Yun C. Sawa

PhD Student in Mathematical, Computational & Systems Biology @CCBS_UCI

[Cell Genomics @CellGenomics]

FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data https://t.co/7gvJkOt0Tc

[Socket @SocketSecurity]

🚨 UPDATE: Mini Shai-Hulud has crossed from @npmjs into @pypi and is still spreading.

Newly confirmed compromised artifacts:

@​opensearch-project/opensearch: 3.5.3, 3.6.2, 3.7.0, 3.8.0 (1.3M weekly downloads)

mistralai: 2.4.6 on PyPI

guardrails-ai: 0.10.1 on PyPI

additional @​squawk/* packages on npm

guardrails-ai 0.10.1 executes malicious code on import. On Linux, it downloads git-tanstack[.]com/transformers.​pyz, writes it to /tmp/transformers.​pyz, and runs it with python3 without integrity verification.

The git-tanstack.​com domain displayed a message signed “With Love TeamPCP,” along with: “We've been online over 2 hours now stealing creds
Regardless I just came to say hello :^)”

The page also linked to a YouTube video and you can probably guess which one.

[Satoshi Kawato 川戸 智 @KawatoSatoshi]

🧬Collinearity Analysis is here in #gbdraw v0.11.0! You can easily detect and visualize synteny and collinearity blocks using LOSATP. You can instantly align the entire diagram based on a shared ortholog!
Try it now: https://t.co/ahqoRxbDHT
#bioinformatics #genomics #microbiology

[Brij Pandey @LearnWithBrij]

Claude Code ships with 5 architectural layers most engineers never open.
Not features. Not settings. Layers — each solving a distinct problem that LLMs alone can't solve. And four of them have nothing to do with prompting.
Here's the full Agent Development Kit:

Layer 1 — CLAUDE.md → The Memory Layer
Architecture rules, naming conventions, test expectations, repo map. Always loaded. Always active.
Two scopes:
• ~/.claude/CLAUDE.md → global
• .claude/CLAUDE.md → project
This isn't context you paste in before every session. It's context that never needs repeating. The agent's constitution.

Layer 2 — Skills → The Knowledge Layer
Each SKILL.md carries a description. Claude matches it at runtime and forks the skill into an isolated subagent. On-demand, never always-on.
Task-specific knowledge without inflating your main context window. Modular by design.

Layer 3 — Hooks → The Guardrail Layer
PreToolUse → PostToolUse → SessionStart → Stop → SubagentStop

This is the layer most teams skip. And the one they regret skipping first.

Hooks are NOT AI. They're deterministic event-driven shell commands.
• Auto-lint on every Write
• Hard-block on rm -rf
• Slack notification on Stop
Event fires → Matcher checks → Command runs
Quality enforced at the infrastructure level. Not the prompt level.

Layer 4 — Subagents → The Delegation Layer
Each subagent gets its own context window, model, tools, and permissions.
Main agent delegates down. Receives results up. That's it.
No infinite recursion — subagents can't spawn subagents. Main context stays clean. Hard boundaries by design.

Layer 5 — Plugins → The Distribution Layer
Bundle your skills + agents + hooks + commands into a plugin. One install. Whole team inherits the behavior.
Think npm packages — but for what your agent knows how to do.

Wrapping everything:
→ MCP Servers on the left (GitHub, databases, APIs, custom integrations)
→ Agent Teams on the right (parallel execution, message passing, shared permissions)

The 5-layer stack in one line:
CLAUDE.md sets rules → Skills provide expertise → Hooks enforce quality → Subagents delegate work → Plugins distribute to the team
Most production failures in agentic systems trace back to one missing layer.

Which one is the gap in your current setup?

[Marios Georgakis @MariosGeorgakis]

This new preprint by Open Targets is one of the most comprehensive efforts to date to connect the dots across all available (>100K) GWAS datasets that have been generated over the last two decades👇

[Wes Bos @wesbos]

Claude Code leaked their source map, effectively giving you a look into the codebase.

I immediately went for the one thing that mattered: spinner verbs

There are 187

[antisense. @razoralign]

LongcallD: joint calling and phasing of small, structural and mosaic variants from long reads https://t.co/GPmPNj8F5w

[Utkarsh Sharma @techxutkarsh]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/MfZHtEFSZC

