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Earlier sources: Hardware
Hardware is the physical foundation of computing: chips, sensors, devices, networks, and the processes that manufacture them. Follow semiconductor research, compute architectures, and engineering developments alongside their software and energy connections.
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 ↗527 classified signals in the fourteen-day window. Recent momentum: steady. 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.
- Hacker News · Posted on Hacker News · First archived
Show HN: Pluto AI model, run tiny MCUs hardware control of natural language ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Surface Laptop Ultra ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
AI agent designs a complete RISC-V CPU from a 219-word spec sheet in 12 hours ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Microsoft event debuts new AI-friendly hardware and Windows changes ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
SERV – The SErial RISC-V CPU ↗
Hacker News discussion.
- GitHub · Repository created · First archived
Phocinae/Phocinae-Largha-150M-v1 ↗
斑海豹 — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. 小海豹，大决断。
- OpenAlex · Published · First archived
Machine Learning-Based Evil Twin Attack Detection and Attacker Position Classification Using an IoT Sensor Network in Non-Urban Terrain ↗
The basis for this work is the growing prevalence of the Evil Twin attack on IEEE 802.11 networks, as it is the initial link in downgrade attacks that force the security protocol from WPA3 to WPA2 in transition mode, enabling handshake interception and an offline dictionary attack on the network password. For the study, a Wi-Fi access point and a device simulating the Evil Twin attack were deployed, and a multi-sensor Internet of Things (IoT) infrastructure was developed to record the received signal strength indicator (RSSI) from the access point. The resulting RSSI dataset was used to train…
- OpenAlex · Published · First archived
Sensor-aware physical modelling for deep learning-based single-photon imaging ↗
Deep learning-based single-photon imaging commonly relies on synthetic time-resolved histograms with incompletely specified sensing physics. We present a sensor-aware framework that represents the source–receiver response, spatially varying background, detector artifacts, and first-detection acquisition within a family of photon measurement operators. A controlled cross-condition protocol independently varies the sensing operators used for training and testing, enabling systematic evaluation of sensing-model mismatch across four representative reconstruction networks. An operator-induced risk…
- OpenAlex · Published · First archived
Evaluating generative large language models as zero-shot semantic odor descriptor generators with applications to odor sensor prediction ↗
Abstract Predicting odor quality from molecular structure remains a fundamental challenge in sensory science. In this study, we evaluated generative large language models (LLMs) as tools for olfactory prediction across multiple tasks. By assigning LLMs distinct cultural personas through role-play prompting, we demonstrate that odorant concentration modulates LLM-generated odor quality outputs in a zero-shot setting. This represents a form of contextual sensitivity absent in structure-based approaches. A systematic evaluation of prompt design revealed that the chemical name is the primary carri…
- OpenAlex · Published · First archived
EnCost: uplink-cost-aware cluster-head selection with sink-trajectory look-ahead for energy-efficient mobile-sink wireless sensor networks ↗
In a mobile-sink wireless sensor network (WSN), the cluster-head (CH)-to-sink uplink periodically enters the distance-fourth-power multipath regime, where electing a CH far from the sink incurs a disproportionate energy penalty. We propose EnCost-CR , a closed-form CH-selection rule that scores each node by its residual energy minus the modelled energy of the uplink it would pay as a head, instead of the normalized distance used by existing energy- and distance-aware rules. We are explicit that this is cost accounting carried out before the decision, not prediction: it uses the sink position a…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Hardware
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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