View Financial HealthMininglamp Technology 배당 및 자사주 매입배당 기준 점검 0/6Mininglamp Technology 배당금을 지급한 기록이 없습니다.핵심 정보n/a배당 수익률-3.5%자사주 매입 수익률총 주주 수익률-3.5%미래 배당 수익률n/a배당 성장률n/a다음 배당 지급일n/a배당락일n/a주당 배당금n/a배당 성향n/a최근 배당 및 자사주 매입 업데이트업데이트 없음모든 업데이트 보기Recent updates공시 • Apr 16Mininglamp Technology Officially Open-Sources Mano-P 1.0 ModelMininglamp Technology has officially open-sourced Mano-P 1.0, a self-developed GUI-aware agent model capable of executing complex cross-platform tasks entirely through pure vision. By seamlessly controlling desktop software, web interfaces, and complex graphical workflows, Mano-P breaks the "see but not do" limitation of AI, empowering users to build personalized AI while guaranteeing data sovereignty and ushering in the era of privatized personal AI. Unlike traditional automation constrained by API calls or HTML parsing, Mano-P utilizes pure visual understanding to operate without external interfaces. It serves as a critical execution foundation for the Agent ecosystem, integrating seamlessly into agents like OpenClaw. This breakthrough eliminates manual intervention bottlenecks, enabling autonomous execution in complex commercial scenarios such as 3D applications and professional tools that were previously inaccessible to standard agents. The model redefines performance ceilings, achieving overwhelming State-of-the-Art (SOTA) results across 13 authoritative multimodal benchmarks. Operating on a dual-version architecture, the 72B model ranked first globally in the OSWorld benchmark with a 58.2% task success rate, leading the runner-up by 13.2 percentage points, while also dominating systems like ScreenSpot-V2 and MMBench. Furthermore, the highly efficient 4B quantized model supports local execution on Apple M4 Pro devices, achieving up to 476 tokens/s prefill speeds with just a 4.3GB memory footprint. As AI integrates into core workflows, data privacy is paramount. Mano-P ensures zero data uploads to the cloud by running locally on Mac devices or via a dedicated USB 4.0 compute stick. This "pure visual understanding plus local execution" architecture guarantees physical isolation from external networks, allowing AI to securely drive complex business processes and self-correct even in offline, high-privacy enterprise environments where data sovereignty is a non-negotiable requirement. Released under the Apache 2.0 license, Mano-P provides out-of-the-box usage modes, supporting commercial use and secondary development without complex API configurations. Mininglamp Technology is initially open-sourcing the Mano-CUA core skill for easy integration into existing workflows like OpenClaw or Claude Code, with local models and SDKs following within the month. Future releases will include underlying training methods and token pruning technology, accelerating the global development of a personalized AI ecosystem.Reported Earnings • Mar 29Full year 2025 earnings: EPS misses analyst expectationsFull year 2025 results: CN¥137 loss per share (down from CN¥0.18 profit in FY 2024). Revenue: CN¥1.43b (up 3.2% from FY 2024). Net loss: CN¥6.41b (down CN¥6.42b from profit in FY 2024). Revenue was in line with analyst estimates. Earnings per share (EPS) missed analyst estimates significantly. Revenue is forecast to grow 25% p.a. on average during the next 2 years, compared to a 24% growth forecast for the Software industry in Hong Kong.