Developer Launches Free Tool to Cryptographically Verify Aadhaar eSign PDFs

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A developer has built SigVerify, a free online tool that verifies whether Aadhaar eSign or DSC-signed PDFs carry cryptographically valid digital signatures. The tool was created after the developer found no reliable, privacy-respecting way to confirm the authenticity of a digitally signed contract. SigVerify extracts signer identity from the X.509 certificate, checks for document tampering via ByteRange hash, and validates the certificate chain up to CCA India 2022. It also correctly handles Aadhaar's short-lived 30-minute session certificates by checking validity at the time of signing rather than the current moment. The tool is available at sigverify.pages.dev and uses Python's cryptography library and OpenSSL on the backend, without relying on Adobe or third-party APIs.

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MiraVault: Open-Source Windows App Organizes Local Media Without Accounts or Cloud

A developer has released MiraVault, an open-source, local-first desktop application for Windows designed to help users organize and watch media stored across multiple folders and drives. The app supports local series and movies, IPTV playlists, subtitle handling, and automatic watch-progress tracking, with VLC integrated for broad playback compatibility. All user data — including library paths, settings, and metadata cache — is stored locally, and no account or hosted backend is required. MiraVault is currently in beta and uses Electron, React 19, and SQLite, with metadata fetched from public sources to avoid the need for private API keys. The project is publicly available on GitHub, and the developer is seeking feedback on areas such as metadata matching, folder organization, and Electron packaging.

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How AlexNet's 2012 ImageNet Win Sparked the Deep Learning Revolution

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Splitting One AI Agent Into Three Parallel Roles Cut Batch Time from 40 to 4 Minutes

A developer building a document processing pipeline for a client found that a single AI agent handling classification, tagging, and summarization worked well at 50 documents per day but took 40 minutes per batch when volume scaled to 500. The root cause was a sequential architecture making 1,500 LLM calls one after another, leaving the model idle most of the time rather than any limitation of the model itself. The solution was splitting the workflow into three specialized agents running concurrently using Python's asyncio, which reduced batch processing time tenfold without changing the underlying model. However, the developer cautions that parallel execution is not always the right approach — tasks with output dependencies, very short LLM calls, or retrieval-bound bottlenecks may perform better when run serially. The key takeaway is that scaling failures in AI agent systems are more often an architectural problem than a model capability problem.

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