And with rclown by @marckohlbrugge you can backup your R2/S3/B2 in turn
LLMS
-
User reports rapid expiration of grok-4-1-fast-non-reasoning AI model
By
–
If it's that, it could be some biological thing where a species is trying to kill itself
-
Request for Model Aliases in AI Tooling
By
–
Good news "Recent news: In October 2025, Glass Lewis announced it would end its practice of issuing uniform proxy voting recommendations, shifting toward allowing clients to tailor advice to their own preferences. In January 2026, JP Morgan Chase's asset management unit cut
-
User Reports Slow Performance After Switching to Grok-Latest AI Model
By
–
Damn "They're co-owned by the Ontario Teachers' Pension Plan Board and Alberta Investment Management Corporation (AIMCo), two large Canadian pension fund manager" And of course Canada + teachers is gonna be left
-
Open Models Panel at GTC 2026 with Industry Leaders
By
–
Join us Wednesday, March 18th at 12:30pm at GTC for “Open Models: Where We Are and Where We’re Headed”, a panel featuring Harrison, Jensen, and the CEOs of Cursor, Thinking Machines Lab, Perplexity, and more. Add it to your schedule https://
nvidia.com/gtc/session-ca
talog/sessions/gtc26-s82480/
… -
Inquiring about Grok 4.3 AI usage and speed without reasoning
By
–
My Dutch stopped evolving after 2013
-
Bot Misunderstanding Despite Clear Instructions
By
–
ive done that too but in this case i was very clear what i wanted and in fact the bot did get it wrong still on the first shot, injected things i never asked for. still, a good reminder.
-
Tested 5 AI models across performance spectrum
By
–
I tested 5 models across the lineup: – Flash (fastest, ultra-low latency)
– 27B (balanced performance)
– 35B-A3B (multimodal powerhouse)
– 122B-A10B (complex reasoning)
– 397B-A17B (frontier-level outputs) Every single one outperformed my expectations for efficiency. Try them -
Qwen 3.5-Flash: Linear Attention + Sparse MoE Breakthrough
By
–
Most companies are scaling models UP to get better performance.
— God of Prompt (@godofprompt) 14 mars 2026
Qwen went the opposite direction.
Their 3.5-Flash model uses linear attention + sparse MoE architecture.
Translation: You get near-frontier performance without needing a data center to run it. pic.twitter.com/rN6cXx8Ox0Most companies are scaling models UP to get better performance.
Qwen went the opposite direction. Their 3.5-Flash model uses linear attention + sparse MoE architecture. Translation: You get near-frontier performance without needing a data center to run it. -
Qwen 3.5 models outperform in AI benchmark tests
By
–
I just spent the morning testing Alibaba's new Qwen 3.5 models against GPT-4o, Claude Sonnet, and Gemini.
— God of Prompt (@godofprompt) 14 mars 2026
The results? Qwen 3.5 is punching way above its weight class especially the small models.
Here's what shocked me about this release:@AlibabaGroup pic.twitter.com/ut3KjAtMK7I just spent the morning testing Alibaba's new Qwen 3.5 models against GPT-4o, Claude Sonnet, and Gemini. The results? Qwen 3.5 is punching way above its weight class especially the small models. Here's what shocked me about this release: @AlibabaGroup