tensorzero

There was a high severity advisory last week https://github.com/tensorzero/tensorzero/security/advisories... though these days can't even tell if this is related or just routine

[I'm his coworker.] We ran Unsloth ourselves on a GPU-by-the-hour server. We have a notebook in the repository showing how to query historical data and use it with Unsloth. It's a WIP PR that we plan to merge soon: https://github.com/tensorzero/tensorzero/pull/2273

TensorZero | https://github.com/tensorzero/tensorzero | Founding Member of Technical Staff | NYC (onsite) | Full-time TensorZero is an open-source stack for industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluation, and experimentation. Open Roles: ‣ Back-end Engineering (Rust) ‣ Design Engineering ‣ Developer Relations (DevRel) Engineering ‣ Front-end Engineering (React) ‣ Product Engineering (Full-Stack) What we offer: ‣ Vast majority of your work → open source ‣ Years of runway ‣ Small and entirely technical team: former Rust compiler maintainer, ML researchers (Stanford, CMU, Oxford, Columbia) with thousands of citations, decacorn CPO ‣ $200-300k base + up to 1% equity + benefits ‣ Onsite (5 days) in New York (Williamsburg, Brooklyn) More information: https://tensorzero.com/candidate-brief A

if you haven't check out our repo -- it's free, fully self-hosted, production-grade, and designed for precisely this application :) https://github.com/TensorZero/tensorzero

This is a really cool idea! I recently came across another project on GitHub: https://github.com/tensorzero/tensorzero that explores a similar direction. You might find it interesting, and perhaps it could offer some inspiration or useful insights for your work as well.

SEEKING FREELANCER TensorZero | https://github.com/tensorzero/tensorzero | Staff Front-end / Design Engineer | Remote or Onsite (NYC) | Full-time or Part-time TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models. We're looking for a contract / freelance Staff Front-end / Design Engineer with the following skillset: ‣ Must have: expert in TypeScript, React, and web fundamentals ‣ Nice to have: familiar with LLMs, experience with Vite / React Router V7 (RemixJS) / Tailwind What we offer: ‣ Vast majority of your work → open source ‣ Flexible arrangement: remote or onsite (NYC), full-time or part-time ‣ Small and entirely technical team: former Rust compiler maintainer, ML researchers with 1000's of citations, decacorn CPO ‣ Engagement expected to last a few mont

TensorZero | https://github.com/tensorzero/tensorzero | Founding Member of Technical Staff | NYC (onsite) | Full-time TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models. We're looking for a Founding Member of Technical Staff with any of the following skillsets: ‣ Back-end / Systems Engineering (Rust) ‣ Front-end / Design Engineering (React) ‣ ML Engineering / Research (Python) What we offer: ‣ Vast majority of your work → open source ‣ Years of runway ‣ Small and entirely technical team: former Rust compiler maintainer, ML researchers with 1000's of citations, decacorn CPO ‣ $200-250k base + up to 1% equity + benefits ‣ Onsite (5 days) in NYC More information: https://tensorzero.com/candidate-brief Apply: https://www.tensorzero.com/jobs

Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation: built-in A/B testing, routing, fallbacks Our goal is to help engineers build, manage, and optimize the next generation of LLM applications: AI systems that learn from real-world experience. In addition to a Quick Start (5min) [1] and a Tutorial (30min) [