Your first AI prototype doesn't need to start with a GPU rental. For supported models, @gmi_cloud serverless endpoints let you start without managing the serving infrastructure. Already using the OpenAI Python client? Configure GMI's endpoint, your GMI key, and an available GMI model. Official Quick Start: docs.gmicloud.ai/quickstart Keep the key server-side, loaded from an environment variable or secret manager. Never ship it in browser code. Check model availability, supported parameters and current billing before sending requests—serverless does NOT mean free. Pick a model from the current catalog rather than blindly copying an old example ID: docs.gmicloud.ai/model-quick… Have custom model weights or need dedicated capacity? Compare the dedicated option: docs.gmicloud.ai/inference-e… What AI feature would you build first? Tell me in the comments.

DeepSeek-V4.1-Flash-1017 uses a Causal Encoder-Decoder design that cuts active parameters to ~8B on prefill and 16B on decode, plus aggressive KV cache compression. This keeps the 1M context cheap for long agent runs while still improving coding benchmarks. The architecture focus is clearly on cost efficiency rather than raw scale. Do you see encoder-decoder MoE becoming the default for agentic models?

Why AI recommendation is always shitty?

A call that sounds exactly like your family member, in trouble, needing money right now — that urgency is the product. Free defense: hang up, call back on a number you know, and keep one family safe word. The voice can be copied. The memories can’t.

let's give @bot a try. First impression, the UI/UX looks amazing. its less cluttered and messy as compared to other bots.

A quick tip: AI works best when you provide it with an example. The example should include both the input and the expected output!

So Luna feels unlimited on Codex… and it takes unlimited time to finish a task too. 😗

AI won't take your job, a dev who uses it might. It writes code fast, but you own what ships: review, security, taste. Comment TASK for the one thing AI can't do from your job. Video powered by @gmi_cloud

FYI, An AI answer with a link still needs a check. Open the source, find the passage behind the claim, and check its date and scope. A real page can be quoted out of context. If you can't find support, don't forward the claim as fact.

Qwen 3.8 is crazy. Using it as the base model for my personal agent. Thanks @gmi_cloud for making it free

When you ask AI to turn notes into tasks, add: "Use only these notes. Give each task's owner, deadline and supporting quote. If something is missing, write 'Not stated'." Then check the quotes yourself. A tidy table can still invent a deadline.

This is huge. $668M and that kind of ARR growth doesn’t happen by accident. Congrats to the GMI Cloud team — well earned. Still early, and that’s the exciting part.

I took the bicycle out. Leaving the desk is when an unattended agent needs a finish line. Claude's Agent SDK defaults max_turns and max_budget_usd to no limit. The loop keeps calling tools until the model returns text with no more tool calls. Fine when you are watching. Not fine when the prompt is "improve this codebase" and nobody is. max_turns counts tool-use round trips only. Their own walkthrough is a four-turn test fix. Set max_turns=2 and the loop stops before the edit that made the tests pass. When the cap hits, ResultMessage.subtype is error_max_turns. The result field is not there. If your wrapper only checks that the process ended, you will file an unfnished run as a success. Same for max_budget_usd. Subagent spend counts. You get error_max_budget_usd, not a quiet completion. The error result still carries session_id, so you can resume. A stop condition is not a quality setting. It pauses the work without lying that it finished. Set both before you walk away. Then branch on subtype. success means it finished. The error subtypes mean it did not. #AgentHarness #AIAgents #Claude #ClaudeCode #OpenAI #Codex

99% cache hit. 😯 229 assistant turns. 229 tool calls. 26.5 million input tokens. $7.86. That’s not a chat. That’s a machine that woke up, looked at a repo, and refused to stop thinking. Anybody want to guess the harness?👀

No more juice value guyzzz :(

The open question is still the meter. Faster and cheaper only helps if usage is visible, stable, and split by product instead of moving around inside one opaque pool.