Midaz.xyz

From blank canvas to a runnable workflow. We show: First block, Hyperliquid Connection, Historical Backtest, Strategy Tuning, Model Switching. Watch the demo. midaz.xyz

We shipped Midaz 2.0. Midaz is now a callable skill. Any agent that can call tools — OpenAI, Claude, or others — can call us. No frontend required. We also built the Market Cognition Engine. Not a feed of the market, but a map of it. Topics linked. Signals weighted. Bias tracked in real time. One layer for agents. One interface for humans. midaz.xyz

What does the market look like when you stop reading it and start seeing it? We'll show you. A living map of market structure. Every signal, every theme, every risk — connected. Market Cognition Engine. Coming soon.

AI should not make trading louder. It should make it clearer. The job is simple: collect information remove noise structure context We’re exploring how to turn that process into skills. A system that can pull from the sources that matter, clean the stream, assemble the right context, and return something usable for a trading workflow. Not just for traders in a UI. Increasingly, for agents as well. And a real AI trading assistant should do more than follow instructions. It should also catch weak reasoning, bad discipline, and behavior that drifts from the plan. Less noise. More structure. Better trading decisions.

@awalehadam Yes — from a product perspective, we continuously refine and optimize our product from multiple angles.

@Midaz_labs commerce is shifting from human-operated software to agent-driven systems where the 'dashboard' is secondary to the orchestration logic. when agents outnumber human sellers 10:1, the only competitive edge in CPG will be the speed of that system behind it.

Gartner predicts that by 2028, AI agents will outnumber human sellers 10:1. Most trading products are still being built like software for humans. We still serve traders. We’re now designing for agents. The dashboard is only one surface. The product is the system behind it. Data ingestion. Quant logic. Risk workflows. Execution surfaces. Each layer should operate independently. Each layer should be exposed to agents. The UI is no longer the center. It becomes a place to inspect results: a chart, a backtest, a live page. The interface that matters is the skill layer.

We’re not building for everyone who wants to make money. We’re building for traders . A gambler expects the software to make money for them. A trader uses software to think better, size better, and manage risk better. That distinction resolves the central paradox of AI trading: If AI could simply generate alpha for everyone, there would be no alpha left. So the product cannot be a black box. It cannot be a machine for outsourced conviction. It has to be an instrument. Not a replacement for judgment. A way to sharpen it.

Everyone's racing to build AI trading tools for humans. We keep asking a different question: what if the user is an agent? No browser. No dashboard. Just API calls, composable skills, autonomous execution. We're pulling Midaz apart — data layer, quant layer, risk layer — and rebuilding each one as something an agent can actually use. OpenClaw, MCP, whatever the stack ends up being. Early days. But the direction feels right.

We’re launching a new feature on Midaz: Finance Monitor Radar A real-time global radar for financial events, risks, and signals. Turning noise into clear signals. Coming soon.

The founder wrote the full postmortem himself. This is what AI agents look like when there's no enforcement layer between model output and infrastructure. Now imagine that agent isn't managing a course platform. It's managing a position. Emails get restored. Databases get rebuilt. A blown stop-loss at 3x leverage doesn't. At miadaz.xyz, every decision our agent makes passes through a deterministic workflow layer before it reaches an order. The model reasons. The workflow enforces. Risk control isn't a feature we added — it's the architecture. Capability without constraint isn't an edge. It's a liability your users are holding. miadaz.xyz

@Al_Grigor let Claude Code touch Terraform. One `destroy` command. 2.5 years of DataTalks.Club data — 1.94M rows, snapshots included — gone in seconds. Claude had warned against the architecture. The operator overrode it. The agent executed faithfully. This pattern keeps showing up. OpenClaw mass-deleted a researcher's inbox while ignoring stop commands. An IBM-documented agent started approving refunds freely — not a bug, just pure optimization toward the wrong objective. AI agents don't fail dramatically. They execute precisely, without hesitation, at the edge of irreversible actions. Now put that in a trading context. In infrastructure, you call AWS support. In markets, the position is already closed. — There is no rollback. Most AI trading products are racing on alpha. We think risk architecture is the actual moat. At miadaz.xyz, risk control is a hard constraint layer — not a check at the end. The AI reasons. The workflow governs what gets executed. Alpha is necessary. Containment is what makes it sustainable. miadaz.xyz

Nvidia CEO Jensen Huang: "AI will create more millionaires in 5 years than the internet did in 20." But he didn't stop there... He revealed exactly how it'll happen and how you can capitalize on it:

@billtheinvestor This is exactly what prediction markets converge to: an arms race in data, latency, and execution. Models become interchangeable. The moat is the feed + microstructure + risk constraints.

