AI Daily Digest - Naval AI List - 2026年04月07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: 13
- 筛选比例: 43.3%
1. @fchollet
原文链接: https://x.com/fchollet/status/2041310893414502595
作者: François Chollet (@fchollet)
互动数据: ❤️ 3 | 🔄 0 | 👁️ 205
原文内容
One thing about DL researchers that has always been surprising to me, is that a lot of them have never been exposed to forms of learning other than fitting the parameters of a curve via gradient descent, and are even unable to conceive that there might exist other options
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2. @Teknium
原文链接: https://x.com/Teknium/status/2041308904060313895
作者: Teknium (e/λ) (@Teknium)
互动数据: ❤️ 14 | 🔄 1 | 👁️ 648
原文内容
If you forgot about this gem hermes cooked up for me
📎 引用推文 - @RajaPatnaik
Very cool stuff out of @NousResearch. They open-sourced a system that lets Hermes agents evolve themselves — no GPU training required.
It uses GEPA to automatically improve skills, prompts, and tool descriptions. Here’s how it works: https://t.co/t217M2r8Zn
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3. @0xDevShah
原文链接: https://x.com/0xDevShah/status/2041216961762496900
作者: Dev Shah (@0xDevShah)
互动数据: ❤️ 15 | 🔄 2 | 👁️ 835
转发自: @Teknium
原文内容
what used to be a complete company is now a skill in hermes. @Teknium really cooked with this one.
hermes is a learning harness. it will watch itself solve something a couple of times and improve its skills with each subsequent iteration. if you have an engineering or
📎 引用推文 - @NousResearch
Introducing the Manim skill for Hermes Agent.
Manim is an engine for creating precise programmatic animations for mathematical and technical explainers, made famous by the @3blue1brown channel. https://t.co/nyNeNthhZB
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4. @ZainanZhou
原文链接: https://x.com/ZainanZhou/status/2041244652309999974
作者: Zainan Victor Zhou (@ZainanZhou)
互动数据: ❤️ 7 | 🔄 0 | 👁️ 434
转发自: @Teknium
原文内容
Oh, @NousResearch ’s Hermes Agent works amazingly good, and much less buggy.
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5. @bensig
原文链接: https://x.com/bensig/status/2041236952998171118
作者: Ben Sigman (@bensig)
互动数据: ❤️ 437 | 🔄 42 | 👁️ 30,300
转发自: @menhguin
原文内容
My friend Milla Jovovich and I spent months creating an AI memory system with Claude. It just posted a perfect score on the standard benchmark - beating every product in the space, free or paid.
It’s called MemPalace, and it works nothing like anything else out there.
Instead https://t.co/trAUZyMHIe
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6. @gidian83427
原文链接: https://x.com/gidian83427/status/2041294596097937830
作者: Gidian Malkavoy (@gidian83427)
互动数据: ❤️ 6 | 🔄 1 | 👁️ 577
转发自: @Teknium
原文内容
@Teknium I’m actually doing something nobody else is doing with Hermes and it’s honestly been next level. Licensed therapist / private practice owner. This is actually saving lives. I’m even underplaying it bc don’t want to dox. Absolutely best tool I’ve ever used. Kudos to this team.
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7. @WillA5412
原文链接: https://x.com/WillA5412/status/2041294312999109070
作者: Will (@WillA5412)
互动数据: ❤️ 6 | 🔄 1 | 👁️ 626
转发自: @Teknium
原文内容
@NousResearch’s Hermes agent is running sooo smoothly on my 10 year old Raspberry Pi, wasn’t expecting that 🤯
it picked up my OpenClaw install and configuration automatically and imported all the settings seamlessly
Congrats @NousResearch @Teknium
💡 核心观点
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8. @bindureddy
原文链接: https://x.com/bindureddy/status/2041298966726521323
作者: Bindu Reddy (@bindureddy)
互动数据: ❤️ 39 | 🔄 5 | 👁️ 2,797
转发自: @ClementDelangue
原文内容
The biggest winners of last week were open-source and cheaper models
MiniMax 2.7
Qwen 3.6
GLM 5 -
Kimi 2.5
Usage is going exponentially up on…. you get about 75-80% performance as the closed models that are 10x more expensive
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9. @JoelDeTeves
原文链接: https://x.com/JoelDeTeves/status/2041272967607583005
作者: Joel - coffee/acc (@JoelDeTeves)
互动数据: ❤️ 59 | 🔄 4 | 👁️ 2,675
转发自: @ClementDelangue
原文内容
Okay, I have no clue how he did it but this is the best 27B distill I’ve used to date.
@DJLougen absolutely cooked with this one.
Note: this is NOT the Hermes Agent reasoning-trace distilled version, but it works incredibly well with Hermes. I haven’t had a single failed tool
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10. @HaihaoShen
原文链接: https://x.com/HaihaoShen/status/2041287564917870803
作者: Haihao Shen (@HaihaoShen)
互动数据: ❤️ 46 | 🔄 7 | 👁️ 4,712
转发自: @ClementDelangue
原文内容
🚩Gemma4 INT4 models are now available! @huggingface @GoogleAI @IntelAI
https://t.co/gmXtFlf1YO
https://t.co/ALKlXfpKBM
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11. @Altimor
原文链接: https://x.com/Altimor/status/2041199915943215163
作者: Flo Crivello (@Altimor)
互动数据: ❤️ 609 | 🔄 38 | 👁️ 77,600
转发自: @ClementDelangue
原文内容
Okay this one seems real. First time ever an OSS model beats Sonnet 4.6(!!) on our evals. Now begins vibe testing, but this is promising.
📎 引用推文 - @arcee_ai
Today we’re releasing Trinity-Large-Thinking.
Available now on the Arcee API, with open weights on Hugging Face under Apache 2.0.
We built it for developers and enterprises that want models they can inspect, post-train, host, distill, and own. https://t.co/jumuYehJdo
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12. @Vtrivedy10
原文链接: https://x.com/Vtrivedy10/status/2041297155730571637
作者: Viv (@Vtrivedy10)
互动数据: ❤️ 10 | 🔄 1 | 👁️ 648
原文内容
this is why we should all be deeply promiscuous across models 👀
every model has its own spiky intelligence, but all we care about is giving users a great experience as fast and cheap as possible
often the best way to do that with a bespoke multi-model harness with the best
📎 引用推文 - @_Evan_Boyle
Introducing RubberDuck in Copilot CLI: a new builtin subagent for cross model family escalations. Sonnet will now proactively solicit input from GPT 5.4 and vice versa.
In our most difficult benchmark subsets, this results in a massive 5% improvement in resolution rates https://t.co/aWvkblIoAy
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13. @_akhaliq
原文链接: https://x.com/_akhaliq/status/2041293489879523519
作者: AK (@_akhaliq)
互动数据: ❤️ 9 | 🔄 1 | 👁️ 1,894
原文内容
gradio.Server
Any Custom Frontend with Gradio’s Backend
build with your own frontend framework entirely like React, Svelte, or even plain HTML/JS, while still benefiting from Gradio’s queuing system, API infrastructure, MCP support, and ZeroGPU on Spaces
blog: https://t.co/EesCcvtnKi
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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: 2026-04-07 00:27 UTC
