Quantum Rise | Chicago or NYC (preferred), Remote for the right candidate | Full-time | No visa sponsorship | https://www.quantumrise.com/
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 went the "build a few PDF statement parsers" route.
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.
I track my propane in an LPG commodity at a fixed price per season. It saved me about $100 once when a transaction wouldn’t balance. I was accidentally partially charged for a short load delivery on one of my tanks at almost double the rate. Even if it seems silly to track at this fidelity in the moment, I wouldn’t have caught this tracking USD alone. Billing mistakes happen and can be costly!
Nice! That sounds really useful; in my case the KWH usage (and price/KWH) I pull directly out of the ConEd bill, so my only chance to notice those sorts of things would be post hoc looking back in time for big jumps in rate or usage I think.
But good to hear the positive story side for this.
You may be able to convert it to CO2 emissions to track your impact :) but for that you'd also need to track how those kWh of electricity were produced ^^
How do you do this? I am just getting into hledger and am curious about tracking this kind of stuff to see how much we would really save with a different electric supplier.
I struggle with tracking the actual cost of my energy usage between changing electric rates, the various solar costs, SRECs, different loans and credits and incentives to be able to make an intelligent decision on what the benefit or cost is of cutting an appliance or adding something new. It’s a lot.
And when it’s fragile even when working. The cost per unit changes with limited notice in various ways (line rate, unit cost, time period that various rates occur, the day, ‘free’ power bonuses etc).
beancount is definitely fun. I also jumped on this bandwagon in 2025 and it's been a great archaeology experiment of digging through old emails and trying to find as much data as I can about what the heck this random checking transaction is from 2012.
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.
I've tried nix-darwin a time or two in the past. Every few years when homebrew makes a "hostile" change and I get upset I consider trying it again (now most recently with changes to gatekeeper). I think I'll get to doing so in the next year or so.
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.)
Very nice, I think I'll be moving my "must-have" homebrew packages to a Brewfile. FYI tho, Homebrew no longer supports Brewfile.lock.json (it was always just a log anyway, not a lockfile). https://github.com/Homebrew/homebrew-bundle/pull/1509
Fun, thanks for letting me know, will remove it :)
(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).
> the comparison here for flakes should be to Homebrew bundles.
The bundler integration for nix-darwin actually just bakes tightly-controlled Brewfiles. It’s still worthwhile though, since part of the “tightly-controlled” means better cleanup when you remove things.
It's not trivial for a mathematician to understand Lean code, but it's something that's possible to learn to read and interpret in a day (without then necessarily being proficient in how to write it).
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.
What are you referring to? Inverting scroll wheel direction in macOS is trivial (and one of the first things I change), you just uncheck the "natural scrolling" checkbox.
I found this a few months ago and use it for something totally unrelated to containers -- namely on my Tailnet I have machines on my home LAN which I want to connect to even though they are asleep. I haven't been bothered to learn whether I can set up Wake on Lan requests triggered from my router to get them to wake up on demand even when I'm not on the actual LAN, so instead, when I want to connect to them, I use wait4x with a TCP connection to wait till the machine wakes itself up momentarily every few minutes, and then finally run what I want (which is usually either git pull or sshing into the machine).
Not directly relevant, but my PTAC in NYC started having issues this summer just as things got hot (of course).
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.
I had something similar happen, but for a gas boiler (hot water radiators). Our was older, but not super old. It would intermittently turn off and we couldn’t figure it out. HVAC Contractor (who we had a maintenance contract with) thought the system was toast and needed a replacement. I noticed a bad capacitor (was blown). HVAC contractor claimed they couldn’t find one through their suppliers. I had two delivered five days later.
When they came back to check the system for a full quote, the tech felt so bad that they just installed the new capacitor for free and we got another few years out of that boiler.
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.
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