No-BS Database of 300+ real-world LLM/GenAI production implementations
I've spent weeks curating technical implementation details of how companies are actually deploying LLMs and Generative AI in production. The database now contains over 300 case studies with detailed technical summaries (230,000+ words) focusing exclusively on architectural decisions, deployment patterns, and real engineering challenges. Key features: * Each case study is technically focused - no marketing fluff * 150+ entries from technical conference talks and panels (saving you 100+ hours of video watching) * Sophisticated filtering by technical stack, RAG implementations, monitoring…
What it does
In the maker’s words, at launch
I've spent weeks curating technical implementation details of how companies are actually deploying LLMs and Generative AI in production. The database now contains over 300 case studies with detailed technical summaries (230,000+ words) focusing exclusively on architectural decisions, deployment patterns, and real engineering challenges. Key features: * Each case study is technically focused - no marketing fluff * 150+ entries from technical conference talks and panels (saving you 100+ hours of video watching) * Sophisticated filtering by technical stack, RAG implementations, monitoring solutions etc. * Summaries generated consistently using Claude for quick insight extraction * All sources remain public and linked for deeper exploration Some unique insights we've found: * Common patterns in LangChain production deployments * Real-world RAG implementation approaches * Emerging best practices in LLM monitoring * Novel solutions to prompt engineering workflows * Production-tested security measures It's a lot to read so we wrote a blog post summarising the main takeaways here https://www.zenml.io/blog/llmops-lessons-learned-navigating-... The database is free and designed to help engineering teams learn from others' practical experiences deploying LLMs. I'm particularly interested in hearing about: 1. What specific implementation patterns you'd like to see analyzed (contribute more case studies via the link on the database's main page) 2. Additional case studies you think should be included 3. How you're handling non-deterministic outputs in production Looking forward to your feedback and contributions!
Does the same job
all alternatives →- UDUnify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment2024 · ▲91
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…
- LSLLMWare – Small Specialized Function Calling 1B LLMs for Multi-Step RAG2024 · github.com · ▲51
Hi, I was a corporate lawyer for many years working with a lot of financial services and insurance companies. In practicing law, I noticed there was a lot of repetition in the tasks I was working on even as a highly paid attorney that could be automated. I wanted to solve the problem of dealing with a lot information and data in a practical way, using AI. This motivated me to start AI Bloks/LLMWare with my husband, who had a deep background in software and is a very early adopter of AI. We have been on this journey with our open source project LLMWare for the past 4 months, producing a…
- A1A 100-Line LLM Framework2025 · github.com · ▲9
I've seen a lot of comments about how complex frameworks like LangChain can be. Over the holidays, I wanted to see how minimal an LLM framework could get if we stripped away everything non-essential. The result is an LLM framework in just 100 lines of code. These 100 lines capture what I see as the core abstraction of most LLM frameworks: a nested directed graph that breaks down tasks into multiple LLM steps, with branching and recursion to enable agent-like decision-making. From there, you can layer on more advanced features like agents, RAG, task decomposition, and more. I’ve intentionally…
- IBI built a tool to find the best and cheapest LLM models for my projects2024 · viewpointhq.com · ▲5
Hey HN, I've been working on something cool that I wanted to share with you all. It's called Viewpoint, an analytics tool for LLMs like OpenAI, Anthropic models, and Gemini. The idea came from the constant flood of new LLM models and the need to figure out which ones work best for my projects without breaking the bank. With viewpoint, I can track token usage, costs, latency(WIP), and traffic over time, making it easier to compare different models and see which ones perform best and save money. The tool works asynchronously, so it doesn't add any latency to your LLM requests, and you have…
- LGLLM-Generated Wikipedia2023 · canonica.ai · ▲6
Hi there, I've decided to jump on the AI train and put something together with low effort & high reward, to see if it can get any traction. What do you think? Is it a promising area? Do you guys have ideas for me? There is obviously going to be sea of LLM generated content out there and one project adding up to it might not necessarily be what world needs. In the same time there is something intriguing about the area. Well, please play with it and let me know what y'all think. Much appreciated.
- IPI published 100 AI generated books on Amazon2020 · ▲20
To be specific, the content is generated by a GPT-2 based model. https://amzn.to/2TCc0v2 Let me know if you have any questions :-)
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
