I have been using Nix for about 2 years now at our company and its pretty amazing. In my experience it doesn't make compiling things much easier than normal, but the difference is that once something builds, it just works. ALL THE TIME. In previous companies I spent a ton of time debugging other devs environments that worked fine on my machine, but cause linker errors, breakage due to transitive deps, slightly different compilation artifacts, etc. Furthermore most tools are language specific leaving you to fend for yourself to get the system dependencies or rely on a separate binary distribution package manager. Nix handles everything we throw at it, we have even configured our dev env to roll to the new nightly Rust every week without any effort from the developer. We also get the performance benefits of compiling everything with march=native. Additionally our dependencies and dev environments are cached, so most of our engineers never have to wait for things to compile. The issue we have ran into is due to the fact we are running a traditional distro instead of NixOS, which causes headaches when your code depends on a kernel module (Nvidia drivers for example) or you want to register systemd units. This has led us to have some hacks in our setup to make it work. Ultimately we are thinking of just making the transition to NixOS. Its unbelievable to me that I can deploy an entire mesos cluster with just two lines of code.

FWIW if you enjoy Nix and are interested in using it in production feel free to reach out to me. brandon [at] standard [dot] ai

Standard Cognition - San Francisco | 130 - 175K + Equity | Infrastructure and HPC | Full-time Onsite

Standard is building the future of retail with autonomous checkout. Our 100% machine vision based system allows shoppers to walk in, grab stuff, and leave, without ever having to wait in a line or scan an item. We figure out what they grab and seamlessly charge them.

We have designed our system from the ground up to be deployable to millions of retailers without major renovations. We are running our own test store here in San Francisco and are rolling out to multiple retailers around the globe.

Help us build the state-of-the-art systems that make this possible!

Skills we are looking for: Rust, Nix Nice to have: Python, Mesos

Software Engineer - Systems Engineering - Build the high performance, distributed inference pipeline that underpins our machine learning models and powers our stores - Integrate python machine learning models with Rust codebase and port legacy infrastructure from fast code into blazing fast code - Optimize hardware utilization (CPU, GPU, Memory)

Senior Software Engineer - Systems Engineering - Design, develop, test and optimize reliable and efficient camera systems for large scale computer vision deployments - Collaborate with computer vision, machine learning and infrastructure teams to architect and integrate sensing software - Evaluate, test and integrate sensors from third party vendors - Design, improve and maintain low latency image acquisition pipelines

Systems Engineer - Site Reliability and Developer Operations - Build observability into products to measure and monitor availability, performance and overall health of systems - Leverage observability to improve operations and drive development towards increased reliability, performance and accuracy - Scale development processes and deployments through automation and orchestration - Participate in incident response strategy and postmortems.

Operations and Release Engineer - Site Reliability and Developer Operations - System provisioning and automated deployments - Maintain and extend automated build configurations and development environments - Maintain, develop and extend Continuous Integration pipelines and testing infrastructure

Senior Security Engineer - Site Reliability and Developer Operations - Identify security risks, threats and develop mitigation plans - Design, implement, maintain, and evaluate security-focused tools and services - Perform security assessments, reviews and code audits

If you're interested in applying, please contact us at brandon [at] standard [dot] ai

Standard Cognition (YC S17) - San Francisco | 130 - 175K + Equity | Infrastructure | Full-time Onsite

We are building the future of retail, our 100% machine vision based system allows shoppers to walk in, grab stuff and leave. We are deployed in our own test store here in San Francisco and gearing up to roll out to our clients around the globe.

Tools: Rust, Nix Bonus: Python, Mesos

Software Eng - Systems Engineering - Develop, test and maintain the high performance engine beneath our distributed machine learning pipelines

Senior Software Eng - Systems Engineering - Camera systems for large scale computer vision deployments - Evaluate and integrate third party sensors

Systems Eng - SRE and DevOps - Build tools to measure and monitor availability, performance and overall health of systems - Scale development processes and deployments through automation and orchestration

Ops and Release Eng - SRE and DevOps - System provisioning, automated config/deploys and testing infrastructure

Data Infra Eng - SRE and DevOps - Identify, test, tune and deploy distributed/clustered storage systems

Senior Security Eng - SRE and DevOps - Identify security risks, threats and develop mitigation plans. Perform security assessments, reviews and code audits - Design, implement, maintain, and evaluate security-focused tools and services

If you're interested in applying, please contact me at brandon [at] standard [dot] ai

On your sieve project

"Below is a screenshot of the time it takes to produce all the prime numbers from 0 to 100,000,000 (100 billion, in other words!)."

100,000,000 != 100 billion

This is amazing, I have been wanting something like this for some time now. Hopefully the protocol can be demystified such that this is less clumsy.

This has the potential to be some day disruptive to Twilio, Plivo, etc.