Developer Builds Multi-Agent AI Interview Coach That Analyzes Resumes

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A developer has created a multi-agent AI system called Interview Coach that generates personalized technical interview questions based on a candidate's resume. Built using Google's Agent Development Kit (ADK), the system uses three agents — a Profiler, an Interviewer, and a Judge — working in a sequential loop to ensure question quality. The Profiler extracts skills from a resume PDF stored in Google Cloud Storage, the Interviewer drafts three targeted questions, and the Judge rejects any that are too generic, prompting a redraft. The project was submitted as part of the DEV Education Track challenge focused on building multi-agent systems. The source code is publicly available on GitHub.

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ADA: Open-Source Data Analyst Tool That Separates LLM Reasoning from Math

A developer has released ADA (Automated Data Analyst), a free, open-source tool designed to address the unreliability of using large language models for numerical data analysis. Users can upload CSV or Excel files and receive dashboards, anomaly detection, forecasts, and plain-English insights, all computed locally using the Python library pandas. Unlike typical LLM-based tools, ADA's optional AI layer only accesses column names and never the underlying row data, ensuring privacy and computational accuracy. The tool requires no API key for its core functionality and is licensed under MIT, meaning anyone can use or contribute to it. The solo developer behind ADA has stated a goal of making the tool increasingly LLM-free to ensure fully deterministic, reproducible results.

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Developers Steven and Vincent built FounderOS, an AI-powered board of directors designed to help solo founders and small teams make high-stakes business decisions without access to real advisors. The system uses eight specialist AI agents — including analysts, a Skeptic, and a Chair — that debate a submitted decision and produce a structured board memo. Built on LangGraph and powered by Qwen models via DashScope, a full board deliberation takes roughly two minutes and costs about two cents. Unlike typical multi-agent demos, FounderOS preserves disagreement in the final output, attributing dissent to specific agents when consensus is not reached. The tool also maintains a markdown-based memory vault per company, allowing the board to reference past decisions when forming new recommendations.

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FROST Framework Introduces Hierarchical 'Family Lineage' Governance for AI Agents

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How to Strip GPS Metadata From Both Parts of an iOS Live Photo Before Upload

iOS Live Photos consist of two paired resources — a JPEG still image and a video clip — both of which can carry location metadata that must be scrubbed before upload. A common privacy gap occurs when the still image is cleaned of GPS EXIF data but the paired video retains location metadata, especially during interrupted or retried uploads. Developers are advised to use Apple's PHAssetResource API to enumerate all asset resources and apply privacy checks to each output individually, not just the visible image. Lifecycle edge cases, such as app backgrounding or crashes mid-upload, must be handled so that a scrubbed still is never paired with an unprocessed original video. Server-side verification is also recommended, as device-only inspection can miss queued originals or metadata embedded in multipart upload manifests.

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