Audioproxy Gem Enables On-Demand Audio Transcoding in Rails Without Background Jobs

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A Ruby gem called Audioproxy allows Rails developers to generate signed, cached audio preview URLs directly from Active Storage attachments without needing a job queue. The tool encodes format options, bitrate, and source URI into a signed URL path, with the first page load triggering transcoding and all subsequent requests served from a variant cache. It supports a range of processing options including trimming, fading, normalization, and expiry, all baked into the signed URL. Waveform peak data can also be generated as a format type, ensuring the visual waveform always matches the processed audio variant. The gem raises descriptive errors for unattached files or unknown options, reducing the risk of silent failures in production.

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Node.js 26.9 enables FFI by default, benchmarks show 37ns per call

Node.js 26.9.0, released on 16 September, makes the node:ffi module available by default, removing the need for the --experimental-ffi flag that was previously required in earlier 26.x versions. The change inverts the flag logic: --no-experimental-ffi is now needed to disable the feature, marking a genuine behavioral shift rather than a documentation update. Benchmarks comparing node:ffi against N-API addons show FFI is roughly 7-8% slower per call at around 37 nanoseconds, meaning it does not offer a performance advantage over existing native addons. However, FFI outperforms N-API by 15-20% in bulk buffer operations, where a single call processes large datasets, making it better suited to high-volume data transfers than frequent small calls. The key practical benefit of node:ffi remains convenience — it eliminates the need for a compiler toolchain, node-gyp, or per-ABI rebuilds in deployment environments.

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Developer fixes hidden UX flaw that drove 80% of users away before first run

A developer noticed a sharp drop-off in usage for one of their Apify data-fetching tools, where only 1 in 5 users who opened the input page actually ran the actor. The culprit turned out to be a raw JSON textarea field requiring users to manually enter a structured array of objects — an unintuitive barrier for casual users. The fix involved adding simple, flat individual input fields that internally map to the existing array format, making the tool accessible without breaking advanced multi-item functionality. After spotting the pattern in one tool, the developer audited their entire portfolio and found four more actors with the same issue, applying the same fix across all of them. The incident highlighted that UX friction invisible in code reviews can only be detected by analyzing where real users abandon a product.

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ProgrammingDEV Community ·

Proxmox VE Auth Bypass Flaw Exposes Tens of Thousands of Instances Online

A critical authentication bypass vulnerability, tracked as PSA-2026-00043-1, affects Proxmox Virtual Environment versions 7.x through 8.0.3, allowing attackers to gain passwordless root access by exploiting a flaw in the tfa-challenge parameter handling. Internet scans using application fingerprints identified roughly 34,000 to 400,000 Proxmox instances publicly reachable online, depending on the query method used. Security researchers cautioned that a raw port 8006 count of over four million is misleading, as it reflects all services on that port rather than confirmed Proxmox deployments. One affected version branch lacks a vendor patch, meaning some exposed systems cannot be fully remediated through upgrades alone. Administrators are advised to enable two-factor authentication on all accounts, restrict public access to port 8006, and audit logs for unauthorized root logins.

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ProgrammingDEV Community ·

Developer shares key lessons from 22-day hands-on AI systems building sprint

A software developer documented a 22-day self-directed learning sprint focused on building practical AI systems, covering topics from LLM fundamentals to retrieval-augmented generation (RAG) and automated evaluation. The experiment corrected several common misconceptions, including the true purpose of RAG — retrieving relevant facts at request time rather than serving as a privacy tool — and clarified how embeddings differ from next-token prediction mechanisms. Using tools like Ollama and local models, the developer demonstrated that LLMs are stateless by design, with apparent memory in chat apps being the result of deliberate state management by the application layer. Tokenization tests revealed that prompt costs and latency vary significantly by language, with Hindi requiring roughly 2.5 times more tokens per word than English. The overall takeaway was that a useful AI feature requires the same engineering discipline as any production software: defined inputs, constrained behavior, observability, and measurable evaluation criteria.

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