Codex CLI가 저장소 작업 중 osFoundry 프로젝트의 스펙 노트, 지식 베이스, 버그 레코드를 직접 조회할 수 있도록 MCP 서버를 연결하는 방법을 설명한다. 연결은 ~/.codex/config.toml에 테이블 하나를 추가하고, 브라우저에서 한 번 로그인한 뒤 프로젝트에 연결을 추가하는 세 단계로 이루어진다. 읽기 작업은 확인 없이 실행되며, 쓰기 작업은 Codex의 확인 창을 거쳐야 한다. 모든 호출은 osFoundry 연결의 최근 활동 기록에 남는다. 또한 enabled_tools 목록으로 사용 가능한 툴을 10개로 제한하는 설정 예시도 함께 제시된다.
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A developer published a write-up describing their own implementation of an isolated sandbox for AI agents, built on Firecracker microVMs. The project was a response to a sandbox architecture that Google had described publicly. The author measured the performance of their build rather than relying on theoretical claims. The post was shared on Hacker News, where it gathered 8 points and no comments at the time of capture. The full write-up is available on the author's personal blog.
At its DevDay event on September 29, OpenAI introduced Dots, agents that run continuously in the cloud, each with its own computer and browser, and connect to many apps through plugins. Users can control when the agent acts alone or asks first, and sensitive actions such as changing passwords remain with the user. Critics such as Signal president Meredith Whittaker argue that deep access to personal data creates privacy and security risks, including exposure of third parties who never consented to an AI reading their messages. Because always-on agents are computationally expensive, OpenAI is also routing some work to a smaller, cheaper model, while companies report AI spending well above their budgets. Experts suggest granting access gradually and treating agent permissions as an architecture decision involving least privilege and cost controls.
Public APIs are exposed to clients that may flood an endpoint with requests, whether from a bug, a faulty script or deliberate abuse. Since .NET 7, the framework includes rate limiting middleware, so no third-party package is needed. The article demonstrates a per-IP fixed-window policy that rejects excess requests immediately with a 429 status and a Retry-After header. It also covers applying the policy to specific endpoints, keeping health checks unlimited, and writing integration tests with WebApplicationFactory.
An engineer describes building DejaVu, an AI on-call agent designed to remember every outage a team has resolved. The author argues that most incidents are recurring failure patterns that appear under different service names and error messages, and that general chatbots lack the context to recognize them. DejaVu works through a recall, reason, and retain loop, using the Hindsight open-source memory system to retrieve semantically similar past incidents and feed them to an LLM. In a side-by-side test, the same model reported 20% confidence without memory versus 98% with memory for a simulated cache bug. The author notes that a keyword search would miss many of the 6 cache-related incidents in a 20-incident dataset, which semantic recall can surface.
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