mR^2

🥷 Tech × Invest × Ride

[池建强 @sagacity]

这篇长文我要强烈推荐一下：

https://t.co/6VrJELTr0K

X 上关于 Jev 发布的热议铺天盖地，但大多数都没说到点子上：“一个 JSON 分类器，1200 万次浏览？没错，我们确实在泡沫里。”普通 LLM 生成“90% 置信”这样的文字；它产出这几个词的概率，并不等于它有 90% 的概率是对的。然而我们恰恰在这个模式上搭建了欺诈筛查、内容审核、路由分派和风险评估：为逐 token 的生成付费，然后把一个未经验证的置信度声明，当成软件可以据此行动的概率。

Jev 的主张是：保留预训练 LLM 的知识，但把生成出来的置信度声明，换成直接从内部表示读出的决策概率。这些概率是针对真实结果训练出来的。给它共享的状态（state）、问题（questions）和允许的答案，它并行返回各概率分布，不生成任何文本。[1] 对于这一整类应用，这同时解决了两个问题：决策信号的可靠性，以及产生信号所浪费的计算。

只有一个问题：它不开源权重，TypeSafe 也拒绝分享他们的研究……所以，我（作者）来（尽力）代劳。

[Miles Ma @miles_mazy]

https://t.co/ECB42N6gYS

[Roan @RohOnChain]

Don't waste 2 years learning to build LLMs like Claude & ChatGPT.

Stanford just dropped a 2 hour course on how to build LLMs from scratch.

• 00:00 - LLM tokenization
• 25:44 - how LLMs decode user prompts
• 35:40 - training pipeline of LLMs
• 1:16:47 - LLM architecture from scratch

Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs.

Bookmark this & give 2 hours today, no matter what. Then read the article below.

[Koichi @fujibee]

https://t.co/R9AUtFE6kk

[阿良｜AI 工作流 @RealYDT]

Google 这次把本地 RAG 的门槛直接打下来了。

原本要占 31GB 内存的向量索引，现在压到只要 4GB。

1000 万份文档，一台普通电脑就能跑。

更狠的是：

→ 单个向量压缩 16 倍
→ 检索速度比 FAISS 快 10%–19%
→ 不用训练
→ 不用重建索引
→ 不需要 GPU 集群
→ 全程本地运行

这意味着以后做知识库、文档搜索、个人 AI 助手，可能根本不需要昂贵的云服务。

Google 开源的不是一个小优化，而是在重新定义本地向量搜索的成本。

[xIA @xiathis]

En vez de 2 horas de Netflix esta noche, mira esta clase magistral de 40 min del fundador de una empresa china de IA valorada en más de $20B

La explicación más clara que he visto sobre enjambres de agentes y sistemas de IA a gran escala.

[nini @nini_incrypto_]

https://t.co/JQRjbrMPfC

[0x_Miko @Mikocrypto11]

https://t.co/HjLiqmtrjo

[Khairallah AL-Awady @eng_khairallah1]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

this is one of the best workflows I've seen in a long time

in this video she breaks down exactly how most people are using Claude:

- the 14% you lose to CLAUDE.md before typing a word
- the plugins that 95% of users have never installed
- the workflows that run without you typing a single prompt
- why typing one prompt and closing the tab is leaving 90% on the table

if you've been using Claude for months and still start every session from scratch, you have at least 28 untouched features. probably 30

instead of another show tonight, watch this

make sure to bookmark it before it gets lost in your feed

full guide in the article below

[Movez @0xMovez]

Google Cloud AI engineer just showed how they go from idea to deployed app at Google in 30-minutes using Claude.

26-minutes. free. by Google AI team.

one person + Claude + Google Cloud = a full engineering org running on a laptop.

worth more than any $500 vibe-coding course.

[Thariq @trq212]

https://t.co/MXt5XS4xBX

[Garry Tan @garrytan]

https://t.co/XTqXqWuQn5

[Khairallah AL-Awady @eng_khairallah1]

🚨 INSTEAD OF WATCHING NETFLIX TONIGHT.

Spend 1 hour with this.

Obsidian + Claude Code = 24/7 personal operating system.

Works while you sleep.

The people who build this tonight will never work the same way again.

Watch it and Bookmark it now.

[Khairallah AL-Awady @eng_khairallah1]

🚨 Anthropic's own team just showed how to actually use Claude Code properly.

30 minutes. free. the person who created Claude Code.

watch the workshop. bookmark it.

worth more than every $500 course you almost bought.

you've been using Claude without knowing 40 of its commands.

Then read the guide below.

[NeilXbt @neil_xbt]

Andrej Karpathy could have charged $10,000 for this course.

He put it on YouTube.

The man who built Tesla Autopilot from scratch.
Co-founded OpenAI.
Understands AI at a level most engineers at Google and Meta never reach.

Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts.

Then dropped it for free.

The gap between people who watch it this week and those who save it for later is not 2 hours.

It is everything those 2 hours quietly unlock for the rest of your career.

[墓碑科技 @mubeitech]

马斯克飞到西雅图，落地就把星链的整个领导层全开了。
当时的星链是个十足的烂摊子。
成本贵了10倍，产量只有需求的十分之一。
离目标差了整整两个数量级。

马斯克放话：“我受够了，这就是瓶颈，我亲自解决。”
他点将Mark Juncosa，SpaceX早期的顶尖人才。
这是个纯正的火箭科学家，压根没碰过卫星。
马斯克带上这支绝对信任的突击队，直接接管了西雅图。

没有交接，全员进作战室。
他们开始推演那套极度硬核的“算法”。
抛开经验，回到卫星设计的第一性原理。
对着图纸逼问每一个细节。
为什么要保留这个零件？
为什么这两个部件离这么远？
为什么要消耗这么多能量？
为什么非得用这种制造工艺？

一群完全外行的火箭专家，靠着疯狂的紧迫感死磕极高标准。
短短几个月，他们彻底跨越了那两个数量级的鸿沟。
硬生生把产品做了出来。
如果现在把星链单独拆分，这已经是一家估值数百亿美元的巨兽。
破局不需要所谓的行业老手。
只需要能把常识和效率逼到极致的人。

[Elon Musk @elonmusk]

Starship is the most powerful moving object ever made

[0xMarioNawfal @RoundtableSpace]

ANTHROPIC PAYS $750,000 A YEAR FOR ENGINEERS WHO UNDERSTAND WHY AI WORKS.

STANFORD JUST PUT THE SAME KNOWLEDGE ON YOUTUBE FOR FREE.

WATCH IT THIS WEEKEND. NOT EVENTUALLY. THIS WEEKEND.

[Khairallah AL-Awady @eng_khairallah1]

This 25-minute Claude Code workshop by Anthropic's own applied AI team will teach

you more about Claude Code best practices and making your AI tools actually work together than everything you've scrolled past this year.

Bookmark this & watch, no matter what.

Then read the guide below.