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The best AI products may start by saying no.
A lot of AI products are designed around one promise: ask for anything, and the system will try to help.
That sounds powerful, but sometimes the better product is the one that knows where it should stop.
1. Saying yes to everything can make a product worse.
If an AI tool tries to write, research, design, schedule, code, summarize, and automate every workflow, it quickly becomes harder to understand what it is actually good at. More capability does not always mean more useful.
2. Constraints can make the output better.
A product that clearly defines what it can do well can make stronger decisions inside that space. Instead of giving you ten possible directions, it can guide you toward the few that actually make sense for the task.
3. Trust also comes from knowing the boundaries.
Users should know when the AI is confident, when it needs more information, and when it simply should not make the call. A useful "I don't know" can be better than a polished answer that sounds right.
The next generation of AI products may compete on two things: how much they can do, and how well they know what not to do.
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Exciting progress from Etheorem!
🚀 Ethereum consensus specs made executable in Lean4, with a growing foundation of formal proofs.
Congrats to the team on this milestone! If you’re into Ethereum, Lean, or formal verification, here’s an open-source effort worth contributing to.
At last we have Etheorem: complete executable Consensus Specs written in Lean4!!
Etheorem started in May, now a team of 7 independent Engineers and Researchers has been working on this (@invisiblgarden and @ethereum Protocol Fellows). It passes all the official test vectors, for Fulu, Gloas and Heze (has not modeled light clients and gossip).
It is based on a framework that abstracts from the spec writer most technical details, uses monadic state machines, allows inheritance between forks and makes adding proofs simpler, and spec code readable.
Currently, Etheorem has only 8 spec functions fully characterized and 29 partially. The SSZ proofs are mostly complete. Etheorem invites the community to work on an open source effort to add more proofs. The base for this is already implemented.
More information at: ethresear.ch/t/etheorem-upda…
Repo: github.com/etheorem/etheorem
Discord: discord.gg/HpjCrEYmDr
Team: github.com/Mouzayan github.com/irajgill github.com/IvanAnishchuk github.com/protocolwhisper github.com/Sahilgill24 github.com/adria0 github.com/leolara @0x_flwr @IvanAnishchuk @0xRajGill @leolarav
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🏙️ Calling all builders in Shenzhen!
Join the Hacker House organized by @OpenBuildxyz and @UpchainDAO a hands-on, in-person sprint to turn your idea into a submission-ready prototype.
📅 October 9–11
Bring your laptop, meet fellow builders, and make it happen! 🚀
Sign up 👉 luma.com/673y4m0q
🏙️深圳站
由 @OpenBuildxyz @UpchainDAO 组织，一场面向开发者的线下黑客共创营，帮你在短时间内，把一个想法变成真正可提交的项目原型。
时间：10.9-10.11
报名链接👉luma.com/673y4m0q
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What did you miss in China’s AI last week?
Huawei went bigger on AI infrastructure, Alibaba open-sourced a medical AI model, and ByteDance’s AI drug discovery spin-off raised its first external round.
Time for last week’s recap 👇
Made with AI
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ByteDance’s AI drug discovery spin-off, Anew Labs, raised $290 million in its first external funding round.
The company is now valued at about $1.5 billion and is working on areas including biomolecular structure prediction, antibody design, and drug discovery.
AI for science is becoming a much bigger part of China’s AI story.
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A few things stood out this week:
🚩 China is investing heavily in the infrastructure behind AI, not just the models.
🚩 AI is moving deeper into healthcare and scientific research.
🚩 More specialized AI companies are starting to attract serious capital.
That’s it for this week.
Follow @OpenBuildxyz for more updates on China’s AI and innovation ecosystem.
See you next Monday.
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Hangzhou, you showed up. 🙌
And that’s a wrap on Metropolis Hacker House Hangzhou — a huge success! ⚡
@monad @monad_zw @Monad_APAC
100+ builders, amazing talks, a deep-dive panel, and 90+ minutes of builder demos.
Massive thanks to every builder, speaker, and community who made it happen.
Hangzhou was just the start. See you next stop！🚀
Join us in Shenzhen on Sep 26：
luma.com/metropolis-shenzhen…
#Monad #Metropolis #AgentPayment #AIAgents #Web3 #HackerHouse #AgenticFinance #Shenzhen #OpenBuild
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Developer communities matter more when information is cheap.
A few years ago, one of the main reasons to join a developer community was simple: someone there probably knew the answer.
Now AI can explain frameworks, debug errors, compare tools, and summarize new protocols in seconds. Information is much easier to get.
But that does not make communities less useful.
🚩1. Communities give you context, not just answers.
AI can explain how something works. People can tell you what actually worked in production, what broke, what they stopped using, and what they would do differently next time. That kind of context is usually harder to find in docs.
🚩2. Trust still comes from people.
When everyone can generate a polished answer, it becomes more useful to know who has actually built with the tool, shipped the product, or dealt with the problem before. Sometimes knowing whose opinion to trust matters more than finding another explanation.
🚩3. A lot of the value was never information anyway.
Communities are where you meet collaborators, find jobs, discover projects early, get introductions, and become known by people building in the same space. AI can help you learn faster, but it cannot fully replace those relationships.
As information gets cheaper, developer communities may become less about finding answers and more about finding the right people, context, and opportunities.
The information is everywhere now.
The people still matter.
#DeveloperCommunity #BuildTogether #OpenSource #TechCommunity #OpenBuild
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🪁 Calling all Open Source builders!
Join the @GoKiteAI Open Source Bounty Program. Build agent tools, contribute code & help grow the ecosystem.
📅 9.15~12.15 3 months of building
👥 50 spots for every cohort
🎁 USDC rewards for eligible contributions
Apply 👉
openbuild.xyz/learn/challeng…
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AI has made it incredibly easy to get an answer.
Ask about a bug, a contract clause, a pricing model, or a go-to-market idea, and you’ll usually get something that sounds useful within seconds.
That part is amazing.
The harder part is figuring out whether the answer is actually right.
Made with AI
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So using AI well is starting to look less like “getting answers” and more like knowing what needs to be checked. Sometimes you test it. Sometimes you go back to the source, etc.
Not every answer needs the same level of trust.
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