AI-powered desktop overlay for coding assessments and interviews
HintLens is an AI-powered desktop overlay that stays on top of the screen to provide answers during coding online assessments and interviews. It uses LLMs to help users avoid switching between apps or browser tabs, reducing stress from strict time limits and challenging questions.
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Open-source AI interview simulator with CLI and local web dashboard
Interview Coach is a free, open-source AI interview simulator that runs in a CLI with a local web dashboard. It uses LLMs to conduct structured mock interviews for any role, supports voice interviews, smart memory, analytics, and PDF reports, and works with any LLM provider or local models.
Luna OS: AI-powered desktop assistant for local use
Luna OS is an AI-powered desktop application that acts as a local assistant. It uses LLMs to provide useful assistance directly on the user's machine, aiming to be a practical alternative to cloud-based assistants. Built for a hackathon, it targets developers and users who want an AI assistant that runs locally.
Real-time stealth meeting copilot for Windows with local ASR and BYOK LLM
MeetingCopilot is a Windows application that provides real-time transcription of the other party in meetings or interviews, using either local FunASR or cloud ASR. It displays suggested answers on a first-person teleprompter that is invisible to screen sharing, powered by a bring-your-own-key (BYOK) LLM. It solves the problem of needing discreet, real-time assistance during remote meetings without being detected.
NeatContext: lightweight desktop app to give LLMs domain knowledge for oncall incident handling
NeatContext is a desktop application that lets LLMs access domain knowledge to handle oncall incidents more accurately. It solves the problem of SRE agents lacking domain-specific context, enabling better incident response without heavy infrastructure.
CouncilAI: local desktop app routing questions to 4 AI models
CouncilAI is a Windows desktop app that runs four local AI models and routes user questions to the appropriate model based on complexity. Simple questions use a fast lightweight model, while complex ones trigger deliberation where multiple models answer and the best response is selected. It operates fully offline on the user's hardware, requiring no accounts or cloud services.