Dev tools · alternatives · 2026

24 alternatives to A radically more scalable alternative to Bayesian Optimization

Experimental Global Optimization Algorithm. Contribute to chrisstroemel/Simple development by creating an account on GitHub.

This software provides a computational approach designed as a more scalable alternative to Bayesian Optimization, a technique commonly used for hyperparameter tuning and optimization problems. It is intended for… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. A radically more scalable alternative to Bayesian Optimization launched in 2018; newer entries below may have overtaken it.

1
Faster LLM evaluation with Bayesian optimization▲131
Recently I've been working on making LLM evaluations fast by using bayesian optimization to select a sensible subset. Bayesian optimization is used because it’s good for exploration / exploitation of expensive black box (paraphrase, LLM). I would love to hear your thoughts and suggestions on this!
2024 · github.com · its alternatives →
2
Xcessiv – Fully managed web application for automated machine learning▲108
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. - reiinakano/xcessiv
2017 · github.com · its alternatives →
3
Speeding up LLM inference 2x times (possibly)▲419
Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
2024 · asciinema.org · its alternatives →
4
Sweeps▲92
Scalable, customizable hyperparameter tuning
2020 · wandb.ai · its alternatives →
5
Boldly go where Gradient Descent has never gone before with DiscoGrad▲232
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
2024 · github.com · its alternatives →
6
A weekend replication of STOKE, a stochastic superoptimiser▲58
Haskell re-implementation of STOKE, the stochastic superoptimizer - blaze/notebooks/tutorial.ipynb at master · bollu/blaze
2019 · github.com · its alternatives →
7
Timber – Ollama for classical ML models, 336x faster than Python▲207
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. - kossisoroyce/timber
Mar 2026 · github.com · its alternatives →
8
Optimize and serve models with Fable quality at half the cost▲71
Hi HN, we built world-model-optimizer, an open source tool to continually improve a specialized model for an agent. It does this by simulating production tool responses through text world modeling (similar to QwenAgentWorld, summary here https://x.com/silennai/status/2073887455884058814). We can then use this to train a router for frontier, OS, and local models (use defaults or pick which ones to optimize against). wmo ingests agent traces, builds the simulation, embeds the traces, runs different models you choose against the simulation scenarios, and then uses a KNN…
Jul 2026 · github.com · its alternatives →
9
Automated smooth Nth order derivatives of noisy data▲146
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com · its alternatives →
10
A reimplementation of Google's sparsehash in C▲22
A simple re-implementation of Google's sparsehash as a learning excercise. - qpfiffer/Simple-Sparsehash
2015 · github.com · its alternatives →
11OH
Optimizing Higher Order Functions with Hypothetical Inverses▲55
2016 · medium.com · its alternatives →
12
Synthesize TikZ Graphics Programs for Scientific Figures and Sketches▲132
Creating high-quality scientific figures can be time-consuming and challenging, even though sketching ideas on paper is relatively easy. Furthermore, recreating existing figures that are not stored in formats preserving semantic information is equally complex. To tackle this problem, we introduce DeTikZify, a novel multimodal language model that automatically synthesizes scientific figures as semantics-preserving TikZ graphics programs based on sketches and existing figures. We also introduce a Monte Carlo Tree Search-based inference algorithm that enables DeTikZify to iteratively refine its…
2024 · github.com · its alternatives →
13
Optimal Stock Weights in a Portfolio, Tangency & Global Min Variance.▲37
A Bayesian based architecture to evaluate the optimal weights of different stocks in a portfolio according to Global Minimum Variance and Tangency optimization problem. - pranjanpr/Quant-MELO-Portfolio
2022 · github.com · its alternatives →
14
A Go implementation of TensorFlow's streaming quantiles estimator▲98
Optimal Quantile Approximation in Streams. Contribute to axiomhq/quantiles development by creating an account on GitHub.
2018 · github.com · its alternatives →
15OA
Opytimizer – A Nature-Inspired Python Optimizer▲23
https://github.com/gugarosa/opytimizer Did you ever reach a bottleneck in your computational experiments? Are you tired of selecting suitable parameters for a chosen technique? If yes, Opytimizer is the real deal! This package provides an easy-to-go implementation of meta-heuristic optimizations. From agents to search space, from internal functions to external communication, we will foster all research related to optimizing stuff. Use Opytimizer if you need a library or wish to: - Create your optimization algorithm; - Design or use pre-loaded optimization tasks; -…
2021 · its alternatives →
16
Monostate AItraining▲59
Fine-tuning, RL, and inference in one CLI
Dec 2025 · github.com · its alternatives →
17RE
RULER – Easily apply RL to any agent▲81
Hey HN, Kyle here, one of the co-founders of OpenPipe. Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement. One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate. RULER is a drop-in reward function that works…
2025 · openpipe.ai · its alternatives →
18
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
19
I wrote an autodiff in C++ and implemented LeNet with it▲36
2024 · gitlab.com · its alternatives →
20
Fast Monte Carlo Permutation Simulator in C▲28
Contribute to monetate/monte-carlo-simulator development by creating an account on GitHub.
2013 · github.com · its alternatives →
21
LeanRL: Fast PyTorch RL with Torch.compile and CUDA Graphs▲53
We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com · its alternatives →
22
Hyperactive – A highly versatile AutoML Toolbox▲7
A unified interface for optimization algorithms and experiments - hyperactive-project/Hyperactive
2021 · github.com · its alternatives →
23
I Wrote a Book on Evolutionary Algorithms with Python Notebooks▲13
Evolutionary Algorithms (EAs) are a fascinating class of algorithms for meta-heuristic optimisation. Perhaps the most difficult question to answer is where do we start? There is so much to cover, and many potential starting points.
2020 · datacrayon.com · its alternatives →
24RA
Relari – Auto Prompt Optimizer as Lightweight Alternative to Finetuning▲32
Hi HN, we are the founders of Relari (https://www.relari.ai). We launched our LLM evaluation stack on HN a few months ago (https://news.ycombinator.com/item?id=39641105), which is now used in production by AI teams at companies like Vanta and PwC. We have since expanded to directly optimizing parts of an LLM pipeline using a data-driven approach. In particular, we see a lot of potential in the Auto Prompt Optimization—which could be an attractive alternative to fine-tuning in many cases—to use data to align LLMs for domain-specific tasks. Here’s a demo video:…
2024 · its alternatives →

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