Minhee Cho @ingray

현재

에이스번 @kiwibird_0810

2 months ago

아 그래 찾았다

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Anthropic just dropped 5 workshops, revealing the latest capabilities of Fable 5: • 00:00 - deep look into Fable 5 • 11:22 - Fable 5 and the capability curve • 30:54 - building managed agents with Fable 5 • 44:29 - real use cases of Fable 5 by teams • 57:43 - how to deploy agents with Fable 5 These 1-hour of sessions will replace 100 articles on how to actually use Fable 5. Watch them today, then read the best practices from the sessions in the article below.

Codez @0xCodez

3 months ago

x.com/i/article/2065…

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都市には「観光客にしか見えていない場所」と「地元民にしか見えていない場所」がある。 Eric Fischer氏の「Locals and Tourists」は、Flickr等の写真のジオタグ（位置情報）を解析し、誰がどこを撮影したかを地図上にプロットした、都市のデータ可視化（データ・ビジュアライゼーション）の先駆的な事例。 🔴赤 = 観光客 🔵青 = 地元民 🟡黄 = 判定不能（どちらの可能性もある場所） 興味深いのは、同じ空間のなかで、赤と青のスポットが微妙にズレた広がりを見せている点。旅先としての非日常の都市と、生活の場としての日常の都市。その「二重性」が写真撮影場所のデータだけでここまでクリアに見えてくるのが面白い。 🗺️ 出典：Eric Fischer, Locals and Tourists, 2010 flickr.com/photos/walking… #データ可視化 #情報デザイン #都市計画 #地図

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그런 생각을 했다. ‘지금이 한국에서 탄생한 소프트웨어 스타트업이 글로벌 1등에 도전하기 가장 좋은 시기가 아닐까?’ ingray.net/2026/06/05/glo…

🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude. Taught by the people who built it. Free. No registration. No paywall. I've seen $300 courses that don't cover what they teach in the first 8 minutes. Watch it and bookmark it now.

어제 샌프란시스코에서 찍은 사진 ‘5개의 도구를 1명의 AI 직원으로 대체하세요‘라는 광고 그 앞의 홈리스

A 22-year-old graduate student in Kazakhstan got so angry at journal paywalls in 2011 that she built a pirate website holding 88 million scientific papers, and last month she turned the whole thing into an AI that lets you ask one question and get the actual research as the answer. Her name is Alexandra Elbakyan, and the website is called Sci-Hub. The AI she just launched is called Sci-Bot. It lives at sci-bot.ru and almost nobody outside academia knows it exists yet. Here is the story, because it is one of the strangest things to happen in science publishing in the last 50 years. Elbakyan was born in Almaty in 1988, the year the Soviet Union started to collapse. She taught herself programming at 12. She read Soviet science books that explained things her family used to call miracles. She got into computer security at university and graduated in 2009 with a degree she barely needed because by then she was already a serious hacker. Alexandra moved to Moscow that fall. Then Germany. Then a research internship in the United States. She was working on brain-computer interfaces, the kind of research that requires you to read hundreds of papers a year just to keep up with the field. And every single one of those papers was locked behind a journal paywall that cost between 30 and 50 dollars to read once. She did the math. A graduate student in Kazakhstan could not afford to read science. The first thing she did was learn how to get around the paywalls one paper at a time. She passed the trick around to other students. They asked her for papers constantly. She got tired of doing it manually. So in September 2011, in three days, she wrote a script that automated the whole thing. A user pastes a DOI. The script logs in through a donated institutional credential. The paper comes back free. The website caches it. The next person who asks for that paper gets it instantly because the previous request already saved a copy. That was Sci-Hub. Three days of code. One graduate student. Done. 15 years later, the cache holds 88 million scientific papers. Almost every piece of scholarly literature published before 2020 is sitting on her servers. Researchers in 190 countries use it. Studies in Nature have shown that roughly half of all academic paper downloads worldwide now go through Sci-Hub, not the publishers who actually own the copyrights. Elsevier sued her in 2015 and won a 15 million dollar judgment. She did not pay. The American Chemical Society sued her and won an injunction. She did not comply. Courts in India, France, Russia, and the UK have tried to block the domain. She just moves it. Sci-hub.se. Sci-hub.ru. Sci-hub.ee. The site has had over 20 domains and is still up. Nature put her on its list of the 10 people who mattered most to science in 2016. The New York Times compared her to Edward Snowden. The Verge called her the pirate queen of science. She has not been to the United States in over a decade because she would be arrested at the airport. The Sci-Bot launch in April 2026 is the part that nobody is talking about. She took the 88 million paper database and put a small language model on top of it. You ask a question in plain English. The model searches the entire shadow library, pulls the relevant papers, synthesizes an answer grounded in real citations, and links you to the full text of every source. Free. No login. No institutional credential. No paywall. Three real scientists tested it for a Chemical and Engineering News article last month. They asked it medical and chemistry questions. The radiologist said the answer he got was usable. The chemist said the gaps in recent literature were obvious but the older science was solid. The publisher community is furious. What she built is what the paid academic AI tools are trying to build. Except the paid ones are limited to what their parent publisher legally owns. Hers is limited to almost nothing. Alexandra still lives somewhere in Russia. She does not give her address. She does not do video interviews. She gives talks over Skype with the camera off. She runs the largest illegal library in human history from a laptop and a donation page. A graduate student who could not afford to read science built the system the entire scientific community now quietly depends on. The publishers have spent a decade trying to shut her down. She just shipped an AI that makes their entire business model outdated.

