Image and Video Generation
Generative image and video models, editing tools, creative workflows, media production, synthetic content, and visual AI product launches.
A Red-Line Guide Turns Aerial Images Into Virtual Drone Flights
ElevenLabs presents a workflow for generating drone-style video from a single AI-made aerial image, using a red line drawn over the image as a virtual camera route. The guide line tells the video model where to move through the scene, while the prompt specifies motion, pacing and visual action; the tutorial positions Gemini Omni Flash as the faster, cheaper option and Seedance 2.0 for longer, higher-resolution shots with native audio.
AI Agents Assemble Documentary Motion Graphics From a Structured Brief
ElevenLabs presents a workflow for producing scrapbook-style documentary videos in which a tested visual prompt, persistent background and repeated reference assets establish continuity before scene generation begins. Claude converts a script and creative direction into a production brief, while ElevenCreative Flows builds the canvas, generates storyboard options and animates selected scenes. The company’s argument is that agent-assisted production can reduce manual setup without removing the creator’s role in supplying assets, choosing variants and revising the edit.
Controlled References Turn One Photo Into Broadcast-Style Football Footage
ElevenLabs’ Alec Wilcock argues that convincing AI football footage depends on production control rather than a single successful video generation. His workflow uses a face image and multi-angle character sheet to hold identity and kit constant, plans keyframes around a three-shot edit before animating them, and fixes visual drift at the image stage. Video, stadium sound and commentary remain separate layers so creators can trim and revise the sequence without rebuilding it.
Meta’s Low-Cost API Tests Frontier Models’ Pricing Power
Ranjan Roy and Alex Kantrowitz argue that AI products are converging on a common agentic workspace just as Meta moves to challenge the premium pricing of OpenAI and Anthropic. Roy says the durable advantage may lie in the organizational context, integrations, and domain expertise needed to make agents useful at scale; Kantrowitz counters that more capable models could eventually absorb much of that implementation work. Meta’s low-cost API strategy sharpens the question of whether frontier labs can retain pricing power when model capabilities and interfaces increasingly resemble one another.
Control Points Are Capturing Value Across the AI Economy
John Coogan and Jordi Hays argue that the AI economy is increasingly organized around control of the layers that route attention, transactions and computing capacity. They point to SK Hynix’s valuation as evidence of memory’s strategic scarcity, Meta’s push into AI and glasses as an effort to escape platform dependence, and OpenAI’s struggle to unify its tools without disrupting established workflows. The same logic, they contend, explains allegations that Phia captured affiliate credit at checkout and Hollywood’s willingness to pay for horror audiences already assembled online.
Seedream 5.0 Pro Adds Sketch-Guided AI Image Editing
ElevenLabs positions Seedream 5.0 Pro as an image-generation model for work that needs revision after the first render, rather than another prompt-only generator. The company says users can direct edits with sketches and annotations, generate text-heavy layouts in 14 languages, and—after launch—separate a composition into transparent, reusable layers. The model is available at 2K resolution, while 4K output and layer separation are not yet available.
OpenAI Launches ChatGPT Work to Execute Tasks Across Workplace Apps
OpenAI is introducing ChatGPT Work, an agent inside ChatGPT that it says can turn broad workplace goals into finished business materials across apps, files, and formats. The company frames the product, powered by Codex and GPT-5.6, as a move beyond question-answering into execution: using selected company context, producing documents, decks, analyses, sites, and reports, revising them through natural-language instructions, and following up as feedback arrives.
Gemini Omni Flash Turns Prompted Briefs Into Full-Context Video Edits
Alec Wilcock presents Google’s Gemini Omni Flash in ElevenCreative as a prompt-driven video editor that can alter footage while preserving the original shot’s motion, framing and subject consistency. His central argument is that the model works best when prompts read like edit briefs: specify the change being requested, then spell out what must not change. Across examples including restyling, relighting, object replacement, subtitles, titles and full Reel edits, Wilcock treats precision and constraints as the difference between a usable edit and a generic regeneration.
