R.

We're extending Claude Fable 5 access on all paid plans, as well as keeping Claude Code’s weekly rate limits 50% higher, through July 19.

Morning. The last 48 hours of Codex and ChatGPT Work have been intense! Three important updates: - Temporarily removing the 5 hour usage limit restriction for all Plus, Business and Pro plans - Rolling out changes that will make GPT 5.6 Sol more efficient across the board and that will be reflected in less usage being used so that it can take you further. Exact impact to be quantified and shared - We hit 6M active users, and are landing a usage reset in the next hour Go do things

WE'VE DONE IT - $1800 MRR 🎉 This is unbelievable, we are adding around $100 MRR every 2.5-3 weeks now. What I'm building now: > Mult-agent data analysis OpenClaw set-up > Instagram influencer campaigns > Testing new Meta creatives Our MRR is going up quicker than my followers, but that's the figure that matters 😅 Thank you everyone! Let's get to $2000 by the end of July 🚀

i took qwen 3.6 27b dense, the king of the 24gb tier, for a full bench spin on my rtx 5090 laptop, head to head against a used $900 rtx 3090. some results i expected. one genuinely surprised me. the setup: q4_k_m, 16.8gb file, llama.cpp with flash attention, q4 kv cache, same flags on both cards, single stream, all measured this week on my own hardware. what i found: > 1. 35.3 tok/s generation fresh, 1,509 tok/s prompt processing on the laptop. > 2. the used $900 3090 beats the flagship laptop at generation, 40.1 against 35.3. generation is memory bandwidth, and the old card simply has more of it, 936 vs 896 GB/s. > 3. the laptop wins prompt processing by 16 percent. blackwell compute is real, it just isn't what generation is hungry for. > 4. filled context is the silent killer. 36 tok/s fresh glides to 18.8 by 128k deep and 12.9 at the full 262k. minus 64 percent. nobody benchmarks this and every agent user feels it. > 5. 128k context costs 18.7gb of vram, the sweet spot for agent work. the wall is 376k. and the footprint is identical on a 3090, 4090 and 5090 within 35 MiB. full picture in the chart, every flag included, everything reproducible. next up: the depth curve on its own, the measurement almost nobody publishes. stay close.

There's a flaw in Codex's (or should I say ChatGPT's?) subagent orchestration. The spawn_agent tool doesn't let you choose the model or reasoning effort. Therefore, every time 5.6 Sol Ultra spawns a subagent, you're getting another Sol Ultra instance. That's why your quota gets drained so fast.