Really excited about today's @OpenAI Codex release that pushes it from a coding assistant toward a real workspace for the kinds of tasks you do on your computer. The team is firing and we have even more in store. 🧵Here are my 11 favorite capabilities we announced today

OpenClaw 2026.4.15 🦞 🤖 Anthropic Opus 4.7 support 🗣️ Gemini TTS in bundled 🧠 Slimmer context + bounded memory reads 🔧 Codex transport self-heal, safer tool/media handling ✨ Pile of update/channel fixes Good boring release. https://t.co/jiLmr1Bxep

I have been using Codex a lot thanks to the Codex for OSS program. I never thought I would say this, but I have been more productive than ever just by using Codex.

🧵 My tips for getting the best results out of Claude Design! I’m on the verticals team at Anthropic which means I serve 7 different products. Claude Design makes it possible! 1. Set up your design system and your core screens. An hour of setup and refinement here is worth it

Anthropic's Claude Mythos isn't a sentient super-hacker, it's a sales pitch — claims of 'thousands' of severe zero-days rely on just 198 manual reviews https://t.co/FMhEyHzlGh

Both Gemini and Grok are underrated. I used to champion Gemini for a while, but lately I've been very happy with Grok, especially since the Grok 4.20 release with multi-agent. And now we have Grok 4.3!

*New Lecture* Stanford @CS153Systems '26, Session 5 (Full Video) Unified Intelligence with Amit Jain (@gravicle) from @LumaLabsAI 01:32 Luma's Origin Story 05:33 Differentiable World Learning 06:36 From 3D Capture To Video 10:40 Dream Machine Flywheel 13:48 Inside The Luma Factory 23:04 Unified Models Explained 32:29 Future Architectures 34:02 Skills and Tools 41:04 Creativity and Exploration 43:03 Sora Shutdown 47:08 GANs Diffusion and Hybrids 51:19 Hollywood Business Model Reset 55:01 What Makes Video Models Useful

grok speech-to-text and tts apis are out. multilingual multi-speaker is huge for me personally. https://t.co/7ABBCUCTO6

🚀我靠！Ollama 原生支持 Hermes Agent 了！ 一行命令就搞定： ollama launch hermes 本地部署，简直爽歪歪😂 检测本地可以跑什么模型可以用 llmfit 或者在线检测👉 https://t.co/qVYlJomz2Q

🦔Goldman Sachs reports that companies are blowing past their AI inference budgets by orders of magnitude, with inference costs in engineering now approaching 10% of total headcount costs and potentially reaching parity with salaries within several quarters. KPMG surveyed 2,100 senior leaders and found US companies plan to spend an average of $178 million on AI over the next 12 months, with Asia-Pacific firms budgeting $245 million and EMEA $157 million. The two reports together show companies are spending more than planned and intend to spend even more. My Take Inference costs approaching headcount parity is an extraordinary number that most finance teams did not model when they approved their AI strategies twelve months ago. The compute crunch, electrical component shortages, and GPU spot prices up 48% in two months are all flowing into corporate operating costs faster than anyone budgeted for, and Goldman's trajectory suggests it accelerates from here. What I find hard to reconcile is that $178 million average sitting alongside enterprise data showing eight in ten workers are either avoiding AI tools or not using them at all. Companies are committing to nine-figure inference budgets while their own employees aren't using what's already been deployed. I've watched this dynamic build all year and my honest read is that a significant portion of this spending is driven by competitive fear rather than demonstrated returns. Nobody wants to be the company that didn't invest in AI when everyone else did. That's how bubbles get funded, and at some point boards are going to demand a number that justifies it. Hedgie🤗