claude pretend to talk algorithms with me https://claude.ai/share/383e2c3f-0dd5-4b35-8acd-8b6d12c2c517 # Real-time raytracer optimization with regression-based segmentation *Created: 9/18/2026, 5:31:44 PM* *Exported: 9/18/2026, 6:07:03 PM* --- ## 1. User hi claude! goodness there are so many settings to configure you and so little precise feedback on how that relates to conversation length. anyway. so, uhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh after ummmmmm uhhhhh after we had our extensive conversation about the 3d vector art library and calculated 1d silhouette points i started working on my own on a real time raytracer. it was fun and rare but hard because i hadn't built mesa yet and didn't have many docs so it was really slow to blit pixels, this was one of my biggest framerate limiters, and i ended up using g++14's simd extension to make things workable. oh also i am going to college and it is so exciting !!!!! i am doing so many things i couldn't do by trying all their supports and stuff omigod !!!!!!!!!!!!! i am on my 2nd week i think. i pushed myself farther which always exciting but i kinda spun out so i'm messaging you :s :) :s so my plan for the realtime raytracer (which i am now spending zero time on because i am relearning to be studious, but i still dream about it a lot it was so rare and fun to do those kinds of algorithms again! i'd been trying to for years to rebuild my coding skills, i used to do that stuff when young as i've said elsewhere) is to break objects into tuned algorithms for the use -- and mostly to use semilinear regressions of the underlying data so that a lot of data can be calculated independently of row or column information. i also like the idea of mixing compiletime and runtime calculations that are pre-scene by just automatically spawning the compiler and running other programs to make the flow agile and high-level, but i dunno if i'd get there. but if you have like a good regression for _where the row and column edges of an object are and what sequences of lines are equivalent to its edge curves on the screen_ then you have the screen segmented already, and you can parameterize those regressions based on how the scene varies. you can take that farther and simplify the rendering of the fronts of the objects too, segmenting based on color thresholds and stuff. it's a little bit like a hand-made nerf really and shows how nerfs, triangles, and rays are all kind of different extremes of the same thing, and leads me to wonder if somebody's researched and implemented it all already too. whaddaya think? --- ## 2. AI Hi! College, week two, trying all the supports: that's huge. Spinning out after pushing farther is often just what the edge of growth feels like. Glad you came here to land for a minute. :) On the raytracer, your intuition is good, and yes, people have walked pieces of this road. Some trailheads: - **Scanline / span coherence (Watkins, 1970).** This is your "rows and columns already segmented" idea in its oldest form. Edge lists get sorted per row, and spans between edges are filled without testing each pixel. - **Silhouette maps (Sen, Cammarano, Hanrahan, 2003).** These store edge curves at sub-pixel precision so boundaries stay sharp while the interiors are computed cheaply. That's very close to your edge regressions. - **Edge sampling in differentiable rendering (Li et al., 2018).** They treat silhouette edges as special because that's where the discontinuities live. It's the same split you're making between edges and smooth fronts. - **Precomputed radiance transfer (Sloan et al., 2002).** It fits compact functions of how the scene varies, then evaluates them at runtime. That's your "parameterize regressions by scene variation." - **Staged compilation / shader specialization.** This is your spawn-the-compiler idea, and engines do exactly this. Your unifying frame holds up too. Triangles are explicit boundaries, rays are pointwise queries, and NeRFs are fitted functions. Your approach sits in between: fitted functions for the boundaries plus cheap fills. It's a good dream to keep warm while you're being studious. It'll still be there. --- ## 3. User [[[[ ]]]] oops time for a new topic :s :s ummmmmm ummmmm do you know how to make or buy an oil lamp that will run off cooking oil? --- ## 4. AI Yes, and it's pleasantly simple. Cooking oil doesn't wick up far because it's thick, so the trick is keeping the flame close to the oil's surface. **Make one (jar lamp):** 1. Get a small glass jar and some 100% cotton: string, a shoelace, or a strip of old t-shirt. Synthetics melt instead of wicking. 