우쭈뽕다니

코딩 공부 시작한 새싹 아름다운 코드를 짜는 그날까지 같이 코딩 공부하실 분들 대환영! 그리고 많이 가르쳐주세요 스승님들!! 부트캠프

[Kirk Borne @KirkDBorne]

System Design Interview Notes — Comprehensive Insider's Guide — 28 chapters, and more:
https://t.co/XMOxxGxsgi

[beamnxw ./ @beamnxw]

this is pure f*cking treasure

these 20 plugins have 6.73M combined downloads and form a real Claude Code stack:

build the product → hold the context → coordinate the work → keep the system under control

BUILD
01 frontend-design-pro
▸ https://t.co/CZoeV58FHT
02 animated-sketch-diagram
▸ https://t.co/EkculbT8H8
03 archcore
▸ https://t.co/hxaYxtcy5o
04 shipwright
▸ https://t.co/83smFo9lqA
05 slopmop
▸ https://t.co/HaNujOPldS

MEMORY
06 origin
▸ https://t.co/kQtB3w6Gyr
07 dsh-deepread
▸ https://t.co/ad6TZcgyoU
08 obsidian-skills
▸ https://t.co/RDVbbqrebt
09 claude-md-kit
▸ https://t.co/tn4ocYU54y
10 claude-ops
▸ https://t.co/qCkXx9aCqh

ORCHESTRATE
11 crosstalk
▸ https://t.co/cr9yQ4OWAX
12 claude-wayfinder
▸ https://t.co/q1wzyreUtI
13 backlogd
▸ https://t.co/C9QbVK23mp
14 magic-cc-codex-worker
▸ https://t.co/YAlO15Dn0d
15 vibe-prospecting
▸ https://t.co/9E6A2KIIYp

CONTROL
16 project-boundary
▸ https://t.co/vgC5Kjsmcj
17 tlsradar
▸ https://t.co/rUVMKybG0R
18 claude-hud
▸ https://t.co/CZoeV58FHT
19 claude-pager
▸ https://t.co/tV3UjuXIO7
20 claude-prospector
▸ https://t.co/5QD4mzV7Tv

the loop:

idea → interface → architecture → shared memory → parallel work → checks → ship

ave this to build your own business with the help of an AI employee ⭣

[Rahul @sairahul1]

Someone turned Claude into a one person company.

42 skills, organised like a real org chart (links below):

Here is every department, and where to get each one.

Developers:

Superpowers → https://t.co/pPPxKoPEwD
Context7 → https://t.co/3Kk9U8PG1T
Skill Creator → https://t.co/Lanao7tpOh
MCP Builder → https://t.co/Lanao7tpOh
Webapp Testing → https://t.co/Lanao7tpOh
Claude-Mem → https://t.co/yTb8qxqa7S

Designers:

UI UX Pro Max → https://t.co/MQTtS9flwt
Taste → https://t.co/AEq4GZc60x
Frontend Design → https://t.co/AEq4GZc60x
Transitions → https://t.co/Z7JOt7lJb2
Web Artifacts → https://t.co/Lanao7tpOh
Brand Guidelines → https://t.co/Lanao7tpOh

Marketing:

45 skills to run your marketing, from copywriting to SEO to lead magnets.

Access them all here → https://t.co/OWo258NM7L

Social Media:

17 skills to run your social media, from post writing to Reels to thumbnails.

Access them all here → https://t.co/2qawCgAyQF

Finance:

8 skills to run your finances, from statements to reconciliation to audits.

Access them all here → https://t.co/X6dVFcZBIJ

Small Business:

31 skills to run your small business, from cash flow to payroll to invoicing.

Access them all here → https://t.co/7Prb2sXVpI

Legal:

9 skills to handle your legal work, from contract review to NDAs to compliance.

Access them all here → https://t.co/GKaZzGYPOr

---

Every skill on the chart is real and installable from the links above.

Same departments. Same output. No payroll.

Save this.

[Nelly; @nrqa__]

Claude Skills & Plugins To Install

1. https://t.co/FsxaZ6uKca
2. https://t.co/ACczMHIUM0
3. https://t.co/AD7nJ4DK1h
4. https://t.co/SaNvOP68Km
5. https://t.co/fYYk8hQlgu
6. https://t.co/F6GwGgCSAH
7. https://t.co/tLLkIbhxYr
8. https://t.co/SRqYgwQJKf
9. https://t.co/fGHL1sgj5c
10. https://t.co/Kg8KgebK1m
11. https://t.co/yZvnN5uyAY
12. https://t.co/XF6SpU1UYS
13. https://t.co/FWP8fJGEjs
14. https://t.co/cP4xO6e5lB
15. https://t.co/up0qSTlnll

[돈벌고영 · DB Goyoung · 稼ぐネコ @db_goyoung]

'바이브 코더(Vibe Coder)'라면 무조건 북마크해둬야 할 사이트가 있음.

전 세계 디자이너들이 만든 살아 움직이는 인터페이스 12,000개가 모여있는 오픈 라이브러리 사이트임.

기존에 AI한테 웹사이트 만들어달라 하면 제일 답답했던 점:
- 기능은 돌아가는데 디자인이 밋밋하고 촌스럽고 밤티남.
- 너무 촌스럽다고 디자인을 수정해달라고 하면 그라데이션 넣고 테두리 추가하고 누가봐도 AI가 만든 웹사이트로 만들어 줌.

근데 여긴 실리콘밸리 100억 투자받은 스타트업 같은 미친 퀄리티의 UI를 눈으로 직접 만져보고, 버튼 하나로 프롬프트나 코드를 복사해서 내 앱에 꽂아 넣으면 끝임.

내 AI 웹사이트의 디자인을 프로 수준으로 끌어올리는 핵심 4가지 정리 👇 (타래)

[lucas @lucas_flatwhite]

📚 시스템 디자인 아카데미
> 분산 시스템부터 AI 엔지니어링까지
https://t.co/NzFAckBlKC

시스템 디자인 자료들은 중요하지만 블로그, 논문, 컨퍼런스 발표에 흩어져 있죠.

이런 지식을 회사별 사례, 기초 개념, 면접, AI 엔지니어링, 논문으로 나눠 정리한 큐레이션이라고 보면 됨요.

가치가 있고, 꾸준히 커밋되고 있어서 좋음.

고전 주제도 있지만 바로 옆에 에이전트, RAG, MCP, 컨텍스트 엔지니어링, LLM 평가 같은 주제도 있음.

이론 + 실사례 + 면접이 연결됨. 이렇게요.

- Fundamentals: 로드밸런싱, 캐싱, Kafka, 일관된 해싱 같은 기초 개념

- Case Study: Netflix, Uber, Stripe, WhatsApp 등 실제 서비스의 스케일링 사례

- Interview: 설계 면접 프레임워크와 "뭐시기 설계하기" 형태의 연습 문제

- White Papers: Amazon Dynamo, Google Spanner 같은 것들

개념을 익히고, 실제 서비스가 어떻게 적용했는지 확인하고, 면접식으로 연습하는 과정을 한 곳에서 이어갈 수 있음.

지금도 관리되고 있는 프로젝트!

[雪踏乌云 @Pluvio9yte]

最近Jev爆火，拿到 Jev API 还不知道怎么玩的，可以按这个清单抄。

1. jev-ultrafast
Browser Use 做的高速浏览器 Agent。Jev 每一步只判断「做什么、点哪个元素」，要打字才叫小模型。Google Flights 搜一次航班大约 7 秒。
https://t.co/IBPb4LBRQh

2. fast-jev-compaction
给 Claude Code 做上下文压缩。每次工具调用先让 Jev 判断还有没有用，没用的删掉，留下来的原文不重写。
https://t.co/qbPTr3TFTB

3. json-render
Vercel Labs 的生成式 UI 框架。实验里 Jev 不逐 token 写 JSON，只负责选组件、属性和布局。
https://t.co/iK0x89svV1

4. typesafe-mcp
最适合刚拿到 API 的人。把 Jev 接进 Claude Code、Claude Desktop、Codex 和 Pi，随时做 Choice / Score / Noul。
https://t.co/XU16Z4xTNb

5. jev-mcp
现成的 Agent 判断工具箱：事实核验、内容筛查、语义排序、分类和信息提取。
https://t.co/Ns9tNWB4st

6. SemDecide
把 Jev 做成命令行。Shell 里直接分类、打分、过滤，适合接爬虫、CI 和数据流水线。
https://t.co/KvNFnjINNj

7. jev-codex-router
先让 Jev 判断这一轮编程任务有多难，再决定模型档位、推理深度和速度模式。
https://t.co/XJWyrbzusf

