If that's your jam then there's also a (non-open-source) "Hero's Hour" which tickles the old Heroes of Might and Magic stylings, works reasonably well on Xbox, where I've been doing most of my gaming lately.

As far as Open Source gaming success stories, I'd put this up there in the Top 5 for "Original IP and Concept" (if that makes sense). Just a stellar labor of love, worth giving it a shot to play!

...and replying to a sibling; yes, I did add it to `.gitignore` (but that's not a guarantee of it going crazy again), and was super surprised that it truly deleted it rather than "safely" doing `mv ... .trash/*` or something.

The reason to dig into the agent reasoning is that I have to treat myself as if I were the one in error (which as you pointed out, I was!), and determine the cause of it along with prevention.

Again; interesting times!

Never had that experience in the whole time using cursor at work so I had to "take the agent to task" and ask it "WTF-mate? you'd better be able to repro that!" and then circle around the drain for a while getting an AGENTS.md written up. Not really a big deal, as the whole project was like 1k lines in and it's not like the code I'd hand-written there was "irreplaceable" but it lead to some interesting discussion w/ the AI like "Why should I have to tell you this? Shouldn't your baseline training data presume not to delete files that you didn't author? How do you think this affects my trust not just of this agent session, but all agent interactions in the future?"

Overall, this is turning out to be quite interesting technology times we're living in.

...time passes...

"""Now that you understand the idea behind these pairings, recommend five more pairings, but don't give any hints as to their connections, just five bullet points with "A vs B" movie titles. Bonus points if there is at least a 10-year gap between them, and they are both not box-office blockbusters (but make sure they are slightly more popular or recognizable movies, not exclusively low-distribution non-critically-acclaimed indie movies)."""

* Children of Men vs Snowpiercer * Lost in Translation vs Frances Ha * No Country for Old Men vs Hell or High Water * The Prestige vs The Illusionist * Drive vs Nightcrawler

...I know guidance is "don't just post AI output", but this is specifically a human-to-human discussion around novel(?) ways to interact with AI/LLM's. I've found they're _really_ good at conceptual-venn-diagrams.

There's a book "Algorithms to Live By" (ie: look for matching socks via BFS/DFS or whatever). Asking the AI: "you know a bunch of algorithms, what are the top three that should have been in the book?" => "what are the weakest that could have been removed?"

Recently during performance reviews, we had to write our self-assessment and had guidance from on high like: "make sure you talk about people skills, technical skills, customer impact, etc." ...so yada yada: "I'm so amazing, I'm so great" => "Dear AI, I've been given this guidance `...`, please compare my handcrafted storytelling against the guidance `...` and tell me where I have missed covering a requirement" => "...now please give help w.r.t. simplifying or cleaning up the section on $INCREDIBLE_TECHNICAL_ACHIEVEMENT b/c I was focusing on describing my personal impact, but need help making it more digestible for others".

The combination of instant, tailored feedback and the fact that they've read the whole internet, "watched" every movie (read the script, read critics reviews, reddit, forum discussions, etc), read most published books, and that they're 80%+ plumbers, doctors, lawyers, car mechanics, etc. make them an unstoppable research assistant, especially when crossing connections that would normally be "expensive" to do so.

Example: ask a [doctor+lawyer+plumber] about the health and legal impacts of lead solder in pipes or whatever. Instead of needing to schedule 3 people's times, wait for them, pay them, etc, you can get instant "free" feedback, educate yourself, and then have a more solid foundation to branch out from there. Such incredibly useful tools!

...I read both of the books recently and it was illuminating to be able to near-instantly explore avenues of insight/criticism of both of the books. Three Musketeers matches fairly closely to Wizard of Oz (vice versa actually), and Monte Cristo raises some really interesting questions if you view "The Count" as basically a fallen angel of divine justice (and the benefits/costs to him via that role).

Since my circle of IRL people who'd recently read both the unabridged books in the last month is infinitesimally small, it was one of my first "arms-length" test cases of "The GPT's" for fitness-for-purpose. I'm still a bit muddy on throwing a bunch of personal data and thoughts to remote servers (or becoming dependent on that interaction pattern), but digging in and analyzing old books was a great kindof gut-check and something I enjoy doing when finishing a book.

I know it's regurgitating a bunch of of reddit comments and academic books/papers (in Dumas's case), but overall- highly recommended!

AA batteries b/c then it'll "last forever", 3M b/c it's basically passive noise cancellation, BlueTooth so it'll connect to phones (hopefully without that digital static that I'll hear with some BT devices). The AM/FM portion is an anti-feature, but mandatory to get access to AA power.

TLDR: Mail storage is the sender's responsibility. The message isn't copied to the receiver. All the receiver needs is a brief notification that a message is available.

While I understand some of the fundamental thoughts behind that comparison, it's slightly wonky... I'm not asking "compress wikipedia really well", but instead "can a 'model' reason its way through wikipedia" (and what does that reasoning look like?).

Theoretically with wikipedia-multi-lang you should be able to reasonably nail machine-translation, but if everyone is starting with "only wikipedia" then how well can they keep up with the wild-web-trained models on similar bar chart per task performance?

If your particular training technique (using only wikipedia) can go from 60% of SOTA to 80% of SOTA on "Explain why 6-degrees of Kevin Bacon is relevant for tensor operations" (which is interesting to plug into Google's AI => Dive Deeper...), then that's a clue that it's not just throwing piles of data at the problem, but instead getting closer to extracting the deeper meaning (and/or reasoning!) that the data enables.

