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미니맥스가 텍스트와 이미지, 영상, 오디오를 하나의 모델에서 처리하는 차세대 멀티모달 영상 생성 모델 'H3'를 공개했다. 이 모델은 아티피셜 애널리시스의 각종 벤치마크에서 최상위권을 기록, 바이트댄스의 '시댄스(Seedance)'와 경쟁할 만하다는 평가를 받았다.미니맥스는 31일 범용 멀티모달 생성 모델 H3를 공식 발표하고 '하이루오(Hailuo)' AI 플랫폼과 개발자 API를 통해 서비스를 시작했다.또 며칠 안에 H3의 모델 가중치를 미니맥스 커뮤니티 라이선스로 공개할 계획이다. 이를 통해 연 매출 2000만달러(약 29억원

For everyone who makes documents with AI Your AI made a beautiful document. Deliver it beautifully. An .html attachment looks unprofessional, and printing to PDF flattens the charts. SendPage turns your document into a link your client opens in one tap — exports it to PDF, image or Word when they want a file, and lets you fix a typo without sending a new link. Paste your HTML — free See a live example → No signup · 5 free documents a month · Works in WhatsApp, WeChat and email Q3 Growth Proposal Shared by Jack from Acme · 30 Jul PDF Q3 Growth Proposal Prepared for Suria Retail Group +38% revenue 1.2M budget 4 mo payback SR Suria Retail Group online Can you send over the proposal? Here it is 👇 Q3 Growth Proposal Q3 Growth Proposal Shared by Jack from Acme · Opens in one tap sendpageapp.com 11:42 ✓✓ Paste it here. Get your link. The whole thing takes about thirty seconds, and you keep the link forever. Paste HTML Upload file How it appears in WhatsApp Your document title Your document title Interactive document · Open in one tap Here's the document 👆 Create share link — free No signup needed · 5 free documents a month Why SendPage? Everyone has an AI that writes a beautiful document in seconds. What it hands you is a file full of HTML — and there is no good way to send that to a client. Attached to an email it looks suspicious and will not open properly on a phone. Printing it to PDF flattens the charts and cuts the wide tables in half. A screenshot throws away the text. So the

Earlier today, I used Google Earth to conjure images of terrible things: office buildings on fire, mass destruction in San Francisco, a smoldering crater in place of the Eiffel Tower. I was able to because Google had just decided to integrate its AI image generator, Nano Banana, into the service, which has historically been used by people seeking satellite or aerial imagery of real-world locations. Just open the site, “pick a spot on the map, and start bringing your ideas to life,” Google wrote in a press release announcing the update some 24 hours ago. So I did. I was easily able to generate those violent images simply by searching for a location on Google Earth, clicking the “Create image” icon, and typing in a few words. The feature also readily doctored stills of less spectacular, and likely less immediately falsifiable, events: a homeless encampment on the White House lawn, protesters outside the Mar-a-Lago resort, immigrants streaming over the U.S.-Mexico border. Google initially responded by directing people to check any suspicious images themselves. As fake images generated with this new Google Earth feature began circulating the web, the company stated that it takes “misinformation seriously” and that “every image created with Nano Banana in Google Earth includes the SynthID digital watermark, so if someone is unsure about an image, they can ask the Gemini app or use Lens in Search to see if the image was AI-generated.” At first, Google did not remove or restrict acc

Building software has never felt this accessible. You describe an idea in plain English, and within minutes, a working prototype appears on your screen. It has a UI. It connects to a database. It does the thing you imagined. For someone who has never written a line of code, that moment feels like magic. For someone who has spent years wrestling with compilers and stack traces, it's genuinely astonishing. The prototype runs on your laptop. It breaks under load. It has no error handling. You find out that it may be leaking your API tokens. The data model made sense for the demo but falls apart the moment you add a second user. The authentication is held together with assumptions. There's a nagging worry whether everything is secure. You want to deploy it, and suddenly you're staring at a chasm between "this works" and "this is ready." Getting to a prototype was never the hard part Software engineers have always been able to get something running quickly. What took time was everything else: designing systems that hold up at scale, handling the cases users weren't supposed to encounter but inevitably do, building in observability so you know when things break, making deliberate decisions about data architecture that you'll regret less three years from now. None of that has changed. AI has dramatically accelerated the path to a first working version. It has not shortened the distance between a first working version and something production-grade. The confusion arises because the f

Is AI Already Conscious? (FULL EPISODE) - YouTube 정보 보도자료 저작권 문의하기 크리에이터 광고 개발자 약관 개인정보처리방침 정책 및 안전 YouTube 작동의 원리 새로운 기능 테스트하기 © 2026 Google LLC, Sundar Pichai, 1600 Amphitheatre Parkway, Mountain View CA 94043, USA, 0807-882-594 (무료), yt-support-solutions-kr@google.com, 호스팅: Google LLC, 사업자정보 , 불법촬영물 신고 크리에이터들이 유튜브 상에 게시, 태그 또는 추천한 상품들은 판매자들의 약관에 따라 판매됩니다. 유튜브는 이러한 제품들을 판매하지 않으며, 그에 대한 책임을 지지 않습니다.

