Join us

ContentUpdates and recent posts about GPT-5.4..
Link
@faun shared a link, 11 months ago
FAUN.dev()

Serving 200 million requests per day with a cgi-bin

UsingGoandRustwith CGI-style requests taps into multi-core CPU might, poking fun at long-held CGI inefficiency myths... read more  

Serving 200 million requests per day with a cgi-bin
Link
@faun shared a link, 11 months ago
FAUN.dev()

OpenYurt Becomes a CNCF Incubating Project

OpenYurt, a CNCF brainchild, shakes up cloud-edge orchestration. It dances with Kubernetes like Fred Astaire and partners with any vendor under the sun... read more  

OpenYurt Becomes a CNCF Incubating Project
Link
@faun shared a link, 11 months ago
FAUN.dev()

Understanding Network Packet Offsets & Safe Parsing in eBPF

eBPFandRustteam up to drive a network packet parser that catches packets at breakneck kernel speed. Welcome to the future of observability and security.XDPsteps in, slicing latency to the bone for real-time inspection... read more  

Understanding Network Packet Offsets & Safe Parsing in eBPF
Link
@faun shared a link, 11 months ago
FAUN.dev()

Atlassian moved 4 million Postgres databases to AWS Aurora

Atlassianpulled off a major coup, relocating 4 million Jira Postgres databases toAWS Aurora. They slashed expenses by taming CPU beasts and carved out a rock-solid 99.99% uptime. A delightful efficiency cocktail. SamsungandTSMCare brooming through some project cobwebs. Samsung's rethinking its Texas.. read more  

Atlassian moved 4 million Postgres databases to AWS Aurora
Link
@faun shared a link, 11 months ago
FAUN.dev()

Hewlett Packard Enterprise completes $14B acquisition of Juniper after settlement of DOJ suit

Hewlett Packard Enterprise closed its acquisition of Juniper Networks following the settlement of a lawsuit by the U.S. Department of Justice. This acquisition will allow HPE to expand its networking business and compete in the AI networking market. HPE officials stated that the merger positions the.. read more  

Link
@faun shared a link, 11 months ago
FAUN.dev()

GitOps Introduction with Argo CD

GitOpsturns deployment upside down. A cunningpull-basedmethod. Tools likeArgo CDautomate app updates by keeping a hawk's eye on Git repos. Toss those convoluted CD pipelines into the trash. If updates stumble—justGit committo roll back. Safe teamwork—no need to touch the cluster... read more  

GitOps Introduction with Argo CD
Link
@faun shared a link, 11 months ago
FAUN.dev()

Building a Cloud Strategy That Delivers

Cloud strategy? It's not about fancy slideshows but shaking up how teams build and deploy. Master new skills. EmbraceSRE practiceslike it's your favorite hobby... read more  

Building a Cloud Strategy That Delivers
Link
@faun shared a link, 11 months ago
FAUN.dev()

Caching is an Abstraction, not an Optimization

Cachingdoes more than rev up performance; it cuts through the chaos of software design, making it tidier and more modular. Sure,LRUandLFUsound like they should open for a prog rock band, but their trusty old formulas stand strong against those wild swings in data access... read more  

Caching is an Abstraction, not an Optimization
Link
@faun shared a link, 11 months ago
FAUN.dev()

Why Kubernetes Throttled My Idle Pods

70% CPU throttlingbaffled me in Kubernetes—minimal CPU usage, yet throttling? Alexandru Lazarev nailed it: ditch the CPU limits. Instant fix. Prometheus paints the spikes, while Grafana smooths them into a bore. Maybe those burstable CPU limits will swoop in to save us soon... read more  

Why Kubernetes Throttled My Idle Pods
Link
@faun shared a link, 11 months ago
FAUN.dev()

Kubernetes complexity killer, Lens by Mirantis embedded AI assistant

Mirantis Lens just got a brain transplant. MeetLens Prism, the AI that slices through Kubernetes like a hot knife through butter—offering real-time insights and commands right in your IDE. Wave goodbye to command-line hell with their slickAWS integration. It blitzes through the setup grind, letting .. read more  

Kubernetes complexity killer, Lens by Mirantis embedded AI assistant
GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.