1
How I use LLMs to learn complex topics
用大模型拆解复杂概念，手把手教你高效自学新领域的实战思路
Article URL: https://laurentiugabriel.github.io/blog/articles/how-i-use-llms-to-learn/ Comments URL: https://news.ycombinator.com/item?id=49234675 Poi…
2
Ask HN: How are you using AI to learn?
看HN网友如何用苏格拉底式提问让LLM当导师，高效啃下硬核新知识。
It's time to start learning advanced topics again and I thought it'd be at least interesting to take advantage of LLM's to help me learn better or fas…
3
Longitudinal Random Forests for Sparse and Irregular Response Trajectories
新方法用随机森林处理稀疏、不规则纵向数据，精准建模响应轨迹变化。
arXiv:2607.21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points. Such…
4
Ask HN: How to get a non-technical friend into tech?
零基础想转行编程？看HN众网友如何为非技术朋友规划FAANG之路的第一步。
My friend wants to learn software engineering and eventually land a job at FAANG / big corp. Trouble is, they have zero engineering or leetcode experi…
5
The Feynman Technique Prompt: How to Make AI Explain Anything in 4 Layers of Depth
用费曼技巧四层提问法，让AI把任何复杂概念讲透，告别看了就忘的假学习。
You asked ChatGPT to explain self-attention. It gave you a paragraph that sounded right. You nodded. Two hours later, you couldn't explain it back to …
6
How do machines learn? Evaluating the AIcon2abs method
评估AIcon2abs方法的论文，直击机器学习核心机制，适合关注算法原理的读者。
arXiv:2401.07386v5 Announce Type: cross Abstract: This study expands on previous work that introduced the AIcon2abs method (AI from Concrete to Abstra…
7
Ask HN: What/how are you teaching your kids in the age of AI?
在AI轻易搞定一切时，还要不要逼孩子学编程基础？一位父亲的真实困惑与社区深度讨论。
I've been feeling more and more lost. Had plans to teach my 10yr python, maybe something fun with games or robotics. But then I look around and I see …
8
Rank-Learner: Orthogonal Ranking of Treatment Effects
ICML 2026论文提出Rank-Learner方法，用正交化实现治疗效果的精准排序，因果推断新突破。
arXiv:2602.03517v2 Announce Type: replace Abstract: Many decision-making problems require ranking individuals by their treatment effects rather than e…
9
Efficient Benchmarking Is Just Feature Selection and Multiple Regression
论文提出基准测试效率优化方法，发现核心就是特征选择加多元回归，简洁有力直击痛点。
arXiv:2605.25773v1 Announce Type: cross Abstract: Efficient benchmarking techniques aim to lower the computational cost of evaluating LLMs by predicti…
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LoMETab: Beyond Rank-1 Ensembles for Tabular Deep Learning
LoMETab将乘法集成从秩1扩展到秩r，为表格深度学习带来新突破
arXiv:2605.14365v1 Announce Type: cross Abstract: Recent tabular learning benchmarks increasingly show a tight performance cluster rather than a clear…