I use FreshRSS. Can’t say I love the interface, but with the open and standardized API, there are dozens of beautiful front ends to choose on any device.
I use FreshRSS. Can’t say I love the interface, but with the open and standardized API, there are dozens of beautiful front ends to choose on any device.
For real? Damn it that’s going to be painful.
Never ask a man his pay, a woman her weight, or a data horder the contents of their stash.
Jk. Mostly.
I have a similar-ish set up to @Davel23 , I have a couple of cool use cases.
I seed the last 5 arch and opensuse (a few different flavors) ISOs at all times
I run an ArchiveBot for archive.org
I scan nontrivial mail (the paper kind) and store it in docspell for later OCR searches, tax purposes etc.
I help keep Sci-Hub healthy
I host several services for de-googling, including Nextcloud, Blocky, Immich, and Searxng
I run Navidrome, that has mostly (and hopefully will soon completely) replace Spotify for my family.
I run Plex (hoping to move to Jellyfin sometime, but there’s inertial resistance to that) that has completely replaced Disney streaming, Netflix streaming, etc for me and my extended family.
I host backups for my family and close friends with an S3 and WebDAV backup target
I run 4x14TB, 2x8TB, 2x4TB, all from serverpartsdeals, in a ZFS RAID10 with two 1TB cache dives, so half of the spinning rust usable at ~35TB, and right now I’m at 62% utilization. I usually expand at about 85%
My favorite city builder in decades. A few notes.
Pros:
Cons:
All in all, I highly recommend it, especially at the modest asking price. If you love city builders, charming and beautiful art, thematic settings, dynamic challenge, and solution engineering, this is a fantastic game for you.
Other games I’ve enjoyed that scratch similar itches:
Get it and have fun is my recommendation.
Seriously. This guy thinks that regulators would have stepped in to stop OpenAI or Microsoft from acquiring a no-name 2 year old startup with two rounds of funding?
Please.
Apparently that wasn’t one of his MBOs, so we can infer the board is a bunch of dumbasses.
Yeah honestly no idea regarding moderation. But the codebase is maintained by a team.
There is a team, not a sole dev.
I’m not saying everything is roses and rainbows, but this is FUD messaging being spread openly by the mbin dev team.
I’ve had great experiences with exactly one vendor of second hand disks.
Currently running 8x14TB in a striped & mirrored zpool.
Really all I do is setup fail2ban on my very few external services, and then put all other access behind wireguard.
Logs are clean, I’m happy.
Yeah, you should be scrubbing weekly or monthly, depending on how often you are using the data. Scrub basically touches each file and checks the checksums and fixes any errors it finds proactively. Basically preventative maintenance.
https://manpages.ubuntu.com/manpages/jammy/man8/zpool-scrub.8.html
Set that up in a cron job and check zpool status periodically.
No dedup is good. LZ4 compression is good. RAM to disk ratio is generous.
Check your disk’s sector size and vdev ashift. On modern multi-TB HDDs you generally have a block size of 4k and want ashift=12. This being set improperly can lead to massive write amplification which will hurt throughput.
https://www.high-availability.com/docs/ZFS-Tuning-Guide/
How about snapshots? Do you have a bunch of old ones? I highly recommend setting up a snapshot manager to prune snapshots to just a working set (monthly keep 1-2, weekly keep 4, daily keep 6 etc) https://github.com/jimsalterjrs/sanoid
And to parrot another insightful comment, I also recommend checking the disk health with SMART tests. In ZFS as a drive begins to fail the pool will get much slower as it constantly repairs the errors.
ZFS is a very robust choice for a NAS. Many people, myself included, as well as hundreds of businesses across the globe, have used ZFS at scale for over a decade.
Attack the problem. Check your system logs, htop, zpool status.
When was the last time you ran a zpool scrub? Is there a scrub, or other zfs operation in progress? How many snapshots do you have? How much RAM vs disk space? Are you using ZFS deduplication? Compression?
Man, all these world class athletes from Russia have these horrible heart conditions at such young ages!
Must be something in the water…
You may enjoy the Red Mars/Blue Mars/Green Mars series from Kim Stanley Robinson.
I confess I only got part way through because it’s more a political thriller with a sci-fi backdrop. But what I read was pretty good.
How could he make matters worse when he has the best words and the biggliest brain?!!
It can. Most people just use the filesystem watcher, but this looks nice. https://github.com/deathbybandaid/tdarr_inform
Highly recommend using tdarr. Not just because the radarr container won’t do it, but because tdarr is so incredibly powerful.
Hard disagree on them being the same thing. LLMs are an entirely different beast from traditional machine learning models. The architecture and logic are worlds apart.
Machine Learning models are "just"statistics. Powerful, yes. And with tons of useful applications, but really just statistics, generally using just 1 to 10 variables in useful models to predict a handful of other variables.
LLMs are an entirely different thing, built using word vector matrices with hundreds or even thousands of variables, which are then fed into dozens or hundreds of layers of algorithms that each modify the matrix slightly, adding context and nudging the word vectors towards new outcomes.
Think of it like this: a word is given a massive chain of numbers to represent both the word and the “thoughts” associated with it, like the subject, tense, location, etc. This let’s the model do math like: Budapest + Rome = Constantinople.
The only thing they share in common is that the computer gives you new insights.
You’re talking about two very different technologies though, but both are confusingly called “AI” by overzealous marketing departments. The basic language recognition and regressive model algorithms they ship today are “Machine Learning”, and fairly simple machine learning at that. This is generally the kind of thing we’re running on simple CPUs in realtime, so long as the model is optimized and pre-trained. What we’re talking about here is a Large Language Model, a form of neural network, the kind of thing that generally brings datacenter GPUs to their knees and generally has hundreds of parameters being processed by tens of thousands of worker neurons in hundreds of sequential layers.
It sounds like they’ve managed to simplify the network’s complexity and have done some tricks with caching while still keeping fair performance and accuracy. Not earth shaking, but a good trick.
And why they dismantle the systems they’re tasked with protecting the moment they can.