Join us

ContentUpdates and recent posts about Arti..
Link
@kala shared a link, 1 month, 3 weeks ago
FAUN.dev()

Why open source may not survive the rise of generative AI

Generative AI is snapping the attribution chain thatcopyleft licenseslike theGNU GPLrely on. Without clear provenance, license terms get lost. Compliance? Forget it. The give-and-take that powersFOSSstops giving - or taking... read more  

Why open source may not survive the rise of generative AI
Link
@kala shared a link, 1 month, 3 weeks ago
FAUN.dev()

I regret building this $3000 Pi AI cluster

A 10-node Raspberry Pi 5 cluster built with16GB CM5 Lite modulestopped out at325 Gflops- then got lapped by an $8K x86 Framework PC cluster running4x faster. On the bright side? The Pi setup edged out in energy efficiency when pushed to thermal limits. It came with160 GB total RAM, but that didn’t h.. read more  

I regret building this $3000 Pi AI cluster
Link
@kala shared a link, 1 month, 3 weeks ago
FAUN.dev()

Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference

Amazon rolled out fine-tuning and distillation forVision LLMslike Nova Lite viaBedrockandSageMaker. Translation: better doc parsing—think messy tax forms, receipts, invoices. Developers get two tuning paths:PEFTor full fine-tune. Then choose how to ship:on-demand inference (ODI)orProvisioned Through.. read more  

Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference
Link
@kala shared a link, 1 month, 3 weeks ago
FAUN.dev()

What Significance Testing is, Why it matters, Various Types and Interpreting the p-Value

Significance testing determines if observed differences are meaningful by calculating the likelihood of results happening by chance. The p-value indicates this likelihood, with values below 0.05 suggesting statistical significance. Different tests, such as t-tests, ANOVA, and chi-square, help analyz.. read more  

Link
@kala shared a link, 1 month, 3 weeks ago
FAUN.dev()

Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

Generative recommender systems need more than just observed user behavior to make accurate recommendations. Introducing A-SFT algorithm improves alignment between pre-trained models and reward models for more effective post-training... read more  

Link
@devopslinks shared a link, 1 month, 3 weeks ago
FAUN.dev()

A FinOps Guide to Comparing Containers and Serverless Functions for Compute

AWS dropped a new cost-performance playbook pittingAmazon ECSagainstAWS Lambda. It's not just a tech choice - it’s a workload strategy. Go containers when you’ve got steady traffic, high CPU or memory needs, or sticky app state. Go serverless for spiky, event-driven bursts that don’t need a long lea.. read more  

A FinOps Guide to Comparing Containers and Serverless Functions for Compute
Link
@devopslinks shared a link, 1 month, 3 weeks ago
FAUN.dev()

How and Why Netflix Built a Real-Time Distributed Graph -  Ingesting and Processing Data Streams at Internet Scale

Netflix built a Real-Time Distributed Graph (RDG) to connect member interactions across different devices instantly. Using Apache Flink and Kafka, they process up to1 millionmessages per second for node and edge updates. Scaling Flink jobs individually reduced operational headaches and allowed for s.. read more  

Link
@devopslinks shared a link, 1 month, 3 weeks ago
FAUN.dev()

What is autonomous validation? The future of CI/CD in the AI era

CircleCI droppedautonomous validation, a smarter CI/CD that thinks on its feet. It scans your code, predicts breakage, runs only the tests that matter - and fixes the easy stuff on its own. If things get messy, it hands off full context so you’re not digging through logs. Bonus: it keeps learning fr.. read more  

What is autonomous validation? The future of CI/CD in the AI era
Link
@devopslinks shared a link, 1 month, 3 weeks ago
FAUN.dev()

Jump Starting Quantum Computing on Azure

Microsoft just pulled off full-stack quantum teleportation withAzure Quantum, wiring up Qiskit and Quantinuum’s simulator in the process. Entanglement? Check. Hadamard and CNOT gates set the stage. Classical control logic wrangles the flow. Validation lands cleanly on the backend... read more  

News FAUN.dev() Team
@kala shared an update, 1 month, 3 weeks ago
FAUN.dev()

FSF Talks GPL Compliance and AI Code at GNU Cauldron

The FSF's Licensing and Compliance Lab engaged with GNU toolchain maintainers at GNU Cauldron to discuss GPL compliance, AI-generated code, and attribution in containerized environments.

FSF Talks GPL Compliance and AI Code at GNU Cauldron
Arti is an official Tor Project initiative to rewrite the Tor client stack in Rust. Its primary goal is to address long-standing safety, reliability, and maintainability challenges inherent in the legacy C-based Tor implementation. By leveraging Rust’s strong compile-time guarantees for memory safety and concurrency, Arti eliminates entire classes of bugs that have historically affected Tor, including many security vulnerabilities.

Arti is architected as a modular, embeddable library rather than a monolithic application. This makes it easier for developers to integrate Tor networking capabilities directly into other applications, services, and platforms. From its earliest versions, Arti has supported multi-core cryptography, cleaner APIs, and a more maintainable internal design.

While early releases focused on client functionality such as bootstrapping, running as a SOCKS proxy, and routing traffic over the Tor network, the long-term roadmap includes full feature parity with the existing Tor client, support for onion services, anti-censorship mechanisms, and eventually Tor relay functionality. Arti represents the future foundation of the Tor ecosystem, prioritizing long-term security, developer velocity, and adaptability.