Blue Origin’s $130 Billion Valuation Prices Reusable Launch Capability
Old market, regulatory and product categories are failing to describe new technical capabilities, John Coogan and Jordi Hays argue in Diet TBPN. Coogan frames Blue Origin’s reported $130bn valuation as a bet on reusable-launch capability and Jeff Bezos’s commitment rather than current revenue, while the failed Getty-Shutterstock merger becomes, in their telling, a test of whether antitrust analysis can account for AI-generated images as substitutes. Their discussion of OpenAI’s GPT-Live and GPT-5.6 turns on a similar shift: models are being judged by whether they can sustain conversation, delegate work and operate software.
Proximity, Trust, and Positioning Drive High-Leverage Business Decisions
After a week in New York, Shaan Puri argues that proximity, trust and positioning are not soft business ideas but operating choices that change what gets built, sold and believed. In a conversation with Sam Parr, Puri uses meetings with creators, restaurateurs and brand operators to make the case for extreme in-person density balanced with protected maker time, for asking what a situation really means, and for treating trust and a single sharp sentence as increasingly valuable commercial assets.
GPT Image 2 And Aleph 2.0 Replace Video Objects From One Still
ElevenLabs’ tutorial argues that object replacement in video can be reduced to a frame-based workflow: use GPT Image 2 to alter a clean still from the source clip, then give that still to Aleph 2.0 as the target look for the full video. The demonstration replaces an apple with a flaming potato while preserving the rest of the shot, and extends the same method to background swaps and green-screen-style edits. The source’s practical caveat is that results depend on a clear reference frame and crisp footage with limited motion blur.
Flows Agent Turns Creative Prompts Into Editable Multimodal Media Workflows
Flows Agent is presented as a conversational workspace for turning a creative prompt into an editable multimodal media workflow. The source argues that its value is not just generating a single video, image, or audio asset, but assembling a structured flow of voice, image, video, and sound nodes that users can revise through natural-language prompts, replace with uploaded assets, and later customize or export.
Midjourney Medical Extends Image-Generation Ambitions Into Full-Body Ultrasound Scanning
TBPN hosts John Coogan and Jordi Hays read Midjourney Medical as a continuation of David Holz’s long-running work on sensing, interfaces and machine perception, rather than a sudden move from image generation into healthcare. Their account argues that Midjourney’s unusual business — bootstrapped, community-driven and cash-generative — has given Holz room to attempt a capital-intensive ultrasound scanning system with ambitions far beyond a conventional clinic device. The episode pairs that bet with OpenAI’s hiring of Noam Shazeer and Dean Ball as evidence that technical talent, policy capacity and institutional advantage are converging in AI.
Flows Agent Turns Creative Briefs Into Editable AI Production Pipelines
ElevenLabs presents Flows Agent as a conversational assistant for building and revising node-based creative workflows inside ElevenCreative Flows. The company’s case is that a user can describe an ad or other asset in natural language, have the agent assemble the models, prompts, nodes, and connections, then keep the resulting pipeline visible for edits, approvals, and reuse. The demo emphasizes cost controls for credit-heavy generation, node-level revisions through chat, and templates that turn a completed flow into a repeatable production system.
Models Will Absorb Today’s Agent Harnesses Within a Year
Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that the current rush to build agent harnesses may have a short shelf life. In an interview with Sequoia Capital’s Sonya Huang, he says models are absorbing the scaffolding around agents and could make much of today’s custom harness layer less distinctive within about 12 months. Google’s own strategy runs on both sides of that claim: Antigravity has become a shared agent layer across products, while Kilpatrick says the durable advantage for builders will move to focus, domain knowledge, risk tolerance and useful outcomes for users.
Codex Turns Campaign Briefs Into Editable Marketing Assets
OpenAI’s demo presents the Creative Production plugin for Codex as a campaign-production workflow for marketing teams, rather than a standalone image generator. Using a fictional Maison Feve chocolate launch, the company shows Codex turning a brief into mood-board directions, revised visual treatments, display-ad variants and an editable Canva handoff. The argument is that marketers can use Codex to carry campaign context through concepting, asset generation and final production edits in one working thread.
