Dev tools · alternatives · 2026
24 alternatives to The problem with the epsilon greedy method
I decided I wanted to roll my own AB testing app for Django (https://github.com/crobertsbmw/RobertsAB) when I was finished, I came across this: http://stevehanov.ca/blog/index.php?id=132 Which is a very convincing article on why AB testing sucks and with a few extra lines, you can improve your algorithm to select the best test so you never go back and update your code (yeah right.) I then thought, how many tests does this thing have to run to truly figure out which is best? I made 4 tests with probability of success equalling 1/2, 1/4,…
EpsilonGreedy is a Python testing tool that compares the effectiveness of the epsilon greedy method against standard A/B testing approaches. It evaluates how many trials each algorithm needs to identify the… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. The problem with the epsilon greedy method launched in 2014; newer entries below may have overtaken it.
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Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - https://news.ycombinator.com/item?id=39528087 As we were building the root-cause phase of our automated debugger, we realized that we developed…
2024 · github.com · its alternatives →
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Ito▲449I'm Evan and I made Ito.ai It's code review that actually runs your code. The result is that it finds more bugs with a smaller false positive rate. You get screenshots, videos, and run logs. I built Ito because manual verification sucks and I thought I could automate it. I've been a professional developer for 15 years and now more than ever, I'm just spending time making sure things work. I looked into all of the other agentic testing tools that click around a url you give them and frankly, they sucked. They just didn't work. I figured to be able to actually testing things you need to run…
Aug 2026 · ito.ai · its alternatives →
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Lift your conversion rate with repeatedly AB tested patterns
2018 · goodui.org · its alternatives →
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Sampling profiler for Python programs. Contribute to benfred/py-spy development by creating an account on GitHub.
2018 · github.com · its alternatives →
- 6CA
Hi everyone, here's a web app I made that generates code for efficiently approximating mathematical functions. This is useful when performance matters more than perfect accuracy, for example in embedded systems. The app uses Chebyshev expansions, which despite their theoretical depth result in suprisingly compact and readable code in practice. This code is generated for you and using it does not require any knowledge of the underlying theory. Source code and more info: https://github.com/stuffmatic/chebyshev-calculator
2024 · stuffmatic.com · its alternatives →
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🐢 bayesAB: Fast Bayesian Methods for A/B Testing. Contribute to FrankPortman/bayesAB development by creating an account on GitHub.
2017 · github.com · its alternatives →
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Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
Apr 2026 · github.com · its alternatives →
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I'm excited to introduce tea-tasting, a Python package for the statistical analysis of A/B tests It features Student's t-test, Bootstrap, variance reduction using CUPED, power analysis, and other statistical methods. tea-tasting supports a wide range of data backends, including BigQuery, ClickHouse, PostgreSQL, Snowflake, Spark, and more, all thanks to Ibis. I consider it ready for important tasks and use it for the analysis of switchback experiments in my work.
2024 · e10v.me · its alternatives →
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A simple Python debugger and profiler that generates animated visualizations of program flow, useful for algorithm learning. - CCExtractor/vardbg
2020 · github.com · its alternatives →
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This is a weekend hack that I'd like to further develop as it's working surprisingly well. Using MCTS, we can explore a space of possible verified programs with an LLM. We check the partial programs at each step, and so steer towards programs that pass the verifier. https://github.com/namin/llm-verified-with-monte-carlo-tree-...
2023 · github.com · its alternatives →
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Haskell re-implementation of STOKE, the stochastic superoptimizer - blaze/notebooks/tutorial.ipynb at master · bollu/blaze
2019 · github.com · its alternatives →
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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 →
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Spectry▲12Get a free AI-powered conversion rate optimization analysis of your website. Discover what
Sep 2026 · spectry.io · its alternatives →
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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 →
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elkai is a Python library for approx. solving travelling salesman problems (TSP) based on LKH 3 - fikisipi/elkai
2019 · github.com · its alternatives →
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Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…
2025 · infinitcode.ai · its alternatives →
- 18LO
Show HN: Leaping - Open-source debugging with LLMs Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our life combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We started by capturing information from running applications to then ‘replay’ relevant sessions later. Our approach for Python involved extensive monkey patching: we’d use OpenTelemetry-style instrumentation to hook into the request/response lifecycle, and capture anything non-deterministic (random,…
2024 · its alternatives →
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I want to share a really dumb, but very practical project I have packaged this summer, to perform operations on strings much faster. I was using Python to work with a multi-terabyte newline-delimited file. Reading, splitting, and shuffling it was a nightmare. So, I wrapped a trivial hardware-friendly heuristic I've been using for the last few years into a CPython library. The part I enjoyed the most is implementing SIMD behavior without SIMD instructions... Using 64-bit words to work at 8-bit granularity. Unlike conventional SIMD, the code would remain the same for ~~almost~~ any hardware.…
2023 · ashvardanian.com · its alternatives →
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We've been trying to evaluate web scraping companies, but when you look at their benchmarks, you can't verify anything, and they mostly exist to prove the company is successful. They put somewhere between 98% and 100% because they pick their own urls, define success their own way, and don't publish the harness. We also saw companies like scrapfly astroturf websites like scrapeway and call them independent. So, we built an open source benchmark that we want to represent the frontier of web data. We're trying to look across all major anti-bot providers and industries, to build a comprehensive…
Jul 2026 · github.com · its alternatives →
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Wrote a program in FreeBASIC to try and find out how Genetic Algorithms work. It's a quick hack that I wrote to try some theories with because the Wikipedia Article on GAs reads like greek to me. So I built it from the general principles i knew at the time. The "genome" per individual is an array of 9000 integers but I used only monochrome pictures with either black or white; An individual also has a fitness score. There are 1000 individuals in the population and only two are selected to reproduce. So that makes 9 million (yes, MEELION as El Reg would write) data points and the scoring…
2015 · its alternatives →
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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 →
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Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
2023 · docs.giskard.ai · its alternatives →
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Contribute to monetate/monte-carlo-simulator development by creating an account on GitHub.
2013 · github.com · its alternatives →
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