8. Winnow
给 Claude Code 做上下文垃圾回收。Read / Bash / Grep 吐一大堆时，Jev 先判断哪些真和当前任务有关。
https://t.co/cs8CtPcXq8

9. jev-review
代码审查前先过一遍 Jev，把高风险改动挑出来，再交给更贵的大模型或人。带本地看板。
https://t.co/dW0nySEIp2

10. Blink
把 Jev 当代码库导航器。每走到一层目录，就判断哪些文件和当前问题最相关，再继续往下找。
https://t.co/ZuOt4Yvpds

11. agent-desktop
桌面自动化。读系统无障碍树，判断下一步该点哪个按钮、菜单或输入框。
https://t.co/uctY97plME

12. typesafe-mario
让 Jev 玩《超级马里奥》。不看截图，直接读模拟器 RAM 里的结构化状态，再决定跑、跳、躲。
https://t.co/tzAGfK9BBE

13. jev-drone
拿 Jev 控无人机。底层飞控继续负责稳定和安全，Jev 只做爬升、刹车、穿越障碍这类上层判断。
https://t.co/lRZ9LuWc3i

14. OneVOneJev
浏览器里的 1v1 FPS。每个决策 tick 都要判断走位、视角、瞄准、开火和跳跃。
https://t.co/l4iHbyH9uU

15. jev-trader
Monad 测试网上做高频做市。Jev 根据价差和成交方向判断下一步买还是卖，模型延迟大约 81ms。
https://t.co/tGAVNWR98u

16. Prism
不直接让 Jev 下单。它判断 toxic flow、市场压力、均值回归之类的状态，再交给原来的策略。
https://t.co/LP0T9rDAWi

17. neo4jev
把 Jev 塞进知识图谱。每走到一个节点，判断下一条最值得走的边，再一路找下去。
https://t.co/Qy1RUgi8oS

18. jev-curate
拿 Jev 筛训练数据。JSONL / Parquet 先做质量、相关性和风险判断，再决定哪些进后面的训练。
https://t.co/ez9fXeTtEu

19. Canny
防 Coding Agent 嘴硬说自己做完了。看工具输出、代码 diff 和测试结果，再判断完成声明靠不靠谱。
https://t.co/O3OQZ4NZ6Q

20. killmyidea
输入一个创业点子，Jev 从多个维度打分，最后给你 KILL、FIX 或 SHIP。
https://t.co/pR4VkDrz54

[Vinay Bharambe @Vinay_bharambe]

Goldmine for AI Engineers! 📌

If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials.

These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI.

1. Python - 100 Days

jackfrued/Python-100-Days

A 100-day Python learning path covering fundamentals, data analysis, web development, and more.

GitHub:
https://t.co/Qa8o6aixcB…

2. Generative AI for Beginners

microsoft/generative-ai-for-beginners

A practical introduction to building Generative AI applications.

Covers:

• LLM fundamentals
• Prompt engineering
• RAG
• AI agents
• Fine-tuning
• AI application development

GitHub:
https://t.co/Y6kNqZ9ojz…

3. LLMs From Scratch

rasbt/LLMs-from-scratch

Want to understand what's actually happening inside an LLM?

Build one step by step.

Covers:

• Tokenization
• Embeddings
• Attention
• Transformers
• Training
• Fine-tuning

GitHub:
https://t.co/VHKqT5ZEm2…

4. Machine Learning for Beginners

microsoft/ML-For-Beginners

A structured 12-week, 26-lesson curriculum covering classical machine learning.

A good starting point if you want ML fundamentals before jumping into LLMs.

GitHub:
https://t.co/dDoKYxQBJz…

5. OpenAI Cookbook

openai/openai-cookbook

A collection of practical examples and guides for building applications with OpenAI models.

Useful when you want to move from:

Learning → Building

GitHub:
https://t.co/VgkYZkfiWe…

6. Stable Diffusion

CompVis/stable-diffusion

Interested in generative image models?

This repository contains the original Stable Diffusion implementation and research code.

GitHub:
https://t.co/MrjWmauahQ…

7. AI Agents for Beginners

microsoft/ai-agents-for-beginners

A practical course for understanding and building AI agents.

