# layer

Published articles for layer.

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## A guide to Transformer architecture in modern language models

DevFeed: [A guide to Transformer architecture in modern language models](<https://devfeed.tech/articles/a-deep-dive-into-the-transformer-architecture-33578.md>)

Original publisher: [Read original article](<https://blog.algomaster.io/p/transformer-architecture>)

Author: Ashish Pratap Singh

Published: 2026-05-14T04:15:11Z

Content type: tutorial

Language: en

Sources: [AlgoMaster Newsletter](<https://devfeed.tech/sources/algomaster-newsletter.md>)

Topics: [Transformer architecture](<https://devfeed.tech/topics/transformer-architecture.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>)

Tags: [architecture-pattern](<https://devfeed.tech/tags/architecture-pattern.md>), [better](<https://devfeed.tech/tags/better.md>), [deep-dive](<https://devfeed.tech/tags/deep-dive.md>), [layer](<https://devfeed.tech/tags/layer.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [parallel](<https://devfeed.tech/tags/parallel.md>), [performance](<https://devfeed.tech/tags/performance.md>), [semantics](<https://devfeed.tech/tags/semantics.md>), [sequence](<https://devfeed.tech/tags/sequence.md>), [syntax](<https://devfeed.tech/tags/syntax.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

This tutorial explains the Transformer architecture, including its original encoder-decoder design for translation and the decoder-only variant used for modern language generation. It describes decoder components such as masked multi-head self-attention, feed-forward networks, layer normalization, and residual connections, and introduces the Pre-LayerNorm pattern.

### Source excerpt

A single 2017 research paper changed the future of AI forever and gave rise to multiple unicorn companies.

## Microservices Deep Dive: Architecting for Scalability and Resilience

DevFeed: [Microservices Deep Dive: Architecting for Scalability and Resilience](<https://devfeed.tech/articles/microservices-deep-dive-architecting-for-scalability-and-resilience-39562.md>)

Original publisher: [Read original article](<https://ankit-rana.com/logs/10-microservices-architecture/>)

Author: hello@ankit-rana.com

Published: 2026-03-16T00:00:00Z

Content type: tutorial

Language: en

Sources: [Ankit Rana | Mechanical Sympathy](<https://devfeed.tech/sources/ankit-rana-mechanical-sympathy.md>)

Topics: [microservices architecture](<https://devfeed.tech/topics/microservices-architecture.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Domain-driven design (DDD)](<https://devfeed.tech/topics/domain-driven-design.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [deployments](<https://devfeed.tech/tags/deployments.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [domain-driven-design-ddd](<https://devfeed.tech/tags/domain-driven-design-ddd.md>), [incident-response](<https://devfeed.tech/tags/incident-response.md>), [independent](<https://devfeed.tech/tags/independent.md>), [isolation](<https://devfeed.tech/tags/isolation.md>), [layer](<https://devfeed.tech/tags/layer.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [observability](<https://devfeed.tech/tags/observability.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [services](<https://devfeed.tech/tags/services.md>), [system-design](<https://devfeed.tech/tags/system-design.md>)

### AI overview

This article explains how microservices replace a tightly coupled monolith with independently deployable services aligned to business capabilities. It covers benefits such as independent scaling, fault isolation, team autonomy, and technology flexibility, while noting the added costs of communication, observability, operations, and data management. It recommends using microservices when domain complexity and scale justify the operational cost, organizing services around bounded contexts, and extracting one domain at a time.

### Source excerpt

Microservices trade deployment simplicity for independent scaling, fault isolation, and team autonomy, and they only pay off once domain complexity and scale justify the operational cost. Align services to bounded contexts, give each its own data store, and extract one domain at a time rather than splitting by technical layer.

