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Products that do what English Placement Suite does
Adaptive CEFR testing that measures cognitive load
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- 2FT
May 2026 · github.com
- 3OS
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com
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- 5UD
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024
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- 7DA
I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
2023 · github.com
- 8AT
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
- 9AN
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai
- 10AN
2023 · github.com
- 11BP
2024 · instill.tech
- 12RE
Recent academic work ([1], [2]) has suggested that LLMs can effectively simulate different Internet subpopulations. For example, you may ask ChatGPT to emulate being a high school teacher explaining Newton’s laws of physics. Building upon this, we created Roundtable, a platform that uses LLMs to predict how people will respond to any arbitrary survey question. To do so, we needed to first reduce bias arising from GPT’s training procedure. Because these models are primarily trained on Internet data, they can be heavily skewed towards the demographics of heavy Internet users (e.g.,…
2023 · roundtable.ai
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AI-powered proficiency scoring for IELTS and Cambridge exams
Jul 2026 · englishexams.org
- 14PE
Spelltest framework simulates conversations between AI ‘synthetic users' in an environment to test and refine LLM-based applications. It ensures your app converse with utmost accuracy and relevance. Post-chat, Spelltest assesses responses, providing qualitative and quantitative feedback on performance. Suitable for both chat and completion modes. When to use: - After modifying your prompt. - When your LLM provider updates. - As a CI step for you repo. All feedback and collaborations appreciated!
2023 · github.com
- 15CL
Hi HN! Run it: OPENROUTER_API_KEY="sk" npx bff-eval --demo We built a tool to help people take LLM outputs and easily grade them / eval them to know how good an assistant response is. We've built a number of LLM apps, and while we could ship decent tech demos, we were disappointed with how they'd perform over time. We worked with a few companies who had the same problem, and found out scientifically building prompts and evals is far from a solved problem... writing these things feels more like directing a play than coding. Inspired by Anthropic's constitutional ai concepts, and amazing…
2025 · github.com
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- 17IR
The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc. Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this. And it works. On real hardware.
Mar 2026 · github.com
- 18CE
Hey HN! We’re been building Composo - a platform that helps teams achieve high performance, guarantee accuracy & minimise the cost of LLM applications. Problem we’re solving: LLM applications are non-deterministic, so evaluating whether results are good or bad is highly subjective and often requires domain expertise. Iterating over 1000s of combinations of prompts, models, temperatures, RAG settings (& many other elements) is therefore very manual & time consuming. How we are solving it: Composo links directly to your application (in a simple to set up, but highly powerful way) which enables…
2024 · composo.ai
- 19OS
The LLM challenge - an online study - aims to answer a simple question: what is the quality corridor that matters to end users when interacting with LLMs? At what point do users stop seeing a quality difference and at what point do users get frustrated by poor LLM quality. The project is an Apache 2.0 licensed open source project available on Github: https://github.com/open-llm-initiative/llm-challenge. And the challenge is hosted on AWS as a single-page web app, where users see greeting text, followed by a randomly selected prompt and a LLM response, which they must rate…
2024 · open-llm-initiative.com
- 20CS
It's extremely difficult for founders, recruiters and hiring managers to screen their candidates for AI proficiency at scale. That's why we built Corepoints. You can create and send OAs where AI usage (with AI chat) is a core metric. You have full control of the testing environment: hallucinations, data leakages, LLM behavior + Grade candidates on aspects such as their answer accuracy (of course), prompting quality, reasoning quality, hallucination susceptibility, token usage, and more. We're currently doing a demo/beta run for about the next month or so that we can iterate off…
Mar 2026 · corepoints.ai
- 21TF
Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…
2024 · github.com
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EU-Native LLM Observability. Stop Flying Blind on AI Spend.
Feb 2026
- 23AS
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io
- 24AB
2025 · github.com
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