The value comes from the manufactured perception of scarcity. You could theoretically trade it directly for goods or services, but nobody ever does. It only ever gets traded for fiat currency. You can really only ever buy them with fiat currency and sell them for fiat currency. Most people don't buy them because they think it's actually useful for anything, they only buy because historically they go up in value and they're pretty sure that'll be true forever. People tell you to buy as much as you can, and never sell because the longer you sit on it, the more it'll be worth someday. The value doesn't come from actually being intrinsically useful or worth anything. They're worth far more than other products of equal or better quality, only because for unknown reasons people have decided that it is. You can make your own at home, but it's very unlikely they'll be worth more than you spent making them. Am I talking about bitcoins or baseball cards? submitted by /u/Asleep_Onion [link] [Kommentare]
I’m working with a dataset inventory of around 500K hours, including egocentric and real-world robot-training footage. I’m curious what formats robotics teams currently need most—manipulation, household tasks, teleoperation, or something else? Comment below or PM me if you’d like to see samples. submitted by /u/WideAmbition1964 [link] [Kommentare]
How do you handle optical-flow dropout in GPS-denied tunnels? Been poking at navigation for tight indoor/underground spaces (tunnels, under bridges) where GPS just drops and there's nothing to fall back on. The annoying part is optical flow basically dies in there: bare concrete, repeating geometry, almost nothing to lock onto. Ends up being mostly lidar plus an illuminated camera doing the work. Testing it in sim first for obvious reasons (not keen on flying real hardware into a concrete wall to find the failure modes). Running it on UE5 with PX4/ArduPilot in the loop. For those who've flown GPS-denied in feature-poor spaces: do you just lean harder on lidar, or is there a VIO setup that actually holds up when the visual texture is that poor? Curious what's worked. submitted by /u/AlexThunderRex [link] [Kommentare]
It’s been 10+ years and crypto/ btc has not proven it self to be useful other than to buy low and sell it to the next person at a higher price hoping the narrative causes the price to rise. 99.99% of people that buy crypto only buy to ope to sell it to the next person at a higher price that’s all it has become . Stocks , realestate other assets classes actually produce some good or service but crypto does nothing …. Look at ADA and Charles lol , poly-GON 🤣🫵🤡. Hope to buy low sell high and bounce . submitted by /u/ozera202 [link] [Kommentare]
I mean wait this is a genuine DOUBT and I'm not predicting anything (because I don't have that much knowledge/experience). I'm actually thinking about what even makes crypto valuable ? It was supposed to be a currency and act like digital decentralized cash.. but most countries don't treat it as a currency but as an asset. Most people just buy crypto for investing(or gambling). While other asssets like stocks, commodities, bonds have some actual value in them that is visible clearly in real world... what does crypto have ? submitted by /u/-InvictusShadow [link] [Kommentare]
Three things broke my faith in published benchmarks recently. One, Kimi K2.7 Code shipped with plus 21.8 percent on Kimi Code Bench v2, plus 11 percent on Program Bench, plus 31.5 percent on MLS Bench Lite. All three are Moonshot's own benchmarks. None were submitted to DeepSWE, which is the one independent coding benchmark that actually produces a meaningful spread between models. When a vendor reports gains on benchmarks they designed and control, the gains are real but the question they answer is "are we better at our own test" not "are we better at your workload." Two, GLM-5.2 hit 51 on the Artificial Analysis Intelligence Index, which is third party, but the model parameters are self reported. The index is good for relative ranking within the artificial analysis methodology. It is not a prediction of how the model performs on the specific distribution of inputs my product sends it. Three, Seed 2.1 just landed and the official information is thin. No clear public eval yet, no third party leaderboard entries I could find. So for now "Seed 2.1 is good" is just not a claim I can verify either way. What I did was build a small eval set from real production traffic, about 240 tasks sampled across our actual usage distribution, frozen so it does not drift. Every model I consider has to run all 240 and I record pass rate, latency, token cost, and a subjective quality score from the person who owns that task area. It is not as rigorous as a published benchmark and it is definitely smaller, but it has one property the published ones do not, which is that it is my distribution. The implementation detail that mattered more than I expected was removing provider variance from the run itself. I route every candidate model through GPTProto so each one gets the exact same 240 prompts in the same order, and the cost and latency come back in one log schema instead of five dashboards. A homegrown shim would do the same job, the point is not the product, it is that a fair comparison only works when everything except the model is held constant. The results have been humbling. The model that wins on our set is not always the one at the top of the public leaderboard, and the gap between first and second place on our set is much smaller than the gap the press releases imply. We also caught one model that benchmarked great but had a nasty failure mode on our long tail of edge case prompts that would have been a production incident if we had shipped it. I am not saying public benchmarks are useless. They are useful for narrowing the field. But the decision of which model to actually put in front of users should be made on your own data, and the eval set has to be frozen and versioned or it will quietly become "things the current model is good at" and stop measuring anything. submitted by /u/Additional-Engine402 [link] [Kommentare]
Human DNA can survive on cave walls for thousands of years - shedding light on prehistoric human activity even where bones, sediments or artifacts are