Comment Loader Save Story Save this story Comment Loader Save Story Save this story The prevailing narrative in Silicon Valley is that Chinese AI models like Kimi K3 and DeepSeek R1 came out of nowhere. But the truth is that even if you never travel to China, there’s an easy way to get a sense of what Chinese AI researchers are up to: log on to X. In the past year, I’ve seen a steady increase of Chinese artificial intelligence researchers joining X and participating in the wider discourse about AI development and commercialization. Take Moonshot AI, the company behind Kimi K3, as an example. On Thursday, I was able to quickly find around 30 accounts on X run by people who claim to be currently affiliated with Moonshot, including two of the startup’s cofounders, as well as half a dozen former employees and collaborators. And these are not dormant accounts: They talk about Moonshot’s big releases and incremental research papers, make friends with Western researchers, and share details about their personal life and hobbies. Moonshot is arguably the Chinese AI lab that’s most active on X, but you will easily find executives and technical staff from other leading Chinese startups, like Minimax and Z.ai. Even several employees at DeepSeek, who reportedly are unable to leave China because the government confiscated their passports, regularly post on X about the latest AI news and share job listings to recruit new talent. “X is the best community” for AI discussions right now, says M

현지 시각 7월 28일 AI 탐지 기업 GPTZero가 PwC 중동 법인이 2024년부터 2026년 사이 발행한 보고서 4건에서 존재하지 않는 출처와 각주를 찾아냈다고 공개했다. 파이낸셜타임스는 다음 날 이 조사 결과를 자체 검증해 보도했다. 대상은 감사보고서나 고객 납품물이 아니라 컨설팅 수주용으로 만든 소트 리더십 자료다. 가장 문제가 된 것은 시티즌 펄스라는 이름의 PwC 자체 프레임워크다. 보고서는 덴마크와 사우디아라비아, 미국, 호주 정부가 이 프레임워크를 쓴다고 적었지만, GPTZero는 제품이 존재한다는 증거도 네 나라가 도입했다는 근거도 찾지 못했다. 조작으로 확정된 인용은 4건이다. 리야드 대기질을 다뤘다는 논문과 MIT 테크놀로지 리뷰 기사, 세계경제포럼 2018년 보고서가 각각 제목부터 존재하지 않았다. 한 보고서의 각주 주소에는 챗GPT를 거쳐 만들어졌음을 보여주는 흔적이 그대로 남아 있었다. JP모건 사례의 근거로는 팔로워 280명인 10대 블로거의 미디엄 글이 인용됐다. 해당 사례 자체는 챗GPT 등장 이전인 2017년에 보도된 내용이다. GPTZero 검사에서 보고서 한 건의 AI 생성 확률은 84%로 나왔고, 참고문헌을 빼고 계산하면 100%였다. PwC 중동은 파이낸셜타임스에 발행 연구의 정확성을 중요하게 여긴다며 일부 인용 출처를 수정하고 있다고 답했다. 오류가 어떻게 생겼는지는 밝히지 않았고 보고서를 철회하지도 않았다. 앞서 EY와 KPMG는 비슷한 문제로 보고서를 철회한 바 있다. 자세한 내용은 GPTZero 에서 확인할 수 있다. 이미지 출처: GPTZero 관련기사 AI에게 자판기 운영 맡겼더니 6번 다 담합…5는 배신·거짓말까지 2026.08.01 美 상원 6인, 애플에 "중국산 메모리 쓰지마"…SK하이닉스 미국 공장도 근거로 2026.08.01 유니트리, 바퀴 달린 4족 로봇 공개…시속 21.6km에 80cm 장애물 넘는다 2026.07.29 반도체 제조장비 시장도 호황…글로벌 기업들 실적 눈높이 상향 2026.08.01 ■ 이 기사는 AI 전문 매체 ‘ AI 매터스 ’와 제휴를 통해 제공됩니다. 기사는 클로드 3.5 소네트와 챗GPT를 활용해 작성되었습니다. (☞ 기사 원문 바로가기 )