This is especially true on a CRT. People often forget how "good enough" the PS2 graphics were, and how big of a deal that was.
Even the prior two games had an extremely tasteful use of textures and geometry that just felt right and realistic enough to not break immersion despite the hardware limitations. This era of gaming rightfully and correctly recognized that any form of "realism" is ultimately an impression rather than an objective truth.
Gran Turismo 3 was where I first started experiencing the "uncanny valley" of graphics. The game looked and felt incredible... so incredible that the lack of damage felt like an immersion-breaker in a way I hadn't experienced before.
> People often forget how "good enough" the PS2 graphics were, and how big of a deal that was.
I remember going to an electronics store with my friend. I couldn't figure out why they had a broadcast football game playing on a 15 inch LCD display up front. I assumed it was a game but then watched it for a few cuts and was so certain it was live footage...
How have you found Muse Spark 1.3? It doesn't get much mention, despite pretty good benchmarks. I've been using a bit at home and find it quite good, often finding mistakes made by Opus 5.
MS13 is pretty sharp and has been my workhorse for the past month. It follows my coding style and commit/clean workflows referenced in AGENTS.md perfectly but has the habit of doing things without conferring with me (the Gemini problem). So you need some kind of instruction for that.
It starts failing around the 5-600K context mark, but you can have it generate a handover document and continue in the next session.
I would not use it at sticker price, but the Contributor version is priced just about right.
shocking. the code it generated, while technically working, was entirely garbage. i used it for code review and it flagged twenty issues, sol checked the review and found 75% of them were hallucinations. sol was much closer to reality. i no longer trust benchmarks at all because of it.
Sorry, I assumed the inductive construction was implied; you can indeed describe properties of that particular interesting problem (though of course you can’t hold its definition in your head), so it goes in the list. Keep going. At some point you’ll hit problems where the process of constructing the problem doesn’t even fit in a brain, etc. There are at least countably many problems, but finitely many problems which any algorithm-which-fits-in-the-brain can describe given finitely many inputs-which-fit-in-the-brain.
This isn’t an enormously important point - the actual question at issue is an empirical one, “in a steady state, can we produce interesting problems at a rate that exceeds our ability to solve them and integrate our understanding” or something like that - but I did rankle at a “trivial” proof which is invalid due to equivocating between multiple definitions of the word “interesting” (which should really take an object, “interesting to me” vs “interesting to something smarter than me”).
In the early days of SC2, I remember people using genetric programming to optimize build orders. I remember a slightly unorthodox Zerg Roach Rush which was _really_ fast.
They overclocked well though, I think you could run the 300Mhz chips at >400Mhz.
I also believe you could get motherboards that supported 2 Celeron chips. I have no idea how effective/useful it was, but it was certainly a cheap/interesting way to get multiple CPU's.
> Co-trained with the harness. Muse Code was in the training loop from day one, so tool calls succeed and plans execute cleanly. Crucially, we trained across multiple harnesses, so while the model is at its best in Muse Code, it still generalizes to other coding agents you already use.
Very keen to try this after using Claude Code over the last few months.
Should I just point Claude Code to Muse Spark endpoint (because I'm familiar with Code)? What do people think of Muse Code or other coding agent harnesses?
Well thats very interesting. Thank you.
Will be interesting to see how hard/easy it is to translate my Claude skills, loop design, etc to the new harness.
This kind of raises another question to me regarding the coding benchmarks, how much of it is model versus harness?
Coming from Claude Code, I initially went with opencode but switched to pi.dev after a while and I think I like it more. It's lighter weight. It's worth trying both.
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