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.

I assume you might mean to ask "why wasn't PyPy adopted in some formal way into CPython" rather than a separate project, for which the answer is at least partially likely to be because it's a completely separate implementation.

  2025-12-04 * "CON ED OF NY CECONY 251203~ Tran: ACHDW"
    Assets:Schwab:Checking                                                                       -72.33 USD
    Expenses:Utilities:Electric:Supply:Rate                                                      143.00 KWH @@ 18.06 USD ; Supply 143.00 kWh @12.629¢/kWh
    Expenses:Utilities:Electric:Supply:Fees                                                        0.68 USD             ; Supply Merchant Function Charge
    Expenses:Taxes:Other                                                                           0.45 USD             ; Supply GRT & other tax surcharges
    Expenses:Taxes:Sales                                                                           0.86 USD             ; Supply Sales tax @4.5%
    Expenses:Utilities:Electric:Delivery:Service                                                  21.95 USD             ; Delivery Basic service charge
    Expenses:Utilities:Electric:Delivery:Rate                                                    143.00 KWH @@ 25.08 USD ; Delivery 143.00 kWh @17.539¢/kWh
    Expenses:Utilities:Electric:Delivery:Fees:Benefit                                            143.00 KWH @@ 0.71 USD ; Delivery System Benefit Charge @0.497¢/kWh
    Expenses:Taxes:Other                                                                           2.29 USD            ; Delivery GRT & other tax surcharges
    Expenses:Taxes:Sales                                                                           2.25 USD            ; Delivery Sales tax @4.5%

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.

Some I wrote by hand using PyMuPDF, some I coerced Claude into writing (again using PyMuPDF) by uploading a sample bill (I'd never put my own data into an LLM but it's nice being able to find a sample bill, gets it close enough to correct that I can do the remaining bits if there are variations in bills over time).

Overall it's effort (and yes certainly a bunch effort for manually downloading transactions). The financial industry is very behind on this stuff clearly. I'm not sure in a few years whether I'll still think it's worth the effort I put in, which has gone down over the past few months as I automate things, but until it stops being fun I'll keep going.

But good to hear the positive story side for this.

(I'll still stick with "I never really have run into a version issue for things I use Homebrew for, for places where it matters, I have whatever-programming-language-lockfile-for-the-project-I-am-developing" for cases where I need to be sure the setup is reproducible, which is why I've clearly never noticed this file was useless).

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.)

That's true though of Lean code written by a human mathematician.

AI systems are capable (and generally even predisposed to) producing long and roundabout proofs which are a slog to decipher. So yes the feeling is somewhat similar at times to an LLM giving you a large and sometimes even redundant-in-parts program.

I'm trying out using the Obsidian Web Clipper extension, which essentially does this (and using it for anything I'd previously have bookmarked).

The compressor would come on for a few seconds then shut off.

After 2 different HVAC companies quoted me $275 to come out (plus hourly and the repair once they find the issue) and then also told me it would be 10 days before they had availability I finally bit the bullet, bought a $30 multimeter, watched a few videos on how capacitor failure is super common and how to hopefully not kill myself, and after confirming with the multimeter and buying the $7 capacitor everything was right back to working with 2 minutes of work.

I did have a moment where I dreaded thinking I'd need to replace the unit and if so whether I'd want a split put in but for $53K I'd better get a third job... Quite glad not to have had to get too far down this road.