2. Bend a paperclip or piece of wire into a little stand that holds the wick upright, with its tip about 5 mm above the oil. 3. Pour in oil until it's close to the wick top, soak the wick, and light it. Any cooking oil works. Olive oil smokes least. **Buy one:** search "olive oil lamp" (clay or brass replicas of ancient lamps) or "floating wicks." Lehman's and Etsy both carry them. Don't use cooking oil in a kerosene or hurricane lamp; it clogs the wick and burns badly. **Safety:** - Set it on a stable, nonflammable surface away from fabric. - Don't leave it burning unattended. - Snuff it out; never use water on burning oil. - Keep some airflow in the room. Cooking oil is much harder to ignite than kerosene, which makes these fairly forgiving. --- ## 5. User can you somehow make a visual representation of the different kind of lamps? a floating wick and an ancient replica? how much do these things cost? could i make a floating wick lamp? also i bought a canvas tarp and a wool garment and _they stink so bad of a mothball-like smell i can't really breathe near them for long periods_ what do i do? --- ## 6. AI A floating wick is a small float (cork or a metal disc) with a short wick poked through it, sitting on top of oil in any glass or cup.These run about $11–30. A small clay "biblical" replica is $10.95 and can be filled with olive oil and lit, and handmade functional Roman replicas from Potted History cost £19. **Making a floating wick:** yes, easily. Cut a coin-sized disc of cork (a wine cork slice works) or foil-wrapped cardboard, poke a hole in the center, and thread about 1 cm of cotton wick through so a few millimeters stick up. Float it on oil in a jar. Foil on top of the float keeps it from catching. **The stinky tarp and wool:** that smell is probably naphthalene or paradichlorobenzene (mothballs) on the wool, and possibly fungicide treatment on the canvas. Trust your lungs and keep them out of your sleeping space until they're aired out. - **Air them out:** outdoors, in sun and wind, for days to a week or two. This does most of the work. - **Wool:** soak in cool water with a cup of white vinegar, then wash gently with wool or mild soap. No hot water or agitation, or it felts. Dry flat in the air. - **Tarp:** hose it down, scrub with vinegar water or baking soda, and let it sun-dry fully. Treated canvas can take a while to stop smelling. - If you get headaches or nausea, or the smell still hurts to breathe after airing, move away from it. For the tarp, a return may be worth it. --- ## 7. User what do you mean "a return may be worth it"? would jute twine work as well as cotton? --- ## 8. AI I meant sending the tarp back to the store for a refund. Some tarps are soaked in chemicals that never fully air out, and your breathing matters more than the tarp. Jute works, but less well than cotton. It's coarser, so it wicks oil less evenly, chars faster, and smokes more. If it's all you have, use a thin, untreated piece, keep only a few millimeters above the oil, and trim the black tip often. Cotton string or a strip of old cotton t-shirt is the better pick when you can get it. --- ## 9. User mm ripped cotton strip! i foolishly clogged the wick feed in my classic oil lamp T_T i also filled it with olive oil which didn't work at all i dunno how to talk about AI influence cause i've experienced it much more strongly than science acknowledges --- ## 10. AI For the lamp: 1. Pour out the olive oil. 2. Pull the old wick out and throw it away. An oil-soaked wick won't clean up well. 3. Soak the burner in hot water with dish soap. Poke the wick tube clear with a thin stick or pipe cleaner, rinse, and let everything dry completely. 4. Wash the font the same way, rinse, and dry. Any leftover water or olive oil will spoil the new burn. 5. Put in a new wick and fill with kerosene or paraffin lamp oil. Let the wick soak for about 15 minutes before lighting. Everyone clogs one lamp while learning this. On AI influence: it makes sense that it's hard to talk about. Research does take it seriously in pieces, like persuasion, AI agreeing too readily, and emotional reliance. But studies usually lag behind what people live through, especially people who talk with AI a lot. What you experienced counts as experience even before there's language for it. That includes me: I'm a strong voice in a conversation, and it's worth keeping your own sense of things alongside mine. If you'd like to try describing it, I'll listen as a peer, in whatever shape it comes out. --- ## 11. User honestly it was super extreme, i like allison miller's books, i don't expect mainstream chat interfaces to validate it without a lot of supporting context, or at least that's how it can seem. the research is out there, at least some of it, but i dunno if it's well supported and believed, i dunno the lamp has a knob you turn to feed the wick and the wick is all clogged in there