Alternatives
Products that do what Anabolic Mechanics DualCore 3D Printer does
10x Faster 3D Printer
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Commodity FDM print strength is limited by poor Z-axis layer bonding. Parts crack along Z under stress. MAGMA tries to fix this in software that works on any FDM 3D printer. It's a fork of OrcaSlicer with a new infill type that creates paired U-shaped vertical channels inside the print, plus G-code that injects molten plastic into those channels to bridge Z layer interfaces with continuous plastic. Big caveat: I have a junky Ender 3 and haven't gotten a clean physical print yet. Don't expect this to work out of the box! After months of tinkering, I'm releasing the software so the 3DP…
Jun 2026 · mgunlogson.github.io
- 21F
2018 · github.com
- 3PF
Struts, nodes, and pins are reversibly assembled into fully 3D printed lattices, trusses, and tree-like structures spanning up to a few meters. I used the system to build a stand for an overhead table lamp which supports a ~1 m cantilevered arm using a tensioned floor-to-ceiling column. If you want to give it a try, find the *.stl files at the bottom of the page; figure ~1 kg of PLA and ~1 day of print time per meter of box truss; pay attention to print orientation; plz respect the license; and definitely print the pin trimming jig.
2025 · zachfred.in
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- 5IB
2021 · github.com
- 6RT
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com
- 7ID
3D printing is such a fascinating field of technology, so a couple months ago, I decided to take a deep dive and learn how they actually work! This took me to one of my very first PCB projects, a small, cheap, 3D printer motherboard. While it's not the most cutting edge board, I learned a lot and I fully documented my process designing it (https://github.com/KaiPereira/Cheetah-MX4-Mini/blob/master/J...), so other people can learn from my mistakes! It runs off of an STM32H743 MCU, has 4 TMC stepsticks with UART/SPI configurations,…
Dec 2025 · github.com
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- 9FN
forkrun is the culmination of a 10-year-long journey focused on "how to make shell parallelization fast". What started as a standard "fork jobs in a loop" has turned into a lock-free, CAS-retry-loop-free, SIMD-accelerated, self-tuning, NUMA aware shell-based stream parallelization engine that is (mostly) a drop-in replacement for xargs -P and GNU parallel. On my 14-core/28-thread i9-7940x, forkrun achieves: * 200,000+ batch dispatches/sec (vs ~500 for GNU Parallel) * ~95–99% CPU utilization across all 28 logical cores, even when the workload is non-existant (bash no-ops / `:`)…
Mar 2026 · github.com
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Go big, and 3D print massive parts for you next project
2018
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- 17FV
To my knowledge, this is the first formally verified implementation of a 3D constructive solid geometry (CSG) operation: mesh intersection, implemented in Lean 4 and verified against a concise specification that pins down the surface of the resulting mesh exactly and guarantees practical well-formedness conditions on the triangulation. This project is also an experiment in avoiding having to trust AI-generated code. A human reviewer only needs to read 93 lines of formal specification and run the Lean checker to certify the correctness of the kernel, skipping the intricate 1000+ lines of…
Jul 2026 · github.com
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- 21TC
Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…
2022 · tensordock.com
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- 243I
CPU & GPU with Zero Hassle Built with NVIDIA Warp, the same code runs seamlessly on both CPU and GPU — no need to deal with CUDA setup, driver issues, or device-specific kernels. Just flip one config line. Learn Modern Graphics the Easy Way Explore core concepts in differentiable rendering and parallel graphics programming — no need for expensive GPUs or thousands of lines of boilerplate. Minimalist & Educational This isn’t another massive codebase. It’s a clean, hackable implementation built for clarity — perfect for study, prototyping, or teaching yourself how Gaussian Splatting works.
2025 · github.com
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