AI agents need more than a good prompt. They need memory.
I tested EverOS Cloud with a fictional e-commerce debugging scenario. First, I saved the debugging experience. Later, I searched for it with a different question and retrieved the earlier context, including the suggested fix and regression test.
That’s the value of persistent memory in AI workflows: experiences can be saved and retrieved when they’re needed again.
Try EverOS Cloud: everos.evermind.ai/login#EverMind#EverOS
Opus 5.5 is making solo game development look ridiculous 💀
From coding to low-poly character animations, AI is helping solo devs build what once took experienced teams months.
Game development is changing FAST 🎮
I tested Lovart 2.0 by giving Arti a fictional developer-tool brand and asking it to build a complete launch kit.
The interesting part wasn't just the designs. Arti researched references, flagged inconsistencies with Canvas Check-up, and helped revise the assets.
A first personal design agent that takes initiative
with memory and self-evolving skills.
#BuiltWithLovart#MarketingTools@lovart_ai
Every creator has a little PTSD from these messages 💀
If you’re a creator on X, you’ve seen these messages.
Also, if you’re running creator campaigns, check out @TheReleaseStack 👀
The best marketing isn’t done alone.
MiniMax M3.1 Flash built a complete 3D pagoda island in one shot
Detailed architecture, lighting, trees, water, and a fully explorable scene from a direct API call with no extra tool calls or harness
Flash models are getting scary good at building things, not just writing code
Space Bunny was just the beginning 🐰
MiniMax M3.1 Flash is bringing frontier-level performance at 1/3 the cost and 3x the speed
$0.15 / 1M input tokens
The Flash race is getting serious 🔥
MiniMax M3.1 Flash: Space Performance + Bunny Speed
🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash.
💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed.
API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens.
Model weights will arrive on Oct 23.
API: platform.minimax.io
M Plan: minimax.io/m-plan
MiniMax Code for desktop: agent.minimax.io/download
AI agents need more than a good prompt. They need memory.
I tested EverOS Cloud with a fictional e-commerce debugging scenario. First, I saved the debugging experience. Later, I searched for it with a different question and retrieved the earlier context, including the suggested fix and regression test.
That’s the value of persistent memory in AI workflows: experiences can be saved and retrieved when they’re needed again.
Try EverOS Cloud: everos.evermind.ai/login#EverMind#EverOS
Everyone talks about AI writing code.
But one of the biggest opportunities might be inside factories.
Machines already generate huge amounts of operational data. AI could help turn that data into better maintenance, planning, and production decisions.
The next AI winners might build the infrastructure behind intelligence - not just the models.
Claude Opus 5.5 built an entire ancient Greek world with code 🏛️
No pre-made assets. Just generated 3D environments, temples, waterfalls, architecture, and cinematic camera movements
This is starting to look less like AI-generated code and more like AI building actual worlds 💀
Claude Opus 5.5 built a real-time ocean simulator in just 30 minutes. 🌊
61 KB. One HTML file. Zero external librarie
FFT wave simulation
Realistic whitecaps and sun reflections
Dynamic sky and clouds
Free-fly camera controls
It even captured screenshots of its own output and used separate AI agents to critique the results
The video looks like a standalone ocean simulation
It’s actually running in your browser with WebGPU 💀