Updates and recent posts about GPT..

Posts
Description

Activity

@goutham-annem started using tool Amazon ECS , 1 week, 1 day ago.

Activity

@eon01 gave 🐾 to The unwritten laws of software engineering , 1 week, 1 day ago.

Link

@varbear shared a link, 1 week, 1 day ago

FAUN.dev()

Build and Deploy a Remote MCP Server to GKE in 30 Minutes

Google walks you through shipping a remoteMCP serveronGKE AutopilotusingFastMCPandstreamable-http, swapping localstdiofor shared HTTP endpoints. The clever bit: theGateway APIhandles managed SSL plusCLIENT_IP session affinity, so one centralized server beats everyone running redundant local copies... read more

Link

@varbear shared a link, 1 week, 1 day ago

FAUN.dev()

How building an HTML-first site doubled our users overnight

Building HTML-first forms using Astro instead of React dramatically increased completion rates and sustainability, highlighting the effectiveness of lightweight, accessible web components for all users, regardless of browser or connectivity... read more

Link

@varbear shared a link, 1 week, 1 day ago

FAUN.dev()

The unwritten laws of software engineering

- Always related - first rollback, then debug. - Backups aren’t real until restored. - You’ll hate yourself for bad logs. - ALWAYS have a rollback plan. - Every external dependency will fail. - If there's risk, use the “4 eyes” rule. - Nothing lasts like a temporary fix... read more

Link

@varbear shared a link, 1 week, 1 day ago

FAUN.dev()

Google hits 50% IPv6

The 50% IPv6 milestone is real, but adoption differs by country. Analysts who report lower figures use population-weighted sampling, while their per-country adoption rates match the higher estimate... read more

Link

@varbear shared a link, 1 week, 1 day ago

FAUN.dev()

Building in the Age of Collaborative Coding

The speed of innovation is crucial for teams, and AI tools have enabled faster work. A collaborative coding model where teams build, review, and ship alongside AI agents is key to staying ahead in workflows. Three shifts have reshaped how teams build, leading to the adoption of a new collaborative c.. read more

Link

@kaptain shared a link, 1 week, 1 day ago

FAUN.dev()

Tigera introduces unified control plane for Kubernetes-based AI agent security

Tigera launched Lynx for general availability, a Kubernetes-native control plane that operators place in the path of AI agent calls so teams can enforce identity and policy... read more

Link

@kaptain shared a link, 1 week, 1 day ago

FAUN.dev()

How Netflix Simplified Batch Compute with Kueue

Netflix migratedmillions of batch jobsfrom their custom queuing system toKueue, a cloud-native job queueing system, as part of transitioning to a more Kubernetes-native infrastructure. Kueue offers features such as preemption, fair sharing, and hierarchical tenants that were missing in their homegro.. read more

Link

@kaptain shared a link, 1 week, 1 day ago

FAUN.dev()

When failover isn’t safe: Building high-availability PostgreSQL on Kubernetes

Datadog made PostgreSQL failover safer by treating replica lag as the promotion gate. A zonal-failure gameday showed that detection and automation could not protect the database if the standby sat behind the primary. The team added lag-aware checks, clearer operator signals, and failure drills so en.. read more

GPT (Generative Pre-trained Transformer) is a deep learning model developed by OpenAI that has been pre-trained on massive amounts of text data using unsupervised learning techniques. GPT is designed to generate human-like text in response to prompts, and it is capable of performing a variety of natural language processing tasks, including language translation, summarization, and question-answering. The model is based on the transformer architecture, which allows it to handle long-range dependencies and generate coherent, fluent text. GPT has been used in a wide range of applications, including chatbots, language translation, and content generation.

GPT is a family of language models that have been trained on large amounts of text data using a technique called unsupervised learning. The model is pre-trained on a diverse range of text sources, including books, articles, and web pages, which allows it to capture a broad range of language patterns and styles. Once trained, GPT can be fine-tuned on specific tasks, such as language translation or question-answering, by providing it with task-specific data.

One of the key features of GPT is its ability to generate coherent and fluent text that is indistinguishable from human-generated text. This is achieved by training the model to predict the next word in a sentence given the previous words. GPT also uses a technique called attention, which allows it to focus on relevant parts of the input text when generating a response.

GPT has become increasingly popular in recent years, particularly in the field of natural language processing. The model has been used in a wide range of applications, including chatbots, content generation, and language translation. GPT has also been used to create AI-generated stories, poetry, and even music.