Building a company used to mean hiring a team. Now it means deploying one. We stopped building disconnected AI “tools” and started hiring AI “employees.” Meet the Agentese workforce for your company: 🎙️ Meeting Copilot - Live meeting assistant Answers hard questions out loud, in real time, during your meetings. No prep and no recaps, just the answer the moment the room needs one. meeting-copilot.agentese.ai 🦞 LifeClaw - Personal call & admin agent Makes your phone calls, bookings, and disputes for you: the cancellation, the appointment, the hold music, all handled. lifeclaw.agentese.ai 🥔 Little Pink Potato - Automated job application agent Applies to jobs for you at a scale no human could, screening thousands of roles, scoring the real matches, applying and negotiating based on your preference. littlepinkpotato.com ☕ Kopi - Approval assistant for Lark Clears the routine approvals piling up in your Lark queue, auto-handling the obvious yeses and flagging only what needs your judgment. hellokopi.com 🔒 CodeAutrix - Smart contract & AI skill auditor Audits and stress-tests smart contracts and AI skills in minutes, surfacing the critical flaw before you ship. codeautrix.com 🍀 Yarrow - Multi-agent simulation platform Ask a question, get a calibrated probability backed by a full chain of evidence and reasoning. Predictions you can grade, not opinions. yarrowlab.ai Stop managing tools, start delegating. With Agentese. 🚀

Micron reports its earnings after market close on Wednesday. Polymarket gives it a 97% probability of beating expectations. When a prediction market assigns such a high probability, how should you interpret it? It’s practically a foregone conclusion. But the real forecasting question is much harder: Is AI compute demand genuinely accelerating, or is this cycle approaching its peak? If Yarrow were to build an evidence pack to investigate this question, here’s how we would approach it. ✅ What belongs in the evidence pack — and what should be excluded (Yarrow only adds verifiable, dated, sourced information): - Micron Q3: Revenue of $41.5 billion (+346% YoY). Q4 guidance: approximately $50 billion. - TSMC July revenue: +44.7% YoY. AI-related revenue now accounts for 50%+ of total revenue. - Hyperscaler capex: Microsoft $55.5B, Google $75B, Meta $64–72B (announced for FY2026). - U.S. data center grid connection requests: 3x higher than in 2024 (DOE data). ❌ Exclude (opinions, undated claims, and unverifiable statements): - "AI demand is a bottomless pit" (Analyst commentary — no settlement criteria). - "This is the next dot-com bubble" (Narrative — not testable). - "Micron price target: $X" (Forecast without a methodology). Yarrow’s evidence pack doesn’t tell you what to think. It tells you what you can start reasoning from. Wednesday’s guidance call will add another data point to the evidence pack. If you have exposure to this thesis, listen for three things: - HBM order backlog — Is it growing, flat, or contracting? - Data center revenue share — Is it continuing to expand? - Any mention of customers delaying capex timelines. If all three are positive, the “AI infrastructure supercycle” thesis gains another quarter of support. If any of them show cracks, the evidence pack changes — and the probabilities should change with it. This is how Yarrow thinks. ☘️

What information to provide to @LifeClaw_AI for best results

Agentese’s take on showing the second attempt in an AI demo

Six months later, has your view on D/ACC vs E/ACC changed? The hardest part of D/ACC, to us, is the incentive to race. A team can believe AI needs safeguards and still accelerate because it expects competitors to move faster. Concern about risks coexists with fear of being left behind. This makes voluntary restraint fragile. Even if everyone prefers mutual caution, nobody wants to be the only one exercising it. D/ACC’s emphasis on accelerating defensive technology offers a path that does not require everyone to slow down together. People have reasons to invest in protection regardless of what competitors believe. But it leaves a difficult question: Can defenses keep pace with the capabilities they are meant to contain? Where restraint is necessary, cooperation must be verifiable, and breaking agreements must carry a cost. Otherwise, the advantage goes to whoever ignores them. We find D/ACC compelling as a direction. We are less convinced it holds under competitive pressure without changing the incentives that drive the race. What’s your take on this?

If Agentese products have their own apps, which one would be your favorite?

Say you want to know: will the Fed raise rates next month? If you're a regular person, you Google it. You find three headlines — one says yes, one says no, one says maybe. You pick the one that sounds most convincing, or the one from the source you trust most. That's not a forecast. That's a vibe check. If you ask ChatGPT, it gives you a confident paragraph. "Based on current economic indicators, it appears likely that..." It sounds smart. But it has no track record, no accuracy score, and if you ask the same question tomorrow with slightly different wording, you might get a different answer. It doesn't know how often it's been right before — because nobody's counting. If you check Polymarket, you get a number — say 55%. That's better. Real money is behind it. But it's just a price. There's no explanation for why it's 55% and not 40%. No evidence trail. No reasoning you can walk through. And if the number changes overnight, you have no idea what drove it. ☘️ Here's what happens inside Yarrow. The same question goes to many independent analysts. They're running on three different AI models from two different providers. None of them can see the market price. None of them can see each other's work. Each one gets the same frozen evidence pack — official data, dated sources, verified statistics. No live search. No Twitter. No vibes. They each have to produce a complete chain: what mechanism drives the outcome → what the data says → what probability they assign. Before any of that ships, the evidence pack runs through five checks: are the numbers internally consistent? Is anything missing that should be there? Is there any information that could only be known after the fact? Has an independent red team tried to break the argument? The final number is the median of the valid analysts — not a vote, not a discussion, not a compromise. Just the middle of six independent judgments. Then it gets frozen. Locked in git. Published. And when the outcome is known, scored — right next to every other prediction we've ever made. Google gives you headlines. ChatGPT gives you confidence. Polymarket gives you a price. Yarrow gives you a probability with the evidence behind it, the reasoning you can audit, and a track record you can check. That's the difference between looking for an answer and building one.