## RCCLX: Innovating GPU Communications on AMD Platforms

DevFeed: [RCCLX: Innovating GPU Communications on AMD Platforms](<https://devfeed.tech/articles/rcclx-innovating-gpu-communications-on-amd-platforms-30493.md>)

Original publisher: [Read original article](<https://engineering.fb.com/2026/02/24/data-center-engineering/rrcclx-innovating-gpu-communications-amd-platforms-meta/>)

Author: Sudharssun Subramanian; Subodh Iyengar; Cen Zhao; Srinath Bayareddy; James Hongyi Zeng

Published: 2026-02-24T21:30:54Z

Content type: article

Language: en

Sources: [Meta AI Research](<https://devfeed.tech/sources/meta-ai-research.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [communications](<https://devfeed.tech/topics/communications.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [communications](<https://devfeed.tech/tags/communications.md>), [data-center-engineering](<https://devfeed.tech/tags/data-center-engineering.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [latency](<https://devfeed.tech/tags/latency.md>), [layer](<https://devfeed.tech/tags/layer.md>), [meta](<https://devfeed.tech/tags/meta.md>), [ml-applications](<https://devfeed.tech/tags/ml-applications.md>), [networking-traffic](<https://devfeed.tech/tags/networking-traffic.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [parallelism](<https://devfeed.tech/tags/parallelism.md>)

### AI overview

Meta describes the initial open-source release of RCCLX, an enhanced version of RCCL for AMD platforms integrated with Torchcomms. The article presents Direct Data Access algorithms and Low Precision Collectives, including approaches intended to reduce communication latency during large language model inference.

### Source excerpt

We are open-sourcing the initial version of RCCLX - an enhanced version of RCCL that we developed and tested on Meta's internal workloads. RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend. Communication patterns for AI models are constantly evolving, as are hardware [...] Read More... The post RCCLX: Innovating GPU Communications on AMD Platforms appeared first on Engineering at Meta.

## Launching ScreenshotWhale.com 🐋

DevFeed: [Launching ScreenshotWhale.com 🐋](<https://devfeed.tech/articles/launching-screenshotwhale-com-37919.md>)

Original publisher: [Read original article](<https://newsletter.jorgecastillo.dev/p/launching-screenshotwhalecom>)

Author: Jorge Castillo

Published: 2025-09-13T15:03:52Z

Content type: release

Language: en

Sources: [Effective Android](<https://devfeed.tech/sources/effective-android.md>)

Topics: [App](<https://devfeed.tech/topics/app.md>), [Android](<https://devfeed.tech/topics/android.md>), [iOS](<https://devfeed.tech/topics/ios.md>), [Internationalization (i18n)](<https://devfeed.tech/topics/i18n.md>), [export](<https://devfeed.tech/topics/export.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [app](<https://devfeed.tech/tags/app.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [editor](<https://devfeed.tech/tags/editor.md>), [export](<https://devfeed.tech/tags/export.md>), [i18n](<https://devfeed.tech/tags/i18n.md>), [image](<https://devfeed.tech/tags/image.md>), [ios](<https://devfeed.tech/tags/ios.md>), [layer](<https://devfeed.tech/tags/layer.md>), [screenshots](<https://devfeed.tech/tags/screenshots.md>), [templates](<https://devfeed.tech/tags/templates.md>)

### AI overview

ScreenshotWhale has launched as a cloud-based tool for creating app screenshots. It includes an online editor, templates, multilingual export, support for iOS and Android form factors, a mirror feature, app store layout settings, and layered editing.

### Source excerpt

The wait is over, screenshotwhale is live today 🎉

## Hello Deep Learning: Reading handwritten digits

DevFeed: [Hello Deep Learning: Reading handwritten digits](<https://devfeed.tech/articles/hello-deep-learning-reading-handwritten-digits-36420.md>)

Original publisher: [Read original article](<https://berthub.eu/articles/posts/handwritten-digits-sgd-batches/>)

Published: 2023-03-30T10:00:04Z

Content type: tutorial

Language: en

Sources: [Bert Hubert's writings](<https://devfeed.tech/sources/bert-hubert-s-writings.md>)

Topics: [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Image](<https://devfeed.tech/topics/image.md>)

Tags: [blog-posts](<https://devfeed.tech/tags/blog-posts.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [images](<https://devfeed.tech/tags/images.md>), [layer](<https://devfeed.tech/tags/layer.md>), [matrices](<https://devfeed.tech/tags/matrices.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [pixels](<https://devfeed.tech/tags/pixels.md>), [spatial](<https://devfeed.tech/tags/spatial.md>)

### AI overview

A tutorial in the Hello Deep Learning series explains how to build a neural network that recognizes all ten handwritten digits. It describes flattening 28x28 images, applying matrix multiplications and ReLU layers, and selecting the highest-scoring output.

