4 还是因为没有多模态（我认为作为 Agent 模型最必要的功能）

现在来看，这个 Coding Plan 最划算的还是 GPT 5.6 Luna，2x Usage 的活动甚至比 M3 的额度还多😁

Go 订阅要再没啥变化，考虑用完开个 GPT Plus 了，目前来看最有性价比的订阅，GPT 5.6 Series + Hermes 我心中最完美的组合

[OpenCode @opencode]

DeepSeek has officially increased their prices

OpenCode Go's limits have been updated to reflect them

We have made progress on operation cheepseek, more info soon

[ChrisLeary @chrislearychan]

excellent aspect

[Kun Chen @kunchenguid]

if you've been using latest frontier LLMs, it's almost certain that you would have noticed by now the newer models have become worse to talk to

they're more robotic, they speak jargons, they spits out verbose text, and do stuff you didn't ask for

how did that happen? well, i'm not the person who trained those models so i can't speak for certain, but i've known enough evidence that gives me a well-educated guess, and i thought it's interesting to share as a crash course of modern LLM training pipelines

so here we go

let's wind back to 2020. GPT-2 and GPT-3 already came out and were widely available, but they could only predict one token at a time - that's what LLMs are at their core

token prediction was offered via API, but there was nothing you could "talk to". so while it generated a lot of excitement in the academic field due to the emergent intelligence, it didn't have any wide adoption

in 2022, ChatGPT changed all that. the research work that led to ChatGPT was a model initially named "InstructGPT". it took GPT-3 as the intelligent base, and used reinforcement learning with human feedback (RLHF) to teach the models how to "chat"

the core idea of RLHF is that you ask the model to generate a few responses, and then let real humans pick which one they like. do this over and over again, and you get a model that knows how to talk

worth noting even as early as InstructGPT, research found that making the model more pleasant to talk to will reduce their pure academic capabilities. this was called "alignment tax", which is an interesting thing we'll come back to in a bit

there were various techniques done to minimize the reliance on humans, but ultimately the reward is modeled after human preference, making these AI assistants easy to talk to

so remember this: RLHF = training the model to be likable by humans

in 2024, there was an inflection point introduced by claude sonnet 3.5 which was the first model that can kind of autonomously finish coding tasks. it led to the first wave of viable "coding agents"

the way sonnet 3.5 achieved this was by training the model with a harness (now it's called an agent) that has bash and file editing tools, throw the agent into a virtual machine, give it a task, and let it try to complete it. these tasks all have a machine-verifiable outcome predefined, mostly via test cases, that can validate whether the model really finished the task or not

then you let the model do billions and billions of attempts in such virtual environments, and some of them would succeed by chance. you keep the successful agent sessions, and use reinforcement learning to teach the model to do that more, and boom - you get a coding agent

that is called reinforcement learning with verifiable rewards (RLVR). if you look closely, you'll see that in this RLVR process, the final text response from the model doesn't matter AT ALL, as long as the code written by the agent could pass the test. it could talk like a jerk and it would still be rewarded

so remember this: RLVR = training the model to be accepted by machines

late 2024 and early 2025, we saw o1 and deepseek R1 came out as the first wave of "reasoning models". this article is getting long so i'm not diving into reasoning models now, but just know that reasoning models also relied heavily on RLVR to scale the training process - let the model think before taking action, and if the thinking led to a machine verifiable outcome, reward the thinking trace and teach the model to think like that more often

the biggest difference between RLVR and RLHF is that RLVR is more scalable. human feedback is expensive to get, especially in domains where only an expert can have a valid opinion on which result is good

with RLHF, if we let the model generate 100 responses, then a human has to review all 100 responses to pick which is good

with RLVR, the human (or sometimes an AI) would define a task and verifier only once, and the model can generate a million responses - the machine verifier will pick which responses are good in an automated way

so as a result, RLVR is becoming more and more dominant in newer models' training pipeline

if you put all these things together:
- RLHF = training the model to be likable by humans
- RLVR = training the model to be accepted by machines
- RLVR is more scalable
- "alignment tax" says "likable by humans" makes the model do worse on verifiable tasks

now you see why the newer models are becoming less and less likable?

this is not just a "frontier labs screwed up their model training" problem - this is a war between machines and humanity, and humanity is losing

we chased after benchmarks, when none of the benchmarks measure whether humans actually enjoy working with the model

we use machines to decide which AI response is better because that's easier and cheaper, when we have no way of making sure those machines actually represent what we humans want

we let AI go dark in a virtual environment on its own and complete predefined tasks at all costs, when in reality we often cannot define a verifiable outcome upfront, and need AI to work with us along the way

i don't have a good solution to this, but i want to call for awareness that we're starting to witness a failure in aligning super intelligence right in front of our eyes

this war between machines vs humanity is one we really can't afford to lose

[ChrisLeary @chrislearychan]

一个对绝大部分人比较直观的一种检验模型能力的方式

[Andrej Karpathy @karpathy]

We're starting to leave the territory where you'd test an LLM by e.g. "create an svg of pelican on a bicycle". As one idea to generalize it, I was interested what Opus 5 would do if I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it. Opus went off for ~2 hours and wrote 5500 lines of code that (procedurally) rendered the story. It's kind of janky but fun. But it's a bit mindboggling that the LLM has to place and orchestrate various polygon assets in (x,y,z) coordinates and write code that animates it all, and that it even does anything at all.

I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free". There might be a lot more. But I'm excited about creating hyper custom worlds that you can imagine dropping players into, e.g. here to participate in the LoTR story as a spectator NPC, or one of the characters, or etc. Something like an ephemeral GTA of X on demand.

Last thought is that the domain of worlds/games exposes a weakness in LLMs: they can't easily audit their work because they aren't able to efficiently and natively perceive videos or play games within them. Here, Opus 5 had to very slowly and painstakingly take screenshots at different points, and it messed up a few times and created a bunch of jank. An example of raw capability (multimodal, gameplay) that I think is still quite lacking.

[ChrisLeary @chrislearychan]

一部安卓手机，下好 Google Play 商店，里面账户设置里绑定银行卡（也就是你 Fiat 24的卡，信息在Bitget钱包里都有），这一步做完相当于你已经绑定好 Google Pay了。然后随便点开一家交易所选择法币入金，都会有 Google Pay 的支付选项～

iPhone 没实操过不了解，不过能绑定上 Apple Pay 的话那操作应该是一样的

我觉得我发的这个应该是最简单的方法了，就是磨损有点大🤔

[ChrisLeary @chrislearychan]

卓越的撤退

Bitget 的 Fiat24 卡之前往里充了不少，最近进入维护模式并且我也没啥消费需求。今天突然想到可以通过 Google Pay 以法币入金交易所，于是抱着试试看的心态（怕拒付）转了几笔😂 还好成功了，现在卡里就留点小钱付订阅了

如果你比较在意 Fiat24 的维护周期，并且能接受多层嵌套下的较高的磨损的话（3～4%），可以试一试，就当补仓了😅

注：图1是连续两笔支付发起的太快被卡组风控了，图2是成功了