Alternatives
Products that do what OpenEntropy – 47 hardware entropy sources from your computer's physics does
I built this to study something most security engineers wave off: whether external factors can nudge hardware entropy sources. Here is why. Princeton’s PEAR lab ran RNG work for about 28 years and shut down in February 2007. People in the lab tried to shift random event generator output, and they reported small deviations after tens of millions of events. https://www.pear-lab.com/ The Global Consciousness Project took a similar idea outside the lab. It has run a distributed network of hardware RNGs since 1998 and looks for correlated deviations around major world events. Most…
- 1DY
Hi HN, this is Shimon and Eyar of Datree (https://www.datree.io/). When I was an Engineering Manager of Infrastructure at ironSource (NASDAQ:IS) for 400 developers, a developer made a mistake, causing a misconfiguration to reach production, which caused major problems for the company's infrastructure. Mistakes happen all the time - you learn from them and hope to never make them again. But how can we prevent a production issue from recurring, or, how about a bigger challenge — how can you prevent the next one from the get-go? In our case, we tried sending emails to our devs,…
2021
- 2OS
We’ve just released an open-source library for solving the Maximum Independent Set (MIS) problem with neutral atom quantum computing, running on both quantum processing units (QPUs) and classical hardware, thanks to emulators. This project is the result of collaboration between Pasqal, academic researchers, and industry partners, aiming to make it practical to experiment with quantum approaches to hard combinatorial optimization tasks. The MIS problem appears in real-world scenarios like scheduling, resource allocation, and network optimization, areas where classical solvers often struggle…
2025
- 3IB
You know that old TI calculator you used in high school, then put in a box and forgot about? Have you ever wished you had an operating system for your calculator with preemptive multitasking, dynamic memory management, a tree filesystem conforming to the FHS, and all the comforts of Unix? Well, good news: that's totally a thing that exists. I've been working on my kernel for about three and a half years now and I'm looking for new contributors to help out. It's written entirely in z80 assembly, and it's both challenging and fun to work on. There's an IRC channel for contributors or people…
2014
- 4IW
Hello! I just published a book on Site Reliability Engineering. https://www.amazon.com/dp/B09QNZC2LL This book is a collection of 28 chapters on SRE concepts, such as observability, monitoring, Service Level Objectives (SLOs), alerting, resilience and debugging. I adapted each chapter from blog posts I've written over the last 4 years. Most of the chapters detailed concepts that I applied while working at $dayjob, so they are 100% proven in real business settings :) ------ Some info on the backstory. Last year I started writing a book on my software development…
2022
- 5MN
Any suggestion is welcome: https://github.com/gbonacini/nuclear_random_number_generator
2022
- 6RS
Hi HN! I've been hacking on this side project for the last month or two with the goal of making it dead simple to use cloud GPUs. I ran into this problem personally during the phd, and built my own tooling around it. I always thought it'd be fun to try to turn that tooling into a more general product... and bitbop.io is the result! All you have to do is run `ssh bitbop.io`, and you get your own personal dev GPU workstation in the cloud. Looking forward to hearing your thoughts!
