I agree that resin cartridges don't handle the resin handling problem.
Nitrile/latex gloves + eye protection + respirator are the absolute minimum for resin printing PPE. A long sleeve shirt wouldn't go amiss for rare flicked resin droplets. If you're not confident in your ability to prevent skin contact with resin, you shouldn't be doing resin printing.
Right. Just opening a bottle of this kind of photopolymer instantly sends DANGER signals from my nose to my brain (and my spine) causing my whole body to almost seize up. I cant believe reading some comments where people seem to be handing this stuff with their bare hands?!
From colleagues in various national bodies, it's very political since it's about "taste" for various features rather than e.g. bug fix in the standard which is fundamentally neutral.
Would myself (not in the committee) argue that contracts as is would most probably be relegated to modules support hell in terms of support and implementation variations, and I think it's in everyone's interest not to repeat that.
The fact that contracts allow non pure functions to be called is just begging to be a large source of bugs to be. I think that if they started with pure functions, then in future standards mechanised some way to call non pure functions? Would be better and more accepted.
Just like reflection, instead of being overly encompassing, we were given at least the beginning to something great. (Though I dislike some syntax and utility functions, as well as the bareness of some reflection, e.g. can't reflect on templates, other than is it a template.)
The big reddit thread on/r/cpp might actually be the _most_ prominent public example of how political it is (and how the committee operates in general), with several people involved with WG21 arguing in the open.
Indeed, the changes around socialization and community formation during the last 30 years perfectly discourage meaningful class solidarity. Everybody will continue scrolling angry content while the middle class hollowing out proceeds.
I'm having bad results with SotA models. Some questions, if you're up for it:
In your workflow, who implements the review feedback - the review subagent or the code-writing-subagent?
Do have a baseline styleguide (like Google's Go style guide) for the review subagents, or is it entirely the subjective things and specific corrections? I remember 6 months ago it seemed like piling general "good taste" code advice into AGENTS.md was considered bad.
Do you move between harnesses or have you gone all in on claude? I've bounced between claude/codex/omp, maybe to my detriment.
> In your workflow, who implements the review feedback - the review subagent or the code-writing-subagent?
I think currently my orchestrator - which spins up subagents for impl, research, review, fixes, etc.
I've been working with a single orchestrator per "project" or "feature" or whatever sort of "thing I'm paying attention to" is (I guess...chat?) and that orchestrator is told to use subagents to do any/all of this type of stuff, it's sole purpose is to just communicate with me, instantiate these types of 'work', and tell me about results or ask for input.
I am just now considering changing this though and considering something even heavier, but for the purpose of "cleaning up / polishing" lazy work by a single pass of "build thing X", this sort of orchestrator with an AGENTS.md or skill that says "always do a review pass after you finish a task by spinning up N sequential (or parallel) follow up subagents to review and another M parallel or sequential fix subagents to 'fix' those things should make a meaningful difference, if a person sees the same types of traps that their model falls into repeatedly.
You don't even need to have some complex harness. Just take a stock frontier model and its default cli or harness, ask it to edit its agents file to tell itself to always work in this manner, and you're done.
I just wanted to thank you for the followup and the clarity on your pattern. So many people say "it's working great for me" without explaining how they go beyond "tell claude to implement feature".
Not asking for answers to these questions, just frustration dumping:
The biggest things I've struggled with are models having taste. For spec writing I was having a lot of issues with them making statements that were interpretable in a superposition of ways, eg "we'll do XYZ with entities that support and need it" when there's 3 possible entities and the model hand-waved at exactly the wrong tokens.
I added AGENTS.md guidance + memories to be unambiguous (with short but good examples) and "no coined shorthand". Now I'm getting a marked increase in specificity, but it's places that don't matter (claude explaining existing code to itself). I'm having difficulty controlling the spew of new text, but feature writing/research still gets fuzzy and lazy around the difficult underspecified aspects of the problem/feature.
Do I just keep dumping examples into reviewer subagent context and have them rewrite and simplify the research subagent spew?
I repeatedly have "Risks and gotchas" sections have a whole paragraph dedicated to things that don't matter, and then a single sentence bullet point that's actually a huge problem when I dig into it.
Do I have a generic hook to make subagents to review bullet points and size them commensurate with impact? If I tell them to "have taste", do they have taste?
I'm just trying to get good code done. I hate these things.
Is anybody getting good code out of these things reliably without a bunch of additional legwork? Like, what a 2020 mid-level software engineer would write? I know it's old fashioned to read code these days.
To be clear, SotA models/harnesses are really good at making things that work one-shot, and their code golf and debugging game is insane.
When I go to implement, even with a good spec, I end up with code that's 80% of the way there in a fractal manner. The modular decomposition is 80% of the way to good code. The function decomposition is 80% of the way there. The computation structure and variable naming within functions is 80% of the way there. I can walk the AI through it and address each level of issues, but it's tedious as hell, and not clearly faster than doing it myself in some cases.
This is with omp/claude/codex, with Fable 5.1/Opus 5.5/6 Astra. The Chinese models do better at staying coherent, but they're a little less smart IME. I've tried many permutations of "fable planning, opus sub-agent implementation"; "pass a branch back and forth between opus and astra, as reviewers and feedback implementers". I haven't tried the "software factory with an architect and 2 juniors" thing. I haven't tried AGENTS.md beyond the /i-have-adhd, iso-24495 (lol at the Opus 5 induced PTSD), "No cleft constructions or Latinate absolutes" and general project orientation. Model character seems to change too fast to make agents worth it, and I see stuff about skills and excessive AGENTS reducing model capabilities.
actually Matt mentions the same problems as Scott Aronson. If an LLM works on a codebase with lots of tiny modules, or a code base that has ten data layers added by previous runs, then poor LLM will make bad decisions.
Now what happens to mathematics, if prior art gets filled up with spaghetti code like proofs that no one can verify? Maybe there is some hallucination somewhere in the middle that has been left unchallenged by the other agents? Nobody can tell.
Getting recognition is a fundamental human motivation to work on hard problems. Being first is a really important part of getting recognition, measuring by the last few hundred years.
You might as well say "the obsession with sex has always held humanity back". Maybe, but it's complicated...
Repost from a different thread: Related only topically, a 1hr YouTube talk I enjoyed a lot: "Hatchling T. rex and the Evolution of Vertebrate Parental Care" by Nick Longrich: https://www.youtube.com/watch?v=zd0dR2n9hLA
He's a paleontologist and the talk starts with some pretty interesting statistical analysis aiming to estimate T rex clutch sizes (66 eggs) and hatchling weights (1.5kg) as well as T rex parental care. It ends with looking at evolutionary history trends in increasing parental investment in radically different animals.
He talks a little slow, so a little playback speed bump won't go amiss.
Related only topically, a 1hr YouTube talk I enjoyed a lot: "Hatchling T. rex and the Evolution of Vertebrate Parental Care" by Nick Longrich: https://www.youtube.com/watch?v=zd0dR2n9hLA
He's a paleontologist and the talk starts with some pretty interesting statistical analysis aiming to estimate T rex clutch sizes (66 eggs) and hatchling weights (1.5kg) as well as T rex parental care. It ends with looking at evolutionary history trends in increasing parental investment in radically different animals.
He talks a little slow, so a little playback speed bump won't go amiss.
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