Updates and recent posts about GPT-5.4..

Posts
Description

Link

@faun shared a link, 10 months ago

FAUN.dev()

Why Is My Docker Image So Big? A Deep Dive with ‘dive’ to Find the Bloat

Docker imagesfor AI often resemble overstuffed suitcases, with a BERT model clocking in at a hefty2.54GB. But trimming them? That’s the key to lightning-fast deployments and lower cloud bills. TheDivetool is your X-ray vision for peeling back layers and spotting the bloat—like those sneaky, useless .. read more

Link

@faun shared a link, 10 months ago

FAUN.dev()

Kubernetes FinOps 2.0: AI-Powered Cost Optimization with Predictive Scaling

Kubernetes FinOps 2.0 leverages AI to slash cloud costs by40–60%throughpredictive autoscalingandspot instance optimization, ensuring peak performance at a fraction of the price. Transition from reactive cloud-cost management to agile, self-optimizing strategies is key for modern software teams aimin.. read more

Link

@faun shared a link, 10 months ago

FAUN.dev()

How Fortune 500 Companies Are Really Using Kubernetes: Insights from KubeCon London

Platform engineeringis practically the law of the land for cloud-native warriors. Crank upscalability, lighten the load on your talent, and hit that mythical99.9% uptimewith a sprinkling ofOpenTelemetry. Meanwhile, yourGPU utilizationhovers at a pitiful5%. Ouch. That's like having a Ferrari stuck in.. read more

Link

@faun shared a link, 10 months ago

FAUN.dev()

Fixing Developer Experience in Kubernetes: How Klutch Helps Developers Stay Productive

Klutchshreds Kubernetes complexity, handing developers the reins with effortless abstractions. It slyly ridesCrossplane'scoattails too... read more

Link

@faun shared a link, 10 months ago

FAUN.dev()

Unveiling the Truth: Kubernetes as a Panacea or a Myth?

Kubernetespunches well above its weight, doling out scalability and resilience. But it trips over complexity, gulps down resources, and fumbles database migration. Meanwhile,Istioswoops in with swifttraffic managementand crystal-clear observability. Sadly, it can't magic away those pesky database bo.. read more

Link

@anjali shared a link, 10 months, 1 week ago

Customer Marketing Manager, Last9

Docker Status Unhealthy: What It Means and How to Fix It

What Docker’s “unhealthy” status means, why it happens, and how to debug failing containers with clarity and control.

Story

@laura_garcia shared a post, 10 months, 1 week ago

Software Developer, RELIANOID

🚨 We're heading to Paris! 🚨

RELIANOID will be at RAISE Summit 2025 — July 8–9 at the Carrousel du Louvre, where AI visionaries and tech leaders unite to shape the future of artificial intelligence. Why attend? 🔹 Validate your AI strategy with proven frameworks 🔹 Build your AI playbook with insights from pioneers 🔹 Connect with..

Story

@laura_garcia shared a post, 10 months, 1 week ago

Software Developer, RELIANOID

𝗕𝗼𝗼𝘀𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗿𝗶𝗻𝘁 𝗦𝗲𝗿𝘃𝗲𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗛𝗶𝗴𝗵 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴

Managing print services in large enterprises can be challenging — bottlenecks, downtime, and update disruptions impact productivity. That’s why implementing a high availability and load balancing solution is a game changer. At RELIANOID, we’ve developed a step-by-step guide to scale Microsoft Print ..

kb Microsoft Print Server load Balancing

Story

@laura_garcia shared a post, 10 months, 1 week ago

Software Developer, RELIANOID

🚨 24th International Congress on Industrial Cybersecurity – LATAM 2025

📍 Medellín, Colombia 📅 July 8–9, 2025 We’re proud to announce that RELIANOID will be participating in the most important event on Industrial Cybersecurity in Latin America: CCIcon 2025! 🛡️ Under the theme "Building the Future of Industrial Cybersecurity", the congress brings together experts, profes..

Link

@anjali shared a link, 10 months, 1 week ago

Customer Marketing Manager, Last9

Logging in Docker Swarm: Visibility Across Distributed Services

Know how to access, troubleshoot, and centralize logs in Docker Swarm for better visibility into your distributed services.

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.