THE INTERNET IS THINKING
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 ↗538 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: Efildr – a reflective EFI loader for PE32 UEFI binaries ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
IBM Debuts Brain-Inspired Chip for Speedy, Efficient AI (2023) ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
ESP-SDR Firmware Turns ESP32 into a 2.4/5 GHz Software-Defined Radio (SDR) ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
j23h67/compute-multipliers-checkpoints — model update ↗
# Pretraining progress is mostly coming from data: checkpoints, data and code The trained models, evaluation data and code behind [*Pretraining progress is mostly coming from data*](https://www.dwarkesh.com/p/pretraining-progress-is-mostly-data) (Dwarkesh Patel and Jerry Han, 2026). How much of the progress in pretraining from 2019 to 2025 has come from data versus model improvements? For each year we take a year-representative open model recipe (GPT-2 to OLMo-2) and a year-representative public data corpus (OpenWebText to Ultra FineWeb), and train combinations of them from scratch at compute
- OpenAlex · Published · First archived
Real-Time Particle Filter Localization on a Cortex-M7 Microcontroller: A Zephyr and Micro-ROS Implementation ↗
Resource-constrained robotic platforms require localization estimators with predictable computational and memory costs. This paper presents the implementation and experimental characterization of a compact hybrid Extended Kalman Filter (EKF)–particle estimator on a single STM32H745 Cortex-M7 core under Zephyr RTOS and micro-ROS, distributed by STMicroelectronics, the Linux Foundation, and eProsima, respectively. This study originates from an existing autonomous-vehicle prototype whose localization pipeline runs on an NVIDIA Jetson platform and comprises two estimators in cascade: a 15-state po…
- OpenAlex · Published · First archived
Machine learning driven replication free storage optimization for wearable healthcare sensors ↗
Wearable sensors (WS) are becoming an important tool for detecting and monitoring physical changes in humans, which, together with improved diagnostic techniques and clinical procedures, represents an important development in the clinical field. The information obtained from these sensors is dynamic and dependent on human activities, making efficient storage crucial for subsequent analysis. However, if data quality management is not properly handled, strategic process failures can reach up to 40%. At the same time, scalability concerns can make it difficult to scale data processing without aff…
- Hacker News · Posted on Hacker News · First archived
Show HN: Local pretrained classifiers, GPU not needed ↗
I was curious about what classifiers could do. It turns out, a lot, sometimes even play Doom. Jeffy is currently a collection of simple classifiers that can do spam detection, intent routing, topic classification, sentiment analysis and even play Doom Jeffy works locally on a regular computer, no GPU needed. It comes with 13 already-trained classifiers, and you can also use Jeffy to train your own classifiers And there’s a lot that can be built o…
- arXiv · Published · First archived
Sum-of-squares compilation of matrix-model dynamics for NISQ and fault-tolerant hardware ↗
The SU$(N)$ matrix-model potential $V=-\tfrac{g^2}{4}\sum_{I,J}Tr[X_I,X_J]^2$, the interaction of the BFSS/BMN family, whose thermal large-$N$ states are dual to black holes, is diagonal in the coordinate basis and is compiled in the literature as a phase polynomial with $O(d^2N^4Q^4)$ rotations per Trotter step. That cost is close to optimal for its gate class: we show that for a generic Trotter step the Walsh support $m_{\min}$ lower-bounds both the rotation count and the two-qubit count of every circuit over $\{CNOT,CZ,CPhase,R_{ZZ},SWAP,X,R_z\}$, ancillas included, and we build a parity ne…
- Hacker News · Posted on Hacker News · First archived
Vx – One Language, Every Chip ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
FSF sponsors GNU Boot, a libre, ethical replacement for the nonfree BIOS or UEFI ↗
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 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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.