    README => AGENTS.md
    HOWTO => SKILLS.md
    INFO => Plan/Arch/Guide
    REFERENCE => JavaDoc-ish

"Hey, we're going to work on Feature X... now some test cases... I've done more testing and Z is not covered... ok, now we'll extend to cover Case Y..."

Let me hover over the 50-100 character commit message and then see the raw discussion (source) that led to the AI-generated (compiled) code. Allow AI.next to review the discussion/response/diff/tests and see if it can expose any flaws with the benefit of hindsight!

It used to be hard to be "web scale" and available, now that's either k8s or a few checkboxes in AWS.

Yahoo used to be able to "coast" on the compellingness of their services because 80% attractive with 100% available and 100% global reach crushes 90% attractive with 95% available and 25% global reach.

I was often confused by the hyperfocus of analysts asking "Is Y! a tech company or a content company?"

What they were really asking was if we should be valuing Yahoo! as 30%+ margin on putting ads next to Yahoo! News articles, or 10x multiplier on originating GMail/Search?

I think "data is the only moat", and in a way that goes back to the "first to market / eBay" POV, and the difference between first to market and fast follower is super interesting!

Per google it was started in 2009, which means any car worth less than $5k around 17 years ago isn't materially impacting new or used car prices today.

...but seriously... there was the "up until 1850" LLM or whatever... can we make an "up until 1920 => 1990 [pre-internet] => present day" and then keep prodding the "older ones" until they "invent their way" to the newer years?

We knew more in 1920 than we did in 1850, but can a "thinking machine" of 1850-knowledge invent 1860's knowledge via infinite monkeys theorem/practice?

The same way that in 2025/2026, Knuth has just invented his way to 2027-knowledge with this paper/observation/finding? If I only had a beowulf cluster of these things... ;-)

Basically there's a ton of traction at the zero-to-one (making the first prototype) and then you start looking at how to "scale" your manufacturing (ie: making 10 at a whack), and then eventually you MAY get to building/assembling 100 at a whack, and up to 1000's or more (where you'd "graduate" to partnering with a "real" manufacturer).

Maybe it's just the way that I'm wired, but I've done 3-4 projects where I've gone down the B.O.M. rabbit hole and have scaled to at least 100 assembled/packaged items.

It seems like a local makerspace is the perfect launch-pad for having flexible "staff" (ie: other makerspace members) that can handle ambiguity and would be invested in the success of a locally owned/managed product!

    Temperature_C != Temperature_F != int8

His POV was that "if you don't win, you lose" and my POV was "second place is better than last place". His response was: "if I play poker to get first place it's wildly different than playing for second or third place [and I may end up in last place wildly more often due to risk % or bad beats]"

I've been more used to "climbing" type performance games (ie: last place => mid-field => second place => first place) and in my gut I wanted my ELO to reflect that (top-half players are better than bottom-half players), however his very valid point was that different games have different payout matrices (eg: poker is often "top-3 payout", and first may be 10x second or third).

I think in my mind I've settled on EV-payout for multiplayer games should match the "game payout", and that maybe my gut is telling me the difference between "Casual ELO" (aka: top-half > bottom-half), and "Competitive ELO" (aka: only the winner gets paid).

Mr. A says, “I resign” or Mr. B says, “I resign” or Mr. A says, “I offer a draw,” and Mr. B replies, “I accept.” That is, under perfect play, each chess position is either a forced win, forced draw, or forced loss. The domain of a perfect chess position evaluation function is these three cases as symbols."""

There's an interesting point I've heard of in Backgammon, somewhat related to this statement. Modern Backgammon offers "the doubling cube" as a play option. https://en.wikipedia.org/wiki/Backgammon#Doubling_cube

...basically if you think you're going to win (aka: you have a 200 centi-pawn advantage), you can offer the doubling cube to your opponent (doubling the stakes of losing). If you're playing to win $5, and halfway through you think "yep, 90% chance I'm going to win this one...", you push the doubling cube to 2x (aka: $10 consequence), and kindof like poker your opponent has to evaluate whether it's "worth it" for them to stay in the game.

You might imagine a "2xELO penalty" where White takes a Queen with a Pawn, and then offers "2x, or I'm gonna beat 'ya!". If Black say "Naaah, you just activated my trap card!" and then either accepts "2x" or pushes back at "4x", then it becomes a little more like poker... you think you can beat me, then prove it!

Not that I'm suggesting changing the rules of Chess, but overall I'm really fascinated by the concept of formalized semi-out-of-band risk-taking to potentially end games early.

So... informally, "Tesla Co-Pilot" => "You're still the pilot but you have a helper", vs "Tesla Autopilot" => "Whelp, guess I can wash my hands and walk away b/c it's AuToMaTiC!"

...it's tough messaging for sure, especially putting these powertools into peoples hands with no formal training required. Woulda-coulda-shoulda, similar to the 737MAX crashes, should "pilots" of Teslas required training in the safety and navigation systems before they were "licensed" to use them?

eg: http://bropages.org/diff

...you need "man" pages for moderately-comprehensive options explanations (backed by /usr/share/doc/$TOOL/README.txt if you're a debian user), but "bro" tends to focus on the "yo, this is what you're actually trying to do here...", including sometimes crossing traditional "this-command" boundaries (eg: in the diff example, offering `diff <( cmd1 ) <( cmd2 )` b/c sometimes that's what people are trying to do).

I can't find one that I submitted but it was something like `bro sed` => `# bro, just use awk! => awk -- '{...}'` ...basically you could go down the wrong rabbit hole, and there's kindof a nice little community of users helping to lift each other up (with upvote/downvote) and focusing on providing relatively simple and salient examples rather than a wall-of-text-options where you know that it's possible, but you don't know how to start. (eg: see `bro ffmpeg`)