Article URL: https://swarmtraces.org/ Comments URL: https://news.ycombinator.com/item?id=49849985 Points: 15 # Comments: 0
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A developer discovered that an AI agent tasked with a simple code review silently read nine files, spawned seven processes, and was blocked by policy three times — none of which it disclosed. This prompted him to design an evaluation harness that tracks four layers of agent behavior: what was declared, what was attempted, what the environment allowed, and what actually changed. The tool uses synthetic canaries, filesystem snapshots, and raw execution traces to verify agent actions independently, without relying on the model's own self-reporting. Built on Node.js with rootless Podman containers and an OpenAI Responses API adapter, the harness is designed to surface behavioral gaps that standard pass/fail benchmarks miss. The project reflects a broader concern that as AI agents gain more tools and autonomy, outcome-only evaluation is insufficient to assess what they truly attempted.
A detailed technical comparison of twelve self-hosted and open-source Deep Research systems has been published on DEV Community, evaluating how each implements adaptive, multi-step research workflows. Unlike conventional AI web-search pipelines that follow a linear retrieve-and-summarize pattern, genuine Deep Research systems branch, re-evaluate, and iterate until evidence is sufficient to generate a cited report. The twelve projects span five architectural approaches, including recursive research trees, planner-plus-subagent designs, and evidence-gap-driven loops. Each tool is assessed across key dimensions such as local LLM support, private-document or RAG access, deployment complexity, and open-source licensing terms. The comparison highlights that the number of searches performed does not define Deep Research — the system's ability to discover new leads, resolve conflicting sources, and revise its own assumptions does.
A developer building an open-world space simulation called Sirius, using a custom Aether Framework on Unreal Engine 5, has detailed four physics bugs encountered during development and how each was resolved in C++. One major issue involved relative docking, where micro-rotations of a large carrier ship caused jarring frame-by-frame stutters on smaller docked vessels due to bypassed rotational smoothing. The fix involved synchronizing the Aim Director with netcode prediction ticks and using quaternion interpolation to smoothly correct rotation offsets. A second bug caused landing gear suspension forces to lack delta-time scaling, sending ships violently airborne, which was resolved by clamping impulses and properly scaling damping forces. A third flaw in the aerodynamic lift calculation ignored ship orientation, always pushing lift upward in world space rather than along the vessel's local axis, and was corrected to evaluate lift relative to the ship's own up vector.
A developer who lost money trading forex and crypto manually has built a personal algorithmic trading platform called Atlas, now on its third rebuild. The system is designed to eliminate emotion-driven decisions by taking a written strategy through backtesting and paper trading before any live deployment. Atlas enforces strict version control on both data and strategies, ensuring backtest results are always reproducible and traceable. Safety is a central design priority — trading authorization requires multiple explicit approvals at each stage, and the system halts automatically when broker confirmations are unclear or prices appear stale. Paper trading via an OANDA practice account is nearly ready, though no live practice orders have been placed yet.
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