Social·Community
Redditor builds Dewey, an AI research system around Codex with 'progressive determinism' architecture
A Reddit user describes building Dewey, an AI-assisted research system for a one-person research business, built around Codex as a generalist rather than a single large agent. The architecture emphasizes persistent project state, validation, and 'progressive determinism' — converting repeatedly successful model behaviors into deterministic software. The post solicits other builders' architectures, particularly cases where agents were replaced by normal software.
26 engagement1 sourceSat, Oct 3, 2026, 11:06 AM
AI Overview
- Redditor building Dewey, an AI-assisted research system for a one-person research business built around Codex rather than one giant agent
- Dewey uses 'progressive determinism': flexible models handle messy work, then stable pieces get converted into software, validators, or bounded workflows
- System separates work from worker: bounded operations can be handled by Codex, cheaper models, specialist agents, scripts, or humans
- Pipeline: persistent state → bounded task → worker → structured result → validation → state update; key metric is human attention per unit of output
Related
Products·building
SOS – stores project state for coding agents between sessions, retrieved via MCP
SOS is a tool that persists a defined 'latest state' of a coding project inside the repository so coding agents like Codex can recover context between sessions. Sessions fetch the state via MCP, and superseded verification results move to history without influencing future work. It targets developers running long-term agent-driven projects where agents lose track of decisions and status.
SOS is a tool that persists a defined 'latest state' of a coding project inside the repository so coding agents like Codex can recover context between sessions. Sessions fetch the state via MCP, and superseded verification results move to history without influencing future work. It targets developers running long-term agent-driven projects where agents lose track of decisions and status.
1 engagement1 sourcehackernewsFri, Sep 11, 2026, 01:16 PM
Products·building
Extra: execution-graph-based AI agent infrastructure
Extra is a framework for building production AI agents that centers on the execution graph rather than the agent itself. It handles multi-tenant isolation, auth, MCP integration, approvals, checkpoints, memory, routing, and deterministic execution, solving the infrastructure complexity around LLMs for developers.
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0 engagement1 sourceredditSat, Aug 1, 2026, 01:37 PM
Social·Community
Hobbyists share real-world architectures for personal AI operating systems
A Reddit user asks how AI hobbyists structure personal AI operating systems in practice, seeking real-world architectures beyond YouTube hype. The discussion centers on moving past frontier-model reliance to address cost, token limits, and privacy.
A Reddit user asks how AI hobbyists structure personal AI operating systems in practice, seeking real-world architectures beyond YouTube hype. The discussion centers on moving past frontier-model reliance to address cost, token limits, and privacy.
5 engagement1 sourceredditWed, Jul 29, 2026, 08:33 AM
Products·building
AI Project Manager for coding agents
Nexus is a tool that acts as an AI project manager for coding agents, maintaining architectural consistency as projects grow. It addresses the problem of agents forgetting design decisions and introducing bugs by providing a structured context. Built with just 2 prompts, it improves coding agent performance more than switching LLMs.
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7 engagement1 sourceredditSun, Aug 2, 2026, 11:57 AM
Developments·Paper
SRHarness: arXiv introduces a runtime harness that lifts LLM symbolic regression accuracy to 93.69% on LSR-Transform
An arXiv paper introduces SRHarness, a domain-specific runtime harness for LLM-driven agentic symbolic regression, built on composable scientific actions, persistent scientific state, and trajectory lifecycle management. On LLM-SRBench it sharply improves symbolic recovery over SR-Scientist and Codex under matched DeepSeek-v4-flash-0731 backbones. The results argue that structured runtime infrastructure, not just better models or tools, drives agentic scientific search performance.
An arXiv paper introduces SRHarness, a domain-specific runtime harness for LLM-driven agentic symbolic regression, built on composable scientific actions, persistent scientific state, and trajectory lifecycle management. On LLM-SRBench it sharply improves symbolic recovery over SR-Scientist and Codex under matched DeepSeek-v4-flash-0731 backbones. The results argue that structured runtime infrastructure, not just better models or tools, drives agentic scientific search performance.
0 engagement1 sourcearxivMon, Sep 28, 2026, 04:03 PM