Items are in a hash table using color/material type/shape as the hashing method optimized for human pattern recognition providing O(1) access. The table is smaller than the number of items causing some collisions. Those items are in a randomly sorted vector. Average case is still around O(1) with an O(n) worst case.

Olauncher (free version) and Pro Launcher (paid version).

Used the free version for years and decided to shell out for the paid version about a year ago.

Primary access to apps is via the search outside of the limited slots for shortcuts (text). Paid version adds some additional slots (icons). Simple and reminds me of a terminal.

Something like graphite.com to create stacked PRs that are reviewable probably would have helped. Can be replicated with local LLMs or remote AI providers with locally configured agentic workflows. Never used graphite personally, but I’ve seen some open source maintainers use it to split up large PRs.

With these sorts of tasks models really seem to suffer from not knowing what packages or conventions have been deprecated. This is really obvious with an immature ecosystem like nix.

This is where custom setups will start to shine.

github.com/upstash/context7 - Pull version specific package documentation.

github.com/utensils/mcp-nixos - Similar to above but for nix (including version specific queries) with more sources.

github.com/…/sequentialthinking - Break down problems into multiple steps instead of trying to solve it all at once. Helps isolate important information per step so “the bigger picture” of the entire prompt doesn’t pollute the results. Sort of simulates reasoning. Instead of finding the best match for all keywords, it breaks the queries down to find the best matches per step and then assembles the final response.

github.com/CaviraOSS/OpenMemory - Long conversations tend to suffer as the working memory (context) fills up so it compresses and details are lost. With this (and many other similar tools) you can have it remember and recall things with or without a human in the loop to validate what’s stored. Great for complex planning or recalling of details. I essentially have a loop setup with global instructions to periodically emit reinforced codified instructions to a file (e.g., AGENTS.md) with human review. Combined with sequential thinking it will identify contradictions and prompt me to resolve any ambiguity.

The quality of the output is like going from 80% to damn near 100% as your knowledge base grows from external memory and codified instructions in files. I’m still lazy sometimes and will use something like Kagi assistant for a quick question or web search, but they have a pretty good baseline setup with sequential thinking in their online tooling.

Like, from Yellow Dog Linux? Was my first Linux distro as a kid (grew up with an Apple computer). Red Hat based so checks out. TIL.

It’s really not that different from a traditional web search under the hood. It’s basically a giant index and my input navigates the results based on probability of relevance. It’s not “thinking” about me or deciding what I should see. When I say a good assistant setup, I mean I don’t use Gemini or ChatGPT or any of the prepackaged stuff that tries to build a profile on you. I run my own setup, pick my own models, and control what context they get. If you check my post history I’m heavily privacy conscious, I’m not handing that over to Google or OpenAI.

The summary helps me evaluate if my input was good and the results are actually relevant to what I’m after without wading through 20 minutes of SEO garbage to get there. For me it’s like getting the quality results you used to get before search got enshitified. It actually surfaces stuff that doesn’t even show up on the front page of a traditional search anymore.

I’m in software development and land on both sides of this argument.

Having to review or maintain AI slop is infuriating.

That said, it has replaced traditional web searching for me. A good assistant setup can run multiple web searches for me, distill the useful info cutting through the blog spam and ads, run follow up searches for additional info if needed, and summarize the results in seconds with references if I want to validate its output.

There was a post a couple days ago about it solving a hard math problem with guidance from a mathematician. Sparked a discussion about AI being a powerful tool in the right hands.

neovim user (inside zellij) and same. More of a full blown IDE than an editor.

Also for the keybind memory impaired like myself:

• github.com/folke/which-key.nvim
• native which-key in emacs

Totally agree with your overall point.

That said, I have to come to the defense of my terminal UI (TUI) comrades with some anecdotal experience.

I’ve got all the same tools in Neovim as my VSCode/Cursor colleagues, with a deeper understanding of how it all works under the hood.

They have no idea what an LSP is. They just know the marketing buzzword “IntelliSense.” As we build out our AI toolchains, it doesn’t even occur to them that an agent can talk to an LSP to improve code generation because all they know are VSCode extensions. I had to pick and evaluate my MCP servers from day one as opposed to just accepting the defaults, and the quality of my results shows it. The same can be done in GUI editors, but since you’re never forced to configure these things yourself, the exposure is just lower. I’ve had to run numerous trainings explaining that MCPs are traditionally meant to be run locally, because folks haven’t built the mental model that comes with wiring it all up yourself.

Again, totally agree with your overall point. This is more of a PSA for any aspiring engineers: TUIs are still alive and well.

There is a praise/validation kink for that.