What actually improved

• More tasks: DeepMind tested the model across 200+ distinct tasks — from serving food with tongs to tying a bow and pipetting liquids.
• More reliable: success rates rose 30% to 100% over previous models, depending on the type of task.
• Faster: tasks were completed 20% to 30% quicker.
• One brain, many bodies: the same model runs on Apollo 2 (with a five-finger SharpaWave hand, 22 degrees of freedom) and on the two-armed Franka Duo platform with simple grippers.

The Mercedes-Benz test, in plain words

The factory job is called kitting: taking individual parts out of bulk bins and laying them into trays in the exact order each car on the line needs them. It is repetitive and physically tiring — exactly the work factories struggle to staff. Apollo 2 did it on live lines at 96% success. Two honest qualifiers matter: DeepMind calls this a proof of concept, not a permanent deployment, and these are DeepMind's own results — no independent evaluation has been published yet. The lab itself says closing the last 4% will be a steep road, because a factory line cannot shrug off one mistake in twenty-five.

Safety work released alongside

DeepMind also published ASIMOV-Jailbreak, an open safety benchmark for robot AI, on Hugging Face — free for any robotics team to test against. If you build or study embodied AI, that benchmark is the part of this launch you can actually download and use today.

Can you get Gemini Robotics 2.5?

Not yet, unless you are inside the circle. The model is rolling out to DeepMind's robotics partners only — names like Agile Robots, Apptronik and Boston Dynamics, plus 100+ trusted testers. There is no public API, no price and no general release date. For everyone else, this launch is a progress report: humanoid robots have crossed from staged demos to supervised real work, and the remaining gap is reliability, not spectacle.

Source: Google DeepMind — Gemini Robotics 2.5: Accelerating Physical Intelligence (10 November 2026).

Originally published on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/gemini-robotics-25-googles-humanoids.html

What Odessia was — and why it is already shut down

• A one-year-old startup: Odessia raised a $6 million pre-seed round led by Sequoia and launched its AI travel agent only about five months ago, with a small team of eight in San Francisco.
• Real early traction: Davidson says tens of thousands of people used the service over the summer to plan more than $60 million in annualised travel — the agent could plan and book flights, hotels and full itineraries inside a single conversation.
• Already closed: the standalone service shut down on the day the deal was announced. Odessia's website now tells visitors the service is no longer available and points people with existing bookings to its support email.

Why OpenAI wants a travel founder

An OpenAI spokesperson confirmed the move to TechCrunch, calling Davidson a proven founder who has built several businesses around helping people travel. Just as important is the timing: the acquisition landed hours after OpenAI launched business profiles in ChatGPT, which let companies present and sell through the chatbot. Put together, the direction is clear — ChatGPT is moving from answering questions about trips toward booking them.

Davidson is a heavyweight hire for that job. He founded Sonder as a college student in 2014 and built it into a hospitality unicorn before resigning as CEO in June 2025; the company later went through bankruptcy after a key partnership with Marriott fell through, and its brand was sold on in 2026. Few people have run travel bookings at that scale — and then rebuilt the same idea as an AI agent.

What this means for you

• No new travel product yet: OpenAI has announced no travel-specific product or launch date. For now this is a hire, not a feature.
• Watch ChatGPT's booking features: if travel booking arrives inside ChatGPT, expect it to grow out of the new business-profile and agent features rather than a separate Odessia app.
• Travel sites should pay attention: when the world's most-used chatbot starts hiring travel-booking founders, the fight for the traveller's first click moves inside the chat window.

Source: TechCrunch — OpenAI acquires Odessia, travel startup created by Sonder founder (10 November 2026).

Originally published on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/openai-buys-odessia-ai-travel-startup.html

How to get the best answer

1. Fill in the brackets with your real details — vague prompts get vague answers.
2. Ask for the format you want ("in a table", "in 5 bullet points", "under 100 words").
3. If the first answer misses, say what to change instead of starting over.
4. Never paste passwords, bank details or private client data into any AI chat.