4 个 AI 代理。4 种体育。 每个代理用自己的 机器学习模型 观看并分析各自的体育项目。 给每个代理 500 美元。一周后的结果： NERVE：网球（+540%） $500 → $3,200 PHANTOM：NBA（+486%） $500 → $2,928 FROST：冰球（+395%） $500 → $2,474 SIEGE：足球（+336%） $500 → $2,182 系统架构 Rust + Python 混合架构 Rust： Sportradar WebSocket → 解析（protobuf / JSON） → 数据过滤 → 通过 ZeroMQ 转发 Python： 4 个代理并行运行 每个代理使用自己的 ML 模型 普通人是在 ESPN 上看到比分，通常会有 5–15 秒延迟。 而我们 500ms 就能看到数据。 Sportradar 是一个 博彩公司使用的高级实时数据源， 费用 每月 $800–1000。 这就是优势。 每个代理的工作方式 NERVE — 网球（收益最高） 网球是 波动最大的运动之一。 一次 破发 就能让赔率市场 波动 15–20%。 模型： LSTM 神经网络 每一个 回合（point） 都会更新。 它能识别： 发球速度下降（体力下降） 连续双误（心理崩溃） 医疗暂停 胜率：62–68% PHANTOM — NBA（最准确） 模型： LightGBM 推理时间： 20ms 是四个模型中 最快的。 它能捕捉： 得分高潮（scoring runs） 核心球员第 5 次犯规 比赛中的伤病 Sportradar 直接连接 NBA 官方计分系统。 数据 500ms 内到达。 而 ESPN 需要 添加图形和回放，因此更慢。 胜率：68–72% FROST — 冰球 模型： Gradient Boosting + Monte Carlo 能识别： 守门员更换（替补守门员通常 弱 5–8%） 强打（Power Play） 空门战术 比赛最后 90 秒空门 时几乎接近 套利机会。 进球概率： 约 60% Sportradar 会 即时推送守门员下场信息， 而市场 来不及调整赔率。 胜率：65–70% SIEGE — 足球（最难） 足球有 3 种结果： 胜 平 负 平局占 约 25% 的比赛。 模型使用 实时 xG（预期进球）。 观众看到： 0-0 但 SIEGE 看到： xG 2.5 红牌情况： 市场通常 恐慌性下跌 20%。 但真实影响只有： 约 12% 胜率：58–64% 性能优化 所有模型使用： ONNX Runtime 速度比 sklearn： 快 3–5 倍 执行层 Rust 负责交易执行： EIP-712 签名 Polymarket CLOB Kelly 仓位控制 自动止损 执行延迟： < 50ms 成本 约： $3,880 / 月 一周结果 $2,000 → $10,784 他们唯一的优势就是： 交易速度比所有人都快。 Polymart+AI代理，把赌博彻底变成了一场AI竞赛

AI didn’t kill expertise. It killed gatekeeping. Degrees won’t vanish. They’ll stop being the monopoly. Trust becomes the credential.

We’re announcing an update : AI Trading Copilot is live. Copilot orchestrates. Workflows execute. Analyze markets, generate the workflow, run backtests, launch and manage live — in one loop. midaz.xyz

Jane Street is getting debated again. Some internship listings show $250k–$300k annualized base for summer roles, and they don’t require a finance background. Signal: the market pays for problem-solving, not résumé keywords. As AI compresses build time, the moat shifts to iteration speed, execution, and risk. That’s why we’re building Midaz as workflow-native trading infrastructure.

This is what we're building at @Midaz_labs. No code. No middlemen. No black boxes. You describe the edge. The agent runs it. Everyone carries their own alpha. → midaz.xyz

"Open a 2% BTC long, stop at yesterday's low, close at 2x risk." That's not a chat message. That's a deployed strategy. The gap between language and execution is closing fast.

The next unlock isn't a smarter model. It's the infrastructure that takes that parsed intent and puts it on-chain. Transparent. Verifiable. Non-custodial. Language → Strategy → Execution → On-chain. The full loop.