AI Daily Digest - Naval AI List - 2026年04月09日
每日精选 Naval AI List 推文，聚焦人工智能、机器学习和技术趋势。
统计概览
- 总推文数: 30
- 24小时内推文: 30
- 精选推文数: 11
- 筛选比例: 36.7%
1. @alexandr_wang
原文链接: https://x.com/alexandr_wang/status/2041909376508985381
作者: Alexandr Wang (@alexandr_wang)
互动数据: ❤️ 7685 | 🔄 854 | 👁️ 2,359,263
转发自: @ylecun
原文内容
1/ today we’re releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. 🧵 https://t.co/fThDXdsxwB
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2. @ScottWu46
原文链接: https://x.com/ScottWu46/status/2042019018471829562
作者: Scott Wu (@ScottWu46)
互动数据: ❤️ 153 | 🔄 20 | 👁️ 19,563
转发自: @alexatallah
原文内容
btw you can see this effect live on OpenRouter:
total # tokens has gone from 1.78T / wk one year ago to 27T / wk today (15.2x).
but % usage of the frontier / most expensive model has gone from 22% one year ago (Sonnet 3.7) to just 4% today (Opus 4.6).
economics works! https://t.co/yKKi4UB6zn
📎 引用推文 - @ScottWu46
Total amt of flops across all the GPUs in the world has grown about 3x per year for the last few years. Total amt of inference demand has probably grown ~10x per year. What happens when those lines cross?
The econ answer is: when demand > supply, price goes up. That might be
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3. @dwarkesh_sp
原文链接: https://x.com/dwarkesh_sp/status/2042034292491255836
作者: Dwarkesh Patel (@dwarkesh_sp)
互动数据: ❤️ 8 | 🔄 0 | 👁️ 1,148
原文内容
I found the discussion about Darwin especially fascinating. Why did it take till 1859 to lay out an idea whose essence every farmer and herder since antiquity must have observed?
The Origin of Species was published in 1859. Principia Mathematica was published in 1687, two https://t.co/uZVrgBJF8w
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4. @akshat_b
原文链接: https://x.com/akshat_b/status/2042003975600283774
作者: Akshat Bubna (@akshat_b)
互动数据: ❤️ 23 | 🔄 3 | 👁️ 1,338
转发自: @charles_irl
原文内容
One cool thing here (besides robots!) is we were able to get sub-5ms latency in some cases using direct UDP connections.
Last year, I hacked together something to prove it works (https://t.co/l0x8o51wiK) and then @_gongy rewrote it into a Rust library.
📎 引用推文 - @modal
The future of artificial intelligence is physical.
@physical_int runs robotic control inference on Modal with >2x lower latency than the lag between your brain and your finger. https://t.co/YvfBdSTc5t
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5. @atmoio
原文链接: https://x.com/atmoio/status/2041935999945625745
作者: Mo (@atmoio)
互动数据: ❤️ 4075 | 🔄 452 | 👁️ 326,775
转发自: @brickroad7
原文内容
Claude Mythos is Delusional https://t.co/7ORqBw22AT
📎 引用推文 - @AnthropicAI
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.
It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
https://t.co/NQ7IfEtYk7
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6. @lateinteraction
原文链接: https://x.com/lateinteraction/status/2042019472798564851
作者: Omar Khattab (@lateinteraction)
互动数据: ❤️ 47 | 🔄 1 | 👁️ 1,440
原文内容
For the last few months, I’ve begun to increasingly dislike the vibe I see in tpot around AI.
Nuance and substance are more absent than ever, and are replaced by shallow hype, including now by many of your largest favorite accounts in this space.
in case it needs to be a meme: https://t.co/sfzu74ZzGP
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7. @lateinteraction
原文链接: https://x.com/lateinteraction/status/2042025859003920574
作者: Omar Khattab (@lateinteraction)
互动数据: ❤️ 18 | 🔄 3 | 👁️ 1,122
原文内容
I think it really helps to say, suppose Claude Code is Einstein x N for N areas you care about.
Don’t you still need to know what you actually want to achieve before you hire him? Don’t you still need basic separation of concerns?
Or else that Einstein is just going to do what
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8. @poetengineer__
原文链接: https://x.com/poetengineer__/status/2041998061153427861
作者: Kat ⊷ the Poet Engineer (@poetengineer__)
互动数据: ❤️ 559 | 🔄 50 | 👁️ 29,065
转发自: @menhguin
原文内容
one direction from this that excites me: a learning base instead of a storage one: not for what you already know, but for what you don’t.
made one for deep reading of plato’s timaeus.
2 things i carried over: non-rag, indexed fs, and /raw-is-sacred to separate sources from https://t.co/OKMen4SiZZ
📎 引用推文 - @karpathy
LLM Knowledge Bases
Something I’m finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating
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9. @jxmnop
原文链接: https://x.com/jxmnop/status/2042021891385508016
作者: dr. jack morris (@jxmnop)
互动数据: ❤️ 138 | 🔄 2 | 👁️ 8,257
原文内容
he’s offended because they didn’t benchmax ARC AGI :(
📎 引用推文 - @fchollet
The new model from Meta is already looking like a disappointment: overoptimized for public benchmark numbers at the detriment of everything else. Knowing how to evaluate models in a way that correlates with actual usefulness is a core competency for AI labs, and any new lab is
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10. @ramez
原文链接: https://x.com/ramez/status/2041946766598402459
作者: Ramez Naam (@ramez)
互动数据: ❤️ 79 | 🔄 7 | 👁️ 8,848
转发自: @brickroad7
原文内容
Anthropic’s Mythos does not appear to show any acceleration of ECI. After normalizing Anthropic’s internal ECI with @EpochAIResearch ’s public ECI, it’s clear that the two metrics are extremely close, and that Mythos is pretty much on trend, just slightly above GPT 5.4. /1 https://t.co/kZXk5L4XpG
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11. @teortaxesTex
原文链接: https://x.com/teortaxesTex/status/2042018982912201166
作者: Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) (@teortaxesTex)
互动数据: ❤️ 17 | 🔄 1 | 👁️ 1,798
原文内容
Waiting to see the first paper that shows meaningful continual training for 1B tokens in an agentic loop.
📎 引用推文 - @daniel_mac8
babe, wake up.
a new form of continual learning just dropped.
> in-place test-time training. https://t.co/VMblTTmcEQ
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关于本摘要
本摘要来自 Naval AI List 的每日精选推文，由 AI 自动筛选和整理。
筛选标准:
- 技术深度：新的方法论、工具使用技巧
- 实用性：可立即应用到工作流
- 时效性：24小时内发布
- 独特性：观点新颖非泛泛而谈
- 可操作性：提供具体步骤或工具
最后更新: 2026-04-09 00:26 UTC
