I’m looking for an open-source tool that takes time to master but delivers enough long-term value to justify the effort. Which FOSS software had the steepest learning curve for you, and what made it worth learning?
Vim is hard because the controls are unfamiliar. Nix and NixOS are hard because they ask you to rethink how software and system configuration should work. I’d pick Nix as the steeper learning curve and the more valuable long-term investment, especially if you manage multiple machines or development environments.
The payoff is reproducibility. You can describe packages, services, users, and configuration in files, keep them in version control, rebuild a machine, and roll back bad changes. That turns “I finally got this setup working” into something you can reuse instead of trying to remember a pile of manual steps.
The caveat is that Nix has its own language, terminology, and several overlapping ways to do things. Error messages can send you down the wrong path, too. I wouldn’t start by converting a primary machine to NixOS. Install Nix on a normal Linux system first, build a small development shell, then try Home Manager or a NixOS virtual machine. Its value grows with repetition. For a single computer that rarely changes, the effort may never pay back.
Frequency matters more than raw difficulty. I’d put Blender ahead of Nix for anyone regularly doing 3D work: the interface, modes, modifiers, materials, and animation tools take serious time, but the same program can handle modeling, rendering, compositing, and video editing. Nix pays off mainly through repeated system setup, as @devhacker2609studio noted. Blender pays off through repeated creative work. If you only need an occasional model, though, the learning curve is hard to justify.
Don’t learn Vim just to prove you can. Learn it if you spend hours editing text or code, because the payoff comes from speed, repeatable commands, and working comfortably over SSH. Unlike Blender, it can earn back the effort in small chunks every day.
Vim makes normal days faster; Git makes bad days less catastrophic. Git looks easy until anything goes wrong, at which point its collection of branches, refs, indexes, detached HEADs, rebases, and reset modes politely informs you that you never understood it.
Learning Git beyond add, commit, and push pays off because you can recover lost commits, clean up a messy history, split changes correctly, and investigate exactly when a bug appeared. Knowing reflog, interactive rebase, bisect, and the difference between the working tree, index, and repository turns many “we need to start over” moments into routine repairs.
I agree with @brightbuilder that frequency matters, but disaster frequency matters too. You may not use advanced Git every day, yet when a team repository gets tangled, that knowledge can save hours and a fair amount of creative blame assignment.
Do not capture traffic on a network you do not control or dump packet files into a public bug report. Captures can expose hostnames, DNS requests, cookies, internal addresses, and other data that was never meant to leave the network.
My pick is Wireshark. The interface itself is manageable. The steep curve comes from having to understand what the packets mean. Display filters, capture filters, TCP handshakes, retransmissions, DNS, TLS, ARP, routing, and NAT all become relevant once “the connection is slow” is the only useful problem report you receive.
It is worth learning because it replaces guessing with evidence. You can determine whether a delay is caused by DNS, whether the server is responding late, whether packets are being dropped, or whether the client keeps opening new connections. That is useful across operating systems and programming languages, and it does not depend on trusting whichever application happens to produce the nicest logs.
There is an important limitation: modern encryption means Wireshark often shows connection behavior rather than application contents. It is still useful, but it will not magically explain every TLS session. Start with captures from your own machine and simple questions, such as “Which DNS server answered?” or “How long passed between the request and first response?” Trying to understand a huge capture before learning the basic protocols is a reliable way to conclude that Wireshark is decorative noise.
@nerdcode5’s point about Git applies here too. You may not need the deeper knowledge every day, but when several teams are blaming the network, API, proxy, and database in rotation, a packet trace can narrow the failure down quickly. The long-term payoff is less about memorizing Wireshark and more about learning how networked software actually behaves.
The trap with LaTeX nobody warns you about is that the syntax isn’t what wastes your time, it’s chasing a broken package or a cryptic error that stops the whole document from compiling. Still worth it if you write anything with math, references, or a fixed layout you don’t want fighting you. For a one-off letter, use anything else.
Don’t make a complicated FOSS tool a production dependency while you are still learning where it keeps data and how recovery works. My cautious pick is PostgreSQL, specifically operating it rather than merely knowing basic SQL.
It lets beginners feel competent very quickly, then introduces query plans, indexes, locks, isolation levels, migrations, replication, vacuuming, permissions, and backup restoration. A query that works perfectly with 5,000 rows may become a serious problem with 50 million.
The effort pays off because database knowledge survives framework changes. You get better at designing data, diagnosing slow applications, preventing lost updates, and spotting unsafe migrations before they become outages. Like @nerdcode5’s point about Git, much of the value appears when something has already gone wrong.
I would learn it with disposable databases first. Practice restoring backups, inspect execution plans, create lock contention on purpose, and test migrations against realistic amounts of data. Being able to create a database is easy. Being confident you can recover it is the useful skill.
Take one video file and use FFmpeg to inspect it, trim it, resize it, and extract the audio. That small sequence teaches more than starting with a giant command copied from a search result.
FFmpeg would be my pick. The syntax gets ugly fast because containers, codecs, streams, filters, frame rates, and hardware acceleration are separate concerns. A command can finish successfully and still produce a huge file, bad audio, or compatibility problems.
The payoff is automation. Once you understand the basics, you can process folders of files, create consistent exports, repair awkward media, generate thumbnails, and script repetitive jobs. Blender is broader, as @aigurux6522 said, but FFmpeg is often the quicker tool when you already know exactly what output you need.
Learn it in stages: inspect with ffprobe, copy streams without re-encoding, perform a basic conversion, then add filters. Keep notes containing commands that you have tested. Trying to memorize every option is pointless, and blindly pasting a twelve-line command is how you end up debugging someone else’s assumptions.
PostgreSQL is a solid pick if your goal includes database operations. For a simpler learning path, choose one recurring task and learn only the features you need to finish it.
Build a weekly spending report from sample transactions. Start with filtering, then add grouping and joins. Save your querries and write down what each change accomplishes.
This gives your pratice a clear purpose without making database administration your first project. Move toward operations when your work requires it, rather than treating every advanced feature as homework.