On June 23, 2016, the UK held a referendum: remain in the EU, or leave. Betting markets gave Remain an 85–90% probability. Pollsters predicted Remain. The UK government had no contingency plan for Brexit, and the prime minister had effectively staked his political career on an outcome he himself could not predict with certainty. Brexit won. 51.9% to 48.1%. The pound plunged 10% overnight, its largest single-day drop since 1985. David Cameron resigned the following morning. Global markets lost $2 trillion in value within two days. And Britain spent the next four years trying to figure out what “Brexit” actually meant. The data telling a different story was already there. Online polls had consistently been closer to the truth than telephone polls. The turnout models had never been properly validated against a nationwide referendum. And anti-immigration sentiment in northern England had been systematically underestimated. Betting odds were anchored to the same telephone polls that were later shown to be wrong. No one stress-tested the consensus. An 85% probability became a “fact,” and every plan was built around it. ☘️ Now imagine if the UK government had used a system like Yarrow before the vote. The system receives one question: “Will the UK vote to leave the EU?” Yarrow has independent analysts approach the question from different bodies of evidence: polling data using two different methodologies, regional turnout models, historical precedents from referendums versus elections, and sentiment data from regions that conventional polling struggled to reach. Three analysts flag online polls showing Leave ahead. Two build turnout scenarios in which older, non-urban voters who leaned toward Leave turn out at higher rates than assumed by the polling models. One points out that betting markets are pricing the same telephone polls—not independent information. The system ultimately returns: Remain: 58%, Leave: 42% And it adds: The 85% consensus probability for Remain depends heavily on a telephone-polling methodology that had never been validated in a nationwide referendum. Cameron might still have held the referendum.But the Treasury might have had contingency plans ready. The Bank of England could have positioned itself in advance. And perhaps $2 trillion would not have vanished from global markets within 48 hours simply because everyone was blindsided by an outcome that, if someone had looked at the full evidence base, was clearly possible. The most expensive predictions aren't the ones that turn out to be wrong. They're the ones that are wrong—and that nobody prepared for.

Give LifeClaw the details. Get on with your day. You need to move a reservation, but “any time on Friday” actually means after work, before 8, and only if there’s no extra fee. Tell LifeClaw those details upfront so it can call the business with a clear request. Here’s what to include before handing over the call. 1. Your personal details Provide your name and the contact or booking information needed for the call. 2. Your saved preferences Share your usual dining times, dietary requirements, seating preferences, and budget so you do not have to repeat them next time. 3. The action you want Specify whether you want LifeClaw to make a reservation, change an existing booking, cancel a booking, or ask about availability. 4. The business details Provide the business name, specific location, and phone number, if available. If not, just let LifeClaw research for you. 5. Any additional instructions Note acceptable alternatives, fees that require your approval, and what LifeClaw should do if your first choice is unavailable. For example: “Call Sakura in District 1 to book a table for two this Saturday. Prefer 7 p.m., but anything between 6 and 8 works. Ask me before accepting a deposit.” Give LifeClaw the details, let it make the call, and then check the result.

Little Pink Potato giúp người dùng tìm việc và chuẩn bị cho các buổi phỏng vấn với các công cụ được hỗ trợ bởi AI. Đây là một trong số ngày càng nhiều sản phẩm AI thuộc hệ sinh thái @Agentese_AI

What should a prediction actually look like? Most AI tools give you a number. "There's a 70% chance this happens." Maybe a paragraph of explanation. That's it. No source trail, no what-if analysis, no way to check later whether 70% was the right call. 👀 Here's what we think a prediction should contain — at minimum — before anyone should take it seriously: 1️⃣ a probability that means what it says. When the system outputs 70%, events like that should happen about 70% of the time. Not "I'm pretty sure" dressed up as a number. Actual calibration, measured over hundreds of questions. 2️⃣ a reasoning chain you can walk through. Not "based on our analysis" — the actual evidence, the actual logic, step by step. Which data points support the conclusion? Which ones cut against it? If you can't trace from evidence to number, it's not a forecast — it's a vibe. 3️⃣ the conditions under which the prediction breaks. Every good forecast comes with its own kill switch. "We'd revise if X happens." "This falls apart if Y turns out to be true." If a prediction doesn't tell you what would prove it wrong, it's not brave — it's unfalsifiable. 4️⃣ a frozen record before the outcome. Anyone can claim they predicted something after the fact. The test is whether the prediction was locked in — with all its reasoning — before the answer was known. No edits, no revisions, no "well what I actually meant was." 5️⃣ a public track record. Not just the wins. The misses too. Side by side. Scored by the same rules. Because a system that only shows you its highlights isn't building trust — it's building a marketing deck. ☘️ This is the standard Yarrow holds itself to. Every prediction frozen in git. Every outcome scored. Every miss published alongside every hit. Not because we're always right. But because a prediction you can't audit is just an opinion with better formatting.

Little Pink Potato допомагає користувачам шукати роботу та готуватися до співбесід за допомогою інструментів штучного інтелекту. Це один із дедалі більшої кількості продуктів на базі штучного інтелекту в екосистемі Agentese_AI.

🔹 Little Pink Potato Little Pink Potato membantu pengguna mencari pekerjaan dan mempersiapkan wawancara dengan berbagai tools berbasis AI. Produk ini menjadi salah satu dari semakin banyak aplikasi AI yang hadir di ekosistem @Agentese_AI 👇

Little Pink Potato помогает пользователям искать работу и готовиться к собеседованиям с помощью AI-инструментов. Это один из растущего числа AI-продуктов в экосистеме Agentese_AI.