### Source excerpt

This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In the previous chapter we described how automatic differentiation of the result of neural networks works. In the first and second chapters we designed and trained a one-layer neural network that could distinguish images of the digit 3 and the digit 7, and the network did so very well.

## How a Neural Network Learns to Classify Images of 3s and 7s

DevFeed: [How a Neural Network Learns to Classify Images of 3s and 7s](<https://devfeed.tech/articles/hello-deep-learning-actually-learning-something-36398.md>)

Original publisher: [Read original article](<https://berthub.eu/articles/posts/first-learning/>)

Published: 2023-03-30T10:00:02Z

Content type: tutorial

Language: en

Sources: [Bert Hubert's writings](<https://devfeed.tech/sources/bert-hubert-s-writings.md>)

Topics: [Learning](<https://devfeed.tech/topics/learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [layer](<https://devfeed.tech/tags/layer.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [pixels](<https://devfeed.tech/tags/pixels.md>)

### AI overview

This tutorial explains how a simple neural network can learn to classify images of 3s and 7s. It introduces random parameter initialization, the role of the weights matrix, and adjusting weights based on image pixels to improve the classification score.

### Source excerpt

This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In this chapter we're going to take the neural network we made earlier, but actually make it do some learning itself. And, oddly enough, this demonstration will again likely simultaneously make you wonder "is this all??" and also impress you by what even this trivial stuff can do.

## Paper summary: End-to-end arguments in system design

DevFeed: [Paper summary: End-to-end arguments in system design](<https://devfeed.tech/articles/paper-summary-end-to-end-arguments-in-system-design-35935.md>)

Original publisher: [Read original article](<https://temporal.io/blog/paper-summary-end-to-end-arguments-in-system-design>)

Author: Dominik Tornow

Published: 2022-08-16T17:00:00Z

Content type: article

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [systems](<https://devfeed.tech/topics/systems.md>), [App](<https://devfeed.tech/topics/app.md>), [modules](<https://devfeed.tech/topics/modules.md>), [context](<https://devfeed.tech/topics/context.md>), [function](<https://devfeed.tech/topics/function.md>), [Transactions](<https://devfeed.tech/topics/transactions.md>), [callback](<https://devfeed.tech/topics/callback.md>)

Tags: [application](<https://devfeed.tech/tags/application.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [components](<https://devfeed.tech/tags/components.md>), [context](<https://devfeed.tech/tags/context.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [function](<https://devfeed.tech/tags/function.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [layer](<https://devfeed.tech/tags/layer.md>), [paper](<https://devfeed.tech/tags/paper.md>), [summary](<https://devfeed.tech/tags/summary.md>), [system-design](<https://devfeed.tech/tags/system-design.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

This article summarizes Saltzer, Reed, and Clark's End-to-End Argument in System Design. It explains how layered architectures place functionality between application and platform layers, emphasizing that some functions require the application layer's complete information and context for complete and correct implementation. It also discusses partial platform implementations and failure analysis.

### Source excerpt

Failure, Failure Detection, Failure Mitigation, Failure Tolerance, Failure Mitigation

## Using Craft CMS as a Headless CMS with the GraphQL API

DevFeed: [Using Craft CMS as a Headless CMS with the GraphQL API](<https://devfeed.tech/articles/using-the-craft-cms-headless-with-the-graphql-api-31317.md>)

Original publisher: [Read original article](<https://nystudio107.com/blog/using-the-craft-cms-graphql-api-on-the-frontend>)

Author: andrew@nystudio107.com (Andrew Welch)

Published: 2020-01-20T05:00:00Z

Content type: tutorial

Language: en

Sources: [nystudio107 | Articles on modern web development.](<https://devfeed.tech/sources/nystudio107-articles-on-modern-web-development.md>)

