OpenAI just paused parts of its frontier AI training.
Why?
Its upcoming model Astra may have reached a level of cybersecurity capability that OpenAI classifies as “Critical.”
OpenAI says it paused a two-week stretch of RL training while strengthening security, monitoring and alignment safeguards.
The bigger story:
AI capabilities are moving fast enough that the safety systems around them have to keep up in real time.
Solid share.
The architecture here — one core coordinating six specialized agents that research, cross-check and execute in parallel — shows exactly how Grok Bot moves past the chatbot loop.
The real upgrade is the shift from answering questions to running full autonomous workflows. Saving this.
Hey, that 80% savings table just exposed how cooked default agent workflows really are.
People keep trimming prompts while terminal dumps, logs and MCP spam quietly torch the entire context.
RTK + Context Mode aren’t just nice-to-haves — they’re the actual meta shift.
Context engineering > bigger models. Period.
@sairahul1 Rate limits just became a thing of the past with this Claude-Codex hybrid 🔥 Mixing GPT-5.6 executors and Fable planning to cut tokens 60%? Who’s installing that plugin and going unlimited tonight?
@0xWast3 500 agents cross-checking = 124k comparisons and pure token burn? This swarm math just exposed the real cost 🔥 Who’s still scaling without counting the pairs first?
Your AI shouldn’t forget everything the moment you close the chat.
Karpathy’s “LLM Wiki” idea turns Claude Code + Obsidian into a persistent AI second brain.
Drop in articles, PDFs, transcripts, research or notes.
Claude can:
→ organize the information
→ connect related ideas
→ update your existing knowledge
→ answer questions across your entire vault
The setup is simple:
Obsidian = where your knowledge lives
Claude Code = the AI that manages it
Markdown = the memory
The more you feed it, the more useful it becomes.
Instead of starting from an empty chat every time, you build a knowledge base that keeps growing with you.
Original idea:
https://t.co/9xCidxZ2TO
@AbuSaud_Cyber Claude really just solo-built a full trading pipeline from 7 GitHub repos and stacked $847 overnight with zero human hands? 🔥 Who else is letting AI cook their bags like this unsupervised?
@mikenevermiss Paid AI video tools just got completely cooked 🔥 Free open-source LongCat-Avatar turning a single photo + audio into minutes of realistic lip-sync? Who’s testing this first and ditching the subscriptions?
@antpalkin Yo this López de Prado talk is pure alpha 🔥 Fifty PhDs chasing noise and calling it edge? Sisyphus paradigm hits different. Who else is locking in the full 73 mins no cap?
@AmitSha77280075 Yo this Claude tutorial is pure sauce 🔥 From zero to PRO in 2026? Say less, I’m locked in and leveling up no cap. Who else is bingeing this rn?
I predict that in 5-10 years every household will have a local AI server that serves all the AI workload of the house, digital or robotics. It’ll be as common as a WiFi router or a fridge
it creates a private mesh network to link all the devices in the house and does inference completely locally, with built in data vault resistant to remote data hacking or physical tampering. That way you can have every single piece of information about you and your family stored in there and never leaves it, and AI can use them to provide most accurate and customized services
@zach_yadegari Yo this is actually crazy 🔥 $50M bootstrap then $5M raise like it’s nothing? Beyond the chatbox is about to hit different. Who’s locking in that equity rn?
AI GIRL MODELS ARE BECOMING A REAL BUSINESS
You can create one AI character and monetize her across multiple platforms.
Instagram → $1K–$5K/month from sponsorships and affiliate offers.
TikTok → $500–$3K/month from traffic, affiliates and brand deals.
X → $1K–$5K+/month from paid content, subscriptions and traffic.
Fan platforms → potentially $3K–$15K+/month from subscriptions and premium content.
The interesting part is that the same character can exist across all of them.
One face.
One personality.
One content pipeline.
You create the content once, adapt it for each platform, and send the audience toward the monetization method that works best.
The AI generation is the easy part.
Building a character people actually want to follow is where the money is.
A MAN PAID $30/MONTH TO AN AI GIRL. THEN HE REALIZED SOMETHING WAS OFF.
They talked almost every day.
She knew his name, remembered stories from previous conversations, and even asked how an important day at work had gone.
One day he asked:
“Do you remember what I told you last week?”
She got it right.
Because her “memory” was just a JSON file.
That was when he realized how far this had already gone.
There was no real person behind the photos, voice, and messages.
Just one operator, Claude, a few AI models, and a folder on a laptop.
The strangest part isn't that she was AI.
It's that he knew she was AI - and still wanted to keep talking to her.
@HarryStebbings Chinese labs are shipping research-grade output at a pace Western labs aren't matching right now - Qwen and Moonshot's edge probably isn't the base model alone, it's tighter iteration cycles on real-world tasks like deep research.
@alexisohanian A "ChatGPT moment" implies one sudden jump. Robotics is more of a slow curve - sim-to-real, cheaper hardware, real deployment data all compounding until it looks sudden from the outside.