- Tech Insights 2026 Week 21
Two weeks ago there were over 2,000 plugins waiting in queue to be manually reviewed for the popular markdown editor Obsidian. The introduction of coding agents and interest for Obsidian as a virtual brain led to an explosion of new plugins being written for the platform, and the review process up until last week was 100% manual. Somewhere in November last year the Obsidian team just stopped reviewing plugins altogether, and started working on a new automated review system that would also process plugin updates, something that was not possible with the old system.
On Tuesday last week Obsidian finally launched their new fully automatic review process that uses a combination of linters and automated test tools to make sure plugins are written to the absolutely highest standards, and that plugins do not try to sneak in malicious code in any update. Each plugin is also given a rating between 1 to 100 based on source code quality. Only plugins with high enough rating are approved for publication.
In parallel with launching the new review process, the Obsidian team also launched a new Community plugin web site, where users can now browse plugins based on popularity, downloads and source code quality. And I am happy to say that my own plugin Notebook Navigator has been locked in as the #1 most popular Obsidian plugin since the day the Community page launched, out of over 3,700 plugins!
On the day the Community plugin page launched, Notebook Navigator was the only plugin with a perfect score rating of 100 / 100. And at the time of writing this newsletter this list has grown to 19 plugins out of 3,700 with a perfect score of 100. And it’s the only plugin in the top-100 most popular plugins with a perfect score. Now in case you didn’t already know this, Notebook Navigator is written 100% with OpenAI Codex. All 230 000 lines of it. And it has had zero critical issues since it’s launch in September last year.
If you have 20 minutes to spare, why not listen to my presentation from the GAIA conference last month where I present in detail how I did this, including the actual process and prompts I used over the past year. This presentation has something for everyone, no matter if you are the CEO of a company adopting AI, or if you are a developer using AI in your daily work.
YouTube: Agentic Engineering: Building Complex Apps by Johan Sanneblad
Thank you for being a Tech Insights subscriber!
Listen to Tech Insights on Spotify: Tech Insights 2026 Week 21 on Spotify
Notable model releases last week:
- ERNIE 5.1 by Baidu. A compressed variant of ERNIE 5.0 that cuts total parameters to roughly one-third while maintaining same performance.
THIS WEEK’S NEWS:
- Anthropic and OpenAI Launch Parallel Enterprise AI Ventures
- MIT Study Finds Regular ChatGPT Use Reduces Brain Engagement and Memory Recall
- Meta Employees Protest Mouse-Tracking Software Tied to AI Training
- Cerebras Goes Public: Largest U.S. Tech IPO Since Uber in 2019
- OpenAI Merges ChatGPT, Codex, and Developer API Under One Team
- Anthropic Releases Open-Source Legal AI Plugin Suite for Claude
- Anthropic Separates Agent SDK Usage Into Dedicated Monthly Credits
- xAI Enters the Agentic CLI Race With Grok Build
- X Open-Sources Full “For You” Feed Algorithm, Including Pre-Trained Model
- GitHub Copilot Desktop App Enters Technical Preview
- Google DeepMind Turns the Mouse Pointer Into a Gemini Input Device
- Thinking Machines Lab Publishes Research Preview of Native Real-Time Multimodal AI
- Krea Releases Its First In-House Foundation Model Built for Aesthetics
Anthropic and OpenAI Launch Parallel Enterprise AI Ventures
The News:
- On May 4, Anthropic and OpenAI each announced separate joint ventures with private equity firms to embed AI engineers inside enterprise clients and drive adoption of their respective models.
- Anthropic’s venture is valued at $1.5 billion, with $300 million committed each from Anthropic, Blackstone, and Hellman & Friedman, plus backing from Goldman Sachs, Apollo Global Management, General Atlantic, GIC, Leonard Green, and Sequoia Capital.
- OpenAI’s venture, called the OpenAI Deployment Company, raised over $4 billion from 19 investors at a $10 billion valuation; named backers include Goldman Sachs, TPG, Brookfield Asset Management, Advent International, and Bain Capital.
