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Autonomous Financial Research Agent on GitHub
Dexter is an autonomous financial research agent, developed by Virattt and hosted on GitHub. It's designed to think, plan, and learn as it works, using task planning, self-reflection, and real-time market data to perform complex financial analyses. Dexter decomposes complex queries into structured research steps, executes those tasks using live market data, checks its own work, and refines the results until it has a confident, data-backed answer. Key capabilities include intelligent task planning, autonomous execution, self-validation, real-time financial data access, and safety features such as loop detection and step limits. To use Dexter, users need Python 3.10 or higher, the uv package manager, and OpenAI and Financial Datasets API keys. Users clone the repository, install dependencies with uv, set up their environment variables, and run Dexter in interactive mode. Dexter can answer queries like "What was Apple's revenue growth over the last 4 quarters?" by breaking down the question into research tasks, fetching necessary financial data, performing calculations and analysis, and providing a comprehensive, data-rich answer. Dexter's architecture includes a multi-agent system with a Planning Agent, Action Agent, Validation Agent, and Answer Agent. The project structure includes a src directory with Python files for agent orchestration logic, LLM interface, financial data tools, system prompts for each component, Pydantic models, utility functions, and CLI entry point. Users can contribute to the project by forking the repository, creating a feature branch, committing changes, pushing to the branch, and creating a Pull Request. The project is licensed under the MIT License.
Agentic Engineering with Codex: A Practical Guide
The article "Just Talk To It - the no-bs Way of Agentic Engineering" by Peter Steinberger discusses his experience with agentic engineering, where AI models write most of his code. He currently works on a TypeScript React app, a Chrome extension, a CLI, a client app in Tauri, and a mobile app in Expo. Steinberger uses the codex CLI as his daily driver, running multiple instances in a 3x3 terminal grid. He has moved away from using worktrees and PRs, instead relying on his agents to do atomic commits. He uses GPT-5-codex on mid settings for most of his work, finding it to be a good compromise between smartness and speed. Steinberger emphasizes the importance of thinking about the "blast radius" when working with agentic engineering, considering how long a change will take and how many files it will touch. He also discusses the benefits of codex over other tools, such as its larger usable context, more efficient token use, message queuing, speed, and language. He prefers codex's approach to agentic engineering, which he finds to be more careful and less eager than other models. The author rarely uses big plan files with codex, as he finds that the model adheres to the prompt better than other models. He also discusses his thoughts on plugins, subagents, and MCPs, finding them to be mostly unnecessary or ineffective. Steinberger emphasizes the importance of writing clear and concise prompts, and provides tips for doing so. He also discusses his use of codex web as a short-term issue tracker and his thoughts on various coding tools and workflows.
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