Ryan

[mousepotato @iluciddreaming]

AI Agent 终于可以免费上网搜索了。

wigolo：本地优先的 MCP 服务器，给 AI agent 完整的网络搜索和抓取能力，零 API 费用。

- 18 个搜索引擎同时跑
- 自动抓取和解析网页
- 本地 ML 重排序
- 结果自动缓存
- 遇到反爬网站自动切换无头浏览器

$0/次查询，不需要任何 API key。

[Santiago @svpino]

How to become GOD-LEVEL with Large Language Models.

Here are 50 hands-on projects with solutions that will teach you how Large Language Models work.

You don't need to solve all 50, but if you do, you'll be at the top 0.01% of the field.

It's all Python + Pytorch + SciKit-Learn + Pandas + Numpy + Matplotlib + Seaborn.

Here are the 50 problems from the book (link below):

Tokenization

1. Three tokenization schemes
2. Book lengths in characters, words, and tokens
3. Pandas frequency tables of token lengths
4. Token lengths in characters and bytes
5. Is tokenization compression?
6. Tokenization and compression in different languages
7. Translating between tokenizers

Embeddings

8. Distribution of cosine similarities
9. Sequential cosine similarity
10. Sequential number cosine similarity
11. Network graphs of cosine similarities
12. RSA to compare GPT-2 & BERT embeddings
13. Word similarity via distance and cosine
14. Linear semantic axes
15. Analogy vectors

Output logits

16. Softmax probability distributions
17. Probabilistic token selection
18. Token prediction accuracy
19. LLM loss function
20. Perplexity over sequences, texts, and models
21. Predict token position with linear and logistic regressions
22. Evaluating models with HellaSwag
23. Measuring language biases

Transformer outputs

24. Cosine similarities within and across layers
25. Category selectivity via cosine similarity
26. Current layer = previous layer + adjustments
27. Impact of layer-specific noise and scaling
28. Effective dimensionality of hidden layers
29. Hidden state dimensionality reduction
30. Sentiment analysis with decision trees
31. Logit lens
32. Patching hidden states in indirect object identification

Attention

33. QKV weights characteristics
34. QKV activation characteristics
35. Raw and softmax attention scores
36. Characteristics of attention adjustment magnitudes
37. Token prediction and attention KL divergences
38. Laminar profile of RSA and category selectivity
39. Token frequency, attention adjustments, QK^T
40. Downstream impacts of head silencing
41. Patching heads in IOI

MLP

42. MLP weights and activations characteristics
43. Characterizing the MLP progression
44. Grammar tuning in MLP projections
45. Minkowski distance, mutual information, and token positions
46. Statistics-based lesioning in MLP neurons
47. Supervised probing with XGBoost
48. "Can" vs. "can't" classification via logistic regression
49. Successive median-replacement of MLP activations
50. Recommender systems with MLP projections

Book link below.

[Wayen @wayen_ai]

Ilya Sutskever 曾断言，只要读懂那 30 篇奠基论文，就能掌握人工智能 90% 的精髓。但面对枯燥的数学公式，大多数人很难将其转化为可运行的代码。

最近在 GitHub 上发现一个叫 Sutskever 30的开源项目，用纯 NumPy 把 Ilya 推荐的 30 篇奠基性论文全部实现了一遍，已 100% 完成。

每个实现都不依赖深度学习框架，只用 NumPy 从零构建：
1.配合 Jupyter Notebook 交互式学习，随时改代码看效果
2.自带合成数据，打开就能直接跑，无需下载数据集
3.覆盖 RNN、LSTM、Transformer、ResNet、VAE、RAG、Scaling Laws 等全部核心架构

GitHub：https://t.co/n4uWTnRPGX

只需要装好 NumPy 和 Jupyter，就能挨个 notebook 跑通，从字符级 RNN 一路看到通用人工智能理论。

想真正吃透 AI 底层原理、不再只会调包的朋友，建议直接 Star 收藏，跟着敲一遍代码，比看一百篇解读文章都管用。

[Mohit Goyal (Harness arc) @ByteMohit]