Anthropic pays $750,000+ a year for engineers who know how to build LLMs from scratch. Stanford just released the exact lecture that teaches it - 1 hour 44 minutes, free, straight from CS229. Bookmark and watch it this weekend. It'll teach you more about how ChatGPT & Claude actually work than most people at top AI companies learn in their entire careers.

Rahul @sairahul1

4 months ago

x.com/i/article/2057…

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instead of watching 2 hours of Netflix tonight, watch this 40-minute masterclass from the founder of a $20B China AI company it's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale useful whether you've never built an agent in your life or have been using Claude every day for the past year I took the key ideas and turned them into a practical guide on how to actually build with Kimi find it below

Kirill @kirillk_web3

4 months ago

x.com/i/article/2056…

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MIT students gave AI a body. the camera sees what’s in front of you. you say what you want. the device moves your fingers with small electric pulses. it plays piano without training. it draws what you describe. it mixes a drink while you watch your own arm do it. the brain is Claude. six people built this in 48 hours. and this is just the hand.

Atlas hauling a 50 lb mini-fridge - Practiced the maneuver for millions of hours in a virtual environment. - Focused the training policy on full-body engagement rather than just hand-grasping, allowing the robot to leverage its entire frame for the lift.

Congrats to Aime!! He said his left forearm is basically broken 😂 Final scores: → F.03: 12,732 packages (2.83 seconds/package) → Aime: 12,924 packages (2.79 seconds/package) This is the last time a human will ever win

Joined a new AI-native company this week and it’s kind of wild how different it feels already. The laptop arrived, I logged in, and an agent basically took over from there. It set up my dev env, pulled repos, fixed dependency issues, got permissions approved, pointed me at the backlog, linked the architecture docs, and surfaced the Slack debates I actually needed to read before touching production. When I needed context on something, I asked the agent and it found the exact thread from months ago explaining why a decision was made, who owned it, the related Linear issues, and the PRs connected to it. I’ve only been here 3 days but it honestly feels like I’ve worked here for a year because the usual friction and scavenger hunt for context just isn’t there anymore. We should probably stop calling this “onboarding” and rename it to “mounting” because this feels a lot more like mounting a distributed filesystem called “institutional memory” than slowly getting drip-fed context over 6 months.

stop what you're doing and look at this image. each dot is 3.2 million people. 2,500 dots = 8.1 billion humans. the grey? 6.8 billion people who have never used AI. the green? 1.3 billion free chatbot users. the yellow? 15-35 million who pay for it. the red? that tiny sliver is us. you think the AI space is crowded because you're in an echo chamber of the 0.06%. the real world hasn't even started. wrote a full breakdown on the data, the opportunity, and 7 businesses you can build from this gap today:

Nozz @NoahEpstein_

7 months ago

x.com/i/article/2025…

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BREAKING: New startup "RentAHuman" allows AI agents to rent humans to perform tasks they cannot physically perform themselves.

포켓몬 GO가 숨겨왔던 거대 AI 데이터 수집의 진실 잠만보 잡는 줄 알았는데 알고보니 AI 데이터 수집 > 1억 4,300만 명이 포켓몬을 잡는 줄 알았음 > 사실은 인류 역사상 가장 큰 AI 시각 데이터셋을 무보수로 구축하고 있었음 > 나이언틱이 8년 동안 수집한 데이터만 300억 장 이상의 이미지와 AR 스캔임 > 유저들이 전 세계의 랜드마크, 상점, 거리, 공원을 모든 각도에서 촬영함 > 시간대, 조명, 날씨 등 기존 지도 회사가 막대한 비용을 들여도 불가능한 데이터를 수집함 > 나이언틱은 이 데이터를 '배달 로봇용 자율주행 AI' 학습에 사용하고 있다고 공개함 > 사람들은 희귀한 리자몽을 쫓는 줄 알았지만, 실제로는 AI 인프라를 건설한 셈임 > 가장 가치 있는 AI 데이터는 연구소가 아니라, 자신이 무엇을 하는지 모르는 대중들에 의해 만들어지고 있음 사람들은 게임을 즐겼다고 생각했지만, 실제로는 전 지구적 규모의 데이터 노동에 참여하고 있었다.

