I think the best way to handle this would be to just encode everything and upload all files. If I wanted some amount of history, I’d use some file system with automatic snapshots, like ZFS.

If I wanted to do what you’ve outlined, I would probably use rclone with filtering for the extension types or something along those lines.

If I wanted to do this with Git specifically, though, this is what I would try first:

First, add lossless extensions (*.flac, *.wav) to my repo’s .gitignore

Second, schedule a job on my local machine that:

1. Watches for changes to the local file system (e.g., with inotifywait or fswatch)
2. For any new lossless files, if there isn’t already an accompanying lossy files (i.e., identified by being collocated, having the exact same filename, sans extension, with an accepted extension, e.g., .mp3, .ogg - possibly also with a confirmation that the codec is up to my standards with a call to ffprobe, avprobe, mediainfo, exiftool, or something similar), it encodes the file to your preferred lossy format.
3. Use git status --porcelain to if there have been any changes.
4. If so, run git add --all && git commit --message "Automatic commit" && git push
5. Optionally, automatically craft a better commit message by checking which files have been changed, generating text like Added album: "Satin Panthers - EP" by Hudson Mohawke or Removed album: "Brat" by Charli XCX; Added album "Brat and it's the same but there's three more songs so it's not" by Charli XCX

Third, schedule a job on my remote machine server that runs git pull at regular intervals.

One issue with this approach is that if you delete a file (as opposed to moving it), the space is not recovered on your local or your server. If space on your server is a concern, you could work around that by running something like the answer here (adjusting the depth to an appropriate amount for your use case):

git fetch --depth=1
git reflog expire --expire-unreachable=now --all
git gc --aggressive --prune=all

Another potential issue is that what I described above involves having an intermediary git to push to and pull from, e.g., running on a hosted Git forge, like GitHub, Codeberg, etc… This could result in getting copyright complaints or something along those lines, though.

Alternatively, you could use your server as the git server (or check out forgejo if you want a Git forge as well), but then you can’t use the above trick to prune file history and save space from deleted files (on the server, at least - you could on your local, I think). If you then check out your working copy in a way such that Git can use hard links, you should at least be able to avoid needing to store two copies on your server.

The other thing to check out, if you take this approach, is git lfs. EDIT: Actually, I take that back - you probably don’t want to use Git LFS.

It’s the new hyped up version of “no-code” or low-code solutions, but with AI so you have more flexibility to footgun.

Not any lazier. Script kiddies didn’t write the code themselves, either.

If you want to generate audiobooks using your own / a hosted TTS server, check out one of these options:

• OpenReader-WebUI - this has built-in read along capability and can be deployed as a PWA that can allow you to download the audiobooks to your phone so you can use them offline
• p0n1/epub to audiobook
• ebook2audiobook

If you don’t have a decent GPU, Kokoro is a great option as it’s fast enough to run on CPU and still sounds very good.

If you’re going to use Kokoro, Audiblez (posted by another commenter) looks like it makes that more of an all-in-one option.

If you want something that you can use without an upfront building of the audiobook, of the above options, only OpenReader-WebUI supports that. RealtimeTTS is a library that handles that, but I don’t know if there are already any apps out there that integrate it.

If you have the audiobook generation handled and just want to be able to follow along with text / switch between text and audio, check out https://storyteller-platform.gitlab.io/storyteller/

What would moving your account here from Reddit entail? What would transfer with you? Your posts? Comments? Followed and moderated subreddits? Upvotes and downvotes?

You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.

I think the better question than “Does the experience system sound like it has potential,” then, is “Does the overall concept / system have potential?”

My gut is probably, but it depends a lot more on what you’re willing to put into it and what you want out of it. What’s your metric for success? If it’s something you want to run yourself and to share online to have a few groups use it, then that’s a lot more achievable than being able to get a publishing deal, for example. And in-between, publishing on drivethrurpg or something similar, at a nominal cost (like $2-$5), would take more effort than the former and less than the latter; and the higher the cost and the higher the number of players you’d want, the higher the effort you need to put in (and a lot of that isn’t just in system building, but in art, community building, marketing, etc.).

