It would have to know the height of that car above ground, how high the air intake is, etc. A lifter offroader could make it through a much deeper body of water than eg. a prius.
Rectangle | On-site Chicago, or US remote in SF and NY | Full-Time | Salary + Equity
We're looking to hire a dedicated full-stack engineer at Rectangle.
If you are (or know!) someone who has thought deeply about distributed coordination, have strong opinions on conflict resolution, or get excited about building systems that are resilient to crazy amounts of chaos, I'd love to talk to you!
Over the last few months we’ve been investing heavily in building our internal infrastructure to power automation, integration, and visibility across the supply chain. We’re rapidly onboarding new customers and are looking for an engineer who’s excited to build alongside them.
You’ll be working directly with customers, solving real-world problems, and scaling the solutions you build. The products you own end-to-end will coordinate billions of dollars of freight, and unlock real revenue and efficiency for leading tech-enabled logistics companies.
This is a great opportunity for someone who loves to operate 0 → 1 while also being directly involved in shaping our platform strategy as we keep stacking investments we’ve made into our infra layer.
Stack: Typescript, Go, Kafka, SQL, AWS
Email hn [@] rectanglehq.com or submit a POST request with X-Application-Key: FDE-CHALLENGE-01 to challenge.rectanglehq.com and see what happens :)
In my experience chatGPT solves the challenge handily, but it does get to the level of problem that GPT requires my code review for, so either I'm not as good at using chat as I could be or they've found the point at which someone has to prove they can actually do code review to filter; nice.
Looks like an amazing product. Been playing around with it for a few mins. The UI is quite buggy and jumps around a lot (Chrome, MacOS), and seems to auto-refresh on the organizations page, which makes curating lists impossible. What's a good way to keep providing feedback?
There was a man who documented his travel to every country in the world. Not long before he was finished, South Sudan gained independence and he had to take a special trip there to complete his journey, which apparently had already completed all the other countries in Africa long ago.
Rectangle | Chicago (US), on-site | Full-Time | Equity
We're looking to hire a dedicated backend engineer at Rectangle. We're looking to solve the communication and collaboration problems in the global supply chain.
If you are (or know!) someone who has thought deeply about distributed coordination, have strong opinions on conflict resolution, or get excited about building systems that are resilient to crazy amounts of chaos, I'd love to talk to you!
Over the past ~6 months my co-founder and I have been hard at work building our product, soon helping coordinate billions of dollars worth of shipments. Our customers love what we've been building for them, we've got revenue, and are backed by leading VCs in the industry.
If you're interested in solving some of the most challenging problems at the intersection of technology and logistics, don't hesitate to reach out.
Rectangle | Chicago (US), on-site | Full-Time | Equity
We're looking to hire a dedicated backend engineer at Rectangle. We're looking to solve the communication and collaboration problems in the global supply chain.
If you are (or know!) someone who has thought deeply about distributed coordination, have strong opinions on conflict resolution, or get excited about building systems that are resilient to crazy amounts of chaos, I'd love to talk to you!
Over the past ~6 months my co-founder and I have been hard at work building our product, soon helping coordinate billions of dollars worth of shipments. Our customers love what we've been building for them, we've got revenue, and are backed by leading VCs in the industry.
If you're interested in solving some of the most challenging problems at the intersection of technology and logistics, don't hesitate to reach out.
In general, you might cross reference with other object mapping libraries (including in other languages) to get ideas on how they approach this problem. Caching mappings is just one common strategy.
The global supply chain has a collaboration problem. It's a mess of TMSs, ERPs, WhatsApp, emails, and spreadsheets. For the last 50 years, it's been solved with brittle EDI integrations to bridge walled gardens. We're taking a different approach. We aim to reshape how the supply chain collaborates and as a result redefine how trillions of dollars of goods get moved around the world.
We’re a small product team who have worked on anything from foundational technology at Flexport, self-driving cars, to core AI experiences and products at Google. We’re looking to bring a high level of craft to an industry that runs on incredibly outdated software.
If you're interested in learning more, send us an email at hn@rectanglehq.com.
A lot of the hype around LLMs and the unfounded promises of them solving complex tasks autonomously seem to stem from a lack of understanding on how these models actually work.
The tech is impressive and has its uses, but we shouldn’t pretend like a token predictor can somehow reason and plan.
Have you ever tried to get whatever fancy LLM to write some code for you and had it generate code that was at the same time plausible to look at but complete bullshit?
This is what I am talking about. It can reason and plan for what is the likely token based off its training data. It is completely unable to evaluate the logic of the code it was generating. It can not reason on the context (in this case, programming).
The same is true for other domains. Law, Medicine, etc. the stricter the field, the less reliable LLMs are, because it cannot reason about the context of what it is writing.
That said, I like LLMs, and I think it was an interesting productivity tool, only having been hyped beyond any reasonable expectations. I find it more useful for less strict contexts (for example, creative writing).
I mean obviously predicting tokens in the context of LLM output requires planning. But planning tokens doesn’t generalize. This is evident when you train an LLM on a small data set and ask it about any unseen variation of it.
There are many examples of slightly modified popular riddles that are easy to solve by reasoning about them, but LLMs always fall back to the most likely output from their training data.
Sure the current generation, but what happens if we expand its scope, so instead of predicting the next token one at a time, it was allowed greater scope? What happens when you feed it summaries of its work as training data so it has long a short term memory, what happens when you feed several networks together creating a dialogue similar to the theories of a bicameral mind, what if we instead of having the llm halt after every prompt it was put in a loop? Its own output along with outside information used as the prompts for fallowing loop
There's no reason they can't. In fact LLMs do seem to be the best at artificial general-purpose reasoning of any attempt so far (pretty much every other attempt has failed miserably). That doesn't mean they're particularly good compared to humans, though. I'd characterize them as very high knowledge, low intelligence.
They absolutely can reason and plan; how do you suppose they predict the next token?
That they’re not autonomously solving complex tasks is a bit of a straw man though, and with a bit of creativity we can easily imagine them being combined with models and modalities that do provide executive function and autonomy.
Well, yes, reasoning and planning abilities exist on a spectrum, so it isn’t so much a matter of where to draw the line as a question of degree. As for LLMs, I think their reasoning and planning is some of the most powerful and human-like we’ve seen so far, even if the hidden mechanisms and constraints are different (in some cases, more limited, but in others, vastly superior).
Our brains however are highly modular (a “committee of idiots”) so who’s to say a portion, and even a significant one, doesn’t operate on similar principles?
Can a collection of around 1.5 billion interconnected cells that predictably respond to signals in their environment using simple rules? How about 86 billion? 36 trillion?
These are ballpark counts of cells in crow’s brain, a human’s brain, and a human body. The question is, is it the cells themselves doing the reasoning and planning, or are they just the machinery this disembodied process happens to be running on? I’d argue intelligence is a distributed phenomenon that our DNA is as much a party to as our brains.
Certainly the question of whether humans use DNA to reproduce or DNA uses humans is a matter of perspective.