NewsForce @Newsforce

6 months ago

POKÉMON GO PLAYERS TRAINED 30 BILLION IMAGE AI MAP Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images. The company is now using that data to power visual navigation for delivery

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<실리콘밸리는 왜 '제품 개발의 종말'을 말하는가? : 10년 만의 SF 출장기> 나를 계속 지켜본 가족보다 오랜만에 만난 친구가 내 변화를 더 잘 캐치하는 것처럼, 이번 글은 10년 만의 방문자가 느낀 ‘변화’에 대한 이야기다. blog.alicorn-space.ai/2026/01/silico…

실리콘밸리에서 전 세계 사람이 다 아는 회사의 개발자와 저녁을 먹었다. 그 회사는 작성되는 코드의 90% 이상이 AI로 작성되지 않으면, CTO가 따로 불러서 생산성을 높이라고 한소리 한다고 한다. AI가 코드 대부분을 작성할 수 있는 환경을 구축하지 목하면 결국 도태되겠다는 생각이 들었다.

업스테이지 Solar 관련(오후에 공개검증 예정) 현재까지 주류 시각 정리 1.현재까지 공개된 기술 자료만 놓고 보면, Solar가 GLM을 베껴 만든 ‘파생 모델’이라는 주장을 뒷받침할 결정적 증거는 없다고 보는 것이 합리적이다. 2.지금 제기된 의혹들은 주로 LayerNorm, 코사인 유사도 같은 일부 수치 지표에 의존하고 있는데, 이 지표들은 구조적 동일성이나 도용 여부를 판단하기엔 통계적 신뢰도와 식별력이 매우 제한적하다는 점이 점점 분명해지고 있다. 3.반면 모델 정체성을 좌우하는 핵심 영역인 Q·K·V 구조, 어텐션 메커니즘, MoE 라우팅, 토크나이저를 살펴보면, 오히려 GLM 파생 모델이라는 가설과 일치하지 않는 결과들이 반복적으로 확인되고 있다. 즉, “진짜 지문”이라고 할 수 있는 핵심 요소에서 동일성을 입증하는 증거가 나오지 않고 있다. 4.결국 초반 의혹 제기는 LayerNorm 중심 해석에 과도하게 의존한 검증 설계의 한계에서 비롯된 것으로 보이며, 현재까지 공개된 기술적 증거만 기준으로 보면 Solar를 GLM 파생 모델로 단정할 근거는 부족하다. 5.다만 이번 논쟁이, 앞으로 대형 AI 모델을 검증할 때 •어떤 증거가 필요하고 •어떤 기준으로 ‘독자 모델’ 여부를 판단할지 업계가 보다 엄밀하고 투명한 검증 체계를 고민하게 만드는 계기가 되었다는 점에서는 분명 의미가 있다. ◾️여기에 하나 더 중요한 점이 있다. 오픈소스 모델을 기반으로 더 좋은 모델을 만들고, 효율성과 비용 최적화에 집중하는 생태계 자체를 부정적으로만 볼 필요는 없다는 것이다. 지금의 LLM 산업은 •완전히 새로 만드는(from scratch) 모델과 •공개된 모델을 기반으로 성능·안정성·비용 효율을 극대화하는 모델이 함께 발전하며 경쟁하는 구조로 이미 진화하고 있다. 따라서 중요한 것은 “출발이 어디였느냐”를 단순하게 낙인찍는 것이 아니라, 투명성, 정직한 출처 표기, 그리고 실제로 더 나은 성능·가치·효율을 만들어내느냐다. 이 관점이 빠지면, 오픈소스 생태계의 본질적 가치와 혁신 동력을 오히려 스스로 훼손하게 된다. 결국 이번 논의에서 우리가 집중해야 할 지점은 논란을 키우고 공방에 매몰되는 것이 아니라, •신뢰 가능한 검증 기준을 어떻게 만들지 •건강한 경쟁과 협력이 공존하는 생태계를 어떻게 설계할지 •더 좋은 기술이 더 빠르게 발전할 수 있는 환경을 어떻게 유지할지 “상호 발전하는 AI 생태계”라는 더 큰 그림에 두는 것이 맞다. 논란보다 중요한 것은, 업계 전체가 서로를 상호 분석/연구하면서도 동시에 함께 성장하는 구조를 만드는 것이다.

GUTE_RachelHan @GuteslaX

9 months ago

이런 이슈가 있었더랬다. 업스테이지의 모델에 대한 의혹을 제기한 사이오닉 에이아이…;;;

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Netflix hit 'KPop Demon Hunters' is TIME's Breakthrough of the Year time-magazine.visitlink.me/yiJSst