From what you’ve shared, it sounds like an interesting system. I could especially see it working in an academy setting where grinding skills to be able to pass practical exams is one of the players’ goals. I also could see it working well by a loosely GMed play by post system, with the players self-enforcing (or possibly leveraging some tools built into the site to track resource pools, experience, rolling, etc.), though I haven’t played in a forum game myself, so I might be way off-base.

Did your system have classes or was it completely free-form in terms of gaining access to those skill trees?

I run a Monster of the Week game and my players get experience throughout sessions, as well as at the end. The mechanics are basically:

• It takes 5 experience points to level up.
• If you fail a roll, you get an experience point.
• If you level up, you get the benefit immediately.
• At the end of the session, everyone gets 0-2 experience points.

I think other PbtA (Powered by the Apocalypse - systems inspired by Apocalypse World) systems do something similar.

I grew increasingly frustrated with the system of only distributing advancement/experience points at the end of a session.

Isn’t the simple fix to this to just distribute experience points as soon as they’re earned?

At some point, I started to divise a play system that relied on a split experience atribution system, with players being able to automatically rack experience points from directly using their skills/habilties, while the DM would keep a tally of points from goals/missions achieved, distributable at session end.

Your system sounds like the way that skill-based video game RPGs (Elder Scrolls games and Arcanum come to mind) handle experience.

In a lot of games I’ve played, I’d rather get experience for in-game accomplishments immediately and to be able to train skills like this during downtime - generally between games.

To those with more experience in TTRPGs: would this be feaseable? Or enticing? Interesting?

I could see people being interested in it. You get instant gratification and a bit of extra crunchiness. A lot of players enjoy that.

With the right skill system I could see this being useful. My main concern is that if you put this on top of a system with relatively few skills, it could encourage people to game it by grinding. There are ways to mitigate that, though.

In a system with fewer skills, instead of just being experience points, the “currency” you earned this way could be used for temporary power ups related to the skill in question.

You could also limit it so you only rewarded players for story-related tasks.

I’d recommend Colemak Mod-DH, personally - it seems ergonomically superior and switching later is a bit of a pain.

You don’t have to finish the file to share it though, that’s a major part of bittorrent. Each peer shares parts of the files that they’ve partially downloaded already. So Meta didn’t need to finish and share the whole file to have technically shared some parts of copyrighted works. Unless they just had uploading completely disabled,

The argument was not that it didn’t matter if a user didn’t download the entirety of a work from Meta, but that it didn’t matter whether a user downloaded anything from Meta, regardless of whether Meta was a peer or seed at the time.

Theoretically, Meta could have disabled uploading but not blocked their client from signaling that they could upload. This would, according to that argument, still counts as reproducing the works, under the logic that signaling that it was available is the same as “making it available.”

but they still “reproduced” those works by vectorizing them into an LLM. If Gemini can reproduce a copyrighted work “from memory” then that still counts.

That’s irrelevant to the plaintiff’s argument. And beyond that, it would need to be proven on its own merits. This argument about torrenting wouldn’t be relevant if LLAMA were obviously a derivative creation that wasn’t subject to fair use protections.

It’s also irrelevant if Gemini can reproduce a work, as Meta did not create Gemini.

Does any Llama model reproduce the entirety of The Bedwetter by Sarah Silverman if you provide the first paragraph? Does it even get the first chapter? I highly doubt it.

By the same logic, almost any computer on the internet is guilty of copyright infringement. Proxy servers, VPNs, basically any compute that routed those packets temporarily had (or still has for caches, logs, etc) copies of that protected data.

There have been lawsuits against both ISPs and VPNs in recent years for being complicit in copyright infringement, but that’s a bit different. Generally speaking, there are laws, like the DMCA, that specifically limit the liability of network providers and network services, so long as they respect things like takedown notices.

Retrieval-Augmented Generation (RAG) is probably the tech you’d want. It basically involves a knowledge library being built from the documents you upload, which is then indexed when you ask questions.

NotebookLM by Google is an off the shelf tool that is specialized in this, but you can upload documents to ChatGPT, Copilot, Claude, etc., and get the same benefit.

If you self hosted, Open WebUI with Ollama supports this, but far from the only one.

If you dislike decentralization, then that’s like being on an instance that’s banned every other instance.

Do you mean that you dislike defederation?

Wow, there isn’t a single solution in here with the obvious answer?

