Hello, not sure this is the appropriate sub but long story short a year or two ago I saw an image on Reddit, I believe it was a painting of a line of crocodile reptilians marching up a dystopian hill to a huge BITCOIN monolith or something. I think about it a lot and was just wondering if anyone had any idea on what I’m talking about thank you submitted by /u/Boring_Fun7004 [link] [Kommentare]

I graduated with my bachelor's in a top 3 CS program and have had a rough recruiting season. I received a full time offer as AI Product Engineer at a tax software company, where they are trying to become more AI native. It's essentially a PM + AI engineering role. Long term I'd love to work at a frontier lab or in a research/more technical role at an AI startup. So, should I take up the offer or pursue my master's at the same school? I am able to defer my master's but don't feel fully comfortable accepting the offer just to only work there for 6 months... At the same time it's not fully aligned with where I want to be long term and feel I can do better, but recruiting was also really difficult. Note, I'm not able to pursue my Master's while working, the company was firm on this TC 126k submitted by /u/jollyjove [link] [Kommentare]

Hi r/robotics ! I’m currently working on a robotic car project for mapping, and I’d like to share my progress and get some feedback from the community. So far, the main issues I’ve encountered (and resolved) are as follows: - Synchronizing the car’s position on the map (as indicated by the gyroscope) with the position of the digitized image based on the car’s position - Managing the motors’ power supply (complex wiring) However, there are still a few issues for which I could use some advice. - It seems that over time, a discrepancy is developing between the robot’s position on the map and its actual position as measured by the gyroscope. Is this an inaccuracy in the gyroscope that could be corrected through code? - The scanner works but remains fairly inaccurate; any recommendations are welcome - The robot’s path tends to veer off course, so I’m considering adding speed encoders to implement a path correction system (I assume the problem stems from the fact that the speed of each motor isn’t always precise) My goal is to build a fully autonomous car capable of mapping its surroundings (I'll add a webcam). Feel free to share any ideas you might have. my target is build a full self driving car able to mapping his environment ( i will adding webcam). Github : https://github.com/enzocolombat/EC-Hub/ submitted by /u/Pasteque9000 [link] [Kommentare]

I participated in Denoising Challenge (gaussian noise level 50), managed to get a decent rank and was looking forward to cite the report in my CV etc, but it seems like the organiser is not planning to release the report, cant see any entry on open access NTIRE page, is the scenario same for other challenges? Does anyone have any lead on the same? submitted by /u/Special_Primary_9249 [link] [Kommentare]

This question is related to topics like language+ models (including multimodal) and things like "circuit" analyses. I think something related might come up in my work (factuality guarantees for model outputs) and I'm trying to orient to the SoTA. I found this old post on trying to deduce, for instance, whether a Transformer-based model "knows" which word a token is in. Even in this simple example, I noticed some meaningful problems (I detail in a footnote1 to not derail my question) - and I've heard that circuit research is pretty fraught. The post claimed to train a logistic regression classifier. What I'm curious about is, how do you balance between the capacity of this probe, and the underlying network? Specifically, I would like to know: Is there theory which grounds inquiries of "what you can learn" in concrete terms? (Perhaps in terms of provable guarantees about overfitting? Or are there Nyquist-type guarantees available about sampling based on frequencies of patterns in language corpora - i.e., can we say we've "seen enough data" to know the network can reliably do something in all cases?) Has any of the existing work factored in attempts to label the "difficulty" of examples? (Perhaps by ensembling some training of models and looking at accuracy on them. I realize bootstrap is insanely expensive for language models due to training costs.) Problems - well, first of all, the number of possible words is so small that I suspect performance looks unrepresentatively good. The classifier seems to gain in performance for words 5/6 after weakening, but that might just be learning "all sufficiently 'extreme' tokens should be words 5 or 6." For another, despite the claim advanced in the article (Nanda concludes the network essentially does learn positions), I happen to have screenshots from recently playing with Google Gemini and asking it how many "r"s and other letters are in Google. Not only did it answer incorrectly - it claimed 1 - but more worryingly, it spelled out G-o-o-g-l-e in answering. This belies a hypothesis of "it's incapable of learning exactly how to decompose tokens, so this question was unfair from a model capacity standpoint" but *still* leads to an incorrect answer! submitted by /u/RepresentativeBee600 [link] [Kommentare]

So this is my 2nd project and final project in high school, quite ambitious i gotta say. I was trying to make a anthropomorphic robotic hand . So i grabbed the palm and finger design from here. But i wanted to make my own thingys where the strings are attached , and add adduction ( fingers get clamped together). I learned how to use fusion and how to 3d print , i didnt know what was clearance. I learned that quickly . I dont have a 3d printer at home so i needed to pay for everything , i spent all my budget for this project , and i was so close to finishing everything but , my strings lacked tension and some 3d printed parts broke and i really dont want to spend more money. I finally decided to postpone the project until september because i got in an engineering school and i hope they have a 3d printer i can use freely. On top of that i think its better to try out some new stuff throughout the summer like i want to make those plasma ball thingys with the glass surrounding it and you can touch it. I am a little disappointed cause i was so close but let's see. I left you some pics too ​ submitted by /u/MINII_man [link] [Kommentare]

and you u/carnageta ? submitted by /u/arthur_fissure [link] [Kommentare]

Michael Saylor Mar. 2026: "Am I offending you if I call it a money market fund?" STR-C is a preferred stock that Saylor repeatedly marketed as being "money market" like (pic 2) it's supposed to be pegged to $100 through tokenomics that pays interest and when the value is about to exceed $100 Microstrategy issues shares to buy Bitcoin. submitted by /u/GabeSter [link] [Kommentare]

What does it say about an industry, if amongst the top 10 crypto projects (excl. stablecoins), one is a meme about a dog and the other had an "infinity-create-coin-bug" in its system since the beginning of the project. Have fun gambling guys, just dont call yourself investors roflmao submitted by /u/Babad00k123 [link] [Kommentare]

I'm not that well versed in ML yet. I know that "Attention is all you need" was based on work that was done with a couple of high end gaming GPUs at the time. I can afford that. Suppose for arguments sake that I have caught up on ML such that I have the competence to recreate state of the art results should I have access to the required hardware, do I still need access to huge amounts of hardware infrastructure to be able to contribute to the field at a foundational level? submitted by /u/Proof-Bed-6928 [link] [Kommentare]