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A Night Without the Nerds – Claude Opus 4.6, Field-Tested

https://konfuzio.com/en/a-night-without-the-nerds-claude-opus-4-6-in-the-field-test/
1•konfuzio•1m ago•0 comments

Could ionospheric disturbances influence earthquakes?

https://www.kyoto-u.ac.jp/en/research-news/2026-02-06-0
1•geox•2m ago•0 comments

SpaceX's next astronaut launch for NASA is officially on for Feb. 11 as FAA clea

https://www.space.com/space-exploration/launches-spacecraft/spacexs-next-astronaut-launch-for-nas...
1•bookmtn•3m ago•0 comments

Show HN: One-click AI employee with its own cloud desktop

https://cloudbot-ai.com
1•fainir•6m ago•0 comments

Show HN: Poddley – Search podcasts by who's speaking

https://poddley.com
1•onesandofgrain•6m ago•0 comments

Same Surface, Different Weight

https://www.robpanico.com/articles/display/?entry_short=same-surface-different-weight
1•retrocog•9m ago•0 comments

The Rise of Spec Driven Development

https://www.dbreunig.com/2026/02/06/the-rise-of-spec-driven-development.html
2•Brajeshwar•13m ago•0 comments

The first good Raspberry Pi Laptop

https://www.jeffgeerling.com/blog/2026/the-first-good-raspberry-pi-laptop/
3•Brajeshwar•13m ago•0 comments

Seas to Rise Around the World – But Not in Greenland

https://e360.yale.edu/digest/greenland-sea-levels-fall
2•Brajeshwar•13m ago•0 comments

Will Future Generations Think We're Gross?

https://chillphysicsenjoyer.substack.com/p/will-future-generations-think-were
1•crescit_eundo•16m ago•0 comments

State Department will delete Xitter posts from before Trump returned to office

https://www.npr.org/2026/02/07/nx-s1-5704785/state-department-trump-posts-x
2•righthand•20m ago•1 comments

Show HN: Verifiable server roundtrip demo for a decision interruption system

https://github.com/veeduzyl-hue/decision-assistant-roundtrip-demo
1•veeduzyl•21m ago•0 comments

Impl Rust – Avro IDL Tool in Rust via Antlr

https://www.youtube.com/watch?v=vmKvw73V394
1•todsacerdoti•21m ago•0 comments

Stories from 25 Years of Software Development

https://susam.net/twenty-five-years-of-computing.html
2•vinhnx•22m ago•0 comments

minikeyvalue

https://github.com/commaai/minikeyvalue/tree/prod
3•tosh•26m ago•0 comments

Neomacs: GPU-accelerated Emacs with inline video, WebKit, and terminal via wgpu

https://github.com/eval-exec/neomacs
1•evalexec•31m ago•0 comments

Show HN: Moli P2P – An ephemeral, serverless image gallery (Rust and WebRTC)

https://moli-green.is/
2•ShinyaKoyano•35m ago•1 comments

How I grow my X presence?

https://www.reddit.com/r/GrowthHacking/s/UEc8pAl61b
2•m00dy•36m ago•0 comments

What's the cost of the most expensive Super Bowl ad slot?

https://ballparkguess.com/?id=5b98b1d3-5887-47b9-8a92-43be2ced674b
1•bkls•37m ago•0 comments

What if you just did a startup instead?

https://alexaraki.substack.com/p/what-if-you-just-did-a-startup
5•okaywriting•44m ago•0 comments

Hacking up your own shell completion (2020)

https://www.feltrac.co/environment/2020/01/18/build-your-own-shell-completion.html
2•todsacerdoti•47m ago•0 comments

Show HN: Gorse 0.5 – Open-source recommender system with visual workflow editor

https://github.com/gorse-io/gorse
1•zhenghaoz•47m ago•0 comments

GLM-OCR: Accurate × Fast × Comprehensive

https://github.com/zai-org/GLM-OCR
1•ms7892•48m ago•0 comments

Local Agent Bench: Test 11 small LLMs on tool-calling judgment, on CPU, no GPU

https://github.com/MikeVeerman/tool-calling-benchmark
1•MikeVeerman•49m ago•0 comments

Show HN: AboutMyProject – A public log for developer proof-of-work

https://aboutmyproject.com/
1•Raiplus•49m ago•0 comments

Expertise, AI and Work of Future [video]

https://www.youtube.com/watch?v=wsxWl9iT1XU
1•indiantinker•50m ago•0 comments

So Long to Cheap Books You Could Fit in Your Pocket

https://www.nytimes.com/2026/02/06/books/mass-market-paperback-books.html
4•pseudolus•50m ago•2 comments

PID Controller

https://en.wikipedia.org/wiki/Proportional%E2%80%93integral%E2%80%93derivative_controller
1•tosh•55m ago•0 comments

SpaceX Rocket Generates 100GW of Power, or 20% of US Electricity

https://twitter.com/AlecStapp/status/2019932764515234159
2•bkls•55m ago•1 comments

Kubernetes MCP Server

https://github.com/yindia/rootcause
1•yindia•56m ago•0 comments
Open in hackernews

How AI Is Changing Bookkeeping

https://www.ledgeriq.ai/blog/MJTgHE8JXagckrXieRDMx/using-ledger-iq/1O7WYgAjtMOI23jpScUoMo/How-AI-is-Changing-Bookkeeping
7•JohnnyRebel•5mo ago

Comments

JohnnyRebel•5mo ago
The story of a bootstrapped AI-first bookkeeping app that lets small business owners talk to their financial data instead of wrestling with spreadsheets. Beta launching this September. Curious if HN thinks this is the future of accounting or just another shiny tool.
wrs•5mo ago
This and other data analysis front ends could be a fantastic application for LLMs + tool use.

