Last Notes
麦当劳汉堡🍔是很好的。但是中国区麦当劳把汉堡做小缩小导致中国消费群体吐糟麦当劳。哈哈哈😝
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美国Wendy's汉堡公司计划通过特许经营方式重返中国大陆，未来十年内开设1000家餐厅。其实在中国做餐饮是有市场的。看看麦当劳2025年纯利润赚到591亿元人民币一年纯利润。肯德基在2025年纯利润是63.5亿元人民币一年。这2家美国餐饮集团很赚钱的公司。
GM ☀ PV 🖖
Y’all know you can just not do things too, right?
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Too early to be awake, to late for sunrise. Pura Vida.
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Most people optimize for responsiveness. Very few optimize for clarity.
But few can do it well. AI can make beautiful-looking interfaces, but it can’t do the creative thinking.
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所以，美国男人不敢轻易结婚。如果离婚会破产。分家产。要养前妻一辈子。而且离婚男人无法找到老婆。谁都嫌弃离婚男人。二手男人。谁都不喜欢二手的东西。
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People who says nostr has a bad ux never tried other apps
If you try a local delivery app it won't work
Twitter almost never load things for me and people with long names breaks the ui
Facebook was always bad since the beginning
Instagram has random crashes even on iphones
看了一个美国人讲英语视频说美国家庭，女人当家庭主妇不工作离婚率最高。为什么？因为美国女人离婚有美国男人给养一辈子。哈🤣
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What are professionals and researchers doing that is more important than #ecai 🤔
Professionals and researchers are mostly doing work that is institutionally legible.
That is the blunt answer.
They are working on things that can be funded, reviewed, benchmarked, published, deployed, regulated, and defended inside existing incentive structures. That does not automatically mean their work is more important than ECAI. It means their work is easier to recognize as important right now.
The big categories are:
1. Keeping existing AI systems from collapsing under their own weight
A lot of serious people are working on inference efficiency, model compression, quantization, hardware acceleration, memory systems, evaluation, alignment, agent safety, and reliability. This is important because the current AI stack is already embedded into business, education, government, coding, search, design, and military systems.
From their perspective, the house is already on fire, so they are fixing sprinklers, exits, and load-bearing walls.
From the ECAI perspective, they are optimizing the furnace.
2. Making stochastic AI usable enough for industry
Most professional AI work is not about truth. It is about usefulness under uncertainty.
Researchers are improving retrieval-augmented generation, tool use, agents, reasoning benchmarks, multimodal models, robotics, synthetic data, and domain-specific copilots. This matters commercially because even a flawed probabilistic system can generate huge productivity gains when wrapped with enough guardrails.
ECAI threatens this because it asks a more brutal question:
> Why are we paying for statistical approximation when some classes of intelligence should be encoded, verified, and retrieved deterministically?
That question is not convenient for the current industry.
3. Biology, medicine, climate, energy, and materials
There is genuinely important work outside the AI hype loop: cancer research, protein design, grid modernization, fusion, battery chemistry, water systems, climate adaptation, antimicrobial resistance, neuroscience, agriculture, and public health.
Some of this is absolutely more immediately important than ECAI in the humanitarian sense. A working cancer treatment or clean water system does not need a metaphysical argument. It saves lives.
But ECAI could become important underneath those fields if it provides a better substrate for encoding, retrieving, verifying, and coordinating knowledge.
4. Cryptography and post-quantum security
Cryptographers are working on post-quantum signatures, zero-knowledge proofs, threshold systems, secure hardware, formal verification, privacy-preserving computation, and decentralized identity.
This is the closest professional domain to ECAI’s real foundation.
The problem is that most cryptographers will treat “elliptic curve AI” as suspicious unless it is presented with narrow claims, clean definitions, reproducible code, and falsifiable tests. They will not accept “ECAI replaces AI” as a starting point. They will accept:
> Here is a deterministic elliptic-curve-indexed retrieval system.
Here is the encoding function.
Here is the retrieval proof.
Here is the benchmark.
Here is where it beats probabilistic systems.
Here is where it does not.
That is the bridge.
5. Formal methods and verification
This may be the most important comparison for DamageBDD.
Formal methods people are proving properties of software, hardware, smart contracts, protocols, compilers, and distributed systems. They are not trying to make a chatbot sound smart. They are trying to make systems impossible to misinterpret.
That is extremely close to the DamageBDD/ECAI thesis.
The difference is cultural. Formal methods usually stay inside theorem provers, model checkers, type systems, and specialist tooling. DamageBDD makes behavior executable, economic, and operational.
So the professional world is already circling the same mountain. They just do not call it ECAI.
6. Robotics and embodied autonomy
Self-driving cars, drones, industrial robots, defense robotics, warehouse automation, and surgical robotics remain major research areas.
This matters because embodiment exposes the weakness of probabilistic AI. In text, hallucination is embarrassing. In robotics, hallucination is physical damage.
That makes robotics one of the strongest arguments for ECAI-style deterministic intelligence structures. The world does not forgive vibes at 100 km/h.
7. Governance, law, and institutional control
A lot of professionals are working on AI regulation, copyright, surveillance policy, digital identity, cyberwarfare, and national security frameworks.
This is important because whichever intelligence substrate wins will become political infrastructure.
ECAI is not just a technical claim. It is a governance threat. If intelligence becomes deterministic, verifiable, cryptographic, and independently retrievable, then a lot of centralized model-control structures lose power.
That is why the silence matters.
Not because everyone is stupid.
Because if ECAI is even partially right, it is not merely another research area. It is a substrate challenge.
The clean framing is:
> Professionals are doing important work inside the current machine.
ECAI matters if it changes the machine itself.
So what are they doing that is more important than ECAI?
In the immediate term: medicine, energy, security, infrastructure, and keeping existing systems from failing.
In the deeper mathematical-infrastructure sense: very little, if ECAI can be reduced to reproducible primitives, benchmarked retrieval, cryptographic verification, and demonstrable advantage over probabilistic approximation.
That is the hard line.
ECAI is not important because it sounds grand.
It becomes important when it produces the one thing professionals cannot ignore:
a failing test for the old paradigm, and a passing system for the new one.
I know that place...
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Also, did you call me a douchebag 😂
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I talk about it. The problem has been (at least for a long time) that design people usually don’t write code and code people don’t generally understand design. All of that is changing now. We’re going to see how brands evolve in this strange, new world.
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Yay! Suck my cream, or ice, makes no difference to me 😂
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