You’ll need a domain name. It doesn’t need to be paid - you can use DuckDNS. Note that whoever hosts your DNS needs to support dynamic DNS. I use Cloudflare for this for free (not their other services) even though I bought my domains from Namecheap.

Then, you can either set up Let’s Encrypt on device and have it generate certs in a location Jellyfin knows about (not sure what this entails exactly, as I don’t use this approach) or you can do what I do:

1. Set up a reverse proxy - I use Traefik but there are a few other solid options - and configure it to use Let’s Encrypt and your domain name.
2. Your reverse proxy should have ports 443 and 80 exposed, but should upgrade http requests to https.
3. Add Jellyfin as a service and route in your reverse proxy’s config.

On your router, forward port 443 to the outbound secure port from your PI (which for simplicity’s sake should also be port 443). You likely also need to forward port 80 in order to verify Let’s Encrypt.

If you want to use Jellyfin while on your network and your router doesn’t support NAT loopback requests, then you can use the server’s IP address and expose Jellyfin’s HTTP ports (e.g., 8080) - just make sure to not forward those ports from the router. You’ll have local unencrypted transfers if you do this, though.

Make sure you have secure passwords in Jellyfin. Note that you are vulnerable to a Jellyfin or Traefik vulnerability if one is found, so make sure to keep your software updated.

If you use Docker, I can share some config info with you on how to set this all up with Traefik, Jellyfin, and a dynamic dns services all up with docker-compose services.

Why should we know this?

Not watching that video for a number of reasons, namely that ten seconds in they hadn’t said anything of substance, their first claim was incorrect (Amazon does not prohibit use of gen ai in books, nor do they require its use be disclosed to the public, no matter how much you might wish it did), and there was nothing in the description of substance, which in instances like this generally means the video will largely be devoid of substance.

What books is the Math Sorcerer selling? Are they the ones on Amazon linked from their page? Are they selling all of those or just promoting most of them?

Why do we think they were generated with AI?

When you say “generated with AI,” what do you mean?

• Generated entirely with AI, without even editing? Then why do they have so many 5 star reviews?
• Generated with AI and then heavily edited?
• Written partly by hand with some pieces written by unedited GenAI?
• Written partly by hand with some pieces written by edited GenAI?
• AI was used for ideation?
• AI was used during editing? E.g., Grammarly?
• GenAI was used during editing?E.g., “ChatGPT, review this chapter and give me any feedback. If sections need rewritten go ahead and take a first pass.”
• AI might have been used, but we don’t know for sure, and the issue is that some passages just “read like AI?”

And what’s the result? Are the books misleading in some way? That’s the most legitimate actual concern I can think of (I’m sure the people screaming that AI isn’t fair use would disagree, but if that’s the concern, settle it in court).

Look up “LLM quantization.” The idea is that each parameter is a number; by default they use 16 bits of precision, but if you scale them into smaller sizes, you use less space and have less precision, but you still have the same parameters. There’s not much quality loss going from 16 bits to 8, but it gets more noticeable as you get lower and lower. (That said, there’s are ternary bit models being trained from scratch that use 1.58 bits per parameter and are allegedly just as good as fp16 models of the same parameter count.)

If you’re using a 4-bit quantization, then you need about half that number in VRAM. Q4_K_M is better than Q4, but also a bit larger. Ollama generally defaults to Q4_K_M. If you can handle a higher quantization, Q6_K is generally best. If you can’t quite fit it, Q5_K_M is generally better than any other option, followed by Q5_K_S.

For example, Llama3.3 70B, which has 70.6 billion parameters, has the following sizes for some of its quantizations:

• q4_K_M (the default): 43 GB
• fp16: 141 GB
• q8: 75 GB
• q6_K: 58 GB
• q5_k_m: 50 GB
• q4: 40 GB
• q3_K_M: 34 GB
• q2_K: 26 GB

This is why I run a lot of Q4_K_M 70B models on two 3090s.

Generally speaking, there’s not a perceptible quality drop going to Q6_K from 8 bit quantization (though I have heard this is less true with MoE models). Below Q6, there’s a bit of a drop between it and 5 and then 4, but the model’s still decent. Below 4-bit quantizations you can generally get better results from a smaller parameter model at a higher quantization.