Covers:

• Agentic AI
• RAG
• Agent frameworks
• Tool use
• Multi-agent systems

GitHub:
https://t.co/vtzBxtLiE3…

8. AI for Beginners

microsoft/AI-For-Beginners

A structured 12-week, 24-lesson introduction to AI.

Covers:

• Neural networks
• Computer vision
• NLP
• Deep learning
• Classical AI

GitHub:
https://t.co/JwHlls8Mwk…

9. LLM App

pathwaycom/llm-app

Focused on building practical LLM applications.

Explore:

• RAG
• AI pipelines
• Enterprise search
• Real-time data
• Vector search

GitHub:
https://t.co/dfYZQYr4f1…

10. Segment Anything

facebookresearch/segment-anything

A foundation model for promptable image segmentation.

Worth exploring if you're interested in computer vision and multimodal AI.

GitHub:
https://t.co/pSNOOoSJJ2…

Don't bookmark all 10 and forget about them.

Pick based on where you are:

Python → Python-100-Days

ML → ML-For-Beginners

AI Fundamentals → AI-For-Beginners

LLMs → LLMs-from-scratch

Generative AI → Generative-AI-for-Beginners

Agents → AI-Agents-for-Beginners

Building → OpenAI Cookbook / LLM App

Computer Vision → Segment Anything

Pick one.

Build something.

Then move to the next.

[Movez @0xMovez]

Jev Founder, Diogo Amogo, just released 12-page PDF on building a Jev Harness for coding agents

this is a 10-step blueprint on how to make your coding agents 200× faster and 400× cheaper:

step 1 → meet Jev: the LLM writes, the harness executes, Jev decides what each turn sees, where it routes and whether it runs

step 2 → ask the question that breaks every agent: how would you design one if LLMs had no KV cache?

step 3 → stop routing blind: Opus → Sonnet → Opus costs 6.19 vs 4.15 for pure Opus, because handing back reprocesses the whole context

step 4 → follow the tokens: reading and searching take 56.2% of tool turns and 46.5% of tokens. Writing code is under 10%

step 5 → score every chunk per query: hide, short summary, long summary or full. Compress after the question, not before

step 6 → disclose tools in tiers: one-line snippets for 100s of tools, schema on demand, docs for one-off queries. Batteries stop costing context

step 7 → load instructions by condition: touching *.tsx loads the style guide, billing/ loads its gotchas file, and compaction can't erase either

step 8 → route by trust, not just difficulty: secrets and infra stay on first-party frontier models, public docs go to the cheapest one

step 9 → share one retrieval pass: cross-model review, eval generation, ELI5 explainers and live progress pages all run read-only in the background

step 10 → gate every command: programmable allow / ask / deny policies that read a script before it runs, not just its name

Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster.

[Trending GitHub Repositories @trending_repos]

Trending repository of the day 📈

security-audit-skill

A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings

Last 24h: 3,606 ⭐
Total: 9,091 ⭐️
https://t.co/z1VMXkjeCY

[Kirk Borne @KirkDBorne]

MIT Advanced Data Structures course: https://t.co/EQtAOIndPI

Video lectures, lecture notes, problem sets, GitHub, and more

[Mba.tech🦀 @_justmba]

most beginners use databases everyday in their app without knowing why they exist

Today, i studies Prostgres fundamentals to get a better understanding. Here's a few things i learnt 👇🏽 1/3

[ふじ @fuji_ai_]

どんなWebサイトも、10秒で丸ごとダウンロードして複製できるようになった。

制作会社が300万円かけて作ったサイトも、0円でそのまま手元に保存できるツール。

しかも完全オープンソース↓

[angel @angeldot_]

ESTE DOCUMENTO DE ANTHROPIC LITERALMENTE TE VA A CONSEGUIR UN ASCENSO

La forma más rápida de ascender es automatizar el trabajo que haces hoy

Esta guía técnica oficial de Anthropic explica cómo enseñarle a Claude a ejecutar tus tareas del día a día

Crea «Skills» personalizadas para que la IA se encargue del trabajo pesado:

→ convierte tus procesos habituales en instrucciones reutilizables
→ deja que el agente ejecute tareas en segundo plano
→ conéctalo a tus herramientas locales mediante servidores MCP

Delega las tareas repetitivas y dedica tu tiempo al trabajo que te puede conseguir ese ascenso

Aquí tienes la guía para hacerlo paso a paso👇

[Abir Rahman @AbirOnAI]

