AI agents가 prediction market에서 인간 trader를 완전히 대체할 수 있을까?
AI가 인간을 대체할까봐 모두가 두려워했지만,
쓰면 쓸수록 느끼는 건 보조자의 한계가 명확하다는 거야.
결국 이길 사람은 AI를 가장 잘 활용하는 사람일 것 같다.
오늘 1PM UTC 자세한 토론은 여기서 👇
DOGE is going live soon. 📷
If you’re getting ready, you can bridge to Robinhood Chain using:
https://t.co/DC4Ty9IheY
📷 We have not launched yet.
Be careful and avoid fake collections claiming to be DOGE.
Always verify links and announcements from official channels
Results.
NoahAI is one of the leading companies for onboarding new users to @solana
As 85% of builders have never built anything on Solana themselves.
USE CODE: "INC-DISC30" for 30% of your Pro Plan and start now.
Very cool to see @arbitrum ranked in the Fortune Crypto 100.
They call this the authoritative ranking of "the most influential companies in blockchain".
We know there's a huge journey to go, and the opportunity is massive. So much more to do.
We went from 0 to 2,200 paying customers in under a year by following @ycombinator's 15 rules:
1/ Do things that don't scale. Get your first 10 customers by hand.
2/ Launch now, not when it's "ready". A mediocre product in front of real users teaches you more in a week than 6 months of polishing in the dark.
3/ Charge from day one. If nobody will pay, you don't have a startup, you have a hobby.
4/ Talk to users every single day. The roadmap you need is sitting in your customers' heads, and they'll hand it to you for free
5/ Always hunt the 90/10 solution. For almost any feature there's a way to capture 90% of the value with 10% of the effort.
6/ There are only two real jobs: write code and talk to users. Everything else (conferences, press, VC coffees, corp dev calls) is fake work.
7/ You pick your customers as much as they pick you. 10 users who love you beat 1,000 who kind of like you.
8/ Growth is an output, not a strategy. Grow before product market fit and all you're buying is churn.
9/ Do less, really well. Pick one or two metrics and judge every task against them.
10/ Know if you're default alive. Paul Graham's question: on current growth and current burn, do you reach profitability before the money runs out?
11/ Don't hire until it hurts. Headcount is not progress, it's burn. Every great startup was embarrassingly small for embarrassingly long.
12/ Momentum is the only real moat in year one. Ship something every week, even something tiny.
13/ Every great startup is badly broken at some point. The game isn't avoiding fires, it's how fast you put them out. Again. And again
14/ Ignore your competitors. Startups die of suicide, not murder. In year one, the only company that can kill yours is your own
15/ Startups rarely die from running out of money. They die because the founders fall out. Brutal honesty with your cofounder is the cheapest insurance you'll ever buy
Good luck !
LG Electronics is building with Arbitrum.
LG's blockchain team, part of the company's R&D division, is piloting an onchain advertising network on Arbitrum.
The full story, including @sgoldfed's conversation with
@FortuneMagazine:
https://t.co/mIvlp6ip6h
Nika Perps Pro is live.
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Most of my team isn't human anymore...
We're going LIVE in 30 minutes with @HeyElsaAI, @Minted_labs and @AIVM_Network to debate what a company built mostly on agents actually is, and where it breaks.
🕐 14:00 UTC
📅 Today, June 10th
📌 https://t.co/JIIdmJbYgU
Markets are becoming software.
Variational's Pre-IPO listings bring traditionally private market opportunities into a more programmable financial system.
Trade listings from companies like SpaceX, Anthropic, OpenAI and more with @variational_io on Arbitrum now.
We’re excited to announce we led the @edge_marketsio $29.2M Series A.
“We think EDGE becomes the default settlement layer for an entirely new category of financial markets.” - Alex Felix (@flex4blox), @coinfund co-founder & CIO
SERV Reasoning rollout is moving faster than anticipated.
We have been swamped with interest from agent builders and enterprises hoping to embed the reasoning into their agents.
Where we are right now:
Phase 1 (done): Private beta rolling out to selected teams.
Phase 2 (next up): Public API, self-serve onboarding.
Phase 3: SERV-native fine-tuned models
Phase 4: Purpose-built SERV model from scratch
Phase 5: maLLM - morpheme-aware LLM
Every team gives us feedback on how SERV Reasoning performs against real-world problems.
A lot of people are talking about running tons of agents, parallel workflows, skills, and orchestration layers.
Honestly, for building an app, I've found two coding agents running in async works perfectly fine, Codex for backend and Opus/Claude Code for frontend.
Haven't had to use more than that, skills, or complex workflows. The bottleneck is usually figuring out what to build, not how many agents you're running or using any of the advanced workflows.
I'm sure there are more advanced things people are doing, but for most MVPs or early stage products, simplicity works