SShortSingh.
Back to feed

Rheinmetall Open-Sources Battlesuite Connected Weapon System Protocol

0
·3 views

German defense manufacturer Rheinmetall has publicly released the protocol documentation for its Battlesuite onboard API, a connected weapon system interface. The release, published on GitHub Pages, covers version 9.10.0 of the onboard API. Battlesuite is Rheinmetall's framework for networked military vehicle and weapon system integration. By open-sourcing the protocol documentation, Rheinmetall makes its technical specifications accessible to developers and potential partners. The move reflects a broader trend of defense contractors publishing interface standards to encourage ecosystem development.

Read the full story at Hacker News

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

How to Secure Kubernetes Services Using Gateway API, Traefik, and OAuth2 Proxy

A technical guide details how to rebuild Kubernetes service authentication using Gateway API, Traefik, OAuth2 Proxy, and Pocket ID, replacing the older ingress-nginx annotation-based approach. The migration is prompted by ingress-nginx being retired, with Kubernetes now recommending Gateway API for traffic management. The setup exposes three HTTPS hostnames under a single domain, allowing OAuth2 Proxy to manage session cookies with a narrow, scoped domain. Traefik's Middleware CRD handles browser-based OIDC login and external authentication filtering, since Gateway API does not standardize these functions natively. The guide was validated on a local K3s cluster and uses standard Kubernetes and Helm commands, making it adaptable to other environments.

0
ProgrammingDEV Community ·

SetrixDB: Go-based set engine delivers microsecond ID intersection with AVX-512

A developer has built SetrixDB, an open-source set engine written in Go, designed to perform exact set intersections over uint64 identifiers at microsecond speeds. The engine uses a Minimal Perfect Hash Function (CHD v2) to eliminate key collisions, achieving 0 collisions across 50 million keys at just 0.5 bytes per key — far more memory-efficient than a standard Go map at 22.3 bytes per key. Intersection operations are accelerated using AVX-512 SIMD instructions via cgo, with a scalar fallback for broader hardware compatibility. Benchmarks conducted on a 2-vCPU AMD EPYC (Zen4) server show bitset AND operations completing in as little as 6 microseconds with AVX-512, compared to 91.6 milliseconds for a hash join approach. The author acknowledges that SetrixDB underperforms Roaring bitmaps in sparse, large-universe scenarios where memory is constrained, but outperforms them significantly for dense ID sets with random 64-bit identifiers.

0
ProgrammingDEV Community ·

SetrixDB: Go-based set engine uses MPHF and AVX-512 for exact ID intersection

A developer has built SetrixDB, an open-source set engine written in Go, designed to perform exact membership checks and intersections over uint64 identifiers at microsecond speeds. The engine uses a Minimal Perfect Hash Function (CHD v2) to map terms to collision-free uint64 IDs, achieving zero collisions across 50 million keys while consuming just 0.5 bytes per key — roughly 44 times less memory than a standard Go map. Intersection operations are accelerated using AVX-512 SIMD instructions via cgo, with runtime dispatch and a scalar fallback for broader hardware compatibility. Benchmarks show the bitset AND kernel completing in around 6 microseconds with AVX-512, outperforming hash joins and sorted merges by a wide margin for dense ID sets. The author notes clear trade-offs: for sparse or randomly distributed 64-bit ID universes too large to fit in RAM, compressed bitmap libraries like Roaring64 remain more memory-efficient alternatives.

0
ProgrammingDEV Community ·

Developer's 2021 AI Presentation Forecast GenAI Boom 20 Months Before ChatGPT

A software developer gave a presentation on artificial intelligence in March 2021, during a full-stack programming bootcamp, at a time when the tech world was focused on crypto and NFTs. The presentation covered OpenAI's beta platform, the evolution from GPT-2 to GPT-3, and live demos using natural language to generate Excel spreadsheets and functional React interfaces. These capabilities were considered remarkable at the time but have since become commonplace in the industry. The talk also outlined core AI concepts including machine learning, deep learning, supervised and unsupervised learning, and real-world applications such as disease diagnosis and autonomous driving. The developer now reflects on the presentation as a record of the trajectory that led to the generative AI surge that followed ChatGPT's launch in late 2022.