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Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction
MarkTechPost tutorial implements a hierarchical NeRF in JAX using jax3d volume-rendering primitives for novel-view synthesis and 3D reconstruction.
The tutorial builds an end-to-end hierarchical Neural Radiance Field using JAX, Flax, Optax, and jax3d's volume-rendering functions (sample_along_rays, volume_rendering, sample_piecewise_constant_pdf). It implements positional encoding, skip connections, separate coarse and fine networks, and view-direction conditioning with hierarchical importance sampling. Training uses JAX JIT compilation, Adam optimization, exponential learning-rate decay, and gradient clipping. Evaluation covers PSNR, depth and opacity visualization, 360-degree rendering, and marching-cubes geometry extraction.
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Axoflow Launches AxoDetect, Bringing Detection Into the Pipeline and Making the SIEM Optional
Axoflow launches AxoDetect in early access, running customer Sigma rules in the data pipeline to cut SIEM ingest costs and make full SIEM feeding optional.
Axoflow announced AxoDetect, now in early access and unveiled at Splunk .conf26, which runs customer Sigma rules directly in the security data pipeline on normalized data. Alerts travel to the SIEM while full-fidelity logs land in AxoLake, a low-cost on-prem-capable security data lake. The company cites a global industrial company cutting SIEM costs 50% and mean time to resolution 85%, and a government agency cutting data volume 80%.
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Axoflow Launches AxoDetect, Bringing Detection Into the Pipeline and Making the SIEM Optional
Axoflow's AxoDetect enters early access, executing Sigma rules in-stream so only alerts reach the SIEM while full-fidelity logs land in the AxoLake data lake.
Axoflow released AxoDetect in early access, announced at Splunk .conf26, which runs customer Sigma detection rules in-stream on normalized security data before SIEM ingestion. Alerts travel to the SIEM while full-fidelity logs land in AxoLake, the company's low-cost security data lake that also runs on-premises. Vendor-cited outcomes include 50% lower SIEM spend and 85% faster resolution at an industrial company, and 80% data volume reduction at a government agency.
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Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks
Sakana AI's PC-ALM adds per-layer Lagrange multipliers to predictive coding, matching backprop on networks up to 1000 layers with layer-local updates.
Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM), a training method that keeps every update layer-local while recovering backprop-aligned credit signals. The team proves multipliers converge to exact backprop adjoints in linear networks and trains 1000-layer residual MLPs on MNIST within about 2 points of backprop accuracy. PC-ALM matched backprop across a width/depth grid from 8 to 128 on MNIST and Fashion-MNIST where standard predictive coding failed in deep, narrow networks, and improved over PC on ResNet-18 with CIFAR-10 and Tiny ImageNet. An MIT-licensed JAX reference implementation reproduces the results on CPU.
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Viggle/Viggle-Animate — new model trending #28 on Hugging Face
Viggle released Viggle-Animate, a 33.1B MiniMax-H3 finetune replacing video characters from one repainted frame, rendering 124 frames in 26 seconds on one GPU.
Viggle-Animate replaces the character in a video using only a driving video and one of its own repainted frames, with no pose estimator, segmentation mask, face tracker, or text encoder. It is a 33.1B full finetune of MiniMax-H3's ref2va transformer, jointly distilled with DMD across two teachers split by noise level, so rendering takes three forward passes per clip. On a B200 GPU it renders 124 frames in 26 seconds, 6.1x faster per clip than Wan2.2-Animate-14B in matched comparisons. The method assumes no person-specific representation, so it generalizes beyond humans; a demo, research write-up, and ComfyUI nodes are available.
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February 2026 CVE Landscape: 13 Critical Vulnerabilities Mark 43% Drop from January
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