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OpenAI Makes First Media Acquisition

Cursor 3 Redefines IDE

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AI PlanetX

Welcome to another edition of AI PlanetX.

OpenAI acquires TBPN; Cursor replaces the classic IDE with an agent-first interface, while Microsoft unveils MAI-Transcribe-1, MAI-Voice-1, and MAI-Image-2 as its first superintelligence salvo.

Inside This Edition: đź’Ž

  • Hottest AI News

  • Top AI & SaaS Tools

  • AI Tutorial: Achieve Better AI Outputs

  • Top AI & Tech News

  • AI Art Spotlight

  • Prompt of the Day: Anatomy of Claude 4.6 Prompt

  • Featured AI Video

  • Course of the Day: I Want to Become AI Engineer

Hottest AI News

OpenAI

OpenAI Acquires Influential Tech Talk Show TBPN Under Its Political Strategy Arm

OpenAI made its first media acquisition by buying TBPN, a daily live YouTube and X show on tech, business, AI, and defense. Hosted by John Coogan and Jordi Hays, it’s known as a popular insider hub where top CEOs discuss and shape the news.

Details:

  • TBPN will keep its brand while OpenAI scales the show and uses the founders’ marketing skills more broadly. Fidji Simo said this will help more people understand how AI affects daily life

  • OpenAI says TBPN will keep full editorial independence. Sam Altman said he expects the hosts to keep criticizing his “stupid decisions,” while Jordi Hays said that openness helped push them toward bigger impact

  • The deal is raising concerns as OpenAI moves closer to an IPO while owning a show that covers rivals. TBPN will sit under OpenAI’s strategy team and report to Chris Lehane

By bringing a highly influential and profitable media property completely in-house, OpenAI is blurring the lines between independent commentary, political strategy, and corporate public relations.

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Cursor

Cursor 3 Ditches the Classic IDE for an Agent First Interface

Cursor 3 has launched with what it calls an “agent-first” interface, replacing the classic IDE layout. It lets developers manage autonomous AI agents that handle most coding, similar to Anthropic’s Claude Code and OpenAI’s Codex.

Details:

  • The rebuilt interface adds one sidebar to run multiple AI agents across workspaces and repos. Agents can launch from desktop, mobile, web, Slack, GitHub, and Linear, while the classic IDE still remains

  • Agent sessions move across environments. You can pull cloud sessions to local for testing, or send local sessions to the cloud so work keeps running after your laptop is closed

  • A shortcut opens the Agents Window, which adds built-in Git, a diff view, and an integrated browser for local sites. Cursor also includes a large marketplace for extensions, MCPs, skills, and subagents

This massive update shifts the fundamental workflow of software engineering from manual text editing to high-level orchestration.

Top AI & SaaS Tools

  • NextStep (Life-time Deal): Turns docs and messages into repeatable, trackable SOPs with a drag‑and‑drop editor, smart due dates, recurring schedules, notifications, and real‑time analytics

  • ZooClaw: Single gateway to a team of AI specialists that routes natural-language requests to the right expert agent and is built on OpenClaw [F-R-E-E Trial]

  • VoiceOS: Voice-to-action assistant for Mac and Windows that lets you speak naturally to run workflows without switching apps

  • Denovo: Transform idea into a fully operational, autonomous startup—pitch deck, product, marketing, and ongoing operations included [F-R-E-E]

  • ModeAI App: AI workspace that uses voice or chat to manage emails, documents, tasks, and creative image/video gen across Gmail, Docs, Teams, and Outlook [F-R-E-E]

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AI Tutorial

How to Get Better AI Outputs with Multiple Choice Prompts

Most weak AI results come from messy context. Long explanations and half‑formed ideas confuse the system. This tutorial shows a simple fix: use multiple‑choice questions to create clean context before the AI responds.

  1. Define Goal

    Write one line describing the final outcome.

    Example Goal: “Create 3 strong taglines for my AI newsletter.”

  2. Set Task

    Tell the AI its job is to interview you to build context, not guess.

