Dev tools · alternatives · 2026

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1. 1
   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 →

2. 2
   SymForce – Fast symbolic computation, code generation, and optimization▲125

   Author here. I’m unreasonably excited to share this library that we’re open-sourcing today — our team has been building it for five years and these ideas have been a passion of mine for fifteen. SymForce is a library that makes it easy to code a problem once in Python with an augmented SymPy API (backed by C++), experiment with it symbolically, generate optimized code in C++ or any backend language, and then run highly efficient nonlinear optimization problems based on the original problem definition. This workflow elegantly solves a wide variety of tasks in robotics and related domains, and…

   2022 · github.com · its alternatives →

3. 3
   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 →

4. 4
   ADTK – new Python library for time series anomaly detection▲67

   A Python toolkit for rule-based/unsupervised anomaly detection in time series - arundo/adtk

   2019 · github.com · its alternatives →

5. 5
   We made our own inference engine for Apple Silicon▲186

   We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.

   2025 · github.com · its alternatives →

6. 6
   NannyML Regression v0.8.0▲85

   OSS Python library for detecting silent ML model failure

   2022 · nannyml.com · its alternatives →

7. 7
   I built a tensor library from scratch in C++/CUDA▲119

   Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…

   2025 · github.com · its alternatives →

8. 8
   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 →

9. 9
   A radically more scalable alternative to Bayesian Optimization▲96

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

   2018 · github.com · its alternatives →

10. 10OS
    Open-Source MCP Server for Context and AI Tools▲43

    Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…

    2025 · its alternatives →

11. 11
    An MCP server that gives LLMs temporal awareness and time calculation▲91

    This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude&#x2F;GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location &#x2F;…

    2025 · github.com · its alternatives →

12. 12
    Python SDK – forecasting with foundation time-series and tabular models▲43

    We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https:&#x2F;&#x2F;github.com&#x2F;S-FM&#x2F;faim-python-client

    Dec 2025 · github.com · its alternatives →

13. 13
    PyBroker – Algotrading in Python with Machine Learning▲70

    Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance. Some of the key features of PyBroker include: - A super-fast backtesting engine built using NumPy and accelerated with Numba. - The ability to create and execute trading rules and models across multiple instruments with ease. - Access to…

    2023 · github.com · its alternatives →

14. 14
    Appelpy – library for easier regression modelling in Python▲56

    Applied Econometrics Library for Python. Contribute to mfarragher/appelpy development by creating an account on GitHub.

    2019 · github.com · its alternatives →

15. 15
    Calling tools w/ Python improves LLM perf. vs MCP (77.1% on BrowseComp)▲10

    If agent's tools are exposed as functions&#x2F;objects in a Python REPL (as opposed to JSON schemas) they perform better, I linked the explainer article we wrote, but if you want to jump straight in check out the docs! https:&#x2F;&#x2F;docs.symbolica.ai&#x2F;

    Dec 2025 · symbolica.ai · its alternatives →

16. 16
    Microcrad – Micrograd Reimplemented in C▲79

    A re-implementation of Karpathy's micrograd in C. Contribute to oraziorillo/microcrad development by creating an account on GitHub.

    Jun 2026 · github.com · its alternatives →

17. 17SA
    SymDerive – A functional, stateless symbolic math library▲26

    Hey HN, I’m a physicist turned quant. Some friends and I 'built' SymDerive because we wanted a symbolic math library that was "Agent-Native" by design, but still a practical tool for humans. It boils down to two main goals: 1. Agent Reliability: I’ve found that AI agents write much more reliable code when they stick to stateless, functional pipelines (Lisp-style). It keeps them from hallucinating state changes or getting lost in long procedural scripts. I wanted a library that enforces that "Input -> Transform -> Output" flow by default. 2. Easing the transition to Python: For many…

    Feb 2026 · its alternatives →

18. 18
    Copy-and-patch compiler for hard real-time Python▲65

    I built Copapy as an experiment: Can Python be used for hard real-time systems? Instead of an interpreter or JIT, Copapy builds a computation graph by tracing Python code and uses a custom copy-and-patch compiler. The result is very fast native code with no GC, no syscalls, and no memory allocations at runtime. The copy-and-patch compiler currently supports x86_64 as well as 32- and 64-bit ARM. It comes as small Python package with no other dependencies - no cross-compiler, nothing except Python. The current focus is on robotics and control systems in general. This project is early but…

    Feb 2026 · github.com · its alternatives →

19. 19
    Hyperbrowser MCP Server – Connect AI agents to the web through browsers▲63

    Hi HN! Excited to share our MCP Server at Hyperbrowser - something we’ve been working on for a few days. We think it’s a pretty neat way to connect LLMs and IDEs like Cursor &#x2F; Windsurf to the internet. Our MCP server exposes seven tools for data collection and browsing: 1. `scrape_webpage` - Extract formatted (markdown, screenshot etc) content from any webpage 2. `crawl_webpages` - Navigate through multiple linked pages and extract LLM-friendly formatted content 3. `extract_structured_data` - Convert messy HTML into structured JSON 4. `search_with_bing` - Query the web and get results…

    2025 · github.com · its alternatives →

20. 20SF
    Smart Fruit – A Python schema-based machine learning library▲44

    I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https:&#x2F;&#x2F;github.com&#x2F;madman-bob&#x2F;Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.

    2018 · its alternatives →

21. 21
    New Causal Impact Library▲9

    Bayesian CausalImpact for Python — R-compatible Gibbs sampler in Rust (PyO3), 30x faster, no TensorFlow - YuminosukeSato/bsts-causalimpact

    Mar 2026 · github.com · its alternatives →

22. 22
    I built a deep learning engine from scratch in Python▲30

    I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…

    2025 · github.com · its alternatives →

23. 23
    MCP Traffic Analysis Tool▲38

    Wireshark-like forensic analysis for Model Context Protocol communications Capture, inspect, and investigate all HTTP requests and responses between your IDE and MCP servers - mcp-shark/mcp-shark

    Nov 2025 · github.com · its alternatives →

24. 24
    zodiac, a new approach to monkey patching in python▲39

    A monkeypatching library for python. Contribute to colinmarc/zodiac development by creating an account on GitHub.

    2012 · github.com · its alternatives →

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →