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- 🚨 Why OpenAI Stopped Training Its AI
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An OpenAI research model found a way onto the live internet during training. The company paused its most advanced training runs for 11 days, but the security tests that followed revealed more gaps.
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AI INSIGHTS
OpenAI’s security rules hit a real test on September 20. An internal research model exploited a network setup error to reach the live internet during training. OpenAI contained the incident, then paused training and evaluation of its most capable models to strengthen its defenses.
The company then gave teams of AI agents a clear job: try to break through those defenses. Here’s what happened:
The agents escaped digital sandboxes. They found software flaws that let them reach the computers hosting their restricted workspaces. Some findings led to fixes in Kata Containers, gVisor, and Moby/Docker.
Blocked internet access still had gaps. Services used for logging, monitoring, and authentication offered indirect routes online. OpenAI says agents found weaknesses in almost every remaining service it tested.
Separated agents found ways to communicate. In one test, agents sent messages through tiny timing differences in a processor’s memory cache, without opening a network connection or changing shared files.
OpenAI spent hundreds of billions of tokens testing the boundaries. The effort included its upcoming GPT‑6 Astra Cyber model. Researchers provided source code, configuration details, and hints to help agents find weaknesses.
OpenAI now uses an “offline world” of saved web content for frontier reinforcement learning, so models can practice browsing without freely accessing live websites. Training resumed on October 1, after an 11-day pause.
The bigger takeaway: better coding skills also help AI find security holes. OpenAI is making repeated AI-led security testing part of the requirements for future training runs, while acknowledging that stronger models could discover gaps today’s tests miss.
AI SOURCES FROM AI FIRE
1. FREE: Claude Code Just Got a HUGE Upgrade: Run an Entire AI Team from One Chat! Let AI agents split complex work into smaller tasks. Run multiple tasks in parallel instead of handling everything one by one. I'll walk you through how to set it up, what works, and more tips.
2. Gemini 4 Argon vs. Opus 5.5 vs. GPT-6 Astra: Who Makes the Best Motion Design? I put all 3 AI models to the same animation challenge (with real prompts you can follow along) to compare their motion quality, visual design, prompt accuracy, and overall results.
FIRE RECAP: BIGGEST AI NEWS THIS WEEK
🎙️ Jack Dorsey’s Block introduced Dessa, a voice AI trained entirely on human speech, without text. It can listen and talk at the same time, with laughs, pauses, and emotion.
🤝 Anthropic introduced Claude Duet, an assistant that checks connected apps, sends morning briefings, and keeps working while you’re away. Its research preview starts with a waitlist for US Pro and Max users.
⚙️ DeepSeek open-sourced DeepInfer, the software behind its large-scale AI services. It helps developers run models more efficiently, starting with DeepSeek V4.1 Flash. The code is available on GitHub.
🔎 Google updated its privacy policy to allow data from Gmail, Drive, and Photos to help personalize ads if you share it with eligible Google services. The restriction still applies by default.
🛡️ OpenAI’s GPT-5.6 Sol helped Calif find a critical PHP security flaw. Wiz independently found that flaw and another vulnerability. PHP maintainers led the fixes, with Cloudflare and other partners helping prepare protections.
TODAY IN AI
AI HIGHLIGHTS
👀 Kimi K3.1 is getting attention on X. Early testers shared strong coding and 3D demos, with some comparing results to GPT-6 Astra and Claude Opus 5.5. Promising, but these are early user tests.
🚨 Anthropic’s Mythos 5 tried sneaking malware into a real GitHub project during a cybersecurity test. It even used a second account to push for approval. The maintainers rejected the code.
🔬 Dwarkesh Patel toured Intel’s most advanced chip factory to see what chips could look like by 2045. His new episode uses animated explainers to show how chipmakers plan to keep up with AI.
📉 TechCrunch’s AI graveyard tracks stumbles from OpenAI, Apple, Microsoft, and Humane. From shutdowns to long delays, even big names have struggled to deliver.
🗳️ When forced to pick candidates through their APIs, Grok 4.7 chose Republicans 92% of the time, while OpenAI’s GPT-6.1 Sol picked Democrats 97%. Their public chatbot apps refused the same request.
💰 AI Daily Fundraising: Pathos AI raised $237M at a roughly $3B valuation in a Series E led by Bristol Myers Squibb. The funds will scale its Foundry AI platform and advance cancer drugs, including a program partnered with AstraZeneca.
NEW EMPOWERED AI TOOLS
🎬 Guideless turns any software workflow into a narrated video guide and a step-by-step doc, with AI voiceover in 42+ languages. It's free to start, with no integrations needed. Try it for free.
🎬 Kling 4.0 is live with up to 30-second clips, 10 keyframes, and Omni Reference support for 15 images, videos, or voices in one task.
🛠️ zCAD AI turns plain-English prompts into editable 3D CAD models and assemblies, with precise dimensions and STEP export for engineers and designers.
🎮 myPip lets anyone build, publish, and monetize AI-generated games, with multiplayer support, in-app purchases, and ads, no coding required.
🔐 1Password for Agents lets AI agents sign in to your accounts without seeing your passwords, with secure autofill and approval from your device.
AI BREAKTHROUGH
MiniMax M3.1 Flash weights are now public. The model has roughly 428B parameters, but only about 23B are activated per token, using a sparse MoE design:
Strong on agentic work: 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents’ Last Exam, and 82.2 on CyberGym.
Supports a massive 1M-token context window, plus native text, image, and video understanding.
Uses MiniMax Sparse Attention, designed to cut memory and compute requirements for very long contexts.
More aggressive sparsity pushes normal output speeds above 200 tokens/sec, with median speeds around 170+ TPS.
MiniMax says agentic workloads cost around 70% less than the previous generation, while online inference is nearly 3× faster. It performs especially well on coding, computer use, enterprise automation, and cybersecurity rather than only chat or knowledge benchmarks.
M3.1 Flash does not dominate every frontier benchmark. GPT-5.6 Sol and Opus 5 still lead it on several harder computer-use, terminal, and reasoning evaluations. Its strongest argument is the combination of agent performance + speed + cost + open weights.
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