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Nobody can follow every World Cup match. Nobody can monitor every prediction market. That's exactly why we built Sorin. Free Chrome extension ↓ chromewebstore.google.com/de…

NVIDIA pitching a max single-threaded CPU for agents is basically an admission that the agent loop is bottlenecked by sequential reasoning, not GPU throughput. Which is real, but most of the agent slowdowns we actually hit aren't compute at all, they're the loop stalling on a tool call while some external API or sandbox takes its time. A faster core doesn't touch that.

Our Sorin Chrome extension just got another major update. This release expands the context behind every market with: • More supporting data points • More sources to validate what's happening • Expanded real-time market context Here's a quick look 👇

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Agents are now producing bug fixes faster than people can review them. In the time it took humans to verify 70,000 security patches, @OpenAI's agents verified 500,000 across major web browsers. At that pace, the human signing off at the end likely isn't reading most of it. Every lab keeps that human anyway. Probably not to catch the errors, but because someone has to be accountable when an agent is wrong. 👇 ------------------- That 7-to-1 gap isn't a one-off. @Google moved production models from TensorFlow to JAX at 6 to 8 times engineer speed, and on @YouTube's models, work that would have taken engineer-months finished in weeks. The agent does the volume, and a human still signs the bottom line. Nobody read 500,000 patches. The volume an agent clears in a day is more than a human gets through in a month, so the person approving Google's migration or OpenAI's security work is mostly taking it on trust. Anyone running a pipeline like this can see the same math. So why keep the human? Most likely for accountability. When something ships and breaks, someone has to own it, and "the agent approved it" isn't a sentence a company can offer a regulator or a court. The human is the name on the decision when it goes wrong, which may be the real job long after the reading stopped being possible. So, when do we go fully autonomous? It sounds like a question about agent capabilities. But on this evidence they're already good enough to outproduce the humans watching them. What's holding autonomy back is that removing the human removes the accountability, and nobody has built the replacement. A human's sign-off carries weight because a person can be held responsible. An agent's approval is just an output, with usually no record of what was checked, against what standard, that anyone can stand behind later. So companies keep a human in the loop as cover, reviewing a fraction of the work, because the alternative is an agent whose approval means nothing when it counts. Most of the field is racing to make agents more capable. Far fewer are building the thing that lets an agent's work be trusted without a human babysitting it. That's part of the infra we build at Sahara AI: verifiable execution and an unforgeable record of what an agent did and what it checked, so its sign-off can be held to account instead of taken on faith. Follow @SaharaAI to keep up with agentic AI, the infrastructure it demands, and where the real building is happening. About Sahara AI: Sahara AI is the agentic AI company dedicated to making AI more accessible and equitable. We build the core protocols, infrastructure, and applications that let personal agents anticipate and execute on your behalf. For this to work, infrastructure has to be trustworthy: verifiable execution, enforceable usage policies, and automatic value distribution across every tool, model, and service an agent touches. Sahara is building a growing suite of agent-powered applications on top of this foundation, including @HeySorinAI, your personal agent for global digital markets. Our solutions currently power AI agents and high-quality data for consumers, Fortune 500 enterprises, and leading research labs, including @Microsoft, @Amazon, @MIT, Motherson, and @Snap.

Prediction markets reward people who can process information faster. That's why we're proud to introduce Sorin Analysis for @Polymarket and @Kalshi. It makes it easy to stay on top of what's driving every market, right where you're trading. Try it free in the Chrome Web Store: chromewebstore.google.com/de…

Micron, one of three companies on earth making specialized memory chips that let AI systems run at scale, says that memory chip supply stays tight through 2027. Read that as a chip story if you want, but the less comfortable read is that memory scarcity doesn't affect everyone equally. It gets allocated to the biggest buyers first. Shortages like this make the current leaders harder to displace and the field gets less competitive. finance.yahoo.com/technology…

Anthropic says Alibaba ran the largest known distillation attack on its models, training their own systems on Claude's outputs without authorization. The problem with closed models is that there’s no verifiable trail showing which outputs ended up in downstream training. Onchain provenance is the fix because it can create receipts for what a model was actually built on. Without that, model theft stays almost impossible to prove cleanly. bloomberg.com/news/articles/…

In the single-model era, one API call usually implied one relatively legible execution path. You knew which model ran. You had a rough mental model of cost. You could reason about latency and usage from the outside. In the orchestration era, none of that holds. A case study 👇 ---- @SakanaAILabs launched Fugu this week. It looks like one model. You call it through a single API. But what you call and what actually runs are two different things. Fugu is a coordinator trained to break a task apart, route pieces of it to a pool of other models, and call instances of itself recursively to handle sub-tasks. One prompt can fan out into a swarm of sub-calls the user never sees. The whole thing still surfaces as one model response. Two developers ran into the consequences of that almost immediately. @LLMJunky said a single prompt consumed 100% of a five-hour quota. @cortesi paid for Sakana's $200 tier, found the API slow, and said he hit his limit in under an hour. Neither could say which models ran, how many times Fugu called itself, or why one prompt cost what it did. The issue isn't specific to Fugu either. When @AnthropicAI briefly opened access to Fable earlier this month, users reported similar quota burns with similarly little visibility into why. When one request fans out into many model calls, and some of those calls spawn more, the unit the buyer sees no longer maps cleanly to the unit of work happening underneath. Forecasting cost gets harder. So does explaining latency, attributing performance, or knowing what actually ran. Sakana's benchmark figures sharpen the question. Shared numbers for Fugu include 54.2 on SWE-Pro, 95.1 on GPQA-D, and 93.2 on LiveCodeBench v6, with testers noting scores above Opus, Gemini 3.1, and GPT 5.4 on each. But Sakana's own base model is around 7B parameters. Frontier-tier results from a coordinator that size strongly suggest that a meaningful share of the work is being done by the larger third-party models it calls. Behind one endpoint, the system may be making its own decisions about how much work to do, which models to invoke, and how many times to invoke them. For hobby use, opaque execution is an annoyance. For production systems touching real money or real workflows, it becomes harder to dismiss. You can't price what you can't predict, and you can't debug what you can't trace. When the run is a sealed box, it also becomes harder to prove what the agent actually did. Fugu is one early launch, and Sakana will likely smooth out the rough edges. But as orchestration becomes the product, execution visibility becomes core infrastructure. The next layer of competition in AI will be won by the teams that can make increasingly complex agentic runs legible enough to trust. Follow @SaharaAI for analysis of agent infrastructure, the AI economy, and where the real building is happening.

One of many updates coming. Excited to show you what we've been working on.

$SAHARA Update: Over the past week, we've worked closely with our team, advisors, and investors, and have finalized our decision on the best path forward for $SAHARA. We're now completing the last steps to put it in place and will share the full update with the community this week. We know the upcoming unlock has been top of mind, and you'll have the full picture before then. Thank you for your patience. We're looking forward to sharing what's ahead. Sahara AI Team