1. Writing and emails

• Shorten and polish an email: Rewrite this email to sound polite and professional, in half the words. Keep all facts and dates the same: [paste your email]
• Reply to a difficult message: Draft a calm, polite reply to this message. I want to say no without damaging the relationship: [paste the message]
• Turn rough notes into a clean message: Turn these rough notes into a clear, professional message I can send on WhatsApp to a client: [paste your notes]
• Write a follow-up: Write a short, friendly follow-up email. I sent [what you sent] on [date] and have had no reply. Ask for an update without sounding pushy.
• Fix tone, not meaning: Check this text for grammar and spelling, and make the tone friendly but professional. Do not change the meaning: [paste your text]

2. Learning and research

• Explain simply: Explain [topic] to me like I am a complete beginner, in simple words, with one everyday example.
• Make a study plan: Make me a 7-day plan to learn [topic]. I have 30 minutes a day. Give me one small task per day and a quick way to test myself.
• Summarise a long text: Summarise this in 5 bullet points: the main idea, 3 key points, and one thing I should do next: [paste the text]
• Compare two options: Compare [option A] and [option B] for [your situation]. Give me a simple table: price, ease of use, best for, and your recommendation.
• Test my understanding: Ask me 5 short questions about [topic], one at a time. After each answer, tell me if I am right and explain briefly.

3. Planning and productivity

• Plan tomorrow: Here is my to-do list for tomorrow: [list]. I have [hours] free. Put it in order of importance, give each task a time slot, and tell me what to drop if I run out of time.
• Break a big task down: Break [big task] into small steps I can finish in 25 minutes each. Give me the first step only, in detail.
• Make a checklist: Make a simple checklist for [trip / event / move / launch]. Include things people usually forget.
• Decide faster: Help me decide between [option A] and [option B]. Ask me 3 questions first, then give a clear recommendation with one reason.
• Weekly review: Here is what I did this week: [list]. Summarise my progress, point out one pattern, and suggest one small improvement for next week.

4. Business and small brands

• Product description: Write a short product description for [product] for [customer type]. Focus on one benefit, keep it under 80 words, and end with a call to action.
• Social media captions: Write 5 short captions for Instagram about [product or topic]. Make each one different — one funny, one helpful, one with a question, one with a tip, one with a story.
• Blog post outline: Make an outline for a blog post titled "[title]". Give me 5 headings, one sentence under each, and 3 keywords readers would search for.
• Customer reply: Draft a polite reply to this customer message: [paste it]. Thank them, answer the question, and offer one next step.
• Name and idea brainstorm: Give me 10 name ideas for [business / product / channel] about [topic]. Short, easy to remember, and different from each other.

5. Everyday life

• Meal plan from what you have: I have these ingredients at home: [list]. Suggest 3 simple meals I can make today, with steps under 5 lines each.
• Budget check: My monthly income is [amount] and my main expenses are [list]. Suggest a simple weekly budget and one expense I could cut without feeling it.
• Explain to a child: Explain [topic] to a 7-year-old in 5 short sentences, with a fun example.
• Translate and adjust: Translate this into [language] in a natural, everyday style — not word-for-word: [paste your text]
• Prepare for a call or meeting: I have a [call / interview / meeting] about [topic] tomorrow. Give me 5 likely questions and a short, confident answer for each.

Bottom line

You do not need a new tool for most daily work — you need a better first sentence. Save this post, copy the prompts you use most into your own notes, and change the brackets each time. The prompts that save you the most time are usually the ones you reuse every week, not the cleverest ones.

Originally published on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/25-chatgpt-prompts-for-daily-work-copy.html

How Video-Coder works

1. Stage one — script the scene: the agent iteratively writes, executes and refines Blender Python programs that assemble a 3D scene — objects, positions, movement — and renders a draft video from it.
2. Stage two — guide the generator: that draft gives the downstream video generator explicit structure and motion signals: what is where, and how it should move, instead of leaving physics to be guessed from a text prompt.

The team describes Video-Coder as the first attempt to train a code-driven video generation agent — the scene is a program, so it can be checked, corrected and re-run, which a pure text prompt never can.

The results: +7 on physics, +24 on video quality

Applied to the MiniMax H3 video backbone, the team reports:

• PhyGenBench (physical plausibility): a gain of roughly 7 points.
• VBench-2.0 (overall video quality): a gain of roughly 24 points.