2. LLMs from Scratch: https://t.co/kfPUVOpIxp

3. Agentic AI Overview (Stanford): https://t.co/YwfdVehhsO

4. Building and Evaluating Agents: https://t.co/6S8FGJRYbE

5. Building Effective Agents: https://t.co/pDTKM04SbE

6. Building Agents with MCP: https://t.co/5tXUZo9Xri

7. Building an Agent from Scratch: https://t.co/BtDWY0u0Du

8. Philo Agents: https://t.co/A4bj3gDOAg

🗂️ Repos

1. GenAI Agents: https://t.co/XbindjiOGu

2. Microsoft's AI Agents for Beginners: https://t.co/6Yaapil6AA

3. Prompt Engineering Guide: https://t.co/9JyzmoITLO

4. Hands-On Large Language Models: https://t.co/cuFS03HiXZ

5. AI Agents for Beginners: https://t.co/6Yaapil6AA

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/vDNcCdZ8Ni

8. Hands-On AI Engineering:https://t.co/GZs4q8rSYc

9. Awesome Generative AI Guide: https://t.co/EdJ1eGDF0m

10. Designing Machine Learning Systems: https://t.co/Nf1szfXGWK

11. Machine Learning for Beginners from Microsoft: https://t.co/gEdhvs78o3

12. LLM Course: https://t.co/4ISw9XBGWe

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/fQ64JT8Hru

2. Google's Agent Companion: https://t.co/NHt05sw0Sx

3. Building Effective Agents by Anthropic: https://t.co/VjaqhSl1jz.

4. Claude Code Best Agentic Coding practices: https://t.co/yBGMtyeWTg

5. OpenAI's Practical Guide to Building Agents: https://t.co/kmqQgcvvwL

📚Books:

1. Understanding Deep Learning: https://t.co/cgSSJ1U1d6

2. Building an LLM from Scratch: https://t.co/IUVA1tpJJ2

3. The LLM Engineering Handbook: https://t.co/G5HGLfTTq9

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/kehojr3v2p

5. Building Applications with AI Agents - Michael Albada: https://t.co/QHaJEitMMN

6. AI Agents with MCP - Kyle Stratis: https://t.co/POn6waJ2w7

7. AI Engineering: https://t.co/rU9HES8VEU

📜 Papers

1. ReAct: https://t.co/q4zflGWTVT

2. Generative Agents: https://t.co/GK5QswcPQ2.

3. Toolformer: https://t.co/mhDey5PGJL

4. Chain-of-Thought Prompting: https://t.co/3kwFbejFqA.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/jIyZzuScoQ

2. MCP with Anthropic: https://t.co/ph5cM4zgx1

3. Building Vector Databases with Pinecone: https://t.co/VtA6lSCaQd

4. Vector Databases from Embeddings to Apps: https://t.co/mr9rePzbc3

5. Agent Memory: https://t.co/hjWM88dY2h

Repost for your network ♻️

[Akshay 🚀 @akshay_pachaar]

How to setup your Claude code project?

TL;DR

Most developers skip the setup and just start prompting. That's the mistake.

A proper Claude Code project lives inside a .𝗰𝗹𝗮𝘂𝗱𝗲/ folder. Start with 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 as Claude's instruction manual. Split it into a 𝗿𝘂𝗹𝗲𝘀/ folder as it grows. Add 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀/ for repeatable workflows, 𝘀𝗸𝗶𝗹𝗹𝘀/ for context-triggered automation, and 𝗮𝗴𝗲𝗻𝘁𝘀/ for isolated subagents. Lock down permissions in 𝘀𝗲𝘁𝘁𝗶𝗻𝗴𝘀.𝗷𝘀𝗼𝗻.

There are two .𝗰𝗹𝗮𝘂𝗱𝗲/ folders: one committed with your repo, one global at ~/.𝗰𝗹𝗮𝘂𝗱𝗲/ for personal preferences and auto-memory across projects.

The .𝗰𝗹𝗮𝘂𝗱𝗲/ folder is infrastructure. Treat it like one.

The article below is a complete guide to 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱, custom commands, skills, agents, and permissions, and how to set them up properly.

[Stephen Turner 🦋 @stephenturner.us @strnr]

How to develop good research questions https://t.co/cj3oATdLqJ (free: https://t.co/TAmZGpQhmq) 🧬🖥️🧪

[Peter Holderrieth @peholderrieth]

We are also releasing self-contained lecture notes that explain flow matching and diffusion models from scratch. This goes from "zero" to the state-of-the-art in modern Generative AI.

📖 Read the notes here: https://t.co/RULWDgn9pm

Joint work with @EErives40101.

[Matt Dancho (Business Science) @mdancho84]

The 10 types of clustering that all data scientists need to know.

Let's dive in:

[Benagen-ONT @benagen3571]

[novel tool] Dogme: a nextflow pipeline for reprocessing nanopore RNA and DNA modifications. #nanopore #DNA #RNA https://t.co/2mZYjxgW5u

[David Sinclair @davidasinclair]

This is where rDNA silencing becomes crucial.

Sir2, the OG Sirtuin, is a chromatin silencing protein that controls the epigenome and suppresses recombination at rDNA. More silencing, less DNA recombination, fewer ERCs, longer lifespan

[David Sinclair @davidasinclair]

Around the same time, Dan Gottschling’s lab showed that aging yeast cells lose transcriptional silencing and accumulate genomic instability.

