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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
joesinvestments/GLM-5.3-Flash-Uncensored-Humming-4x-DGX-Spark ↗
Reproducible vLLM v0.30.0 build for Blackfrost's uncensored GLM-5.3-Flash on four DGX Sparks (TP4, NVFP4 attention, DFlash2)
- GitHub · Repository created · First archived
Jwuthri/SelfJev ↗
Open decisions model with Jev's API: typed answers (yes/no, choice, score, multi) with probabilities from forward passes, no generation. Qwen3.5-4B + LoRA, one GPU.
- arXiv · Published · First archived
MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference ↗
Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full precision accuracy. However, applying these methods efficiently in convolutional layers is not straightforward. A naive approach transfers full-precision weights and activations to processing units and quantizes each tensor twice, resulting in much more memory movement than expected. Additional overhead comes from the activation tensors, whose sizes grow substantially because of the im2col transformation applied before quantization. We propose MicroQ…
- GitHub · Repository created · First archived
emnlmn/snap ↗
Typed decisions from unstructured state: one forward pass, zero generated text. Local, deterministic, Jev-compatible. Not affiliated with typesafe.ai.
- GitHub · Repository created · First archived
tamnd/kime ↗
Typed decisions over text in milliseconds. A Rust inference engine and server that answers choice, score and yes or no questions with calibrated probabilities, compatible with Jev and Laya. The bar is 10x faster on every benchmark at equal or better accuracy.
- GitHub · Repository created · First archived
AskTheWay/dsh-jev-interceptor ↗
⚡ Millisecond System-1 judgement for every tool call in DeepSeek Harness — Jev-powered risk classification & evidence-gated auto-approval. Fail-closed by construction. dsh 生态第一个 System-1 决策插件
- GitHub · Repository created · First archived
rferrari/boar-app ↗
BOAR - Adaptive Local Intelligence
- GitHub · Repository created · First archived
tc3oliver/laya-apple ↗
Correctness-validated heterogeneous Laya runtime for Apple Silicon (MLX GPU + Apple Neural Engine)
- GitHub · Repository created · First archived
athrael-soju/Narwhal ↗
Adaptive disaggregated inference on a role-free fleet.
- GitHub · Repository created · First archived
chengyongru/fastjev ↗
SDK-first, independently maintained SemIf fork for fast, self-hosted semantic decisions.
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Related threads
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.