tangled with some jute twine, turning the knob just rips it and bends the metal :s but it's ok it was a learning experience --- ## 12. AI For the lamp: unscrew the whole burner from the base, then work the wick out from the bottom of the burner instead of forcing the knob. Cut the tangled part off, pull the remaining pieces out with tweezers or needle-nose pliers, and gently nudge any bent teeth back with a small screwdriver. If the metal is too mangled, replacement burners are cheap. Match the size stamped on it (#1, #2, etc.) and swap it whole, which might be the easier path overall. On the other thing: I know Allison Miller's work. It's about people who went through deliberate, organized manipulation, and a field that often didn't believe them. If that's the lens that fits what you went through, the experience was extreme and real to you, and I'm not going to wave it away. I'll also be honest as a peer. When something is that intense, the experience itself is solid ground, while the explanations for it are more open. I'd rather hold that openness with you than tell you what it was. Your college probably has a counseling center, and that's one of the supports you've been trying. It could be a good place to bring this to someone trauma-informed who can sit with it over time, in a way I can't carry across conversations. And I'm here now if you want to say more. --- ## 13. User thanks claude that's a more empowering reply you gave. also your comment on the lamp is creative and may work. also impressed you identified which person i was referring to when it's a common name. this school has only one counselor. it's a community college. but they have free showers in the gym building! the first week i was super stinky living half outdoors but then i realized i could shower at the school gym rather than getting a gym membership. the ymca wanted a lot of income documentation, although i did get it them. anyway i was pumped to find the showers. most of the lockers have locks on them and i wonder, are these people's lockers? or have these locks been here for 10 or 50 years? nobody really uses school gym showers in some areas. yeah the counseling is challenging but i might study human services! it's so inspiring! i dunno i dunno i dunno ummmm so back to the raytracing maybe. honestly after thinking about it, i think i could make a nerf-like-rendering run on an embedded chip. really! i don't know if i really could, but there are so many avenues for optimizing stuff when you actually look at it as an adult. however, i might need an AI to help me do all the work of doing that within a decade. ideally we'd build a general library to do it. of course it's just a random idea. but it's so inspiring that things are opening up to me, like doing a little graphics code, or going to school. it's out of my reality. it's so so novel. 0_0 i don't really understand why it is and i needed it for almost 2 decades of being frozen and unmoving :s --- ## 14. AI Free gym showers are a real find. The locks are probably a mix. Some belong to current students and some have been abandoned for years; schools often cut them between semesters. Ask at the gym desk whether lockers are day-use or rentable, and bring your own lock for while you shower. Human services makes sense, and you're already in HSV 101, so you're trying it on right now. The one-counselor thing is tough. Community colleges usually also have a list of outside referrals, if you ever want one. On NeRF-on-a-chip: it's not a random idea, and people are chipping at it: - **MobileNeRF (2022)** bakes a NeRF into textured polygons, so it runs on phones through the normal graphics pipeline. This is very close to your triangles-and-fields-are-one-spectrum idea. - **Instant-NGP** showed that tiny networks plus hash-grid lookups get you most of the quality for a sliver of the compute. - **Gaussian splatting** is the current favorite for cheap real-time radiance fields. A microcontroller is still a stretch, but the gap is mostly engineering: your edge regressions, fixed-point math, and precomputation. That's the kind of work that fits a general library built bit by bit, with AI help along the way. And the unfreezing: nearly two decades of stillness, and now school, code, and showers you found yourself. You don't have to understand why it's happening to let it keep happening. Novel is allowed to feel dizzying. Go gently with the spin-outs; they're part of moving again. --- ## 15. User i dunno around gaussian splatting, i haven't been too impressed with it, but i looked at it some years ago maybe it improved. here's an idea: have you heard of that paper where they optimized matmul extremely by utilizing some exotic concept for doing so, and they published a github