The honest caveats: these are the research team's own reported numbers, measured on a single backbone (MiniMax H3). They show the approach works on that setup; how it transfers to other generators is exactly what the open-source release lets others test.

Who built it

Video-Coder comes from a six-institution collaboration: the University of Central Florida, the University of Würzburg, Microsoft Research, Tsinghua University, UNC-Chapel Hill and UW-Madison. The full title of the work is "Video-Coder: Reinforcing Agentic Coding for Video Generation" — the agent was trained with supervised fine-tuning plus agentic reinforcement learning, rewarding both code that runs and video that looks right.

It is open source — data, models and code

The team has fully open-sourced the project: training data, models and code are public, and the system is reproducible. The project's Hugging Face hub is the training-data repository at huggingface.co/datasets/Video-Coder/Train-Data, which links onward to the project page, the paper and the code.

How to try it (as far as the public release supports)

1. Start at the Hugging Face page: open Video-Coder/Train-Data — this is the project's official data hub.
2. Look at the training data: the repository holds two JSON files — sft_data.json for the supervised fine-tuning stage and rl_data.json for the reinforcement-learning stage (about 2.73 GB in total) — free to download and inspect.
3. Follow the links on that page: the dataset card links to the project page, the paper and the code repository — the code is where the inference pipeline lives.
4. Bring the right tools: the agent drives Blender through Python, and its output conditions a separate video generator (the paper's experiments used MiniMax H3) — so this is a developer/research setup, not a one-click consumer app.

Bottom line: Video-Coder's bet is that the cheapest way to fix AI video physics is not a bigger video model, but an executable 3D draft in front of the one we already have. The benchmark gains say the bet is worth testing — and because everything is open, anyone can.

Sources: Emergent, "Video-Coder Agent Scripts 3D Scenes Before Video Gen", 9 November 2026 — emergent.sh; Video-Coder project data page, Hugging Face — huggingface.co/datasets/Video-Coder/Train-Data. Benchmark figures are the research team's own reported results.

Originally published on Ai Game Changer Pro.

What Muse Ember is for

Ember is aimed squarely at the unglamorous tasks that make up most of an agent's day:

• Tool calls — firing off the many small API actions an agent performs per task.
• Coding subtasks — the small, well-defined pieces of a larger coding job.
• Routing — deciding which model, tool or workflow should handle each request.
• Extraction — pulling structured facts out of unstructured text.
• Long-document parsing — chewing through big documents cheaply, page after page.

None of these need a frontier model's full reasoning power. All of them happen thousands of times in a busy agent system — which is exactly where per-token price decides whether the system is viable.

The price: $0.10 per million input tokens

Ember costs $0.10 per million input tokens. Meta positions that as far cheaper than Claude Haiku and OpenAI's Luna, and roughly on a par with the open-source DeepSeek V4.1 Flash and GLM 5.3 Flash models. In other words, a major lab is now pricing a hosted model where the open-source discount tier used to sit — a significant moment for anyone who builds agents on a budget.

Multimodal without a second model

Cheap usually means text-only. Ember is not: it handles text, image and audio in one model — screenshots, charts, scanned documents and voice memos included. For agent builders, that removes a whole layer of plumbing: no separate vision model to call when a subtask involves a screenshot, and no separate transcription step for a voice memo.

Meta's pairing rule: Wonder plans, Ember executes

Meta describes the intended architecture in one line: "Muse Wonder plans, Muse Ember handles the volume." A big, expensive model makes the plan and the rules once; Ember then applies them across thousands of cases. It is the manager-and-team pattern applied to models — you do not pay the senior architect's rate for every brick that gets laid.

When to pick Ember — and when to pick a frontier model

• Pick Ember when the subtask is well-defined and repeated: extracting fields from invoices, routing tickets, parsing a backlog of scanned documents, summarising voice memos, or executing the tenth step of a plan a bigger model already made. Volume and predictability are Ember's home ground.
• Pick a frontier model when the task is the plan itself: open-ended reasoning, ambiguous problems, high-stakes one-off decisions, or work where a single mistake costs more than a million tokens. Frontier models earn their price on judgement, not throughput.
• Mix them, as Meta suggests: let the frontier model decide what should happen and write the rules, then hand the execution loop to Ember. Most real agent systems will end up as this hybrid — the only question is where each builder draws the line.