It’s also a market where getting the wrong answer could result in huge liability, so at this point you’re really rolling the dice that you’re a great LLM whisperer. (There’s no such thing as an LLM engineer, at least not yet.)

FredPret•5mo ago
LLM engineer -> silicon psychologist who can sometimes sell the beast into making the year-end postings pass all tests?
JohnnyRebel•5mo ago
We sidestep the “silicon psychologist” issue: the LLM simply interprets questions, while all numbers come from structured data. AI explains results, but it can’t rewrite the books.
FredPret•5mo ago
Sounds like a powerful model if you can get it right
presentation•5mo ago
Yeah, I’m biased since my startup is a very non-AI payroll app, but trusting my finances to an LLM sounds frightening and the money saved is not much since just hiring an accountant whose neck is on the line to get it right just isn’t that expensive.
JohnnyRebel•5mo ago
Fair point—though to be clear, the LLM isn’t doing the math, just the interface. The numbers come from structured data, so accuracy isn’t left to chance. Where this really helps is for small business owners who are overwhelmed by QuickBooks data entry and classification. Our goal is to continually improve the experience, making bookkeeping as simple as possible.
JohnnyRebel•5mo ago
I totally agree; the liability is real, which is why we don’t let the LLM “invent” numbers. We use the model as the interface, but all financial data comes from a structured database. In practice, it works like RAG: the LLM interprets the user’s question, retrieves the right data, and explains the result in plain English. That way the math is deterministic, the answers are grounded, and the AI layer just makes it accessible.
wrs•5mo ago
I can see that this is potentially a good sweet spot for the current state of AI. More complex and custom enterprise BI queries can get totally bollixed up in interpretation — even humans can’t agree on definitions so there’s no way to know if the query is “correct”. Perhaps in small business accounting SaaS you have the luxury of saying “this is the model, no substitutions please” and produce clearly interpretable answers.
JohnnyRebel•5mo ago
That’s exactly the question we’re testing. Will this feel like the future of bookkeeping, or just another tool? We’re launching the beta in a week and are eager to see how real users respond. Curious what you think would make this genuinely useful vs. gimmicky?
mmcn•5mo ago
Enabling an agent to query financial data really helps on the analysis side. How are you tackling the data ingestion side? The challenge I’ve seen again and again is logging financial data from different sources in a consistent way such that it is able to be aggregated and queried. I’ve been curious if AI can help there.
JohnnyRebel•5mo ago
We’re tackling ingestion primarily through direct bank connections. Users connect their bank and financial accounts, and transactions flow into our system automatically. From there, we store the data in a structured database and normalize it into a consistent internal format so it can be aggregated and queried reliably.

Right now, the ingestion layer handles most of the heavy lifting—parsing the raw feeds, mapping fields into a standard schema, and ensuring consistency across institutions. Our next version will include layering AI to help with classification and enrichment (e.g. categorizing ambiguous transactions, detecting anomalies, and filling in context where the raw data is thin).

So it’s a mix: the ingestion pipeline makes the data uniform, while AI helps make it more useful and accurate for analysis. As we move toward our “agentic” roadmap, we see AI playing a bigger role in automating the messy parts of ingestion as well.

realitysballs•5mo ago
Solid AI use case. As someone that is trying to tack on AI/automation to legacy accounting software, this might be the better way to do it.
JohnnyRebel•5mo ago
We thought about just building an AI plugin for QuickBooks, but decided to build our own platform instead. Harder path, but bigger upside.
xtiansimon•5mo ago
Well. Big ad.

> “Traditional bookkeeping software assumes its users are trained accountants.”

That’s not the way QuickBooks community talks about their software. I’ve not been in their forums for a while, but a common refrain was to stop users from _trying_ to think like accountants. They would say it’s not the way of QB.

JohnnyRebel•5mo ago
Fair, QuickBooks does market their software towards every small business owner not just accountants. But even so, small business owners still spend hours on entry and categorization. It's enough that most community colleges teach QuickBooks courses. We want to go further: let owners ask questions in plain language and get answers instantly, no menus or reports needed. Our upcoming versions will include agentic capabilities to send invoices, and do much more all from one AI UI.
kkfx•5mo ago
What could possibly go wrong with letting an LLM decide how to record transactions? Someone who sold a new car for a dollar might have an idea...