공시 • Mar 26Mininglamp Technology, Annual General Meeting, Jun 12, 2026Mininglamp Technology, Annual General Meeting, Jun 12, 2026.공시 • Mar 16Mininglamp Technology to Report Fiscal Year 2025 Results on Mar 26, 2026Mininglamp Technology announced that they will report fiscal year 2025 results on Mar 26, 2026Board Change • Nov 03No independent directorsFollowing the recent departure of a director, there are no independent directors on the board. The company's board is composed of: No independent directors. 5 non-independent directors. Executive Director, Senior VP & Chief Client Officer Jie Zhao was the last director to join the board, commencing their role in 2024. The company's lack of independent directors is a risk according to the Simply Wall St Risk Model.지급의 안정성과 성장배당 데이터 가져오는 중안정적인 배당: 과거에 2718 의 주당 배당금이 안정적이었는지 판단하기에는 데이터가 부족합니다.배당금 증가: 2718 의 배당금 지급이 증가했는지 판단하기에는 데이터가 부족합니다.배당 수익률 vs 시장Mininglamp Technology 배당 수익률 vs 시장2718의 배당 수익률은 시장과 어떻게 비교되나요?구분배당 수익률회사 (2718)n/a시장 하위 25% (HK)2.7%시장 상위 25% (HK)6.8%업계 평균 (Software)2.8%분석가 예측 (2718) (최대 3년)n/a주목할만한 배당금: 회사가 최근 지급을 보고하지 않았기 때문에 하위 25%의 배당금 지급자에 대해 2718 의 배당 수익률을 평가할 수 없습니다.고배당: 회사가 최근 지급을 보고하지 않았기 때문에 배당금 지급자의 상위 25%에 대해 2718 의 배당 수익률을 평가할 수 없습니다.주주 대상 이익 배당수익 보장: 배당금 지급이 수익으로 충당되는지 확인하기 위해 2718 의 지급 비율을 계산하기에는 데이터가 부족합니다.주주 현금 배당현금 흐름 범위: 2718 에서 지급을 보고하지 않았기 때문에 배당 지속 가능성을 계산할 수 없습니다.높은 배당을 제공하는 우량 기업 찾기7D1Y7D1Y7D1YHK 시장에서 배당이 강한 기업.View Management기업 분석 및 재무 데이터 상태데이터최종 업데이트 (UTC 시간)기업 분석2026/05/20 00:40종가2026/05/20 00:00수익2025/12/31연간 수익2025/12/31데이터 소스당사의 기업 분석에 사용되는 데이터는 S&P Global Market Intelligence LLC에서 제공됩니다. 아래 데이터는 이 보고서를 생성하기 위해 분석 모델에서 사용됩니다. 데이터는 정규화되므로 소스가 제공된 후 지연이 발생할 수 있습니다.패키지데이터기간미국 소스 예시 *기업 재무제표10년손익계산서현금흐름표대차대조표SEC 양식 10-KSEC 양식 10-Q분석가 컨센서스 추정치+3년재무 예측분석가 목표주가분석가 리서치 보고서Blue Matrix시장 가격30년주가배당, 분할 및 기타 조치ICE 시장 데이터SEC 양식 S-1지분 구조10년주요 주주내부자 거래SEC 양식 4SEC 양식 13D경영진10년리더십 팀이사회SEC 양식 10-KSEC 양식 DEF 14A주요 개발10년회사 공시SEC 양식 8-K* 미국 증권에 대한 예시이며, 비(非)미국 증권에는 해당 국가의 규제 서식 및 자료원을 사용합니다.별도로 명시되지 않는 한 모든 재무 데이터는 연간 기간을 기준으로 하지만 분기별로 업데이트됩니다. 이를 TTM(최근 12개월) 또는 LTM(지난 12개월) 데이터라고 합니다. 자세히 알아보기.분석 모델 및 스노우플레이크이 보고서를 생성하는 데 사용된 분석 모델에 대한 자세한 내용은 당사의 Github 페이지에서 확인하실 수 있습니다. 또한 보고서 활용 방법에 대한 가이드와 YouTube 튜토리얼도 제공합니다.Simply Wall St 분석 모델을 설계하고 구축한 세계적 수준의 팀에 대해 알아보세요.산업 및 섹터 지표산업 및 섹터 지표는 Simply Wall St가 6시간마다 계산하며, 프로세스에 대한 자세한 내용은 Github에서 확인할 수 있습니다.분석가 소스Mininglamp Technology는 1명의 분석가가 다루고 있습니다. 이 중 1명의 분석가가 우리 보고서에 입력 데이터로 사용되는 매출 또는 수익 추정치를 제출했습니다. 분석가의 제출 자료는 하루 종일 업데이트됩니다.분석가기관Liping ZhaoChina International Capital Corporation Limited
공시 • Apr 16Mininglamp Technology Officially Open-Sources Mano-P 1.0 ModelMininglamp Technology has officially open-sourced Mano-P 1.0, a self-developed GUI-aware agent model capable of executing complex cross-platform tasks entirely through pure vision. By seamlessly controlling desktop software, web interfaces, and complex graphical workflows, Mano-P breaks the "see but not do" limitation of AI, empowering users to build personalized AI while guaranteeing data sovereignty and ushering in the era of privatized personal AI. Unlike traditional automation constrained by API calls or HTML parsing, Mano-P utilizes pure visual