https://github.com/dnc1994/jarvis

我最近也做了相关的实践，目前 scope 比上面这位要小得多，只负责管理三类 todo：ad hoc、recurring、project based。一个需求是它会基于 obsidian，因为我本来就是重度用户。

配合 *claw，现在不管走到哪，在 telegram 里就可以按照符合自己设想的信息架构的方式去管理 todo

我甚至感觉开源也没什么意义，agents 的厉害之处就在于每个人都可以高度定制自己用的方案。

周末无聊 vibe coding 了个玩具，模拟 EVA 里 MAGI 系统的三方表决。你输入一个提案，MELCHIOR、BALTHASAR、CASPAR 三个子系统会从各自人格出发思考然后投票，推理过程接了 Gemini 3.1 Flash Lite 实时流式输出，看起来挺有那味儿的。

https://github.com/dnc1994/magi

装 nanoclaw 的时候，发现启用新功能比如对接 telegram 不再是通过在配置里打开一个选项，而是让 agent 直接修改本地代码来实现功能。这一点很有意思，几乎就是 config as code 的彻底反面 — code as config。联想到一些生物学的类比，写了这篇文章。

以前软件的"基因"是固定的，所有人跑的是同一份 binary。但当 AI agent 开始直接改源代码本身，每个人跑的软件就开始各自突变、各自演化 — 这是软件的物种分化。

由此引出三个有意思的推论：只在你这台机器上出现、别人永远无法复现的 bug；版本号失去意义，取而代之的是追踪每个实例的演化谱系；以及代码最终可能优化到只有 agent 看得懂、人类再也无法理解的程度。

最后提了三个对 2027 年的预测：Verification-as-a-Service、Frozen Core 架构、Reverse-SaaS。

https://linghao.io/posts/software-speciation

一月底最后一个周六有了一个灵感，想做个解放双手，优化了 AirPods 录音，边散步边和自己对话的 App。打开 Cursor coding 了一天，第二天就出门去 SoHo 散步就用上了，然后就完全离不开了，一天不落用到今天，录了300多条录音，200多个不同地点，近100小时和自己的对话。
这两个半月工作之余的 side project 时间全放在了这个 App 的打磨上，和自己和它的关系也有忽近忽远的变化，有意思的是也用它自己记录了下来。最近在读荣格，说自性化的目标是走向完整，走向完整指的是充分体验所有情感，如何充分体验所有情感？我给的答案是记录。放下手机，出门散步，踏出的每一步，对自己说出每个字，周围记录下来的环境音，路上随手拍的照片，都是值得记录下来的此时此刻。
随手录音，AI转录，适配了各种麦克风，privacy first，取名叫 Yuho，Logo是个伦敦的大肥鸽，昨天刚刚上线了，欢迎试玩，有机会一起散步🚶 如果用着感觉还不错，记得向我要 Pro 的兑换码

https://yuho.io

https://arxiv.org/abs/2503.02113

The core idea:

Deep learning does not work because neural nets somehow escape generalization theory. It works because very flexible models can still generalize when they have soft inductive biases — preferences for simple, compressible, structured solutions.

Key points:

- 🧠 Overparameterization is not automatically a problem.
Having more parameters than data points does not necessarily mean the learned function is complex. Parameter count is a bad proxy for the complexity of the actual solution.

- 📈 Benign overfitting is not unique to neural nets.
Models can perfectly fit training data, even noisy data, while still generalizing on structured data. Similar behavior appears in linear models, Gaussian processes, high-degree polynomials, and other classical model classes.

- 🔁 Double descent is not just a modern deep learning anomaly.
The pattern where test error falls, rises, then falls again as model size increases also appears outside neural networks. It can be understood through effective dimensionality, compression, and the geometry of learned solutions.

- 📦 Compression is central.
A huge model can generalize if the solution it finds is simple or compressible. The rough intuition is:
expected error ≈ training error + complexity/compressibility penalty.

- 📚 Some older theories already help explain this.
PAC-Bayes and countable hypothesis bounds are more useful here than raw VC dimension, Rademacher complexity, or parameter counting, because they focus on which solutions are likely/simple rather than just how large the hypothesis space is.

- 🎯 The paper’s recommended lens:
Don’t only restrict what a model can represent. Instead, allow a very rich hypothesis space, but bias the learner toward simpler solutions that fit the data.

- ✨ What is still distinctive about deep learning?
Not overparameterization or double descent by themselves, but things like representation learning, in-context learning, broad cross-domain usefulness, and mode connectivity in loss landscapes.

My takeaway:

Deep learning’s famous generalization puzzles may not require rewriting the textbooks from scratch. They may require reading the right parts of the textbooks more carefully — especially the parts about priors, compression, and soft preferences over solutions.

关于层出不穷的各式 AI memory system 的一些思考：我们应该把更多的精力放在设计更好的 eval 上，从而让最强的 memory system 进化出来
https://linghao.io/posts/memory-systems-should-be-evolved

https://linghao.io/posts/taxonomy-differences-matter
以前觉得 taxonomy 只是无聊的分类学，开始做 LLM quality 以后几乎每天都在思考和跟同事争论 loss taxonomy 的细枝末节。这几天放假闲下来仔细想想，taxonomy 无处不在，至关重要。

https://alexzhang13.github.io/blog/2026/harness/

> A good harness is a harness that reduces unfamiliar problems to familiar ones and reduces complex problems to simple ones. In other words, even if the state s is out-of-distribution (OOD) to what any individual language model call was trained for, a good harness produces observations o that are locally in-distribution (LID), which we define as every individual LM call over this observation being in-distribution with respect to the training data.