2024 · twitter.com
- 7MF
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
- 8EAEntropy A.I▲12
2017 · github.com
- 9AO
Hey hackers, the world needs more AI researchers with good taste, and hardcore software folks have some of the best. Many software friends mentioned they learn better from implementations than from papers, but existing open-source examples rarely go beyond basic nanoGPT-level demos. To help bridge that gap, I spent the last two months full-time reimplementing and open-sourcing a self-contained implementation of every major modern deep learning technique from scratch. The result is beyond-nanoGPT, containing 20k+ lines of handcrafted, minimal, and extensively annotated PyTorch code. I'd love…
2025 · github.com
- 10AF
Hi HN! I'm Nick, one of the cofounders of Bitping, and we'd like to share a tool that we've been working on for the past couple of years that lets you test network connectivity from real user devices around the world. We originally were part of a hackathon focused on building a product that is powered by micropayments and we settled on a network that could ping other network devices and users could be paid in fractions of a cent for performing said work. After winning the hackathon, we built an uptime monitoring service and discovered some discrepancies between our data and traditional…
2024 · developer.bitping.com
- 117D
hi all. i’ve been shipping a small open project that tries to answer that question with evidence, not vibes. in 70 days it reached \~800 stars. the core claim is simple: many AI failures are not noise. they repeat because the geometry and ordering underneath are stable. if so, we should be able to name each failure mode, set acceptance targets, and stop shipping the same bug twice. ### what it is * a compact Problem Map of 16 reproducible failure modes in RAG and agents. * each item has a minimal fix and measurable gates. examples: * Semantic ≠ Embedding: metric and normalization mismatch.…
2025 · github.com
- 12WR
Hi HN, I previously wore SWE/SRE hats on GCP. Later, I was on the other side, managing thousands of machines. The friction points of onboarding and operating cloud systems are personal problems to me. I’ve found it hard to keep track of all the random cloud resources floating around, especially as my team’s assets proliferated. Occasionally, there is a resource affecting an outage but no one remembers where it is. I am constantly frustrated by existing tooling. APIs can work, after you’ve navigated the byzantine documentation, but I often find myself doing ad-hoc tasks which are best…
2022 · listvms.com
- 13WB
Over the past few months, as we scaled our internal AI Agents, we hit a dead end: Running LLM-generated arbitrary code in Docker is basically running naked on security due to container escape risks. But using full traditional VMs takes minutes to boot and eats too much memory to support high-density concurrency. We loved the developer experience of SaaS sandboxes on the market, but they are closed-source, expensive, and have too high a barrier to entry for self-hosting. So, our team decided to build our own. After months of grinding, using RustVMM and KVM, we built a blazing-fast,…
Apr 2026 · github.com
- 14OS
Posted before, but wanted to share if you want an open source alternative to OpenAI fine-tuning, give Unsloth a try! Phi 3.5 was just released, and is distilled from GPT4. Unsloth makes finetuning 2x faster, uses 70% less VRAM + has no accuracy degradations. We rewrite all backprop steps and reduce FLOPs and write everything in Triton (JIT low level CUDA). If you want to own the weights after fine-tuning, give Unsloth a spin! I have free Colabs and Kaggle notebooks as well at https://github.com/unslothai/unsloth
2024 · colab.research.google.com
- 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
- 16S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 17IG
Hello HN, I work on vision systems for structural inspection. A common pain point is usually that while we have a lot of "healthy" images, we often lack a reliable "Golden Set" of rare failures (like shattered porcelain) to validate our models before deployment. You can't trust your model's recall if your test set only has 5 examples of the failure mode for example. So to fix this, I built a pipeline to generate datasets. In this example, I took 7 real-world defect samples, extracted their topology/texture, and procedurally generated 200 hard-to-detect variations across different…
Feb 2026
- 18LF
Hi HN, I just wanted to share what I have been working on for the past few months: A firmware analyzer for embedded Linux systems that helps uncovering security issues running entirely in the browser. This is a very early Alpha. It is going to be rough around the edges. But I think it provides quite a lot of value already. So please go ahead and drop a firmware (only .tar rootfs archives for now) and try to break it :)
Mar 2026 · xray.boldwark.com
- 19CT
I come from a machine learning background - PyTorch code, leaving a training job running overnight, and Jupyter Notebooks. I hadn't touched much frontend before diving deep into start-ups. It was similar for my co-founder Nick, who spent time working on semiconductors. I started building, and noticing patterns in AI outputs. Enough to be able to understand how a hook works, how to manage state and why Typescript is great. But whenever it came to optimising a piece of code, debugging state issues or designing a codebase from scratch, my mind went blank. I went to ChatGPT Study Mode to seek…
Apr 2026 · chestnut.so
- 20DE
Hi HN! I built this after seeing several references to Election Truth Alliance on social media, and after reading their analysis, I just couldn't get the problems I saw in it out of my head. So I downloaded the data, and rebuilt their full analysis from scratch. Their critical error is a simple misunderstanding of the Law of Large Numbers: values collected in large samples converge to the true probability in the sample distribution. (not to be confused with the Law of Very Large Numbers: which states that unlikely things happen given enough time. That confused me too) Technical Details: - No…
2025 · sullivan.zip
- 21AH
This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…
2025
- 22
- 235L
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
- 24IM
Hey HN! I made a completely open sourced alternative to Weights and Biases with (insert cringe) blazingly fast performance (yes we use rust and clickhouse) Weights and Biases is super unperformant, their logger blocks user code... logging should not be blocking, yet they got away with it. We do the right thing by being non blocking. Would love any thoughts / feedbacks / roasts etc
2025 · github.com
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