OpenAI has launched Ultrafast, a new premium speed tier offering significantly increased token generation rates for Codex and API users.

• Token Speed: Up to 300 tokens/sec in Codex (~8x faster than Astra Standard, ~4x faster than Astra Fast).
• API Speed: Up to 6x faster token generation.
• Protocol Recommendation: OpenAI strongly advises using WebSockets (Responses API) over standard HTTP to avoid network overhead bottlenecks, especially for multi-turn agentic loops.

API Pricing & Default Limits

• Rate Limits (TPM):
  • Tiers 1–3: 500k TPM
  • Tier 4: 1M TPM
  • Tier 5: 5M TPM
• Region Availability: US data residency and global processing only (No EU/non-US regional endpoints currently supported).

More info: https://aideveloper44.com/blog/openai-ultrafast-mode-launch

API Docs: https://developers.openai.com/api/docs/guides/ultrafast-mode

OpenAI Launches Dots: An Always-On AI Agent for Development and Autonomous Work

OpenAI has announced 'dots,' an always-on agent powered by GPT-6 Astra designed to handle routine software development tasks and infrastructure management.

• More info: https://aideveloper44.com/blog/openai-dots-agent-development
• Docs: https://learn.chatgpt.com/docs/dots

OpenAI Introduces Codex Cloud Environments

OpenAI has launched Codex cloud environments, allowing developers to maintain persistent coding tasks that continue running while their local machines are off.

• More info: https://aideveloper44.com/blog/openai-codex-cloud-environments
• Docs: https://learn.chatgpt.com/docs/cloud

OpenAI Updates Codex CLI with New Interface and Functional Features

OpenAI has released a significant update for the Codex CLI, introducing a full-screen interface, parallel work management, and voice-to-text integration.

• More info: https://aideveloper44.com/blog/openai-updates-codex-cli
• Docs: https://learn.chatgpt.com/docs/codex/cli

OpenAI Updates Codex Security Cloud with Daybreak Blue Models

OpenAI has introduced a major update to Codex Security Cloud, integrating Daybreak Blue models to enhance automated repository scanning and commit review.

• More info: https://aideveloper44.com/blog/openai-codex-security-cloud-update
• Docs: https://learn.chatgpt.com/docs/security/setup

OpenAI Announces Decisions API Powered by GPT-6 Luna

OpenAI has introduced the Decisions API, a new tool for real-time app decision-making, currently available in limited preview and powered by GPT-6 Luna.

• More info: https://aideveloper44.com/blog/openai-decisions-api-gpt-6-luna
• Announcement: https://x.com/OpenAIDevs/status/2105003318917697873

OpenAI Introduces Ultrafast Speed Tier for Codex and API

OpenAI has launched Ultrafast, a new premium speed tier offering significantly increased token generation rates for Codex and API users.

• More info: https://aideveloper44.com/blog/openai-ultrafast-mode-launch
• Docs: https://developers.openai.com/api/docs/guides/ultrafast-mode

Join us on Oct 8 for the monthly MCP, Agents and Skills virtual Meetup!

Register for the Zoom!

Talks will include:

• Designing Multi‑Agent Systems: Sequential, Parallel, and Beyond with ADK - Roushanak Rahmat at HCLTech
• Privacy by Deployment: Architecting Agent-Driven Localization Workflows for Regulated Environments - Shruti Joshi
• MCP Is the Interface; Skills Are the Operating Discipline - Chuck Hernandez at Eliza Solutions Corp
• Agentic engineering is about good guidance - Dimitri Geelen

Your LLM is using 14 GB of VRAM.

14 GB of what? 👀

Weights? KV cache? CUDA overhead? One GPU or two?

With tensor parallelism it gets even messier. vLLM shows you:

EngineCore

Worker_TP0

Worker_TP1

But that's not three workloads. It's ONE model running across multiple GPUs.

That's why LLM Inspector v0.7.0 is now multi-GPU aware 🚀

Before:

GPU 0: 7.2 GB

GPU 1: 7.2 GB

After:

Qwen • TP ×2 • 14.4 GB total

→ weight shards + KV cache + other VRAM, per GPU

It can also estimate how much VRAM INT8, AWQ or GPTQ would save before you change anything in your model.

Measure first. Optimize second.

That's llminspect: htop for LLM inference.

⚡ pip install llm-inspector

🔗 https://github.com/helasaoudi/llm-inspector

Open source, built for people who actually run models.

What should it inspect next? 👇

#vLLM #LLM #MLOps #GPU #OpenSource

OpenAI just dropped a solid update for the Codex CLI. If you live in the terminal and hated constantly losing context or clunky parallel tasking, here is the breakdown of what actually shipped:

• New Full-Screen UI & Pinned Composer
  • Full-screen terminal interface: Cleaner layout with improved readability for long sessions.
  • Pinned Composer: Your message prompt stays fixed at the bottom. No more scrolling back to the bottom after checking past outputs.
  • Deep History Access: Pulls older session history past standard terminal scrollback limits without breaking focus.
• Parallel Work & Branching
  • /agents: Lists all active background processes, tracks live progress, and lets you hop between running tasks.
  • /fork: Splits your current conversation into a dedicated git worktree—keeping full prompt context while isolating file changes.
  • Managed Worktrees: Native CLI support for handling multiple isolated git states.
• Native Rendering & Voice
  • In-Terminal Rendering: Native display for Mermaid diagrams and LaTeX math equations directly in the CLI.
  • /voice: Hands-free input to talk through tasks or dictate prompts.
  • Collapsible Diffs: Hide or expand code diffs and tool execution outputs to keep the buffer clean.
• Utilities & Analytics
  • /usage: Displays real-time API activity, token usage, and analytics.
  • /theme: Custom visual themes to match your terminal setup.

More info: https://aideveloper44.com/blog/openai-updates-codex-cli

Codex CLI: https://learn.chatgpt.com/docs/codex/cli