TheBloke on Huggingface has a lot of GGUF quantization repos, and most, if not all of them, have a blurb about the different quantization types and which are recommended. When Ollama.com doesn’t have a model I want, I’m generally able to find one there.

I recommend a used 3090, as that has 24 GB of VRAM and generally can be found for $800ish or less (at least when I last checked, in February). It’s much cheaper than a 4090 and while admittedly more expensive than the inexpensive 24GB Nvidia Tesla card (the P40?) it also has much better performance and CUDA support.

I have dual 3090s so my performance won’t translate directly to what a single GPU would get, but it’s pretty easy to find stats on 3090 performance.

From https://www.yalemedicine.org/news/covid-vaccines-reduce-long-covid-risk-new-study-shows

At the pandemic’s onset, approximately 10% of people who suffered COVID-19 infections went on to develop Long COVID. Now, the risk of getting Long COVID has dropped to about 3.5% among vaccinated people (primary series).

…

Then, the team conducted analyses to uncover the reasons for the observed decline in Long COVID cases from the pre-Delta to Omicron eras. About 70% of the decline was attributable to vaccination, they found.

To be clear, I’m measuring the relative humidity of the air in the drybox at room temp (72 degrees Fahrenheit / 22 degrees Celsius), not of the filament directly. You can use a hygrometer to do this. I mostly use the hygrometer that comes bundled with my dryboxes (I use the PolyDryer and have several extra PolyDryer Boxes, but there are much cheaper options available) but you can buy a hygrometer for a few bucks or get a bluetooth / wifi / connected one for $15-$20 or so.

If you put filament into a sealed box, it’ll generally - depending on the material - end up in equilibrium with the air. So the measurement you get right away will just show the humidity of the room, but if the filament and desiccant are both dry, it’ll drop; if the desiccant is dry and the filament is wet, it’ll still drop, but not as low.

Note also that what counts as “wet” varies by material. For example, from what I’ve read, PLA can absorb up to 1% or so of its mass as moisture, PETG up to 0.2%, Nylon up to 7-8%… silica gel desiccant beads up to 40%. So when I say they’ll be in equilibrium, I’m referring to the percentage of what that material is capable of absorbing. It isn’t a linear relationship as far as I know, but if it were, that would mean that: if the humidity of the air is 10% and the max moisture the material could retain is 1%, then the material is currently retaining 0.1% moisture by mass. If my room’s humidity is kept at 40%, it’ll absorb moisture until it’s at 0.4% moisture by mass.

That said, this doesn’t measure it perfectly, since while most filament materials absorb moisture from the air when the humidity is higher, they don’t release it as easily. Heating it both allows the air to hold more moisture and allows the filament (and desiccant) to release more moisture.

The above post says it has support for Ollama, so I don’t think this is the case… but the instructions in the Readme do make it seem like it’s dependent on OpenAI.

What have you done to clean the bed? From the link to the textured sheet, you should be cleaning it between every print - after it cools - with 90% IPA, and if you still have adhesion issues, you should clean it with warm water and a couple drops of dish soap.

Has the TPU been dried? I don’t normally print with TPU but my understanding is that it needs to be lower humidity than PLA; I use dryboxes for PLA and target a humidity of 15% or lower and don’t use them if they raise above 20%. The recommendation I saw for TPU was to dry it for 7 hours at 70 degrees Celsius, to target 10% humidity (or at least under 20%) and to print directly from a drybox. Note that compared to other filaments, TPU can’t recover as well from having absorbed moisture - if the filament has gotten too wet, it’ll become too brittle if you dry it out as much as is needed. At that point you would need to start with a fresh roll, which would ideally go into a dryer and then drybox immediately.

You should be able to set different settings for the initial layer to avoid stringing, i.e., slower speeds and longer retraction distance. It’s a bit more complicated but you can also configure the speed for a specific range of layers to be slower - i.e., setting it to slow down again once you get to the top of the print. For an example of that, see https://forum.prusa3d.com/forum/prusaslicer/bed-flinger-slower-y-movement-as-function-of-z/

What’s the max speed you’re printing at? My understanding is that everything other than travel should all be the same speed at a given layer, and no higher than 25 mm/s. And with a bed slinger I wouldn’t recommend a much higher travel, either.

In addition to a brim, have you tried adding supports?