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Products that do what We built the Keras of LLMs does
Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
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- 2WM
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat
- 3IS
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
- 4CM
I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…
2023 · waveworks.dk
- 5OS
Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai
- 6
- 7TO
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:…
2024 · github.com
- 8HP
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai
- 9GB
Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
2024 · github.com
- 10IB
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…
2024 · viewpointhq.com
- 11KA
2019 · github.com
- 12IB
I had 14,000 photos sitting on a drive and wanted an excuse to play with local vision models and Elixir/Phoenix. I originally tried to get LLaVA to tell me if a photo was 'good' or matched my style, but quickly learned that LLMs have terrible taste. I ended up demoting the LLM to just extract metadata, and built a custom CLIP/Ridge Regression pipeline to actually learn my preferences based on how I rate things. The stack is Phoenix/Oban on the orchestrator side, and Python/FastAPI/Instructor for the AI workers. Happy to answer any questions about the architecture,…
Apr 2026 · qwelian.com
- 13IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
- 14WW
Hey all! @sridatta and I wrote a book/zine called Forest Friends on system evals for LLM-driven apps. But it's a bit more whimsical, a bit more visual, and very much inspired by the meme of LLMs being a shoggoth polished into a smiley face with RLHF. LLM system evals are important as companies move past the flashy AI demos to reliable production apps. System evals keep coming up as the answer for what you "should do", but it's not exactly a standard part of the software engineering toolkit. So we pulled from @sridatta's seven years as a research engineer at Google, plus a ton of best…
2024
- 15IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 16CR
hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
2024 · github.com
- 17IM
Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!? To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits: 1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant…
2024 · kitops.ml
- 18OA
A friend and I are launching an alpha first thing in 2011. We're trying to gain some momentum and whatnot, so we're opening early registration as of tonight. We're planning to develop a Bayesian network to derive suggestions. If you're interested in seeing our progress, check it out. Edit: Any and all suggestions, criticism, advice, etc is highly appreciated!
2010 · osmoar.com
- 19AO
Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
- 20UA
Hello! So, I am experimenting with new approaches to automated machine learning, where you don't need anything besides either data or prompt This is a first platform from my auto ml serie called Łukasiewicz (Jan Łukasiewicz was a logician who created Polish notation) I much appreciate your feedback and if you want to try it out, I created a -75% off promo code "HACKERNEWS" on credits, so it hopefully will cover at least some of computing expenses, but more importantly will let you play with the platform at almost no cost Thank you!
2024 · lukasiewicz.tmlc.pl
- 21KA
Knit was created to solve pains of other LLM playgrounds. Some of the highlights: - Smart prompt builder, create prompt with simple requirement and few shot learning, fast and effortlessly. - Function call simulation, visualize the function callings and you can also setup a mocked value to return. - Support OpenAI/Anthropic/Azure models. - Manage prompts with projects and members. - And so much more! I have been developing Knit by myself for over 4 months now, and am looking for ways to improve it. Any feedback is appreciated.
2023 · promptknit.com
- 22AV
2018 · github.com
- 23HF
We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models. There's how it works: 1. You upload the dataset with preconfigured format into HuggingFaсe [1]. 2. Choose your LLM (e.g. LLaMa 70B, Mistral 7B) 3. Place your submission into the queue 4. Wait for it to get trained. 5. Then you get your trained model there on HuggingFace. Essentially, why would we want to do it? 1. We already have an experience with training big LLMs. 2. We could achieve near-perfect infrastructure performance for training. 3. Sometimes GPUs have just nothing to…
2023 · higgsfield.xyz
- 24IB
Fully leaned into vibe coding this time around. Started on v0, at some point ejected into running locally (getting v0's exported folder to run locally was a challenge), then iterated with Cursor over the course of a few hours. Really neat how much can be accomplished just conversationally these days.
2025 · macrodata-refinement.arjit.me
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