- OpenAI acquired Tomoro, an applied AI consulting firm, giving the Deployment Company roughly 150 Forward Deployed Engineers from launch.
- Both ventures plan to acquire additional engineering and consulting firms; OpenAI’s venture is reported to be in advanced discussions on multiple acquisitions.
- Anthropic’s stated approach targets mid-market companies in healthcare and industrial sectors, with engineers co-designing tools alongside clinicians and operations staff.
My take: Effectively adopting AI agents within the enterprise is one of the most difficult tasks you can do today. As a consultant you must know how to navigate around executive management teams, calculate return on investment, choose the right architectural solution, plan for implementation, deliver and scale. In my own experience there are very few companies that have the skills to do this the right way.
Both these labs are approach work according to Palantir’s Forward Deployed Engineer playbook: send an engineer into the client, sit with the operators, ship software around the model, and stay until it works in production. If you want to learn more about the Palantir model, look at how they use Forward Deployed Engineers. I think this is where enterprise AI is heading: less buying services from the outside, more sitting inside the business until the technology actually changes how work gets done.
Read more:
- OpenAI launches the OpenAI Deployment Company to help businesses build around intelligence | OpenAI
- Anthropic Unveils $1.5 Billion Joint Venture With Wall Street Firms – WSJ
- Dev versus Delta: Demystifying engineering roles at Palantir | by Palantir | Palantir Blog
MIT Study Finds Regular ChatGPT Use Reduces Brain Engagement and Memory Recall
https://arxiv.org/abs/2506.08872
The News:
- A research team from MIT Media Lab studied the neural and behavioral effects of using GPT-4o for essay writing among 54 participants, divided into three groups over four sessions spanning four months: an LLM group, a search engine group, and a brain-only group.
- EEG measurements showed the LLM group had up to 55% lower brain connectivity compared to the brain-only group, with the weakest alpha and beta network coupling, including in occipito-parietal and prefrontal areas associated with memory and visual processing.
- 83% of LLM users could not quote from essays they had just written, and 78% of those switching from LLM to unaided writing in the final session also failed to quote their own work.
- LLM group essays were statistically homogeneous within topics, while the brain-only group produced significantly more varied content. LLM users incorporated 2-3 times more named entities (specific facts, dates, names) yet showed less original thinking.
- Participants who used the brain-only approach for three sessions and then switched to AI in session four showed higher memory recall and re-engagement of occipito-parietal and prefrontal areas, compared to participants who had used AI throughout.
“In our current EEG analysis we focused on reporting connectivity patterns without examining spectral power changes, which could provide additional insights into neural efficiency.”
My take: This is a small study with only 54 participants, the article is not peer-reviewed, and the team lacked spectral power analysis in the EEG data. The article ended up being very good click-bait because who doesn’t want to post headlights like “MIT researchers say using ChatGPT can rot your brain”.
However, there was one interesting piece in this study, and that was that they showed that participants who did their own thinking first and only switched to AI later still had strong recall and brain engagement. This suggests that the real risk isn’t using AI, it’s using it as a replacement for thinking rather than an amplifier of it.
Read more:
- Let’s talk about the MIT study on ChatGPT (and what you can do about it) : r/Btechtards
- MIT researchers say using ChatGPT can rot your brain
Meta Employees Protest Mouse-Tracking Software Tied to AI Training
https://finance.yahoo.com/news/exclusive-meta-u-employees-organize-195905738.html
The News:
- Meta installed software on U.S. employee computers that tracks mouse movements, clicks, keystrokes, and captures occasional screenshots within work-related apps and websites, under a program called the Model Capability Initiative (MCI), as part of a broader effort to build AI agents that perform workplace tasks autonomously.
- MCI also records employee activity on external platforms including Google, LinkedIn, Wikipedia, GitHub, and Slack.
