投稿者: ミハエル・ラングマイエル

Nano Banana is Google’s new image model built for contextual editing and photo‑real generation. In plain English: you tell it exactly what to change in a picture, and it edits only that while keeping everything else intact — lighting, perspective, textures, text on products, even a person’s face. Creators are already posting crazy examples on X and YouTube. Below are the most useful workflows I’ve seen, written for “how‑to” intent: what to do, why it works, and prompts you can try today. How to try Nano Banana (fast) We did Ai image generators one prompt faceoff between Nano Banana Pro and 3 other models. The results are suprising! Prompt basics Be specific with your edits. Instead of saying “change […]

The open-source AI race just got more interesting. Chinese startup DeepSeek has unveiled DeepSeek-V3.1, its biggest upgrade yet, bringing sharper reasoning, stronger coding skills, and new support for tool-calling and agent workflows. Unlike its earlier release that felt experimental, V3.1 arrives as a serious contender. With a 128K context window, hybrid reasoning modes, and an API that’s dramatically cheaper than its Western rivals, the model is designed to push open AI closer to the performance of GPT-5, Grok 4, Claude Opus, and Gemini 2.5 Pro. See our DeepSeek pricing guide for full API costs and free tier details. Early benchmarks show V3.1 not just catching up but competing head-to-head in coding and reasoning tasks, […]

AI benchmarks are usually abstract. They test math puzzles, programming problems, or reading comprehension tasks most people never encounter in real life. But the newest yardstick for long-term AI performance is surprisingly ordinary: running a vending machine. The benchmark, called Vending-Bench, was created by Andon Labs to test whether AI agents can handle one of their hardest unsolved problems — staying coherent and effective over long stretches of time. Each agent is tasked with operating a simulated vending machine business: tracking inventory, placing orders, setting prices, collecting revenue, and paying daily fees. On paper, these are trivial management chores. In practice, they push models to their limits when extended across 20 million […]

Most people are disappointed with GPT-5 because they prompt it like GPT-4. It feels random, overconfident, or stuck in rabbit holes—and then gets blamed. The model changed; the prompting didn’t. GPT-5 acts like an agent. It plans, calls tools, carries state, and will press ahead unless you define the finish line. Big context and built-in browsing won’t save you if you don’t steer it. Without structure, you’ll see shallow guesses one moment and overwork the next. You don’t need a heavy playbook. A tiny bit of structure works: three dials that set research depth, autonomy, and how it reports progress. Turn them and you get steadier outputs, faster turnarounds, and […]

Unlike GPT-4o, GPT-5 was trained to be a worker, not a chatterbox. It plans, calls tools, follows rules, and carries state across long tasks. If you keep throwing casual prompts at it, you’ll leave a lot of performance on the table. The model responds best when you give it structure, constraints, and a clear finish line. Treat it like a teammate with a mission and operating rules, not a magic box. Below is the playbook: one framework, three core tags, a compact starter template, and a couple of inline examples that show the pieces working together. This guide pulls together the strongest patterns we’ve seen—how to control research depth, dial up […]

It sounds like something from a sci-fi movie: the same tech billionaires shaping our digital future are quietly preparing for a much darker one. From private bunkers in Hawaii to fortified basements and New Zealand hideouts, several AI leaders are investing heavily in survival infrastructure. Some of the most influential people in tech are quietly preparing for worst-case scenarios. They are heavily involved in AI, automation, and platforms that billions rely on daily. Their decisions to invest in bunkers or remote shelters raise a practical question: why now, and what are they preparing for? Let’s break down what’s really going on—who’s building what, where, and why—and then explore the bigger picture […]

OpenAI’s launch of GPT-5 changed how ChatGPT works. Instead of choosing between models like GPT-4o, GPT-4.5 or o3, you now get one unified GPT-5 system. It responds quickly to simple prompts, but can automatically switch into a slower, more thoughtful mode called GPT-5 Thinking for complex questions. If you’re on a paid plan, you can also turn this mode on manually. And on Pro (and Team) plans, you also get GPT-5 Pro, which uses more compute for the hardest problems. Behind the scenes, ChatGPT is now smarter about how it handles your requests. Most of the time, it gives fast replies using a lightweight model. But when the task is harder—like writing code or […]

Over the past few years, large language models like ChatGPT have become increasingly capable at answering a wide range of questions—from writing code and analyzing data to offering life advice. But even as their capabilities have grown, one thing remains inconsistent: the quality of responses. Users have often reported that answers from ChatGPT can vary significantly in depth, specificity, and usefulness, even when the same model and settings are used. A recent exploration into this inconsistency has revealed a fascinating insight: by adjusting the structure of the prompt, users can reliably activate a more thoughtful and analytical mode of reasoning within the model. The key lies in guiding the model […]

When OpenAI released GPT-4o in 2024, it was widely praised for its creativity, speed, and emotional warmth. For many, it felt like a breakthrough in AI interaction—lively, expressive, and deeply human-like. Then, almost overnight, GPT-4o vanished from the ChatGPT interface, replaced by GPT-5, which—despite major upgrades—has left a significant part of the user base feeling cold, alienated, and frustrated. This article covers why this shift has upset so many users, what GPT-5 does differently, and most importantly, how to keep using GPT-4o today—even if OpenAI no longer features it in ChatGPT. Why People Are Upset About GPT-5 Users feel misledOpenAI had previously implied GPT-4o would stick around. But it disappeared […]

On August 5, 2025, Google DeepMind introduced Genie 3, a major leap in AI-generated interactive environments. The release came on a day packed with significant AI announcements, including Anthropic’s Claude Opus 4.1 and OpenAI’s open-weight GPT-OSS model. While those releases focused on multimodal performance and open-access NLP, Genie 3 targets a different space: real-time virtual world generation. Designed to turn simple text prompts into explorable, dynamic 3D environments, Genie 3 is part of DeepMind’s growing focus on world models — systems that simulate environments for both human users and AI agents to interact with. Unlike previous iterations, Genie 3 supports multiple minutes of consistent simulation, real-time rendering, and the ability to inject events into the environment […]