This is a problem I kept seeing come up and never saw a good answer to: if you die tomorrow, does anyone you trust know how to access your wallet? Your seed phrase? Your exchange accounts? Most people either write it on a piece of paper (and hope nobody finds it early), put it in a will (public record, lawyer fees, months of probate), or just... do nothing and hope for the best. I built Deadman Ravn (deadmanravn.com) to solve this properly. How it works: You write a message — wallet seeds, account instructions, whatever you want to pass on — and it gets encrypted and stored. You assign it to one or more contacts. Then you set a check-in interval: once a month, every 90 days, whatever fits your life. If you stop checking in, it escalates: grace period, then a countdown, then your message is automatically delivered to your contacts. If you're alive and just forgot, you get email and Telegram warnings at every stage so you can stop it with one click. Why it's built for this community specifically: Crypto payments only — BTC, USDC, USDT, 300+ coins. No card required Messages encrypted with AES-256-GCM before storage — not readable server-side No phone number, no Google/Apple login required Telegram notifications built in Free tier lets you set up one switch fully. Takes about 5 minutes. Genuinely curious how people here are currently handling this — seed phrase handoff is one of those problems everyone knows they should solve and almost nobody actually does. submitted by /u/gbhaddie [link] [Kommentare]

Welcome to the Daily Crypto Discussion thread. Please read the disclaimer and rules before participating. Disclaimer: Consider all information posted here with several liberal heaps of salt, and always cross check any information you may read on this thread with known sources. Any trade information posted in this open thread may be highly misleading, and could be an attempt to manipulate new readers by known "pump and dump (PnD) groups" for their own profit. BEWARE of such practices and exercise utmost caution before acting on any trade tip mentioned here. Please be careful about what information you share and the actions you take. Do not share the amounts of your portfolios (why not just share percentage?). Do not share your private keys or wallet seed. Use strong, non-SMS 2FA if possible. Beware of scammers and be smart. Do not invest more than you can afford to lose, and do not fall for pyramid schemes, promises of unrealistic returns (get-rich-quick schemes), and other common scams. Rules: All sub rules apply in this thread. The prior exemption for karma and age requirements is no longer in effect. Discussion topics must be related to cryptocurrency. Behave with civility and politeness. Do not use offensive, racist or homophobic language. Comments will be sorted by newest first. Useful Links: Beginner Resources Intro to r/Cryptocurrency MOONs 🌔 MOONs Wiki Page r/CryptoCurrency Discord r/CryptoCurrencyMemes Prior Daily Discussions - (Link fixed.) r/CryptoCurrencyMeta - Join in on all meta discussions regarding r/CryptoCurrency whether it be moon distributions or governance. Finding Other Discussion Threads Follow a mod account below to be notified in your home feed when the latest r/CC discussion thread of your interest is posted. u/CryptoDaily- — Posts the Daily Crypto Discussion threads. u/CryptoSkeptics — Posts the Monthly Skeptics Discussion threads. u/CryptoOptimists- — Posts the Monthly Optimists Discussion threads. u/CryptoNewsUpdates — Posts the Monthly News Summary threads. submitted by /u/AutoModerator [link] [Kommentare]

I have released a model that I have been working on for ~6 months off and on. I've been enjoying listening to spatial music, but there is a lot of music out there with no real quality spatial mix. So I decided to make a model to convert stereo to spatial. I started by making a flow-matching diffusion model that operated in the latent space of a separately trained VAE (EAR-VAE). I used the VAE to encode the stereo input in to 1 latent and then separately on each channel of the 7.1.4 output. This design is similar to a paper I found about another stereo -> spatial project (ImmersiveFlow (they have code links in the paper but they 404 for me so I made the codebase from scratch)). Since the VAE was out of distribution with the output (was trained to encode\decode stereo tracks directly, not individual channels and certainly not individual channels of a 7.1.4 mix), the results hit real quality bottlenecks. But it showed that the mapping was possible. Rather than stick to modeling in latent space, I decided to pivot to modeling raw waveforms. This ended up fixing all of the quality issues I was seeing with the latent version, but at the cost of more training compute and instability. The waveform version of the model would train to around 60K-80K steps with loss going down like normal, validation generations looking good\improving, and then it would quickly become unstable and the loss would shoot up. I tried direct waveforms, scaling the waveforms by a multiplier, aggressive grad clipping, lower learning rates, and all of those experiments failed the same way. Luckily, I came across a recent paper called WavFlow. The authors of this project also had issues with modeling raw waveforms with flow-matching diffusion, and they solved it by using amplitude lifting (scale each audio track to an rms of 0.33 then multiply by 3) (the WavFlow paper used a clip of 1.0 before the scale which leads to a model space of -3 - 3, but I used a clip of 4.0 which leads to a model space of -12 - 12 (from my testing this led to better binaural outputs)). Once I implemented amplitude lifting like the paper, the training stability issues vanished. The waveform model was trained on 7,669 tracks for ~20 days on 2x A6000 gpus - 10 days stage one, effective batch of 16, 10- 18- 26- and 34-second training sequences - 10 days stage two, effective batch of 16, 122-second training sequences - There is optional mix-style conditioning for controllable outputs (waveform version only) - Direct binaural output (waveform training is more compute expensive than latent training so binaural proves the idea\theory and the same codebase and data can be used in the future to train a 7.1.4 version (once I have access to the compute)) I also made a Windows desktop app for inferencing with the model and consuming the results. Everything has been released apache 2.0. Huggingface link for waveform model: https://huggingface.co/francislabounty/stereo2spatial-v2-binaural Huggingface link for latent model: https://huggingface.co/francislabounty/stereo2spatial-v1 Github repo for training\inferencing: https://github.com/francislabountyjr/stereo2spatial Github repo for the Windows app: https://github.com/francislabountyjr/stereo2spatial-app App Homepage: https://stereo2spatial.francislabounty.com Case study for the waveform model: https://francislabounty.com/blog/stereo2spatial-v2 Case study for the latent model: https://francislabounty.com/blog/stereo2spatial Playlist of generations made from the model (will be updated with more tracks over time): https://www.youtube.com/playlist?list=PLQ-HHjPijrAg If you have any questions\etc. leave a comment and I will do my best to get them answered! Disclaimer: I know AI written posts are generally frowned upon so this post was 100% written by me (mistakes and all). But the case studies and READMEs from the above links were made with AI assistance. submitted by /u/kittenkrazy [link] [Kommentare]