Open LLM Gateway offers self-hosted, Apache 2.0 LLM control plane with no user cap

·1 views

A developer has released Open LLM Gateway, a fully open-source, self-hostable LLM control plane licensed under Apache 2.0, after finding existing tools like LiteLLM imposed seat-based licensing restrictions beyond five users. The project was born out of a real operational problem: teams using multiple LLM providers across Google, Azure, and AWS had no unified way to track API key ownership, token usage, or per-product spending. The gateway supports proxying for OpenAI and Anthropic APIs, encrypted key management, cost and token analytics, and an admin portal with role-based access and optional Google login. It is deployable on Cloudflare Workers and Vercel, with the full admin UI included in the open-source release — not just the proxy layer. The project is positioned for organizations that need a self-hosted LLM gateway without enterprise licensing walls, though features like spend limits and guardrails are not yet available.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer builds browser-only PDF compressor that never uploads your files

A developer added a client-side PDF compression tool to ToolHub, designed so that no file data ever leaves the user's browser. Each PDF page is rendered as an image using a pdf.js pipeline, recompressed via a canvas-based quality slider, and reassembled into a new PDF using pdf-lib. Testing on a 4.7MB image-heavy PDF achieved a 72% size reduction, with output verified using pypdf to confirm image integrity. The approach comes with a noted tradeoff: because pages are converted to images, text in the compressed output is no longer selectable or searchable. The tool is recommended for image-heavy or scanned PDFs and openly discloses this limitation to users upfront.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Google's TimesFM 3.0 Offers Zero-Shot Time Series Forecasting Without Training

Google Research has released TimesFM 3.0, a pretrained foundation model that forecasts time series data without requiring any model training or hyperparameter tuning by developers. Built on a decoder-only transformer architecture similar to GPT, the model processes time series data in 32-step patches rather than individual points, making it efficient over long histories. It was pretrained on over a trillion time points spanning retail, finance, web traffic, and energy sectors, enabling zero-shot forecasting across unseen datasets. The model natively outputs nine quantile estimates at every forecast step, providing built-in prediction intervals without additional configuration. Developers can install and use TimesFM via a simple pip command, passing raw NumPy arrays to receive forecasts, including the latest improvements introduced in the August 2026 version 3.0 release.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Talent Is Pattern Recognition, Not Memory Capacity, Argues DEV Community Post

A widely discussed piece on DEV Community argues that memory and talent are fundamentally different cognitive abilities, with memory serving merely as storage while talent involves recognizing patterns, building abstractions, and transferring knowledge across domains. The article uses a graph analogy to explain that a truly skilled thinker has fewer but better-connected ideas, rather than many isolated facts. It draws a parallel to machine learning, noting that models — and people — who only memorize training data tend to fail when faced with unfamiliar problems, a phenomenon known as poor generalization. The author contends that real-world problem-solving demands the ability to map new inputs to underlying principles, not just retrieve cached answers. Ultimately, the piece concludes that the ability to discover connections between ideas, not the volume of information retained, is the stronger indicator of mastery.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Volunteer Recovers 431 School Archive Photos Using Free Upscaling Tools

A volunteer took on the task of restoring 550 low-resolution yearbook scans for a local school archive, all on a zero-dollar budget. The photos, averaging around 600 pixels on the long edge, were too small to print or display usefully, despite having been carefully scanned from fifty years of yearbooks by a parent over a summer. Rather than upscaling all files blindly, the volunteer wrote triage scripts to sort images into actionable categories before applying any processing. Of the 550 scans, 431 were successfully upscaled to a printable resolution, 62 required no enhancement, and 19 were deemed unrecoverable. The project highlights a common pitfall in archival digitisation: scanning preserves only what physically exists, and small originals will always yield small files regardless of scanner quality.

0 comments Read more at DEV Community