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Liked on XI made a mistake. When Jack and I decided to organize this $100K Hackathon, my biggest concern was cheating. Unlike a regular hackathon, everyone participates from home. And because the winner is whoever makes the most PROFIT (not revenue), there are so many ways to manipulate the numbers. So I added a $99 entry fee. One thing I’ve learned from building businesses is that people tend to be more committed when they pay for something. I thought it would filter out spam, attract serious builders, and make the competition fairer for everyone. But I completely missed the other side. 3 years ago, I was a living with my apps. Today, I’m financially free. My life changed so much that somewhere along the way, I lost perspective on what $99 means to someone else. For someone in India, someone struggling to pay their bills, or someone trying to make their first dollar online, $99 can be a lot of money. And those are exactly the people I want to help! I’ve spent years sharing what I’ve learned, reviewing random people’s landing pages, and organizing free meetups around the world. Not because there’s money to make, but because I genuinely love seeing people build things and succeed. And somehow, I was so focused on keeping cheaters out that I didn’t realize I was also keeping genuine builders out. That’s my mistake. So I’ve decided to make the $100K Hackathon completely FREE :) Everyone who already paid got a full refund. I’m still worried about cheating. That problem hasn’t disappeared, and Jack and I will have to figure out better ways to verify profits and keep the leaderboard fair. But that’s our problem to solve. Thank you to everyone who shared their thoughts. Sometimes you need people to remind you of things you’ve forgotten. I genuinely hope this hackathon changes someone’s life. ❤️Liked on XIt’s time to bring back Cafe CursorPosted on Xyo @pendev i can't live share edit a .pen file? it's the most needed featurePosted on XI'm coining a word "Agentsplaining" Agents condescendingly assuming the user don't know fuck and repeating the obviousReplied on X@jayhxmo @raycast Hard to explain. It's the small detailsPosted on X@raycast iOS keyboard is insanely good. Might resub Raycast pro just cuz of thisReplied on X@ishuagra02 I saw a theory that F5.2 is a post train of 5.1, but O5.5 is a distillation of F5.5(unreleased new pretrain) and thats why O5.5 > F5.2Liked on X@anaclumos don't know what is actually improved 👀Posted on Xam i the only one underimpressed with 5.2 fableReplied on X@bvvst great minds think alike https://t.co/YRFWhb1mcOLiked on XClaude Fable 5.2 is out and it's the perfect time to run this god-level prompt on your codebase: "I want you to audit our codebase, is anything here unnecessary, overly complicated in our current setup, or based on weak assumptions? Challenge them. - What can be deleted entirely? - What can be simplified now that unnecessary pieces are gone? Then make the changes. Prefer deleting over simplifying, simplifying over optimizing, and optimizing over automating. This product is being used by thousands of customers, do not cut any corners." This prompt is one I run often and leads to the best results.Replied on X@akshayadinesh19 @bot can we please BYOM for botsLiked on XTo be fair, the Chinese models have a much lower refusal rateLiked on XF*CK IT, im creating a library of AI slop indicators. my feed is full of taste larpers. stay tuned https://t.co/5Muv8nQRm3Liked on XJENSEN! THATS NOT WHAT WE MEANT BY CHINESE MODELS!!!!!!!Liked on Xmy parents had to declare bankruptcy when I was a kid, so they had every reason to pressure me towards a more stable career path instead, they were so patient with me, even as i failed startup after startup for years so getting to tell my mom that everything turned out okay was a really special moment :) for those who can't read Korean, her first message roughly translates to: "Really!!! Congratulations my son. So much :') Thank you God!" i know I've been posting about my exit for like a week now, but it really is a big deal for me and my family! i'll share more about how this all happened and some tactical advice later this week, but for now i'm just riding out all the emotions to quote my mom, thank you God!!!!Liked on X@thefarmers_Dog it stands for Egg GzampleLiked on X🦍 Introducing Memorilla: memory as a modality for LLMs Memorilla turns everything a frozen LLM needs to remember into just 16 memory embeddings ⚡ It uses up to 4,212× fewer tokens and is 27× faster than full context inference while more accurate 🎯 Pushes the Pareto frontier over RAG and agentic memory in personalization, long-document and knowledge QA A thread 🧵:Liked on XThere's a huge product opportunity downstream of Tailscale etc too. At @tldraw we've started designing for "high trust networks" and it's been a beautiful thing. 1. Features that would be a nightmare on the internet are okay on my computer and mostly okay on private networks. Websites that execute user-generated scripts, control production systems, allow for users to send desktop notifications, spend tokens, or directly prompt my agent. Crazy stuff that would only make sense if everyone was in a committed polycule. 2. When everyone on the server can be trusted to have _external_ reasons for good behavior (eg keeping their job, or maintaining friends, family, church, mafia org), the software can be designed in a default social way. Most apps default to isolation, low horizontal visibility, highly restricted collaboration. Makes sense if the social contracts are inside of the app, but if you can trust that they live outside of the app then you can get wild. Example being small group chat names as an emergent comedy phenomenon because anyone in the group can change the name. 3. This can work for commercial products, too, not just internal stuff. Security matters more than ever but the way we've seen that manifest is that more companies are more confident in their own solutions than anything we could provide, to the point where owning data and access is a better value proposition than having it managed. Always has been true for big companies but now even small companies can get a good secure network set up pretty easily, and the hooks we need to build are easy enough. tldraw offline got us started down this road (and shout out to @pendev for first introducing me to the idea of desktop + mcp). We've been bringing that product to private networks and it's been exciting to see how much we can get away with. Keep an eye outReplied on X@saradu birds eye viewLiked on XApplications close in 2 days!! If you have a cool bio idea, and $5-10k will go a long way for you, please apply!!! https://t.co/NCvmrNC53cLiked on XWe've been early adopters of Rust since 2018 and a lot of our infra, voice tech, and even some of the current client is already Rust, the holy grail was getting the whole client there. That means Electron goes away, and so does React Native, mobile keeps native UI but shares app code with desktop via Rust. We hope to get it into testing with users soon™. (And it's not Tauri, it's GPUI, so good guess everyone.) Alright, back to staring at the WoW Forever login screen.Reposted on XOh boy. https://t.co/X7SSskGuGvLiked on XRIP .si domain squatters
INCL의 구성