- On May 12, employees distributed protest flyers across multiple U.S. offices, placing them in meeting rooms, on vending machines, and atop toilet paper dispensers, urging colleagues to sign an online petition against the software.
- The flyers cited the U.S. National Labor Relations Act, stating “workers are legally protected when they choose to organize for the improvement of working conditions.”
- A Meta spokesperson stated: “If we’re building agents to help people complete everyday tasks using computers, our models need real examples of how people actually use them — things like mouse movements, clicking buttons, and navigating dropdown menus.” Meta also confirmed the data will not be used for performance evaluations and that safeguards are in place to protect sensitive information.
- Meta has announced plans to lay off approximately 8,000 employees, roughly 10% of its global workforce, with cuts scheduled to begin May 20, 2026.
My take: Just when you didn’t think Meta could sink any lower they start recording every single motion of all their US employees in the hope of training future AI models to do all their work. What sounds like the script of a very dystopian Sci-Fi movie is actually happening right now in 2026. Now consider that in 2 years time we would have a model that could do all desktop work that a white-collar at Meta did in 2026, is this really the best use we could have for that AI? To sort Excel sheets and assemble PowerPoints?
I am a strong believer in reinforcement learning with verifiable rewards (RLVR) and I believe that AI models learn best by doing, not by analyzing millions of mouse pointer clicks. I believe the only net effect this will have is more people leaving Meta, and only those who cannot find any other job will be forced to stay as puppeteers in Zuckerbergs AI playground.
Cerebras Goes Public: Largest U.S. Tech IPO Since Uber in 2019
The News:
- Cerebras Systems, a Sunnyvale-based AI chip maker founded in 2016, listed on Nasdaq under the ticker CBRS on May 14, raising $5.55 billion in the largest U.S. tech IPO since Uber’s listing in 2019.
- Shares priced at $185, opened at $350, and closed at $311, a 68% gain on the day. The first-day opening represented roughly 89% above the IPO price.
- The company raised its price range twice before pricing: first from $115–$125 to $150–$160, then to the final $185.
- Cerebras designs chips using a Wafer Scale Engine (WSE-3), a single silicon wafer containing 900,000 AI cores and 44 GB of on-die SRAM connected at 21 PB/s bandwidth. At batch size 1, the WSE-3 delivers approximately 2,100 tokens/sec on Llama 3.1 70B, compared to roughly 550 tokens/sec on a single NVIDIA H100 SXM5.
- In January 2026, Cerebras announced a multi-year deal with OpenAI valued at over $20 billion, covering up to 750 megawatts of computing power through 2028. Spending could reach $30 billion, which would grant OpenAI warrants for an equity stake.
- Customers include OpenAI, G42, Amazon Web Services, and Mohamed bin Zayed University of Artificial Intelligence.
- Cerebras filed to go public in September 2024 but withdrew the S-1 in October 2024 after disclosures showed that G42, a single customer, accounted for more than 80% of revenue in 2023 and the first half of 2024. The company refiled in 2026 with a diversified customer base and revenue of $510 million in 2025, up 76% from $290 million in 2024.
My take: Cerebras’s commercial focus is inference (“answering questions”) rather than training. Their main selling point is WSE-3, which fits 900,000 AI cores on a single chip with 44 GB of on-die SRAM at 21 PB/s internal bandwidth. This gives them a significant latency advantage for models that fit entirely on-die, but larger models that exceed 44 GB must spill to external memory which is orders of magnitude slower than the on-die figure. Thus, the IPO valuation seems to mostly reflect their strong revenue growth and the OpenAI deal rather than proven performance at frontier model scale.
OpenAI Merges ChatGPT, Codex, and Developer API Under One Team
https://twitter.com/mark_k/status/2055351143610327110
The News:
- OpenAI is consolidating its ChatGPT, Codex AI coding agent, and developer API into a single product organization, with Greg Brockman formally taking over product strategy.
- In an internal memo, Brockman stated the product lines are “naturally converging” and that the merger is intended to concentrate resources on agents.
- Thibault Sottiaux, previously head of Codex, now leads the combined core product and platform team, and is overseeing a “super-app” that would unify Codex, ChatGPT, and the Atlas browser into a single desktop experience.
- Nick Turley, who led ChatGPT’s consumer growth, has moved to head enterprise products and is stepping away from consumer initiatives; Ashley Alexander, former VP of Instagram, has taken over consumer products.
- The ChatGPT mobile app launched a Codex tab in preview on iOS and Android, letting users monitor active coding tasks, review outputs, approve commands, and change models from their phones while Codex runs on a connected desktop or cloud machine.
My take: Last week I wrote that OpenAI would probably launch remote control in their mobile app soon and that they over time probably will “abandon and phase out the regular ChatGPT app for desktop and mobile”. Now on May 14 OpenAI launched mobile support: to remotely control Codex running on your desktop computer, and on May 15 they announced that they are merging the ChatGPT team, the Codex team and the Developer API team into one single organization. I think this is the right move and it will make it easier for OpenAI to keep their focus going forward. Everything is centered around automations, and with the speed things are moving right now – sitting as disconnected teams just won’t cut it.
Read more:
Anthropic Releases Open-Source Legal AI Plugin Suite for Claude
https://github.com/anthropics/claude-for-legal
The News:
- Anthropic published claude-for-legal on May 12, an Apache 2.0 open-source GitHub repository with 12 practice-area plugins, 70+ named agents, and 20+ MCP connectors for legal workflows, accumulating 5.6k GitHub stars and 774 forks within days of launch.
- The plugins cover commercial, corporate, employment, privacy, product, regulatory, AI governance, IP, and litigation work, with additional tools for law school clinics and students.
- Named agents include the Vendor Agreement Reviewer, NDA Triager, DSAR Responder, DPA Reviewer, PIA Generator, Claim Chart Builder, Demand Letter Drafter, and Trademark Clearance Screener.
- Deployment runs through Claude Cowork and Claude Code (one-click install) or the Managed Agents API for headless and scheduled deployments; all outputs are framed as drafts for attorney review, not legal advice.
- A Thomson Reuters partnership connects Claude to Westlaw primary law, Practical Law, and KeyCite through the CoCounsel Legal MCP connector, with source attribution required on every citation.
- Claude carries persistent context across Microsoft Word, Outlook, Excel, and PowerPoint, so a redline drafted in Word does not need re-explanation when referenced in an Outlook cover note.
My take: This Legal AI Plugin Suite by Anthropic is a direct attack against dedicated legal AI platforms like Harvey, valued at $11 billion and serving roughly two-thirds of the AmLaw 100, and Legora, which closed a $600 million Series D at a $5.6 billion valuation in April. Legora builds their own workflow tools on top of Claude Opus 4.7, and by open-sourcing claude-for-legal under an Apache 2.0 license, Anthropic commoditizes the product these companies sell commercially. At the speed at both Anthropic and OpenAI are moving at the moment, any company that has any service based around their models risk getting sidestepped any month in the coming year.
Anthropic Separates Agent SDK Usage Into Dedicated Monthly Credits
https://support.claude.com/en/articles/15036540-use-the-claude-agent-sdk-with-your-claude-plan
The News:
- Starting June 15, Anthropic separates programmatic Claude usage from subscription usage limits, creating a standalone monthly “Agent SDK credit” for Pro, Max, Team, and Enterprise plans.
- The credit applies to Claude Agent SDK (Python and TypeScript), the claude -p non-interactive command, Claude Code GitHub Actions, and third-party apps that authenticate via the Agent SDK.
- Credits are per-user, not poolable across teams, do not roll over, and require a one-time opt-in through the user’s Claude account before June 15.
- Credit amounts by plan: $20 for Pro, Team Standard seats, and Enterprise usage-based seats; $100 for Max 5x and Team Premium seats; $200 for Max 20x and Enterprise seat-based Premium seats. Enterprise Standard seats are not eligible.
