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Products that do what We built a tool for fast-forwarding 95% of tests (MIT) does
Hi, we are working on a tool for speeding up test runs, by skipping tests unaffected by code changes. Effectivly, Saving 80-95% of the time, by skipping 80-95% of tests. We started a few months ago, and have managed to get into a few production CI systems. All our prospects and users are on holiday right now. So we decided to repackage and open-source for local test running. available here (https://github.com/nabaz-io/nabaz) under MIT license. One line change: pytest -v -> nabaz test --cmdline "pytest -v" Stalk us on GitHub, or just Star us. Ask questions, we'll answer in…
- 1LD
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
- 2IB
I got sick of the old software development loop: Change code -> Run tests -> wait -> wait some more -> look at failures. I decided to build a tool that will enable you to: Change code -> look at failures. No wait time, no explicit test running. Under the hood: - Runs the whole test suite and collects code coverage per test. - For each auto file save, analyzes the changes on the tests. - Runs changed tests in the background. - Display results, the loop time from change to test results is approx 250ms. Instead of: code -> alt+tab -> arrow up -> rerun all the tests -> wait ... -> test results…
2022 · github.com
- 3SU
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
- 4LA
I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…
Feb 2026 · github.com
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I wanted QR codes to be really simple to implement everywhere and for everyone. So I spent some time on my weekends trying to write a fast QR generator module for Apache, and here are the results: ab -n 5000 -c 100 http://lilqr.com/qr Requests per second: 4301.78 [#/sec] (mean)
2011 · lilqr.com
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2017 · github.com
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2024 · github.com
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2025 · github.com
- 11SC
2015 · github.com
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2021 · github.com
- 13TC
Hi HN, I spent my easter weekend stuck in the house with COVID and I decided to play with llama.cpp [1] and fauxpilot [2] to see if I could get LLM code assist working on pure CPU. As a proof of concept I'd say I've proven that it's possible. However there's still a lot to do. The auto complete is quite slow at the moment. PRs welcome. [1] https://github.com/ggerganov/llama.cpp [2] https://github.com/fauxpilot/fauxpilot
2023 · github.com
- 14FS
I wanted to test mobile apps in plain English instead of relying on brittle selectors like XPath or accessibility IDs. With a vision-based agent, that part actually works well. It can look at the screen, understand intent, and perform actions across Android and iOS. The bigger problem showed up around how tests are defined and maintained. When test flows are kept outside the codebase (written manually or generated from PRDs), they quickly go out of sync with the app. Keeping them updated becomes a lot of effort, and they lose reliability over time. I then tried generating tests directly from…
Apr 2026 · github.com
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2015 · github.com
- 16OL
I've been working on Fast LiteLLM - a Rust acceleration layer for the popular LiteLLM library - and I had some interesting learnings that might resonate with other developers trying to squeeze performance out of existing systems. My assumption was that LiteLLM, being a Python library, would have plenty of low-hanging fruit for optimization. I set out to create a Rust layer using PyO3 to accelerate the performance-critical parts: token counting, routing, rate limiting, and connection pooling. The Approach - Built Rust implementations for token counting using tiktoken-rs - Added lock-free data…
Nov 2025 · github.com
- 17FS
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
- 18IM
I built BuzzBench because I was frustrated with how complex performance testing tools have become. And I was ending up writing my own scripts to test endpoints and manually check out resource usage at the time of testing. Checkout demo: https://www.youtube.com/watch?v=yAnbZMoQvmQ
2025 · buzzbench.io
- 19KA
I built this because Cursor, Claude Code and other agentic AI tools kept giving me tests that looked fine but failed when I ran them. Or worse - I'd ask the agent to run them and it would start looping: fix tests, those fail, then it starts "fixing" my code so tests pass, or just deletes assertions so they "pass". Out of that frustration I built KeelTest - a VS Code extension that generates pytest tests and executes them, got hooked and decided to push this project forward... When tests fail, it tries to figure out why: - Generation error: Attemps to fix it automatically, then tries again -…
Jan 2026 · keelcode.dev
- 20IB
After fine-tuning GPT for a personal project, I realized how tedious it is to write plain text in a massive JSON file. That's why I built this app for my own use, and I want to see if others could benefit from a tool like this as well ;)
2024 · finetuna-ui.com
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I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev
- 22RM
Apr 2026 · github.com
- 23PC
Hi HN! We’re the team behind CodSpeed (https://codspeed.io), a continuous performance testing tool. Today, we're really excited to launch our new product: p99 (https://p99.chat), an assistant for software performance optimization. Through CodSpeed, we have been working with hundreds of projects doing performance optimization. What struck us was how fragmented the tooling landscape is. You would identify a performance regression in their CI, or worse, in production, then disappear into a rabbit hole of benchmarking frameworks, sampling profilers, memory profilers,…
2025 · p99.chat
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A CI gate for testing AI prompts and preventing regressions. - mukundzha/crilio
12d ago · github.com
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