As it happens I'm working on a chessdb.nu, which is supposed to be a set of tools and skills for llms to annotate, even Socratically coach chess positions and games.
Two observations, 1) I think the post is really just trying to explain that the LLM is not limited to the next token in its planning. It can have an idea of where it is going all the way to the last period and token prediction is just the step by step to get there. Even more interesting is that the LLM can change plans midstream (cf: seahorse emoji). And 2) without going so far as saying it is existentially making choices, it is showing complex reasoning and planning which is a few steps above a Markov Chain.
As for myself, I like the analogy of french curves and splines being fixed and added to with the LLMs internal reasoning and then walked token by token. But LLM's don't actually have to be that continuous and smooth, although in fairness when it isn't, it's usually a failure mode.
If your spec is really what your computer is, you should be able to run the models you need to.
Just slow down, have patience, and be opinionated on what you want it to look like, from code to architecture to user experience.
I hit more failure modes than when I use Claude, but there are always failure modes, and the answer is always slowing down, learning what it's doing, and having an opinion.
I will use, sometimes, free web search tools to architect, plan, and generally get ideas. Their ability to search the web directly helps. And to code, I sometimes build a cheap RAG with the language documentation (nushell needs help most of the time) and that helps.
Again, in general, just slow down and take your time. Like a jr dev, just cut it into bite-sized pieces with clear goals, all of which a more capable model can help with.
GLM worked well and did some impressive one-shots. Gemma can be more sophisticated in what it writes, but Qwen is less acrobatic and stable. Ornith looks interesting but I was never able to run it.