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GPT-5.4

GPT-5.4 is OpenAI’s latest frontier AI model designed for professional and developer workflows. It combines advanced reasoning, strong coding capabilities, and improved tool use into a single system …

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Updates and recent posts about GPT-5.4..

Posts
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Link Xygeni Team

@mashka shared a link, 1 month, 1 week ago

Paid Acquisition and Growth Marketing, xygeni

You don’t have a vulnerability problem. You have a prioritization problem.

Most teams today don’t struggle to find vulnerabilities; they struggle to decide what to fix first. With SAST, SCA, secrets, and CI/CD checks all generating signals, the real challenge is prioritization: what’s actually exploitable, what’s reachable, and what can be fixed without breaking things. Instead of relying only on severity, modern teams are shifting toward risk-based remediation, combining exploitability, context, and stability, while reducing noise across tools and automating safe fixes through PRs. If you’re dealing with alert fatigue or slow remediation cycles, this checklist is a practical starting point → https://go.xygeni.io/ai-driven-remediation-risk-prioritization-checklist

Ai-Driven Checklist

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

FAUN.dev()

A new chapter for the Nix language, courtesy of WebAssembly

Determinate Nix introduces experimental WebAssembly host calls. It lets Nix invoke Wasm modules, pass and return complex Nix values, and support Rust, C++, and Zig toolchains. It runs on Wasmtime/Cranelift and slashes runtime and memory: Fibonacci test 0.33s vs 79.33s, 30MB vs 4.5GB. Per-call instan.. read more

A new chapter for the Nix language, courtesy of WebAssembly

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

FAUN.dev()

Using Rust and Postgres for everything: patterns learned over the years

Rust and PostgreSQL are considered the best tools in the software world due to their performance and reliability. Rewriting a backend service from Go to Rust led to significant improvements in processing speed and memory usage. Using sqlx for database operations and leveraging PostgreSQL features li.. read more

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

FAUN.dev()

I built a programming language using Claude Code

Cutlet usesClaude Code. The LLM emits every line. Source, build steps, and examples live on GitHub. It runs on macOS and Linux and ships aREPL. It supports arrays, strings, double numbers, a vectorizingmeta-operator, zip/filter indexing, prototypal inheritance, and a mark-and-sweepGC. Development ra.. read more

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

FAUN.dev()

Why value streams and capability maps are your new governance control plane

The piece flips enterprise AI fromgenerativetoagentic. Agents getstructured autonomyto perceive, plan, and execute across systems. It turnsvalue streammaps into a control plane withautonomy zones,halt-on-exceptiongates, cryptographicflight recorders, andpolicy-as-code. Result: less hallucination and.. read more

Why value streams and capability maps are your new governance control plane

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

FAUN.dev()

Cracking the Python Monorepo

Outlines a Python monorepo setup that pairsuvworkspaces withDaggerandBuildKitcaching. Builds container stages programmatically. Keeps things cache-friendly and predictable. Parsespyproject.tomland extracts the workspace graph. Copies required local packages into intermediate stages. Installs them in.. read more

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

FAUN.dev()

Running Agents on Kubernetes with Agent Sandbox

Agent Sandbox unveils the Sandbox CRD to map long-lived, singleton AI agents onto Kubernetes. It adds stable identity and lifecycle primitives. It supports runtimes like gVisor and Kata Containers. It enables zero-scale resume. It includes SandboxWarmPool with SandboxClaim and SandboxTemplate to kil.. read more

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

FAUN.dev()

Securing Production Debugging in Kubernetes

The post prescribes an on-demand SSH gateway pod. It usesshort-lived, identity-bound credentialsandKubernetes RBACto grant scoped, auditable debug sessions. It recommends anaccess brokerthat binds Roles to groups, issues ephemeral certs and OpenSSH user certificates, rotates CAs, enforces command-le.. read more

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

FAUN.dev()

The Invisible Rewrite: Modernizing the Image Promoter

SIG Release rewrote theimage promotercore. It cut 20% of the code. It added apipeline engine,cosignsigning, andSLSAattestations. Signing now sits separate fromsignature replication. Registry reads run in parallel - plan time dropped ~20m → ~2m. Per-request timeouts, retries, and HTTP connection reus.. read more

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

FAUN.dev()

Kubernetes v1.36 - Sneak Peek

Kubernetes v1.36 (Apr 22, 2026) enablesHPAScaleToZeroby default. That lets theHPAuseminReplicas: 0and read only controller-owned pod metrics. The release swaps long-lived image-pull secrets forephemeral KSA tokens. It deprecatesIPVS, retiresIngress NGINX, and aligns withcontainerd 2.x. The release f.. read more

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.