Apple’s New Siri Tests Who Controls the Default AI Assistant
John Coogan and Jordi Hays read Apple’s WWDC as a test of whether the company can turn its long-delayed Siri promise into a defensible AI interface without giving up control of defaults, privacy, and the iPhone camera. The Diet TBPN segment argues that Apple’s AI story is less about a single keynote than about older bets now becoming technically possible, while Anthropic’s Claude Fable release and Meta’s data-center training push show the same shift toward long-running inference and physical AI infrastructure.
A Python Decorator Replaces the GPU Deployment Container Loop
RunPod’s Audrey Hsu argues that GPU inference development should not require a commit, container build, registry push and server provisioning cycle for every model change. In a demo of Flash, RunPod’s Python SDK, she shows how adding a `@flash.endpoint` decorator to an async function can package that function as a GPU-backed cloud endpoint while the rest of the application stays in the developer’s IDE. Her broader case is that teams should experiment on Pods or low worker counts, then move to Serverless when they need autoscaling inference across many GPU workers.
ElevenLabs Adds Studio and Flows Agents to Automate Creative Production
Luke Harries used ElevenLabs’ Warsaw summit to argue that AI creative production is moving beyond prompt-based asset generation toward agent-directed workflows. Presenting ElevenCreative, he introduced Studio Agent and Flows Agent as layers above models and editing tools, intended to help teams ideate, script, prompt, edit, localize, and reuse campaigns. His case was that marketers’ role shifts from executing each production step to directing and approving systems that can produce hero assets, performance variations, and localized creative continuously.
Sanders’ 50% AI Stock Plan Turns Training Data Into a Political Fight
Jason Calacanis argued that Anthropic’s call for an AI slowdown and Bernie Sanders’ proposal for public ownership of major AI companies show AI politics moving toward jobs, ownership and redistribution. He dismissed Sanders’ 50% stock-tax plan as unworkable but said its premise could resonate with voters who believe AI companies built enormous value from public and creative inputs while threatening employment. Yoland Yan’s ComfyUI demo supplied the production-layer version of the same control question, presenting generative AI as a workflow where exposed parameters and reproducibility matter more than prompt-box convenience.
ComfyUI Bets on Open-Source Control for AI Video Workflows
Despite its Anthropic-titled hook, the source’s developed argument is about product interfaces that give users more control over complex systems. ComfyUI co-founder Yoland Yan argues that serious AI video creators need open, node-based workflows rather than simplified freemium tools; INTVL founder Louis Phillips makes the case for turning tracked routes into contested fitness territory; and the fact-checker bounty highlights live verification as a control layer for streamed claims.
Hackathon Caps Models at 32B Parameters to Reward Tinkerable AI Apps
Build Small is a Hugging Face and Gradio hackathon organized around a hard constraint: every model used must be under 32 billion parameters. Yuvraj Sharma framed the rule as a way to move AI building away from dependence on giant hosted models and back toward systems that participants can inspect, fine-tune, run locally, and ship as working Gradio Spaces. Sponsor presentations from Black Forest Labs, OpenBMB, OpenAI, NVIDIA, Modal, JetBrains, and Cohere largely reinforced that premise, offering small models, credits, tools, and prize categories meant to turn the constraint into runnable projects rather than demos in name only.
Native Multimodal Models Extend LLMs but Still Lack Unified Representations
Victoria Lin of Thinking Machines uses a Stanford CS25 seminar to argue that native multimodal models have extended much of the large-language-model recipe into images, audio, video and action, but have not yet unified multimodal intelligence. Her account is that tokenization, Transformers, autoregressive conditioning and scaling transfer only partly: images, video and action require different representations, objectives and sometimes modality-specific parameters. The result, she says, is a field moving beyond text-only systems while still relying on text as its strongest abstraction for reasoning.
Microsoft Bets Enterprise Agents Will Run Through the Cloud
John Coogan reads Microsoft Build 2026 as a sign that Microsoft is trying to make the cloud, not the phone, the center of enterprise AI agents. On Diet TBPN, he argues that Project Solara, Scout, OpenClaw support and Microsoft’s own models point to a platform strategy built around Azure, Microsoft 365 data, security boundaries and cost-efficient deployment rather than frontier-model supremacy. The open question, he says, is whether agent hardware and workflows can win adoption outside environments where companies can mandate them.