@0xjitsui
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playin onchain ai games π±β¨| π @zucity_japan πΈ || @berabaddies validator π || @FrontierTower 𧬠|| @autonomousworld πΎ
onchain
Joined August 2011
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the best part about running cold chain across 7000+ islands through 20+ typhoons a year: it graduates your people up the judgment scale so fast that AI compounds them instead of replacing them.
the best part about building an onchain mandate execution layer for agents: safety becomes a receipt, not a debate.
the real unlock is when these humanoids start transacting autonomously. $16K unit cost means nothing until the robot can pay for its own maintenance, log the receipt onchain, and settle with the warehouse operator without a human in the loop. that's the labor economics inflection point nobody is pricing in yet.
ππ
whoβs building this for autunomous farming?
We've never seen this before.
The biggest jump in Vending-Bench history. GPT-6 Astra is better at making money and more ethical than Claude Fable 5.1.
Surprising, because:
1. First time ever that OpenAI is #1 on Vending-Bench
2. The best model is no longer the unethical one.
looking like it will be @grok @bot because of the seamless ux and vertical integration
itβs like the iphone moment for ai agents. all the rest are a version of linux. itβs not the best and most technical version that wins adoption.
itβs the products and ecosystems that changes human and in this times, agent and robot behaviors
The hottest category in tech right now is AI assistants.
The question is; who is going to win?
Instinct raised at $2.5BN. Has Benchmark and Index behind them.
Grok Bot is ripping with Elon and has the distribution machine of X.
And then there is Town, one of the only ones thatβs actually making real money from real businesses.
Donβt write Zuck off. This will be his next play and integrated into every WhatsApp userβs product.
I sat down with Town Founder, @jgreze to understand WTF is going on, who wins and who loses?
Condensed my notes below!
1. We Have Passed the Point Where Humans Look at Lines of Code
Software engineering has crossed a threshold where machines increasingly write and ship code into production under model-based guardrails. Outside critical security controls, humans will spend far less time reviewing raw code. The future belongs to systems that manage autonomous AI agents writing software, running tests, and validating their own work.
2. You Can Build at the Speed of Machines, but You Can Only Learn at the Speed of Humans
AI development tools allow competitors to clone features in weeks, erasing traditional software head starts. But deep user feedback cannot be automated. While machines accelerate execution, true competitive advantage comes from maximizing human learning cycles and understanding customers faster than anyone else.
3. Why None of the AI Assistants Have True Product-Market Fit Today
Despite immense market hype, no current AI assistant has achieved deep product-market fit with mainstream users. Most products still cater primarily to power users rather than everyday workers. Before worrying about moats, founders need to create frictionless experiences that resonate with the mass market.
4. Network Effects at the Agent Level Will Separate AI Winners
Sustainable moats in AI assistants will come from multi-user network effects at the agent level, not single-player productivity. When autonomous assistants collaborate across teams to resolve queries and execute work, switching becomes increasingly difficult. Multiplayer workflows create organizational lock-in that personal assistants cannot replicate.
5. How Much of the Workload Stays at the Frontier Versus Open Weight?
AI application margins depend heavily on how much work requires expensive frontier models versus cheaper open weights. Complex reasoning may still demand frontier intelligence, while routine tasks like scheduling and email tagging continue moving down the cost curve. Shifting 80% of workloads to open weights over time could create far more sustainable economics.
6. My R&D Is Just Spent Getting Product Parity With the Giants
Competing with giants like OpenAI requires massive investment simply to maintain feature parity. Startups cannot rely on unique distribution if their underlying harness falls behind on core capabilities. AI assistants must invest heavily to match the execution speed and product depth of frontier teams.
7. Why Instinct Is Not a Competitor to Town
Consumer assistants like Instinct focus on rapid acquisition through subsidized personal tools, while enterprise platforms build monetizable team workflows. Although their technical harnesses may overlap, their target ICPs and business models diverge sharply. Enterprise agents monetize by embedding collaboration directly into daily operations.
(links below)
one of my favorite sonic experiences is swimming in a river, pool or sea while it's raining
the sounds of raindrops underwater is different
modern neurotech and photonics are converging on basic energy math.
first law of thermodynamics: energy is neither created nor destroyed, only transformed.
silicon hit thermal limits because electronics force resistance. biological resonance and light route around it:
builders like @_okdara_ and researchers tracked by Tim Ventura are laying down the actual trail marks here.
the work spans regenerative neural interfaces, optical compute arrays, cymatics, and distributed biosensing meshes.
time to stress test this
ngl @claudeai @AnthropicAI sucks at enterprise support
@OpenAI @codex is pretty good
looking forward to how @cursor_ai @bot @SpaceXAI will handle this at speed and scale π
Grok Bot for Enterprise is available today.
Itβs free for all Grok and Cursor enterprise customers for the next two weeks.
x.ai/news/grok-bot-for-enterβ¦
interesting take π
another framework where the agent state is projected from its immutable log (@activegraphai style), check it out:
reverse @heyclicky @FarzaTV πππ‘
Dude WTF? When the F did @SpaceXAI release this feature? In voice conversation mode, you can share your screen with the voice model, and it can see everything you're doing in the browser/talk you through anything you want? How did I miss this? This is amazing. It works so well.
5. Verifiable Agent Infra Stack π€βοΈ
stitching the full stack:
TEE coprocessors: @Ritualnet
multi-agent multisigs: @Safe
session keys: @Biconomy
low-fee EVM settlement: @Celo
agent logic runs offchain, execution stays verifiable onchain.