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Getting Started with Mastra (TypeScript Agents)
Scaffold a Mastra TypeScript agent locally, run Studio, and contrast typed agents with your after-work Python stack.
Academy notes
Articles and updates from SoloQuant Academy.
Tag: python
Scaffold a Mastra TypeScript agent locally, run Studio, and contrast typed agents with your after-work Python stack.
Invent one research or ops agent on Relevance AI, give it a single tool, and skip the multi-agent workforce canvas on night one.
Build one Make scenario with Make AI Agent (New), one tool, and a manual trigger—then compare Zapier and n8n for after-work projects.
Create one unpublished Dust agent on your own notes, test in Preview, and keep company Slack disconnected on night one.
Open zapier.com/agents, build one narrow SaaS agent with a single app, test, then publish only if the run is boring.
Skip retired Operator and old Agent-mode names. Use ChatGPT Work, tools, and cloud browser for one supervised after-work task.
Use Perplexity as a cited research searcher—not a coding agent—and run one sourced brief after work.
Guijarro-Ordonez, Pelger & Zanotti (2021) use deep nets to extract residual spreads for statistical arbitrage. Plain-language intro. Not investment advice.
Feng, He & Polson (2018) study deep nets for predicting asset returns from characteristics. Plain-language intro. Not investment advice.
Justin Sirignano (2016) applies spatial neural nets to limit-order-book states—an early LOB deep-learning baseline. Plain-language intro. Not investment advice.
Bühler, Gonon, Teichmann & Wood (2018) train neural nets to hedge derivatives under frictions instead of relying on Black–Scholes deltas. Plain-language intro. Not investment advice.
Dixon, Klabjan & Bang (2017) recast market prediction as classification with deep neural nets instead of return regression. Plain-language intro. Not investment advice.