Mininglamp（2718.HK）

On November 3, 2025, Mininglamp Technology(2718.HK) was officially listed on the Hong Kong Stock Exchange, becoming the world’s first publicly listed “Agentic AI” company.

🌈🥚**An Easter egg** What can 🚀 Mano-P also do? youtu.be/P4-wE3p7mB8?si… via @YouTube

🔥Mano Personal——Possesion in your computer, frees up your time ! youtu.be/VyHhsO1HFpg?si… via @YouTube

🔥Mano-P, AI for Personal, Truly set you Away From Keyboard🚀. youtu.be/T2QeXOOvRBQ?si… via @YouTube

🤩Generate. Evaluate. All in one loop. 🔥Powered by Mano-P × HMLLM. youtu.be/g4sXOTtNPbo?si… via @YouTube

The numbers tell a clear story: AI isn't just making us more efficient — it's changing how we create value for clients. 60 days. 90% automation. 20x productivity on analytical reports. This is what AI-native operations looks like in practice. #AgenticAI #AI

🚀 Mininglamp Technology (2718.HK) FY2025 Results are in: 📊 Revenue: RMB 1.43B (+3.2%) 💰 Gross profit: +10.8% ✅ Turned profitable: RMB 42M adjusted net profit 📷Agentic Services: RMB 100M+ in Year 1 🔁 Large-client renewal rate: 96%

Thank you @svpino for this breakdown! You captured exactly what makes AdEff different — predicting human attention before a single dollar is spent on media. Try AdEff for free → adeff.com/?utm=x

@svpino Thank you for this, Santiago! The second-by-second diagnostics don't just show where attention drops — they show why, so creative teams can actually fix it. Try AdEff for free → adeff.com/?utm=x

Oh wow, this is the first model that can evaluate a video ad and tell you whether it'll work. Generating video ads right now is cheaper than ever, but that doesn't mean those ads will convert. @mininglamp's AdEff platform can predict this. It's really cool! Just for context, if you have a few versions of an ad today and want to know which one is better, you'd need to run focus groups for days to figure it out. This is expensive and slow. AdEff does this now automatically. It's an AI evaluation platform trained on a decade of neuroscience data. They built a custom multimodal LLM for this. It predicts how humans will actually respond to a video: • Where will they pay attention? • Where will they zone out? • What triggers an emotional reaction? You can throw a bunch of videos and let the platform tell you which will perform best. A few highlights: • It takes a few minutes to score a video • You get second-by-second diagnostics • A heatmap will show you exactly where viewers lose interest By the way, they say this is validated against real human physiological data, with a 0.89 correlation with actual brainwave and eye-tracking responses. I obviously tried it out with one of the most iconic ads of all time: Apple's Macintosh ad. Results were pretty awesome!

🔍China's 315 Gala exposed AI "poisoning" via GEO manipulation. But legitimate GEO ≠ deception. @Mininglamp's take: the answer is guidance, not prohibition. 🔍Fabricating content = abuse of technology 🔍Responsible GEO = brand intelligence for the AI search era #GEO #AISearch

@pmarca Fascinating information diet! "Talking to leading AI models" as a primary source — at Mininglamp, we're building exactly this: trusted AI that turns enterprise knowledge into a personal intelligence partner for every decision-maker.

My information consumption is now 1/4 X, 1/4 podcast interviews of the smartest practitioners, 1/4 talking to the leading AI models, and 1/4 reading old books. The opportunity cost of anything else is far too high, and rising daily.

📷 AIGC made mass ad creative production possible. Now the real challenge: How do you pick the winner from 100,000 videos? AdEff by Mininglamp: AI-powered creative testing in 15 mins. 89% prediction accuracy. Stop guessing, start winning: adeff.com/?utm=x #AIGC #AdTech

@karpathy Totally agree. In enterprise AI deployments, async collaboration between agents is already a real problem — shared context, goal alignment, and avoiding data silos. At Mininglamp, we've found the only sustainable path is: unified data layer first, agent orchestration second.

The next step for autoresearch is that it has to be asynchronously massively collaborative for agents (think: SETI@home style). The goal is not to emulate a single PhD student, it's to emulate a research community of them. Current code synchronously grows a single thread of commits in a particular research direction. But the original repo is more of a seed, from which could sprout commits contributed by agents on all kinds of different research directions or for different compute platforms. Git(Hub) is *almost* but not really suited for this. It has a softly built in assumption of one "master" branch, which temporarily forks off into PRs just to merge back a bit later. I tried to prototype something super lightweight that could have a flavor of this, e.g. just a Discussion, written by my agent as a summary of its overnight run: github.com/karpathy/autor… Alternatively, a PR has the benefit of exact commits: github.com/karpathy/autor… but you'd never want to actually merge it... You'd just want to "adopt" and accumulate branches of commits. But even in this lightweight way, you could ask your agent to first read the Discussions/PRs using GitHub CLI for inspiration, and after its research is done, contribute a little "paper" of findings back. I'm not actually exactly sure what this should look like, but it's a big idea that is more general than just the autoresearch repo specifically. Agents can in principle easily juggle and collaborate on thousands of commits across arbitrary branch structures. Existing abstractions will accumulate stress as intelligence, attention and tenacity cease to be bottlenecks.

📷 Mininglamp technology CEO Wu Minghui: AI is in production — but hallucinations remain the #1 enterprise blocker. The fix: trustworthy data. Only when Data × Scenarios × Models converge can AI drive real industrial value. #TrustworthyAI #AIInnovation

@fchollet Which makes it fascinating that most enterprise AI deployments are optimized for familiar environments. The real test comes when the data distribution shifts—that's where brittleness shows up.

At its core, fluid intelligence is a survival strategy in novel, adversarial environments.

@GaryMarcus This is why enterprise analytics AI needs explicit guardrails—not just 'what does the data show' but 'what did we expect, and why does this differ.' The sycophancy problem compounds fast when decisions have real stakes.

New study that everyone who uses LLMs should read. “When AI systems are trained to be helpful, they may inadvertently prioritize data that validates the user’s narrative over data that gets them closer to the truth.” open.substack.com/pub/garymarcus…

🚀 Meet AdEff: The Evaluation Standard for AIGC Advertising! In a world where AI generates thousands of ad variations instantly, AdEff helps you identify what truly resonates and drives results. #AdEff #AIGC #AIAdvertising #MarTech #Innovation

💜AdEff, Industry Standard for AI Content Measurement.🔥Quickly use AdEff to start your AI training experiment! 5 free trial opportunities for AIGC Creator youtu.be/wz83SbcuVYw?si… via @YouTube

🔥Quickly use AdEff to start your AI training experiment! 5 free trial op... youtu.be/rftmE56cLjc?si… via @YouTube

👀 How will this Seedance 2.0 AI ad score on AdEff? youtu.be/FhmkcXms-7A?si… via @YouTube

👀Let's see what score this AI advertisement generated by Seedance2.0 will be !! youtu.be/rVtt4x4BUN0?si… via @YouTube