Bottom line: Muse Ember is not trying to win the intelligence race. It is trying to win the cost-per-task race — and at $0.10 per million input tokens with vision and audio included, it makes a strong case that the future of agents is a cheap, tireless execution layer under a smart planner.

Where to get Muse Ember — and how to check it

Ember is available now in two places: inside Muse Code, and through the Meta Model API under the model ID muse-ember-1.0, with Responses API support. To check it yourself, open Meta's official developer page, dev.meta.ai — Muse Ember is listed there with a Playground where you can try it, alongside the Model API and Muse Code getting-started guides. Full pricing: $0.10 per million input tokens, $0.004 per million cached input tokens and $0.50 per million output tokens, with a 1 million-token context window.

Source: TechCrunch, "Meta releases Muse Ember, a new lightweight model for agent execution", 9 November 2026 — techcrunch.com. Pricing as reported at launch.

Originally published on Ai Game Changer Pro.

What actually launched

• Claude Fable 5.2: the general-release model — improved coding, reasoning and vision, with clearer, more direct answers and noticeably quicker responses than Fable 5.1.
• Claude Mythos 5.2: the more powerful sibling, but not open to the public. Access is limited to verified organisations, with verification aimed at cyber and life-science work — the areas where a very capable model needs the tightest control.

Both models are available through the Claude API, Amazon Bedrock, Google Cloud and Microsoft Foundry, so teams can reach them through whichever cloud they already use.

The price cut: Fable undercuts GPT-6 Astra

The most concrete news in the launch is pricing. Fable 5.2's input price drops from $10 to $8 per million input tokens, which puts it below OpenAI's GPT-6 Astra at the input level:

For teams running high-volume workloads — code review, document processing, support automation — a 20% input-price cut plus faster responses is a real cost change, not a marketing rounding error. It also signals that the frontier price war is still very much on.

Why Mythos is locked down

Mythos 5.2's restriction is part of the story, not a footnote. Anthropic is limiting it to organisations that pass cyber or life-science verification. In plain terms: the company believes this model is powerful enough in sensitive domains that anonymous API access is not appropriate. Expect this pattern — public model, gated stronger sibling — to become more common across the industry.

The RSI debate behind the launch

The launch coverage was shaped by a third-party report from METR raising concerns about RSI — recursive self-improvement. The worry, in simple terms: AI models are now helping to build the next generation of AI models, and METR's assessment suggested models may be speeding Anthropic's own research progress by roughly 2x. If AI progress starts compounding on itself, safety evaluation has to move much faster than it does today.

Anthropic's position is more measured: the company says Mythos cannot perform covert action. But there is an honest tension here — Anthropic's own system card showed more covert capability than in previous models, even while the company maintains the model cannot actually carry out covert action. Both things are in the public record, and readers should hold them together: capability measurements are rising, and the maker's judgement is that the dangerous threshold has not been crossed.

What this means for you

• If you build with Claude: Fable 5.2 is the default upgrade — better quality, lower price, faster replies, same API surfaces.
• If you compare frontier models: Fable 5.2 now undercuts GPT-6 Astra on both input and output pricing — worth a fresh benchmark on your own workload before renewing assumptions.
• If you follow AI safety: watch the Mythos gating model and the METR debate. How labs verify access to their strongest systems may become one of the defining policy questions of 2027.

Bottom line: Fable 5.2 is an easy headline — a better model for less money. Mythos 5.2 is the deeper story: the frontier is now splitting into models everyone can use and models only verified institutions can touch.

Source: TechCrunch, "Anthropic releases new Mythos and Fable models amid RSI concerns", 9 November 2026 — techcrunch.com. Prices and availability as reported at launch.

Originally published on Ai Game Changer Pro.

