JulianWasTaken

Quantum Rise is an AI-forward consulting startup serving the mid-market in the US. We see companies in multiple industries struggling to understand how to leverage artificial intelligence in their day-to-day operations, trying to understand what their existing workforce now needs to know, or trying to understand whether they're about to be disrupted and need to change how their existing processes work. Many have business problems which "traditional" machine learning could help with even prior to the current GenAI hype cycle, but for whatever reason hadn't prioritized doing until now. Many have employees looking to incorporate agentic AI safely and effectively across different business areas. We try and help getting a handle around these issues and designing outcome-driven solutions.

Agentic AI Solutions Manager: You'll work directly with clients to benchmark, train and deploy agentic systems for industry-specific problems. We're looking for someone deeply embedded in agentic engineering practice across vendors and platforms. Companies are looking for ways to leverage agentic systems to upskill their practitioners and need help navigating a new, powerful scary world.

AI Solutions Engineer: You're someone who has experience across the entire spectrum of applied automation, machine learning and agentic engineering.

We think of these two roles as complementary -- agentic AI solutioners help clients understand the forefront, whereas pure AI solutioners help them pick the right solution not the one they saw in the news.

Both are front-facing development roles for someone who loves both tech and tackling real business problems. We love people with prior experience in multiple industries.

To apply, send me an email at julian.berman at quantumrise.com, mentioning HN and including your CV.

Quantum Rise is an AI-forward consulting startup serving the middle market, across multiple industries. Clearly many companies are struggling to understand how to leverage artificial intelligence in their day-to-day operations, to understand what their existing workforce now needs to know, or to understand whether they're about to be disrupted. Many have business problems which "traditional" machine learning could help with even before the current GenAI hype cycle, but for whatever reason hadn't prioritized doing so until now. We try and help getting a handle around these issues and designing outcome-driven solutions.

Agentic AI Solutions Manager: You'll work directly with clients to benchmark, train and deploy agentic systems for industry-specific problems. We're looking for someone deeply curious about agentic systems across vendors and platforms. Companies are looking for ways to leverage agentic systems to upskill their practitioners. This is a front-facing development role for someone who loves both tech and tackling real business problems. To apply, send me an email at julian.berman at quantumrise.com, mentioning HN and including your CV.

Data Engineer: You'll work directly with clients and application developers to design and build data solutions for AI-enabled applications, Lakehouses, Warehouses, and analytics that solve industry-specific problems. We're looking for someone passionate about delivering outcomes that combine best practices for deterministic data management and governance with AI functionality across tools, vendors, and platforms. Our clients need to leverage AI to solve specific problems using data. This is a client-facing development role for someone who loves to work with data technologies and tackle real business issues through genuine innovation. To apply, send John an email at john.swift at quantumrise.com, mentioning DE and including your resume/CV.

I think a nice thing about beancount is that given how simple it is you can almost even ignore whole parts of it. In my case I chose to write my own importing tooling essentially without learning at all about the built-in one: https://github.com/Julian/alubia. I had no intention to make that approachable for lots of users not named me (in fact none of my actual importers are present) but it's been very fun to watch my ledger get more and more accurate.

But I think just in fairness, the comparison here for flakes should be to Homebrew bundles. My packages are managed in a bundle: https://github.com/Julian/dotfiles/blob/main/Brewfile and then locked by a lockfile: https://github.com/Julian/dotfiles/blob/main/Brewfile.lock.j... and installing is just `brew bundle install`. All native Homebrew functionality. In practice I have never had an issue with non-reproducible builds across my machines (partly because the tendency on macOS is to run the latest versions of things and stay up to date).

(But again I do find nix-darwin interesting to try for other reasons.)