AI Post — Artificial Intelligence

🔥The widely discussed Chinese AI agent Manus AI has officially launched inside Telegram. They introduced a new feature called Manus Agents, allowing users to connect the assistant to Telegram in under a minute via QR code. Once connected, the agent operates directly within the messenger interface. It functions as a Fully fledged AI agent: you can give voice commands, send photos and documents, request research, generate reports, create PDFs, and handle complex tasks. There are two operating modes: • Max — designed for more complex, multi-step tasks • Lite — optimized for faster, simpler requests The feature is already available across all Manus subscription plans. @aipost🏴

🇺🇸Pentagon vs AI Guardrails: A $200M Standoff The U.S. Department of Defense is pressuring top AI labs to allow their models to be used for “all lawful purposes” including weapons development, intelligence collection, and battlefield operations. At the center of the dispute: Anthropic. Here’s what’s happening: What the Pentagon wants • Broad authorization to deploy frontier AI models across classified and unclassified networks • Fewer built-in guardrails on classified “secret” systems • Assurance that models won’t refuse tasks mid-operation From the military’s perspective, operational reliability is critical. If a model declines a request during a live mission, swapping systems or renegotiating edge cases isn’t practical. Where Anthropic draws the line Anthropic is holding firm on two non-negotiables: • No fully autonomous weapons • No mass domestic surveillance of Americans The company says discussions have focused on usage boundaries, not specific missions. But the Pentagon is reportedly frustrated after months of talks and per Axios, Anthropic’s contract (valued up to $200M) is now at risk. This isn’t just a contract dispute. It’s a test of who sets the rules for frontier AI: Governments demanding operational flexibility or companies enforcing hard ethical constraints. AI is moving from lab demos to battlefield infrastructure, and the governance layer is now the real battleground. Source. @aipost🏴

📢Google DeepMind evealed a major leap in AI reasoning. Their internal model, “Aletheia,” built on Gemini Deep Think, is moving beyond Olympiad math into real-world scientific research. It reportedly scores up to 90% on IMO-ProofBench Advanced, tackles PhD-level problems, and applies scalable reasoning across domains. But this isn’t just benchmark performance. “Aletheia” has: • Autonomously solved open mathematical problems including four from the Erdős database • Contributed to publishable research papers • Worked on challenges in algorithms, economics, ML optimization, and even cosmic string physics Just 2.5 years ago, chatbots struggled with basic arithmetic. Now, we’re seeing AI operate as a scientific collaborator. This signals something bigger than better math skills. We’re entering a new phase of the scientific workflow where AI acts as a force multiplier for human intellect: • Handling large-scale knowledge retrieval • Performing rigorous verification • Stress-testing proofs and hunting counterexamples • Connecting ideas across disciplines That frees researchers to focus on conceptual depth and creative direction. This isn’t automation replacing scientists, it’s augmentation redefining how discovery happens. @aipost🏴

🧠 ChatGPT prompts to help you learn anything 10x faster 1. Explain Like I’m 5 “Summarize [topic] in the simplest terms possible, like you’re explaining it to a 5-year-old.” 2. Visualize the Process “Create a step-by-step mental image or visual map of how [concept] works.” 3. Break It Into Chunks “Divide [topic] into 3–5 main parts and explain each part briefly.” 4. Relate to Real Life “Explain how [topic] connects to daily life or a practical situation I might encounter.” 5. Teach It Back “How would I teach [topic] to someone who knows nothing about it?” 6. Ask the Critical ‘Why’ “Why does [concept] matter, and what are its key implications in [field]?” 7. Find the Patterns “What patterns, formulas, or rules can I extract from [topic] to remember it better?” 8. Simulate or Practice “Give me a simple example, scenario, or exercise to apply [topic] right now.” 9. Use Analogies “Compare [topic] to something familiar or unrelated to make it easier to understand.” 10. Break Myths “What are three misconceptions about [topic], and what’s the truth behind them?” 11. Turn It Into a Story “Write a short story or scenario where [topic] is applied in a relatable context.” 12. Challenge It “What are the common misunderstandings or mistakes people make about [topic], and how do I avoid them?” 13. Prioritize Learning “What are the 2–3 most important concepts in [topic] I should focus on first?” 14. Find the Gaps “What are the most overlooked aspects of [topic] that are crucial to understanding it?” @aipost🏴

🔥A new SOTA image-editing model named FireRed-Image-Edit just got released. Outperforms many top closed source options like Qwen-Image-Edit and Seedream 4.0, on GEdit benchmarks. It’s a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios. Built straight from text to image foundations instead of simple add on patches, it shows much stronger native editing power. Available under Apache 2.0 license, this model runs completely local and delivers major improvements over earlier tools. GitHub: https://github.com/FireRedTeam/FireRed-Image-Edit Huggingface: https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0 @aipost🏴

OpenClaw’s creator, Peter Steinberger is joining OpenAI to help bring AI agents to more people. @aipost 🏴

OpenClaw’s creator, Peter Steinberger is joining OpenAI to help bring AI agents to more people. @aipost🏴

Another insane Seedance 2 example. Actors will simply license their image moving forward and AI models will do all the actual “acting”. @aipost🏴

Seedance 2 is already making full cinematic short films. Time is running out on Hollywood... @aipost🏴

OpenAI recently warned congress that China is distilling US models. @aipost🏴

🇨🇳 China's AI powered car inspection system scans through over 1,000 functions in just 3 hours. @aipost🏴

Dario predicts trillions in AI revenue likely before 2030, and “country of geniuses” by 2028. @aipost🏴