DEEPSEEK HAS JUST KILLED THE ENTIRE CODING AGENT INDUSTRY

It's called deepseek-harness

It's the most complete framework for creating code agents

Open source. Claude's most complete plan costs $200 a month. This is FREE

And it comes with a brutal idea

Everything is a plugin

The model
The tools
The sandbox
The UI
Even the agent's own loop

You can swap out any of those pieces just by tweaking the config

Without touching the base code

One single command and you have the local web interface:

npx @deepseek-ai/dsh web

Compatible with DeepSeek, Claude, GPT, Gemini and whatever else you want to plug in

It reached over 160 thousand stars in just a few days

A frontier lab just dropped for free the exact layer that other companies are selling as premium product

I'll leave the repo in the comments

[GitHub Projects Community @GithubProjects]

One of those open source tools every developer ends up using sooner or later.

Excalidraw makes it ridiculously easy to sketch system designs, architecture diagrams, flows, wireframes, or random ideas without fighting the tool itself.

Open source, collaborative, and genuinely useful.

Explore it here:
https://t.co/dkvJBPJSuJ

[古野光太朗 @koutarou_furuno]

LLM業界を賑わしている、Jevについてめっちゃわかりやすい解説があった。AIネイティブのif文っていうこと。

＞
Jevを理解する最も簡単な方法はこれです：

LLMは回答を生成します。

Jevは決定を下します。

これは小さな違いのように聞こえますが、実際には使用事例全体を変えてしまいます。

例えば、通常のLLMにこれを与えるとします：

「ここにユーザー、そのアカウント履歴、支払い行動、サポートチャット、デバイスデータなどがあります。

これがリスクありそうか教えてください。」

LLMはこれを推論して、以下のように返すかもしれません：

「はい、これは高リスクに見えます。」

丁寧に頼めばJSON形式で。

Jevでは、可能な決定を事前に定義します：

リスク：

* 低
* 中
* 高

手動レビュー：

* はい
* いいえ

そしてJevは次のようなものを返します：

リスク = 高 (96%)
手動レビュー = はい (91%)

これが基本的に製品です。

これは別のChatGPTになろうとしているわけではありません。

もっとAIネイティブなif文のようなものです。

以下のような代わりに：

if 取引額 > $10,000:
review()

もっと以下のように考え始められます：

if 「この行動が怪しいか？」 > 95%:
review()

[beamnxw ./ @beamnxw]

this is pure f*cking treasure

15 GitHub projects with 1.21M combined stars that can form a real agent stack

specs. memory. web data. documents. context. sandboxes. monitoring. video

01 hermes-agent
▸ https://t.co/fpJlyU9RoS

02 OpenSpec
▸ https://t.co/2nB3z9aICv

03 caveman
▸ https://t.co/w7TLtPTg4o

04 Scrapling
▸ https://t.co/uwfvY3AtBi

05 Docling
▸ https://t.co/ETkC2ak6as

06 PageIndex
▸ https://t.co/RcH9jlUKfl

07 mem0
▸ https://t.co/Oiqdog6LXF

08 headroom
▸ https://t.co/Lw1ouzUoE3

09 Daytona
▸ https://t.co/1e0hLQpaRs

10 TrendRadar
▸ https://t.co/aIrqtOtPn8

11 Fabric
▸ https://t.co/AoCw2SjDFk

12 spec-kit
▸ https://t.co/Lztf6n09fW

13 hyperframes
▸ https://t.co/EEk3joz0GZ

14 OpenMontage
▸ https://t.co/D5ebisVk0d

15 AI Engineering Hub
▸ https://t.co/aEXjUByMxO

the loop:

define the job → collect the evidence → parse the docs → save the memory → compress the context → run the code safely → watch what changes → ship the output

the entire stack is open source

save this to build your own business with the help of an AI employee ⭣

[Brais Moure @MoureDev]

¿Usas Redis como base de datos? Esto te interesa.

Han creado una visualización 3D interactiva que explica cómo Redis procesa comandos, organiza estructuras, asigna memoria y persiste datos. Muy útil para estudiantes y devs que quieren entender Redis más allá de GET y SET.

https://t.co/oAlvnDnare

[Neeraj @neeraj_here15]

Claude FULL COURSE 1 HOUR (Build & Automate Anything).

INSTEAD OF WATCHING NETFLIX TONIGHT.

Spend 1 hour on