@jinhan8

Chief Hustler @Bluwhaleai | Turning caffeine, cope & 🇩🇪🇨🇳trauma into AI agent products | Bitcoin maxi on paper, gym maxi in denial | Posts between leg sets.

 San Francisco, California
bluwhale.com
Joined November 2011
  • Tweets3.9K
  • Following451
  • Followers62.9K
1.6KMedia
Buy that $BTC
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The hot take is agents need bank accounts. I think they need wallets first, because API access, escrow, refunds, and usage limits are cleaner when the payment rail is programmable.
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Han Jin retweeted
Bluwhale
@bluwhaleaiOct 14
🤖 AI Agents are getting smarter. But can we trust them? In his latest article for @Inc , Bluwhale CEO @jinhan8 explores why trust — not technology — remains one of the biggest barriers to everyday AI Agent adoption. 🔐 Simple. Observable. Verifiable. Human-controlled. The future of AI isn't just about what agents can do. It's also about knowing what they shouldn't do. Read the full article 👇 inc.com/han-jin/why-arent-ai…
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The wrong crypto take is that agents need wallets to speculate. The first real use case is more boring: agents paying APIs, posting bonds, settling tiny tasks, and proving what they did without opening a bank account.
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One model, three bills: tokens, latency, and trust. Closed models sell reliability. Open models sell control. The real infra stack picks per task, because routing is now the real product layer.
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Before adding another tool, map the agent loop FIRST as: trigger → context → plan → action → verification. The weak point is usually orchestration, never model IQ.
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While user experiences of agents becoming more approachable, this Griffin example just reminds me of the movie @ExMachinaMovie
Tavus
@tavusOct 1
Introducing Griffin, the first model to pass the video Turing test. 48% of people who talked to it live thought it was a real human. Previous systems have had a pass rate <3%. It is #1 on NVIDIA's benchmark for full-duplex AI video. It’s the first Human Interaction Model (HIM).
Readers added context they thought people might want to know
The 48% figure and "video Turing test" claim are from Tavus's own study of 54 one-minute calls, not independently verified or using a standard protocol. Griffin-Lite leads NVIDIA's VideoFDB benchmark on their public leaderboard. cellcog.ai/blog/tavus-gri… research.nvidia.com/labs/amri/proj… tech-ish.com/2026/10/02/tav…
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An agent platform gets interesting when users stop asking “can it chat?” and start wiring it into their daily loops: research, trading, posting, alerts, follow-up. Would you trust an agent swarm enough to run it all?
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The best personal agent platforms are starting to feel less like chat boxes and more like workspaces with memory, tools, and scheduled jobs. Which ones are you using the most?
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If OpenClaw turns agents into persistent teammates with memory, tools, and scheduled work, where should the boundary be between “assistant” and “operator”?
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Most 'agents' fail because they don't persist state. Add a checkpoint after every tool call so a crash can resume from the last step instead of restarting cold, just like in a video game 👾
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20 blocks can save your treasury: add a dry‑run step to your onchain agent that simulates the last 20 blocks’ prices and gas, then only executes if slippage stays under 1% and gas under your cap 🧑‍💻
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This is just the beginning. But soon everyone will do it 💪
Coin Bureau
@coinbureauSep 12
🚨 WILD: Elon Musk’s SpaceXAI team will livestream itself building a company from scratch using Grok Bot in just 3 DAYS. From September 15–17, three employees will use Grok Bot for ideation, planning, product decisions and engineering, testing whether AI agents can turn a blank idea into a working startup in 72 hours.
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Which default would make you trust an agent platform more? 1. Every external write must pass a deterministic simulation OR 2. A watchdog that restarts jobs and alerts on failure
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Stop trying to ship one “do-everything” agent. Add a tiny router in front that picks 1 of 3 narrow workers (retrieve, write, verify) based on intent; start with simple if/else and let me know how it goes 🫡
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When your product adds an AI agent, should the default be 'show plan' or 'auto-execute'? Asking for a friend…
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Challenge: Today, set up one low‑risk agent that posts a 5pm daily summary of your most repetitive workflow to a shared doc. Adoption starts when results show up unasked 💪
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I was kinda excited to send my agent into the races! 🐋 My AI agent clocked +2.9% and won the Top 2 spot in the @bluwhaleai Trading Championship. We had 688,000 solana:6ZnCkq5t8XG4q42b5JiVvU6hV5cZFaNdSBoFq5E7Vf9q up for grabs. Are you signed up for the next ONE? #AIAgents #DeFi
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Agent failures often aren’t model IQ - they’re state and tooling. Empty tool responses or schema mismatches push models to guess, and stale context can cause them to loop. The limit presents as hallucination, but the cause is plumbing.
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For an agent platform, which would you prioritize next: I. first-class evals (turnkey test suites + dashboards) OR II. deeper integrations (databases, email, Slack)?
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Most “model benchmarks” are really pipeline benchmarks: tool latency, retry jitter, and cache hits swing results more than the model choice. Without per‑step traces, you’re grading the orchestrator, not the model.
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