INCL의 구성

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Over a year old. May be outdated

정보

Lunit 기술 블로그 글을 빠르게 공유하려고 번역하고 줄인 글입니다.

실험은 어떻게 돌아가나

은 클라이언트, 백엔드, 작업 인스턴스 세 덩어리로 되어 있다. 클라이언트로 실험을 제출하고 추적하고, 백엔드는 요청을 처리하고 자원을 관리하고, 작업 인스턴스는 VM에서 실험을 실제로 돌린다. 여기서 "실험"과 "작업"은 같은 말이고, 모델 학습뿐 아니라 추론이나 스크립트 실행도 포함한다.

실험 하나가 실행되는 흐름

  1. 작업 요청. 사용자가 스크립트와 필요한 자원을 정해서 백엔드로 보낸다.
  2. 코드 업로드. 작업 디렉터리가 객체 스토리지로 동기화된다.
  3. VM 프로비저닝. 백엔드가 요구 자원에 맞는 VM을 만들고 환경을 준비한다.
  4. 코드 다운로드. VM이 객체 스토리지의 코드를 블록 스토리지로 복사한다.
  5. 모델 학습, 추론. VM이 작업을 돌리고, 학습 데이터는 파일 스토리지(NFS)에서 읽는다.
  6. 결과 동기화. 체크포인트와 출력을 객체 스토리지에 올린다.
  7. 종료 보고. VM이 끝났다고 알리고 바로 자원을 반납한다.

구성요소

클라이언트

  • CLI. Python Click 기반이라 터미널에서 쓰기 좋고 자동화하기도 편하다.
  • 웹 UI. React, MobX, Material UI로 만들었고 실시간 그래프를 보여 준다.

정보

2025년부터는 shadcn 기반 Next App Router로 옮겼다. 이 작업 이야기는 .

클라이언트와 백엔드 사이

클라이언트는 REST API로 작업 제출, 상태 조회, 모델 관리 요청을 보낸다. API 서버는 객체 스토리지와 관계형 DB에 붙어 있고, 로깅 서버는 로그와 메트릭을 NoSQL DB에 저장한다.

백엔드

Kubernetes 클러스터 위에서 돌고, 이렇게 나뉜다.

  • API 서버 (Django REST). 인증, 메타데이터 저장, 아티팩트 제공.
  • 스케줄러. 작업 큐를 관리하고, 전체 할당량과 사용자별 할당량을 적용하고, 온디맨드와 중 뭘 쓸지 고른다.
  • 작업 실행기 (Celery). VM 생성, 코드 복사, 의존성 설치, 오류 처리.
  • 로깅 서버 (Gin). 동시에 쏟아지는 로그와 메트릭 수집.

API 서버, 스케줄러, 실행기, 작업 인스턴스 사이의 흐름

API 서버가 작업을 큐에 넣으면 스케줄러가 자원을 확인해서 실행기에 넘기고, 실행기가 VM에서 작업을 돌린다. 끝나면 VM이 실행기에 종료를 알리고 자원을 돌려준다. 중지 요청은 API 서버 → 실행기 → VM 순서로 전달된다.

실험의 수명주기

Gin 로깅 서버는 초당 수천 건의 로그와 메트릭을 받아서 실시간 모니터링을 받쳐 준다. 로깅을 따로 떼어 내서 API 서버 부담을 줄이고 확장성도 높였다.

작업 인스턴스

VM 안에는 작업 실행기, INCL Python SDK, 로그와 메트릭 수집기가 들어 있다. SDK로 구성, 메트릭, 아티팩트를 코드 몇 줄로 기록할 수 있다. 그리고 GPU 사용률이 기준치 아래로 떨어지면 VM이 알아서 꺼져서 돈이 새지 않는다.

스토리지는 용도별로

스토리지용도특성
객체코드, 모델 장기 저장저렴, 느림
블록실행 중 임시 데이터빠름, 단일 인스턴스 전용
파일(NFS)학습 데이터다중 인스턴스 동시 접근, 비쌈

그래서 이렇게 나눠 쓴다

코드는 실행 전에 블록 스토리지로 복사해서 I/O 병목을 없애고, 작업이 끝나면 결과를 객체 스토리지로 옮겨서 비용을 아낀다. 학습 데이터는 파일 스토리지에 두고 여러 VM이 동시에 읽는다.

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