As an AI Infrastructure Engineer

Please learn:
- GPU architecture, VRAM fundamentals, CUDA & memory hierarchy
- Quantization (INT8/FP8/4-bit), batching & continuous batching
- vLLM, TensorRT-LLM, SGLang, llama.cpp & inference optimization
- KV caching, speculative decoding, prefix caching & token throughput
- Distributed training (DDP, FSDP, DeepSpeed, ZeRO)
- Model serving (Triton, vLLM, KServe, Ray Serve, SGLang)
- Kubernetes, Docker & GPU orchestration
- NCCL, InfiniBand & high-speed networking
- Multi-GPU & multi-node inference
- LoRA, QLoRA, PEFT & fine-tuning pipelines
- Vector databases, embeddings & RAG pipelines
- Prompt caching, semantic caching & cost optimization
- Observability (OpenTelemetry, Prometheus, Grafana, Langfuse, Phoenix)
- LLM evaluation, benchmarking & A/B testing
- Model routing & fallback strategies
- MCP, AI agents & workflow orchestration
- Data pipelines, Kafka & streaming inference
- Security, guardrails & prompt injection mitigation
- CI/CD for ML (MLOps), MLflow & model registries
- Linux, networking & storage fundamentals

[kozue @0xkozue]

https://t.co/CVNHS65aIt

[Aaron Lemke @aaronlemke]

I put all my RL creatures into the same scene.
Feels like there is some kind of interesting game design here. Tending to / training your RL menagerie.
Made with @threejs @webgl_webgpu MuJoCo from @GoogleDeepMind trained on @runpod

[Aaron Lemke @aaronlemke]

Using Fable 5 to add fur and a new voice to my RL creature. The voice is based on the neural network activation (pictured top left). Made with @threejs @webgl_webgpu @runpod trained with JAX and MuJoCo from @GoogleDeepMind

[Jordan Nanos @JordanNanos]

Recently people have caught on to the fact that some of the people taking home millions every year working at an AI lab aren’t actually working on the core pretraining team, or even touching the model weights. They’re just an engineer! They don’t even do math!

Instead they’re building the infrastructure to serve the models to hundreds of millions of people.

Try running an open model with vLLM or SGLang on k8s. Hard enough on its own.

Then add load balancing, autoscaling, logs and metrics. Then add performance optimizations: pd disagg, MTP, KV aware routing, KV offload, etc. And now make the load balancing global across a fleet of datacenters with diverse hardware (even just a few different generations of NVIDIA GPUs)

The result is an unbelievably complicated system that only a few people can keep in their head and reason about.

But when they do, and they find a 1% performance improvement that touches millions of users and billions of $ worth of hardware, it certainly pays for itself

[John Schulman @johnschulman2]

PPO had a second wave in the LLM era for reasons unanticipated by the original paper
- the importance-ratio objective fixes biases from numeric error, async training, and forward pass noise
- the clipping objective affects entropy through a mechanism that we didn't know about at the time of publication (DAPO, https://t.co/sBo9DeFS5Y)

[Yong Zheng-Xin @yong_zhengxin]

Joining @openai next month!

after seeing people's reaction to Alisa's post about her experience, I also wrote down some of the surprising things I wish I know before my research scientist job search: https://t.co/vyFV6lYpWD

[Arnesh @Arnesh_24]

She's joining OpenAI and made her interview prep notes complete free.

Absolute legend.

https://t.co/Oo146nWsQZ
https://t.co/hWgrrYzdFl

[Harnoor Singh @iHarnoorSingh]

we have had high school dropouts in SF doing research in areas like post training / reasoning / RL

you can as well, this is indeed one of the most help blog to land in a frontier lab

top learning resources:
LeetCode 75 / Neetcode Blind 75
Stanford CS336: Language Modeling from Scratch
Self-Attention & Transformers
The Illustrated GPT-2
Backpropagation
Introduction to Policy Gradient for LMs
Lightweight Guide to understanding GRPO and RL principles
How to Scale Your Model

[Alisa Liu @alisawuffles]

I'm joining OpenAI next week!🥹 The job search turned out to be really challenging but also super rewarding, so I wrote a small blog to share what I learned along the way and hopefully make the process a little less mysterious for the next person. https://t.co/6FigSBdenD

[lele @ethan_an16]

这个妹妹太卷了，20 岁自学两年计算机进入微软做 AI 工程师。

看了下她的 Github 仓库确实有点东西，她把她这两年学习计算机以及 AI 的经验，通通毫无保留的分享在了仓库，值得我们学习回味。

上大学的新生以及在校大学生，真的包括我们这种已经工作的老油条也应该学习。
https://t.co/kLs9aTpbXg

[DAIR.AI @dair_ai]

Great paper on long-term memory for LLM agents.

