Alternatives
Products that do what Narev does
Rapid A/B testing for LLMs
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Hey HN, we've just finished building a dynamic router for LLMs, which takes each prompt and sends it to the most appropriate model and provider. We'd love to know what you think! Here is a quick(ish) screen-recroding explaining how it works: https://youtu.be/ZpY6SIkBosE Best results when training a custom router on your own prompt data: https://youtu.be/9JYqNbIEac0 The router balances user preferences for quality, speed and cost. The end result is higher quality and faster LLM responses at lower cost. The quality for each candidate LLM is predicted ahead of time…
2024 · unify.ai
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Compare LLMs on your data, measure, and pick the best.
Apr 2026 · trismik.com
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Everything you need to drive revenue with A/B testing.
Apr 2026 · optibase.io
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I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…
2024 · github.com
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2023 · promptperfect.jina.ai
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2016 · github.com
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I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
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I got sick of the old software development loop: Change code -> Run tests -> wait -> wait some more -> look at failures. I decided to build a tool that will enable you to: Change code -> look at failures. No wait time, no explicit test running. Under the hood: - Runs the whole test suite and collects code coverage per test. - For each auto file save, analyzes the changes on the tests. - Runs changed tests in the background. - Display results, the loop time from change to test results is approx 250ms. Instead of: code -> alt+tab -> arrow up -> rerun all the tests -> wait ... -> test results…
2022 · github.com
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2012 · keepsafe-engineering.tumblr.com
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https://medium.com/@theaniketgiri/three-months-ago-i-wanted-...
Oct 2025 · github.com
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