My boss sent me this post on LinkedIn since he knows I buy Bitcoin. This accuracy seems insane, and he's calling for lower. As someone who does DCA, this seems like a better option, but he only talks with companies. Thoughts? He's thinking of calling him and wants my thoughts, and I've no idea what to say. Update: Post link - www.linkedin.com/in/jackson-osborn submitted by /u/Thiccmas [link] [Kommentare]

After enough cycles you lose track of which exchange or lender blew up when, so I went back and put the major custody failures from 2014 to 2023 into one timeline with the actual figures. Running through them: Mt. Gox (2014): around 850,000 BTC gone. It wasn't a dramatic hack, the coins drained out over years behind balances that were simply faked. Anyone with funds on it saw no repayment until 2024. QuadrigaCX (2018): the CEO died and the company claimed he was the only one who could access the cold wallets, so ~CAD 169M was supposedly stuck. Then the auditors looked, and the wallets had been empty for eight months before he died. The Ontario regulator called it a Ponzi. 2022, the whole cascade: Three Arrows Capital (a hedge fund) over-leveraged itself into the ground and defaulted on the lenders that were funding it with customer deposits. Celsius, Voyager, BlockFi, and Genesis went down behind it inside about eight months. The "earn yield on your crypto" products had been lending those deposits into that leverage the entire time. FTX (2022): roughly $8B hole, customer funds routed to Alameda, SBF got 25 years. One detail worth knowing if you had money there: the estate is repaying creditors over 100%, but in dollars valued at the November 2022 petition date, when BTC was around $16k. It later ran past $90k, so the recovery measured in actual coins is far below what people lost. The annoying part is how little it changes from one to the next. The deposits were never just sitting there, you had no way to verify the custody, and when it broke you were an unsecured creditor in a bankruptcy instead of someone who owned coins. We have to talk about the other side too, because "just self-custody" isn't free advice. You need exchanges to actually buy and sell. Lose your seed phrase and there's no support line and no court to recover it. Some people are genuinely better off with a regulated, insured custodian. Full story: https://www.learnbitcoin.com/rabbit-hole/mt-gox-ftx-graveyard Corrections welcome. submitted by /u/LearnBitcoinCom [link] [Kommentare]

Since the steam sale is live I wanted to post a Dev log on my personal project https://nextsteamgame.com/ sharing some outcomes from the web traffic and how I changed the project from the great feedback I got! I made a post about a month ago explaining how I made this opensource explainable search engine built around steam reviews to people find new video games, Not through Relevancy but through aspect based similarity. Check out the old post for a better explanation if you want! https://www.reddit.com/r/MachineLearning/comments/1tb8k3n/steam_recommender_using_similarity_undergraduate/ I wanted to say thank you to all the people of r/datascience and r/MachineLearning that gave me feedback and tried out my tool! I improved the UI/UX of the website to make the vectors more clear and controllable, I Implemented a thumbs up and down feature on recommendations to see if users even like the tool. I also wanted to share the after effects of promoting this tool on reddit! from the 2,652 searches I got in the website 913 of them resulted in steam clicks! the games that were discovered were all in a uniform distribution and did not share much of a pattern showing me that the engine did its job in helping people find niche games across all genres! (More images attached to post to see data viz) I wanted to disclose that I made this tool to not make any profit of some kind, but it does use posthog so I can collect diagnostics now. submitted by /u/Expensive-Ad8916 [link] [Kommentare]

Token-based billing is causing my company to reevaluate small language models. I came across this paper that shows SLM supervised fine-tuning on traces from orchestration of frontier models can be nearly as performant and much cheaper. Has any tried this in the real world? submitted by /u/ThirdWaveCat [link] [Kommentare]

What if there was a new programming language where the meaning of each token was so dense (or perhaps so specific) that an LLM could write robust code with fewer tokens and faster inference? Assuming there’s enough training data, do you think something like this allow an LLM to write better code faster? Rationale: 1) It would allow for faster inference. Fewer tokens required to do the same thing in Python = finish faster. 2) It would allow for more information in a 1M context window. Whatever you could fit in 1M tokens of Python, you could do 100x that in this theoretical language. 3) It would effectively remove the “noise” from human readable language (semi-colons, curly braces for example) which I would think would make the LLMs coding ability stronger. I could be wrong about this of course. submitted by /u/Spongebubs [link] [Kommentare]

