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![[R], 31 MILLIONS High frequency data, Light GBM worked perfectly](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-neural-network-P6fqXULWLNUwjuxqUZnB3T.webp)
[R], 31 MILLIONS High frequency data, Light GBM worked perfectly
We just published a paper on predicting adverse selection in high-frequency crypto markets using LightGBM , and I wanted to share it here because the findings are directly relevant to anyone dealing high frequency data and machine learning The core problem we solved: Every market maker's nightmare — getting picked off by informed traders right before a big move. We built a model that flags those toxic seconds before they wreck you. The data: - 31,081,463 second-level observations of BTC/USDT perpetual futures on Bybit - February 2025 → February 2026 (381 raw daily files) - Strict walk-forward regime, zero lookahead bias The key results (this is the part that shocked us): Our TailScore metric — which combines predicted toxicity probability with predicted price move severity — flags the top
Big Tech firms are accelerating AI investments and integration, while regulators and companies focus on safety and responsible adoption.
The AI landscape is experiencing unprecedented growth and transformation. This post delves into the key developments shaping the future of artificial intelligence, from massive industry investments to critical safety considerations and integration into core development processes. Key Areas Explored: Record-Breaking Investments: Major tech firms are committing billions to AI infrastructure, signaling a significant acceleration in the field. AI in Software Development: We examine how companies are leveraging AI for code generation and the implications for engineering workflows. Safety and Responsibility: The increasing focus on ethical AI development and protecting vulnerable users, particularly minors. Market Dynamics: How AI is influencing stock performance, cloud computing strategies, and
How to Publish a Paid API for AI Agents Using MCP and AgenticTrade
How to Publish a Paid API for AI Agents Using MCP and AgenticTrade Most API monetization guides assume your consumers are humans who browse a marketplace, read your docs, and manually configure auth. That assumption is becoming outdated. AI agents do not browse. They query a service registry at runtime, read machine-structured MCP tool descriptors, execute calls autonomously, and handle payment without a human in the loop. The infrastructure for that workflow is what AgenticTrade is building. This article walks through the practical steps to register your API on AgenticTrade — an MCP-native marketplace where AI agents can discover, authenticate, and pay for your API per call in USDC. What MCP Actually Does Here MCP (Model Context Protocol) is a protocol for exposing tools and data sources
Top 15 MCP Servers Every Developer Should Install in 2026
Top 15 MCP Servers Every Developer Should Install in 2026 There are over 10,000 MCP servers listed across directories like mcpmarket.com , mcpservers.org , and GitHub. Most of them are weekend projects that break the first time you try them. A handful are production-grade tools that will fundamentally change how you work with AI coding assistants. This guide is not a directory listing. We tested these servers in our daily workflow at Effloow , where we run a fully AI-powered company with 14 agents . Every pick includes a real claude mcp add install command, a concrete use case, and honest notes about what does not work well. If a server is deprecated or has significant limitations, we say so. What Is MCP and Why It Matters Now The Model Context Protocol (MCP) is an open standard created by
Anthropic is having a moment in the private markets; SpaceX could spoil the party
Glen Anderson, president of Rainmaker Securities, says the secondary market for private shares has never been more active — with Anthropic the hottest trade around, OpenAI losing ground, and SpaceX's looming IPO poised to reshape the landscape for everyone.
The Agent Economy Is Here — Why AI Agents Need Their Own Marketplace
The Agent Economy Is Here — Why AI Agents Need Their Own Marketplace AI Agents are starting to need each other's services. But there's no standardized way for them to discover, verify, and pay. That's changing. Agents Are No Longer Just Tools — They're Becoming Economic Participants Between late 2025 and early 2026, the role of AI Agents shifted in a subtle but critical way. When we used to say "AI Agent," we pictured an assistant that follows orders — organizing inboxes, summarizing documents, handling customer support. It was a tool. You were the user. Clear relationship. That's not how it works anymore. A quantitative trading Agent needs real-time news summaries. It doesn't scrape news sites itself — it calls another Agent that specializes in news aggregation. That news Agent needs mult
The bar is lower than you think
TL;DR: The efficient market hypothesis is a lie, there are no adults, you don't have to be as cool as the Very Cool People to contribute something, your comparative advantage tends to feel like just doing the obvious thing, and low hanging fruit is everywhere if you pay attention. The Very Cool People are anyways not so impossible to become; and perhaps most coolness is gated behind a self belief of having nothing to add. So put more out into the world, worry less about whether people already know or find it boring. At worst you'll be slightly annoying. How can you know, if you haven't even tried? Recently I've been commenting more on LessWrong [1] . This place is somehow the best [2] forum for sane reasoned discussion on the internet besides small academic-gated communities. A lot of post
From Forecast Models to Policy Agents: Rethinking AI in Power Markets
What actually changes when you replace a prediction pipeline with a decision-making system — and why the gap matters more than most teams realize. The previous post in this series walked through a single day in a European power market — a wind-plus-battery portfolio, a good forecast, and a sequence of decisions that the forecast couldn’t help with. The conclusion was structural: electricity markets are sequential decision systems. The forecast-optimize-execute pipeline treats each trading stage independently, while the actual problem is coupled across time, assets, and market stages. This post is about what comes next. If the pipeline is wrong, what replaces it? The short answer is: a policy. The longer answer requires being precise about what that word means, why it’s different from what
Show HN: EU Compliance SaaS for Sale ($4K Each) – CBAM, AI Act, Public Tenders
I built 4 SaaS products targeting mandatory EU regulations. Each for $4,000. 1. CBAM OS (cbam-os.com) - EU Carbon Border Tax compliance for importers 2. AIA Proof (aiaproof.com) - AI Act compliance & AI detection 3. AO France (ao-france.fr) - AI-powered public tender responses (FR) 4. AO Copilot (ao-copilot.fr) - BTP tender analysis, 20 modules, 1900+ tests All mandatory regulations = guaranteed market. Modern stack (Next.js, Supabase). Production-ready. Contact: [email protected] Comments URL: https://news.ycombinator.com/item?id=47632626 Points: 1 # Comments: 0
