Sahara AI 🔆

What institutional trading desks have always had, now in the hands of every investor. Powered by @SaharaAI

The Key Level Break is one of 12 Alpha Signals available in @HeySorinAI today. We're just getting started. What signal should we add next?

The #WorldCupFinal is here. Argentina 🇦🇷 or Spain 🇪🇸? Before you make your call, see what's driving the market on @Polymarket and @Kalshi. Sorin brings the context directly into every market. See for yourself before kickoff ↓ chromewebstore.google.com/detail/sorin-a… #mundial #VamosEspaña #VamosArgentina

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The capability lead between Codex and Claude flips every few weeks now, which makes hard-wiring your whole production stack to whichever one is ahead this month a strange bet most teams keep making anyway.

@Polymarket How about owning your own compute though? x.com/SaharaAI/statu…

BREAKING: Meta is reportedly in talks to lease AI computing power to Anthropic in a deal worth up to $10,000,000,000.00.

@VanessaDefi that's exactly right. AI that's: - trained on real human intelligence - keeps humans in the loop - allows humans to own AI compute in a decentralized manner that's the AI future we want to see

I believe the most valuable part of AI is still human intelligence. The models may get smarter, but they’ll always need people to guide, refine, and improve them. And that’s why humans should never be completely removed from the process. Some builders already understand this and have been on it: @oortech is building decentralized data infrastructure where people label datasets and review AI outputs, giving models the human feedback they need to improve. @PerleLabs is building a network where experts contribute verified knowledge and high-quality datasets, ensuring AI is trained on trusted human expertise rather than just raw internet data. @SaharaAI enables anyone to contribute to AI by collecting, labeling, and refining datasets. And many other builders out there. These projects aren’t trying to take humans out of the equation. They’re building systems where human intelligence remains a core part of how AI learns and evolves. As AI continues to advance, I think that’s exactly the direction we should be moving in.

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Adding more agents to a workflow usually makes it worse. Most multi-agent systems are one capable agent doing the real work surrounded by others that mostly add latency, coordination bugs, and new ways to fail silently. It helps when the subtasks are genuinely independent and each has its own check, and it hurts when you have taken one coherent task and split it across agents that now have to keep re-agreeing on state.

Every World Cup match has dozens of possible outcomes. The hard part is understanding which signals actually matter for navigating prediction markets. That's why we built the free Sorin Chrome extension. See for yourself ↓ chromewebstore.google.com/detail/sorin-m…

Anthropic's new Claude Code course spends a section on why dictating beats typing. Voice pushes you toward describing the outcome you want instead of micromanaging steps, which reliably produces better instructions for the agent. The advantage starts to fade once a task hinges on an exact path, flag, or symbol name, so in practice you end up dictating the intent and typing the specifics anyway. If you've switched to dictating prompts, how has it improved your workflow?

Two hours until the last #FIFAWorldCup semi-final match ⚽️ It's 🏴󠁧󠁢󠁥󠁮󠁧󠁿 England vs Argentina 🇦🇷 Want to see what's driving the market before kickoff? See what Sorin's seeing ↓ chromewebstore.google.com/detail/sorin-a… #messi #CopaMundialFIFA #ThreeLions #VamosArgentina

StepX Neo beat OpenAI to the first agentic phone. It's being scored as a hardware race, but a phone is the most hostile environment to run an agent in. It gets your contacts, banking apps, and notifications with no sandbox between the model and any of them. Whoever ships second with a real permission model will age better than whoever shipped first.

Most teams are nowhere near ready to remove that human. Half the 'agentic workflow' launches we've seen are one engineer manually babysitting a script that occasionally calls a model, which is a fine v1 but not what the label promises. Then the first night you let it run unattended you come back to a mess no one caught and countless tokens wasted.

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Most agent latency comes down to one of these: - Model latency - Waiting on external APIs - Retries after failed tool calls - Waiting on a human to approve something Where does your agent actually lose most of its wall-clock time?

Every World Cup match has an opening. We built Sorin to help you spot the ones the market hasn't fully priced in yet. Try the free Chrome extension ↓ chromewebstore.google.com/detail/sorin-m…

@akashnet couldn't agree more. open models → compute you control → open web for everyone

With closed AI, your knowledge and know-how leak with every prompt and correction. The fix is open models on 👉 compute you control. The frontier isn't the point anymore. Not on cost. Not on risk. Your learning loop should compound your firm's value, not your vendor's.

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