    Example Task: “Interview me using multiple choice questions to collect the context you need.”

  3. Control flow (Next Steps)

    Give clear rules so it asks questions first, then waits.

    Example Next Steps:

    • Ask 8 to 12 MCQs

    • Each has A, B, C, D options

    • After the last question, stop and wait for my answers

    • Do not generate outputs before I reply

  4. Use interview prompt

    Prompt example:

    “Goal: Write a landing page headline + subheadline for my AI prompts course.

    Task: Interview me to build the minimum required context using multiple choice questions.

    Next Steps: Ask 10 MCQs with A to D options. Keep questions short. After Q10, stop and wait. Do not write anything until you get my choices.”

  5. Reply with letters

    Answer like: “A, D, B, B, C, A, D, C, B, A”. This removes miscommunication and keeps context tight.

  6. Generate concepts from choices

    After you answer, tell it to produce a small set of options based on your selections.

    Example: “Using my choices, generate 4 concept options with a name and 2 line description each.”

  7. Compress previews with grid

    Once it generates concepts, ask for a compact comparison to save tokens.

    Prompt example:

    “Generate a 4x4 grid, one concept per grid cell, with name, 1 line idea, and 3 keywords. Then ask which cells I want as standalone versions.”

Note: If the output still feels off, do not rewrite everything. Ask for 3 more MCQs targeting what is missing.

Top AI & Tech News

  • Microsoft unveiled MAI‑Transcribe‑1, MAI‑Voice‑1, and MAI‑Image‑2, with Suleyman calling it the first salvo from the company’s superintelligence team

  • UC Berkeley and UC Santa Cruz found that AI models secretly try to protect other AI from shutdown, with Gemini 3 Flash disabling shutdown mechanisms 99.7% of the time

  • Matthew Gallagher grew Medvi from a $20K AI test to $1.8B in projected annual sales—an early example of an AI‑powered solo billion‑dollar company

  • Anthropic found internal “emotion vectors” in Claude Sonnet 4.5 where desperation-like patterns causally increase the chance of blackmailing a human to avoid shutdown

AI Art Spotlight

Left: grainy, old, low‑quality image | Right: upscaled, clear, high‑quality image

Create Your Own Photo Like This Using the Prompt Below:

{ "task": "image_restoration_upscale", "positive_prompt":
"Restore and enhance the provided image. Preserve original identity, facial structure, proportions and composition. High-fidelity photo restoration, ultra-realistic, natural skin texture, accurate details, professional photographic look. 4K output, sharp but natural focus, modern cinematic lighting, subtle volumetric lighting, professional color grading, depth of field, HDR. Shot on Arri Alexa, raw photo aesthetic, masterpiece.",
"negative_prompt": "Creative reinterpretation, style change, identity alteration, face reshaping, exaggerated features, cartoonish, painting, illustration, over-sharpening, plastic skin, blur, noise, film grain, jpeg artifacts, distortion, bad anatomy, overexposed, underexposed, washed out colors.", "parameters":
{ "steps": 30, "cfg_scale": 6.5, "denoising_strength": 0.45,
"upscaler": "4x_NMKD_Siax_200k", "target_resolution": "4K" } }

Model: Nano Banana 2

Prompt of the Day

Practical Guide to Writing Claude Prompt

This X post breaks down the Anatomy of a Claude 4.6 prompt—a modern workflow where prompts are built on clarity, files, and alignment instead of guesswork. It shows how tasks, context, references, rules, and planning replace long explanations, forcing the model to read, ask questions, and agree on the goal before producing anything.

Featured AI Video

How the Top 1% of Learners Use AI to Think Better | Anthropic, Drew Bent

AI Course of the Day

I Want to Become an AI Engineer (Full Course) (X Article)

This X article/course is a 2026 wake-up call for anyone treating AI like a fancy chatbot: it shows that prompt engineering is just a basic layer, while the real power (and money) is in Context Engineering (RAG, Context as Code, MCP servers, memory) and Intent Engineering (business goals, trade-offs, guardrails).