In the recent Springer/Meteor email, it says: The deadline for the upload of the camera-ready manuscripts and source files is 30 June. This is a hard deadline and will not be extended. However, in the same email, the Meteor submission line for my paper says: submission due: June 27, 2026 A previous email from the ECCV Program Chairs also stated that the camera-ready deadline had been extended to 30.06 AoE and that this deadline is final. Does anyone know whether June 27 is just an internal/default Meteor due date, or whether it is the actual deadline for uploading in Meteor? Since the email says there is only one upload and the first upload is final, I want to avoid uploading too early if June 30 is the correct deadline. this is really confusing. submitted by /u/National-Resident244 [link] [Kommentare]

I've been working on a project that started with what I thought was a transformer problem. People usually talk about attention sinks, representation collapse, low-rank activations, weird key norm distributions, etc. as separate attention pathologies that need separate fixes. I don't think they're actually about attention. I think they're what any content-based router does when it's making decisions with a similarity metric that's blind to magnitude. The observation that kicked this off is surprisingly simple. You can rewrite softmax attention as a Boltzmann distribution over Euclidean distances only if all the keys have the same norm. Expanding the distance, ||q-k||² = ||q||² - 2 + ||k||² The query norm disappears inside the softmax. The key norm also disappears, but only when every key has roughly the same magnitude. Standard attention just throws the key norm term away regardless. That means it's routing using a metric that can't "see" key magnitude. Once I started looking for that assumption in real models, it was violated basically everywhere. My hypothesis became: If your routing metric is blind to magnitude, the model has to compensate somehow. And that compensation consistently shows up as: routing concentrating onto a few positions, representations collapsing into a low-rank subspace, key norms becoming highly stratified. Those aren't three unrelated phenomena, hey're different symptoms of the same geometry. The cool part is that it isn't just transformers. I looked at five different routing mechanisms. Transformers: 9 pretrained models (GPT-2 Small → XL, Pythia 160M → 2.8B). Every single one develops the same signature. GATs: Compared graph attention against depth/width-matched GCNs on three heterophilic WebKB graphs. The attention models collapse more than the fixed-aggregation controls. Mamba: No explicit attention here, but you can reconstruct the hidden routing operator. The effective "key" ends up being Δ·B. If I freeze Δ while keeping everything else fixed, the concentration almost completely disappears. So the selective routing is what's creating the effect. RWKV: This one surprised me. If I sweep the learned time decay, the depth where concentration starts shifts dramatically. Strong decay delays it, weak decay makes it happen much earlier. So the decay acts like a positional brake sitting on top of the same content attractor. AttnRes (Qwen3 variant): Probably my favorite result. It routes over depth instead of tokens, and its keys are RMS-normalized, so key norm variation is literally zero by construction. It still develops strong routing hubs. That was the moment where I stopped thinking norm stratification was the cause. It's just one way a router can compensate. Across all of these architectures, what changes isn't whether collapse happens, but when and how strongly. Those seem to be controlled mostly by whatever positional bias or decay mechanism the architecture already has (RoPE, time decay, recency bias, etc.). The paper is about 20 pages including the appendix. It has the measurement details, null baselines, causal ablations, retraining controls, and some converging evidence from recent work (AttnRes, QKV sharing, memory caching, etc.). I'd love feedback, especially from people who've worked on attention, state-space models, or graph transformers. Code: https://github.com/parzi-val/all-routes-lead-to-collapse Paper: https://arxiv.org/abs/2606.22325 submitted by /u/entropy_- [link] [Kommentare]

Not trying to advertise, but I’m sharing what we build because I genuinely believe it’s incredibly cool. Currently, we’re using it as a skin for robotics, primarily for tactile data collection. However, our long-term goal is to make it the skin layer for robots. submitted by /u/Sea-Lab-1972 [link] [Kommentare]

Hello beautiful community! Looks like we are on track from my previous post based off of the 4 year cycle. Try to get organized enough for the inevitable by this early fall. Good luck and remember to be patient! These drops eventually correct back to the upside. Especially after the halving. submitted by /u/jphillips8648 [link] [Kommentare]