(bookmark it)

Coarse summaries drift and unconstrained updates corrupt, so AtomMem makes the unit of memory small.

A Fact Executor pulls high-value atomic facts out of long interactions, organizes them into hierarchical event structures and temporal user profiles, then activates an associative memory graph at retrieval time to connect fragmented pieces.

It reports state-of-the-art on the LoCoMo multi-session benchmark while staying cheap enough to deploy.

Paper: https://t.co/F73NhNdcMR

Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

[Phoenix Yin @Phoenixyin13]

如果满分10分，这篇来自麻省理工学院的论文的SMT的idea与writing我真的会给到8分。

论文提出 Supervised Memory Training 的想法是用 Transformer 当超级老师，先并行提炼出每一时刻最该记住的记忆状态，然后只用一步监督学习教 RNN，结果是训练完全并行，梯度路径从 O(T) 缩短到 O(1)，长距离依赖明显学得更好。

之前训练 RNN ，我会觉得它像教一个人背一整本长小说，必须从头到尾一页一页慢慢背，背错了前面还会影响后面。

这次的SMT先让Transformer把整本书的关键记忆点提炼好，然后只教 RNN 从第 t 页该记住什么，翻到第 t+1 页该怎么更新记忆，每一步可以同时教，就学得又快又好。

让RNN重新能高效训练和扩展，极其适合需要固定内存、边处理边推理的场景，比如Agent，手机，机器人，或者实时AI等等。

全文最有 mechanistic 味道、最值得看的部分，我认为是Fig 9 的 compression-as-scaling-axis 和 Fig 14 的 memory 几何。

我们可能重新拥有既 expressive 又高效的循环模型了，不再被迫全靠 Transformer 的 KV cache 吃显存。

不知道这会不会让RNN重获新生，但这篇论文的idea感觉比较有趣。

[Kafka @kafkasudo]

徐亦达教授（Prof Richard Xu），香港浸会大学数学系教授，“TadReamk Limited”创始人

他在 GitHub 上只有一个真正爆款项目：`machine-learning-notes`（9663 stars，1767 forks），一部 2000+ 页的机器学习、概率模型和深度学习的幻灯片合集，附带视频链接

除此之外的 13 个仓库，大多是无描述、无更新、无代码的“三无产品”。他拥有 5347 个 followers，但关注 0 人——绝对的知识输出者，社交黑洞

[Saiyam Pathak @SaiyamPathak]

Bookmark this: Local LLM resources for 2026

- llama.cpp: inference + GGUF
- Hugging Face Hub: model cards + weights
- Ollama: easiest local start
- r/LocalLLaMA: hardware + benchmarks
- Ahead of AI: deep explainers
- KubeSimplify: DGX Spark series

Pick one. Build this week.

[Karan🧋 @kmeanskaran]

Avg job description for AI/ML Engineers in 2026

- Python programming, SQL, NoSQL
- ML, Sklearn, computer vision, NLP, Transformers, PyTorch
- FastAPI, Docker, Kubernetes, CI/CD, Cloud deployment
- FAST PACED capabilities
- Gen AI libraries and LLMs
- Problem solving, ETL, Data Engineering

Simply means you've to level up in your skills so fast and learn complete AI/ML lifecycle with deployment and not just a toy project. There's no alternate to this.

And yes you need to learn all these things at once even while they will ask DSA or distribution systems in an interview.

Which means,
Prepare
DSA (Medium) + ML algorithms + Deep Learning + Gen AI + LLMs + MLOps + Cloud + ML system design + Distributed systems

There's a no shortcut and that's the truth!!!

[Xiuyu Li @sheriyuo]

https://t.co/aJEXibSlmN