understanding to operate without external interfaces. It serves as a critical execution foundation for the Agent ecosystem, integrating seamlessly into agents like OpenClaw. This breakthrough eliminates manual intervention bottlenecks, enabling autonomous execution in complex commercial scenarios such as 3D applications and professional tools that were previously inaccessible to standard agents. The model redefines performance ceilings, achieving overwhelming State-of-the-Art (SOTA) results across 13 authoritative multimodal benchmarks. Operating on a dual-version architecture, the 72B model ranked first globally in the OSWorld benchmark with a 58.2% task success rate, leading the runner-up by 13.2 percentage points, while also dominating systems like ScreenSpot-V2 and MMBench. Furthermore, the highly efficient 4B quantized model supports local execution on Apple M4 Pro devices, achieving up to 476 tokens/s prefill speeds with just a 4.3GB memory footprint. As AI integrates into core workflows, data privacy is paramount. Mano-P ensures zero data uploads to the cloud by running locally on Mac devices or via a dedicated USB 4.0 compute stick. This "pure visual understanding plus local execution" architecture guarantees physical isolation from external networks, allowing AI to securely drive complex business processes and self-correct even in offline, high-privacy enterprise environments where data sovereignty is a non-negotiable requirement. Released under the Apache 2.0 license, Mano-P provides out-of-the-box usage modes, supporting commercial use and secondary development without complex API configurations. Mininglamp Technology is initially open-sourcing the Mano-CUA core skill for easy integration into existing workflows like OpenClaw or Claude Code, with local models and SDKs following within the month. Future releases will include underlying training methods and token pruning technology, accelerating the global development of a personalized AI ecosystem.
Reported Earnings • Mar 29Full year 2025 earnings: EPS misses analyst expectationsFull year 2025 results: CN¥137 loss per share (down from CN¥0.18 profit in FY 2024). Revenue: CN¥1.43b (up 3.2% from FY 2024). Net loss: CN¥6.41b (down CN¥6.42b from profit in FY 2024). Revenue was in line with analyst estimates. Earnings per share (EPS) missed analyst estimates significantly. Revenue is forecast to grow 25% p.a. on average during the next 2 years, compared to a 24% growth forecast for the Software industry in Hong Kong.
공시 • Mar 26Mininglamp Technology, Annual General Meeting, Jun 12, 2026Mininglamp Technology, Annual General Meeting, Jun 12, 2026.
공시 • Mar 16Mininglamp Technology to Report Fiscal Year 2025 Results on Mar 26, 2026Mininglamp Technology announced that they will report fiscal year 2025 results on Mar 26, 2026
Board Change • Nov 03No independent directorsFollowing the recent departure of a director, there are no independent directors on the board. The company's board is composed of: No independent directors. 5 non-independent directors. Executive Director, Senior VP & Chief Client Officer Jie Zhao was the last director to join the board, commencing their role in 2024. The company's lack of independent directors is a risk according to the Simply Wall St Risk Model.