Together, these studies revealed that genome instability at repetitive DNA is a fundamental aging driver

[Rimsha Bhardwaj @heyrimsha]

🚨BREAKING: Claude can now write your entire job application like a top recruiter.

Here are 10 prompts that turn a job description into a tailored CV, cover letter, and interview prep guide in under 5 minutes (Save this)

[Vaidehi @Ai_Vaidehi]

This is one of those “this changes everything” moments.

Microsoft just broke a core assumption in AI.

You needed GPUs to run big models.

Not anymore.

They open-sourced BitNet,
an inference framework that runs a 100B parameter LLM on a single CPU 🤯

No GPU
No cloud
No expensive setup

Just your laptop.

Here’s the trick:

Instead of 16-bit or 32-bit weights...
BitNet uses 1.58 bits

Yes, seriously.

Weights = -1, 0, +1

That’s it.

No heavy matrix math.
Just simple integer ops your CPU already handles easily.

And the results?

• 100B model → 5–7 tokens/sec on CPU
• Up to 6x faster than llama.cpp
• 82% less energy usage
• Runs on x86 + ARM (MacBook)
• Memory reduced by 16–32x

But here’s the insane part:

👉 Accuracy barely drops.

Their model (BitNet b1.58 2B4T) competes with full-precision models trained the “normal” way.

So what does this unlock?

• Fully offline AI (privacy ↑)
• No more API bills
• AI on phones, IoT, edge devices
• Access in low-internet regions

We’re watching AI move from
“cloud-only” → “runs anywhere”

The GPU monopoly just got… shaky.

And this is open source.

Let that sink in. 🚀

[Peter Holderrieth @peholderrieth]

🚀MIT Flow Matching and Diffusion Lecture 2026 Released (https://t.co/bKgs2wghvY)!

We just released our new MIT 2026 course on flow matching and diffusion models! We teach the full stack of modern AI image, video, protein generators - theory and practice. We include:
📺 Videos: Step-by-step derivations.
📝 Notes: Mathematically self-contained lecture notes
💻 Coding: Hands-on exercises for every component

We fully improved last years’ iteration and added new topics: latent spaces, diffusion transformers, building language models with discrete diffusion models.

Everything is available here: https://t.co/bKgs2wghvY

A huge thanks to Tommi Jaakkola for his support in making this class possible and Ashay Athalye (MIT SOUL) for the incredible production! Was fun to do this with @RShprints!

#MachineLearning #GenerativeAI #MIT #DiffusionModels #AI

[摸鱼巨匠🔨 @SunNeverSetsX]

刚开始接触 Claude code 的朋友，我强烈推荐你去看看这个项目— learn-claude-code

这个课程不是在教你用 Claude code，而是教你从零实现一个类似 Claude Code 的 AI 编码 Agent

项目分成 12 个 session，每个 session 只加一个机制，代码从几十行逐渐到完整版，每节课都有独立可运行的 Python 文件：

s01. 基础 Agent Loop + 1 个 Bash Tool（最简版，跑起来就行）

s02. Tool 注册与调度

s03. Todo 规划（让 Agent 先想计划）

s04. Subagents（子 Agent 拆分大任务）

s05. Skills 动态加载（需要知识时再注入）

s06. Context Compact（上下文压缩）

s07. Tasks + 依赖图（持久化任务）

s08. Background Tasks（后台异步执行）

s09. Agent Teams（多 Agent 团队）

s10. Team Protocols（团队沟通协议）

s11. Autonomous（自主认领任务）

s12. Worktree + Task Isolation（工作树隔离，完全不互相干扰）

最后还有 s_full.py 把所有功能合在一起

仓库地址：https://t.co/JAwrAAEzpB

在线学习平台：https://t.co/WTmxZfX7GO（强烈推荐先打开这个看可视化）

中文 README：https://t.co/C8OKtKk3qs

[KK.aWSB @KKaWSB]

Google今天放大招了！今天一次性发布了5个Agent Skill设计模式。

现在30多个Agent工具（Claude Code、Gemini CLI、Cursor）已经统一了SKILL.md格式，格式不再是问题——真正的挑战是里面写什么。

5个模式：

Tool Wrapper：给Agent装上某个库的专家知识，按需加载，不占context

Generator：模板+样式指南，每次输出结构一致

Reviewer：把检查清单外置，换一份清单就变成另一个审核专家

Inversion：先采访你，收集完所有需求才开始执行

Pipeline：强制分步骤，每步有检查点，不允许跳过
这5个模式可以组合使用——Pipeline里嵌Reviewer，Generator前加Inversion。

与其把复杂逻辑塞进一个system prompt，不如用对结构。

推荐阅读👇