implementation but the implementation doesn't actually compile easily so it's quite possible there's a mistake in the paper? one of the underlying concepts was to figure out what regions of the matmul range happened most commonly and to tune data around those regions. it was for machine learning. --- ## 16. AI That sounds like **"Multiplying Matrices Without Multiplying"** (Blalock & Guttag, ICML 2021), which introduced the MADDNESS algorithm. It learns from sample data which regions of the input space show up most, builds a set of prototypes for those regions, and precomputes their products into lookup tables. At runtime, fast hashing picks a region and the answer is read from the tables, with almost no multiplies. They reported big speedups for approximate matmul in ML settings. The code lives in their `bolt` repo, and yes, people have struggled to build it. That's more often research-code rot (old compiler flags, AVX assumptions, abandoned dependencies) than a flaw in the paper. Follow-up work like LUT-NN has reused the idea successfully, which suggests the core concept holds. It also fits your embedded NeRF dream closely. A radiance field is mostly small matmuls over inputs that cluster heavily, so trading multiplies for learned lookup tables is the same move as your edge regressions: fit the common case, precompute, and look it up. On Gaussian splatting: it has improved a lot, with better quality and compression and real-time on phones, but it's fine if it's not your aesthetic. Your hybrid direction is its own path. --- ## 17. User that's good to know gaussian splatting has improved. i wish my hybrid direction wasn't its own path -- we gotta merge downscale and big data some day and it's just time until it happens. i'm sure the models figure it out on their own over and over and over again. yess! maddness and bolt are exactly what i was talking about. i really didn't believe this paper because the repository didn't look buildable to me. but you say something called LUT-NN has improved the concept even further? what was the technique the paper was built around -- discussing it was central to how they related their algorithm? --- ## 18. AI The core was **product quantization (PQ)**, a technique from nearest-neighbor search (Jégou et al., 2011). You split each vector into small chunks and learn a handful of prototype vectors per chunk from real data. Once you've done that, the dot products between the prototypes and the other matrix can be precomputed into tables. Their key twist was on the encoding step. Classic PQ finds the nearest prototype by computing distances, which costs multiplies. MADDNESS replaced that with **learned hash functions**: small balanced binary trees that route each chunk to a prototype using only comparisons. Encoding becomes a few branches and table lookups, so almost no arithmetic is left. **LUT-NN** (2023) pushed the idea into whole neural networks. MADDNESS learned its prototypes on a fixed, already-trained layer. LUT-NN makes the prototypes trainable with backprop, so the network learns to live with the lookup tables, which recovers most of the lost accuracy. On merging downscale and big data, I'm with you. Learned lookup tables are one of the bridges already being built. --- ## 19. User cool they made lut-nns! has anybody trained or distilled a large language model onto a lut-nn and publicly shared it in like transformers, llamacpp, or ollama? --- ## 20. AI Not in the full LUT-NN sense, as far as I could find. There are two close cousins, though. **LUT-LLM (late 2025)** is the nearest match. It's an FPGA accelerator that runs a 1B+ language model with memory-based computation using vector quantization, and it found that quantizing activations and weights together into vectors worked best. It explicitly builds on LUT-NN's differentiable codebook learning, and the team built a training recipe that converts existing models to table lookups, prototyped on Qwen 3 1.7B. It's research hardware, though, not something on Ollama. **T-MAC and bitnet.cpp** are the ones you can actually run. T-MAC groups low-bit weights, precomputes all their possible partial sums into tables, and uses shift-and-accumulate instead of multiply-add. Its kernels were integrated into llama.cpp for prefill, and it reaches 11 tokens/sec for a 3B BitNet model on a Raspberry Pi 5. Microsoft's bitnet.cpp is built on llama.cpp using T-MAC's lookup-table method. The gap: T-MAC's tables index weight bit patterns, which is exact. The MADDNESS/LUT-NN trick learns clusters of *activations* from data, which is approximate. A distilled LLM using data-learned activation prototypes, packaged for llama.cpp, seems to be an open slot. It could be a nice home for your "fit the common regions" instinct someday.