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Earlier sources: AI infrastructure
AI infrastructure comprises the compute, storage, serving systems, and operational tools used to train and deploy models. Follow inference engines, distributed training, observability, and the costs and constraints of running AI workloads.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗41 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- GitHub · Repository created · First archived
andrei-borcea/liyab ↗
🔥 Run large open language models on your phone. A C++20 GGUF inference engine that streams models larger than RAM from flash, paces itself by the device's thermal forecast, and powers a private on-device assistant. Android · iOS · macOS · Linux.
- GitHub · Repository created · First archived
loopai-hq/pulsar ↗
Pulsar (formerly fastkernel): the fastest inference engine for Qwen3.8-27B and Qwen3.6-35B-A3B on Apple Silicon
- GitHub · Repository created · First archived
VecSzn/PolyStrata ↗
Run large AI models on your own PC: Qwen3.8-Flash-Next, GLM 5.3 Flash, Qwen3.6, Ornith 1.5 and Gemma 4. One-click install for Windows and Linux, OpenAI/Anthropic API on localhost, image input. Built on Strata.
- arXiv · Published · First archived
LBA-CBF: Rapidly Adaptive Safety Filters via Parallel Dynamics Inference ↗
Control barrier functions (CBFs) certify commands through an assumed dynamics model, so an abrupt, unmeasured regime change can undermine the certificate exactly when safety matters most. We present Look-Back Adaptive Control Barrier Functions (LBA-CBF), which rank a finite bank of candidate dynamics by recent prediction error over a short look-back window and enforce the high-order CBF condition against every model within a tolerance of the best, spanning best-fit adaptation to full-bank robust filtering. The dynamics may depend nonlinearly on the unknown parameters, and no switching model or…
- arXiv · Published · First archived
Prediction-powered inference for time series across space ↗
The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period. We have access to unlabeled covariates over a longer time period. Data is observed over many spatial locations. For instance, crop yield might be observed over a large geographical area for recent years, but weather data (which is informative about crop yield) is available for a much longer period. The goal is to estimate, at each spatial location, the expected label (e.g., crop yield) in the future and provide a valid confidence interva…
- GitHub · Repository created · First archived
joshuaswarren/omarchy-cluster ↗
Run one local MLX model across Apple Silicon Macs on macOS and on Omarchy Linux, served as one OpenAI-compatible endpoint
- GitHub · Repository created · First archived
Yuepixel/tensorfold-qwen38-exl3-lookup ↗
Qwen3.8-Flash-Next EXL3 3.05bpw on ONE DGX Spark (GB10): ~2.4x prefill (700->1777 t/s), prompt-lookup drafter, native vision/video, 262k ctx. Unofficial TensorFold v0.6.5 fork, 18 patches, byte-exact receipts.
- arXiv · Published · First archived
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding ↗
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy variables to identify effects under hidden confounding. However, nonparametric proximal estimation can be challenging in practice: recovering causal estimands such as the conditional average treatment effect (CATE) requires solving an ill-posed integral equation that is data-hungry, hyperparameter-sensitive, and optimization-unstable. Bayesian inference for such models provides a desirable alternative, mitigating these…
- OpenAlex · Published · First archived
CAMEO: Compressive Energy-Aware Multi-Task Deep Inference in Resource-Constrained Energy Harvesting Systems ↗
Multi-task Convolutional Neural Network (CNN) inference on Energy Harvesting (EH) devices has received limited attention, particularly in scenarios where energy availability fluctuates significantly. This poses substantial implementation challenges, especially in deploying and running inference under varying energy conditions while performing multiple correlated tasks. In this work, we present an end-to-end framework that enables multi-task inference to inherently conserve computation under dynamic energy conditions. To facilitate this adaptation in multi-task settings, we propose inter-task a…
- Hacker News · Posted on Hacker News · First archived
The-engineering-behind-LLM-inference ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about AI infrastructure
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.