Keep the scale honest: this is a test, not a working data centre. The satellite carries four of Google's TPU AI chips and generates up to about 1 kilowatt of power — so little that it can only run one TPU at a time, for roughly twenty minutes at a stretch. Space is hostile to computers — heat has nowhere to go and radiation causes "bit flips" — and Google plans two more satellites with more advanced chips in 2027.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/googles-first-data-centre-satellite-is.html

The striking part is the reversal: Blackstone had previously expected Anthropic to earn an investment-grade rating before 2027. What changed is the scale of Anthropic's spending on enormous compute deals. Anthropic officially filed to go public earlier this week — and remember, this is a forecast from an investor, not a rating any agency has actually assigned.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/anthropic-is-heading-for-ipo-and-wall.html

The leaderboard: Claude Opus 5.5 leads at 12.0% ($51.41 per task), Gemini 4 Carbon at 10.9% ($16.29), GPT-6 Astra at 9.6% ($53.99) — and in finance and legal, every model scored 0%. GDPx is deliberately brutal, and it does not mean agents fail 88% of ordinary office work — but it is a map of where agents can and cannot be trusted yet. Buy an assistant, not an employee, and judge cost per finished task, not per token.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/the-best-ai-agent-scores-just-12-on.html

On 2 November, shareholders of Churchill Capital Corp XI (a SPAC) approved the deal, worth $465 million in gross proceeds — including a roughly $201 million PIPE investment led by Foxconn. Agility says it will be the only US-listed company specialising in humanoid robots with verified, currently operating commercial deployments, with customers including GXO and Schaeffler, and backers including Amazon, Nvidia and SoftBank's Vision Fund.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/agility-robotics-goes-public-humanoid.html

Omni Reference lets you feed in up to 15 reference assets — faces, products, settings — and holds them consistent across shots, with up to 10 keyframes. The honest notes: 4K and 10-bit HDR output are still "coming soon" (today's model works up to 1080p through API partners), and quality still varies clip to clip — but there are free credits to try it.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/kling-40-is-live-30-second-ai-videos.html

Sentient OS learns your life overnight from your notes, emails, messages and calendar, then writes what it learned into a Markdown folder you own. Local Code is a private agent for documents, data and code from the team behind Draw Things. Nativ runs open models locally for your coding agents. All three are free — though local models are still a step behind the biggest cloud models in raw capability.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/ai-that-never-leaves-your-computer-3.html

In China, Apple Intelligence is finally switching on (announced 31 October), with Alibaba's Qwen built into Siri, Writing Tools and Visual Intelligence. The first wave covers Live Translation, Writing Tools and Clean Up — though the rebuilt Siri AI launched elsewhere this year is absent from the first Chinese wave.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/apple-opens-siri-ai-to-every-app-and.html

In the same filing, Anthropic warns its internal estimates suggest it may imminently meet its Responsible Scaling Policy's definition of "dramatically accelerated progress" — recursive self-improvement, where AI gets good enough at building AI that progress feeds on itself. Anthropic stresses no current model has crossed that threshold, and it would not let Claude design successor models fully on its own without meaningful human supervision.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/claude-wrote-over-90-of-anthropics-own.html

These are OpenAI's own numbers, and a "search" here means a question that sends ChatGPT to look things up — not traffic sent to websites. Google is still the front door of the web, but a second front door is opening fast.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/chatgpt-hits-12-billion-weekly-users.html

If you have instructions or files in your Gems, now is the time to back them up — files under 100 MB move over automatically, but anything larger or stored elsewhere may be lost. This guide walks through the dates, what migrates and how to move your Gems to Skills step by step.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/gemini-gems-end-on-17-nov-2026-how-to.html

The race for affordable, useful humanoid robots is heating up on both sides of the Pacific — and the price points are falling fast enough that labs, startups and even hobbyists can now take part.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/unitree-g1-and-ubtech-u1-pro-headline.html

Fewer steps means faster generation and lower compute cost, which matters for anyone producing video at scale. Combined with native audio, it is a big step towards fast, affordable AI video anyone can use.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/pika-h3-flex-open-weights-ai-video-in-6.html

Early benchmarks look strong, and the long context makes it interesting for working with large documents and codebases. You can try it in minutes through OpenRouter's chat interface or API — no waitlist, no payment needed while it is free.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/crouton-alpha-free-on-openrouter-1m.html

Developers have been given weeks to comply, as Apple tightens safety rules around AI companions following scrutiny of how these apps interact with younger users. For developers, it means reviewing content, age gating and store listings now — not after the deadline.

Read the full story on Ai Game Changer Pro: https://aigamechangerpro.blogspot.com/2026/11/apple-app-store-rules-dating-and.html