Topics: [Content Management System](<https://devfeed.tech/topics/cms.md>), [GraphQL](<https://devfeed.tech/topics/graphql.md>), [API](<https://devfeed.tech/topics/api.md>), [Front end](<https://devfeed.tech/topics/frontend.md>), [Self-hosted](<https://devfeed.tech/topics/self-hosted.md>)

Tags: [added](<https://devfeed.tech/tags/added.md>), [api](<https://devfeed.tech/tags/api.md>), [authoring](<https://devfeed.tech/tags/authoring.md>), [cms](<https://devfeed.tech/tags/cms.md>), [craft](<https://devfeed.tech/tags/craft.md>), [formalized](<https://devfeed.tech/tags/formalized.md>), [frontend](<https://devfeed.tech/tags/frontend.md>), [gives](<https://devfeed.tech/tags/gives.md>), [graphql](<https://devfeed.tech/tags/graphql.md>), [headless](<https://devfeed.tech/tags/headless.md>), [here-s](<https://devfeed.tech/tags/here-s.md>), [insights](<https://devfeed.tech/tags/insights.md>), [layer](<https://devfeed.tech/tags/layer.md>), [practical](<https://devfeed.tech/tags/practical.md>), [structured](<https://devfeed.tech/tags/structured.md>), [website](<https://devfeed.tech/tags/website.md>)

### AI overview

This tutorial explains how to use Craft CMS as a headless CMS through its GraphQL API. It discusses the API's structured data access, its use with separate frontends, and the distinction between Craft CMS's first-party implementation and the CraftQL plugin.

### Source excerpt

Craft CMS 3.3 added a GraphQL layer that gives your website a formalized, structured API out of the box. Here's how to use GraphQL + Craft CMS as a "headless" CMS

## Threat Modeling at Layer 8

DevFeed: [Threat Modeling at Layer 8](<https://devfeed.tech/articles/threat-modeling-at-layer-8-37030.md>)

Original publisher: [Read original article](<https://shostack.org/blog/threat-modeling-at-layer-8/>)

Author: Adam

Published: 2019-07-12T00:00:00Z

Content type: opinion

Language: en

Sources: [Shostack & Friends Blog](<https://devfeed.tech/sources/shostack-friends-blog.md>)

Topics: [Application Security](<https://devfeed.tech/topics/application-security.md>)

Tags: [appsec](<https://devfeed.tech/tags/appsec.md>), [layer](<https://devfeed.tech/tags/layer.md>), [podcast](<https://devfeed.tech/tags/podcast.md>)

### AI overview

A discussion of online conflict, including bullying, trolling, and threats, on the AppSec Podcast. The speakers consider how to engineer responses to these problems and invite collaboration.

### Source excerpt

Discussing online conflict on the AppSec Podcast

## Raw sockets in Go: IP layer

DevFeed: [Raw sockets in Go: IP layer](<https://devfeed.tech/articles/raw-sockets-in-go-ip-layer-35498.md>)

Original publisher: [Read original article](<https://darkcoding.net/uncategorized/raw-sockets-in-go-ip-layer/>)

Author: Graham King

Published: 2014-03-01T23:41:48Z

Content type: tutorial

Language: en

Sources: [Graham King](<https://devfeed.tech/sources/graham-king.md>)

Topics: [Go Language](<https://devfeed.tech/topics/go-language.md>), [Internet](<https://devfeed.tech/topics/internet.md>), [Protocol (disambiguation)](<https://devfeed.tech/topics/protocol.md>)

Tags: [encoding](<https://devfeed.tech/tags/encoding.md>), [go](<https://devfeed.tech/tags/go.md>), [ip](<https://devfeed.tech/tags/ip.md>), [layer](<https://devfeed.tech/tags/layer.md>), [protocol](<https://devfeed.tech/tags/protocol.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

A Go tutorial on using raw sockets at the IP layer. It demonstrates receiving ICMP and TCP packets, interpreting headers and ports, sending crafted packets, and using Go's binary-encoding tools.

### Source excerpt

Diving Deep: Exploring Go's IP Layer Capabilities