Article URL: https://militarywatchmagazine.com/ Comments URL: https://news.ycombinator.com/item?id=49034895 Points: 8 # Comments: 0
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A developer has released an open-source tool called LLMProxy on GitHub to address 502 read timeout errors that occur when querying slow reasoning large language model APIs. The solution uses Server-Sent Events (SSE) stream aggregation in Python to handle long-running LLM responses without connection failures. The project is publicly available at the developer's GitHub repository under the username lordraw77. The author is actively seeking community feedback, feature requests, and improvement suggestions for the tool.
In September 2025, a self-propagating worm called Shai-Hulud compromised over 500 npm packages by stealing maintainer credentials to publish malicious versions, prompting a CISA alert. The incident prompted scrutiny of whether a similar supply-chain attack could hit the NuGet ecosystem for .NET developers. NuGet does offer several defenses, including package signing with default verification since .NET 8, ID prefix reservation to block namespace impersonation, and OIDC-based Trusted Publishing to eliminate long-lived CI credentials. However, these controls only verify who published a package and that it was not tampered with in transit — they cannot determine whether the published code itself is malicious. Critically, a compromised or bad-faith maintainer account can still push harmful code that passes all current checks, and NuGet packages can carry executable logic that runs at build time, not just at runtime.
Developers can integrate OpenAI Codex with a self-hosted Outline wiki using the Model Context Protocol (MCP), offering an alternative to Claude-based setups that require a paid subscription. The setup requires Codex, Node.js, a running Outline instance, and an Outline API token generated from the account preferences panel. Users configure the connection by editing a config.toml file located in the .codex directory on either Windows or macOS, adding MCP server details and the API token as an authorization header. Once configured, Codex can search, read, edit, create, and move documents within Outline across the app, CLI, and IDE extensions without repeating the setup for each client. After restarting Codex, users can verify the connection by running 'codex mcp list' or typing '/mcp' in the CLI to confirm the Outline tools are loaded and functional.
Developers building Retrieval-Augmented Generation (RAG) chatbots often rely on informal manual testing, asking a handful of self-selected questions and judging answers by intuition rather than measurement. This approach fails to detect silent regressions that can occur whenever prompts, embedding models, chunk sizes, or document batches are changed. A structured evaluation framework requires a fixed 'golden dataset' — pre-written question-and-answer pairs scored consistently every time — rather than subjective spot-checks. Offline evaluation, conducted before deployment, helps catch retrieval and generation failures early, while production monitoring captures real-world edge cases after launch. Without systematic grading, teams risk discovering critical failures only through user complaints, often days or weeks after a breaking change was introduced.
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