- Once the monthly credit is exhausted, additional Agent SDK usage flows to standard API billing at pay-as-you-go rates, but only if the user has enabled “extra usage.” If not enabled, Agent SDK requests are suspended until the credit refreshes.
- On the same day as the announcement, Anthropic also increased Claude Code weekly limits by 50% through July 13 for Pro, Max, Team, and seat-based Enterprise users, requiring no opt-in.
My take: If you subscribe to an Anthropic Max20 subscription for $200 per month, you can easily go through $500 worth of API usage from that subscription on a daily basis, especially if you employ agents like OpenClaw. Anthropic are almost out of GPU resources, and it makes no sense for them to hand it all out for people to experiment with autonomous agents at their expense. So starting on June 15 all autonomous use of Claude will go have to go through paid API access, which definitely makes sense. I do however read dozens of surprised and angry posts every day on social media about this change, but it shouldn’t have come as a surprise to anyone.
xAI Enters the Agentic CLI Race With Grok Build
The News:
- xAI released Grok Build, an agentic command-line interface for coding, app building, and workflow automation, currently in early beta and restricted to SuperGrok Heavy subscribers.
- The tool runs directly in the terminal and includes a Plan mode that lets users review, comment on individual steps, or rewrite an entire execution plan before it runs, with all changes displayed as clean diffs.
- Grok Build auto-detects existing AGENTS.md files, plugins, hooks, Skills, and MCP servers when launched inside a repository, requiring no additional configuration.
- For larger tasks, Grok Build dispatches work to parallel subagents, which can each operate in separate Git worktrees simultaneously.
- It supports headless mode for scripting and automated pipelines, plus full ACP support for building custom bots and orchestration applications.
My take: Both Anthropic and xAI are going all-in on the terminal interface, with Anthropic adding agent view in the latest version and xAI launching their first CLI tool last week. The benefits with a command-line tool is of course simplicity of developing it, the main drawbacks are that it cannot easily control your computer or host a browser. OpenAI and GitHub are both moving the other direction, from CLI to the desktop app. Which one is right I don’t know, but we’ll see in half a year or so which direction most users preferred.
Read more:
X Open-Sources Full “For You” Feed Algorithm, Including Pre-Trained Model
https://github.com/xai-org/x-algorithm
The News:
- On May 15 xAI published a major update to the xai-org/x-algorithm GitHub repository, making the full inference pipeline for X’s “For You” feed runnable locally for the first time, under the Apache 2.0 license.
- The update ships a unified entry point (phoenix/run_pipeline.py) that runs retrieval and ranking in cascade, replacing two previously disconnected scripts (run_ranker.py and run_retrieval.py) from the January 2026 release that required manual coordination and undocumented configuration.
- A downloadable mini Phoenix model is included via Git LFS: 256 embedding dimensions, 4 attention heads, 2 transformer layers, approximately 3 GB. It is not the production model but runs the complete recommendation stack on a consumer GPU.
- The system eliminates all hand-engineered features. Phoenix uses a two-tower transformer for retrieval (user embedding vs. post embedding via dot-product similarity) and a Grok-derived transformer for ranking, predicting 15 engagement probabilities per post including reply, dwell time, share, and report.
- A “Candidate Isolation” attention mask ensures each post’s score is independent: candidates cannot attend to each other during ranking, only to the user context. This makes scores deterministic and batch-independent.
- The May release also adds a new home-mixer/ads/ module for ad injection with brand safety tracking, the Grox subsystem for spam and safety classification, new post metadata hydrators, and a reusable Rust-based candidate pipeline framework (Source, Hydrator, Filter, Scorer, Selector, SideEffect).
- The repository has accumulated over 16,200 stars, 2,800 forks, and 183 watchers. Written 62.9% in Rust and 37.1% in Python.
My take: If you are working with a similar architecture, you could probably learn a thing or two by looking into how xAI built the X recommendation engine from the ground up here. But if you planned to use this repo as a guide to your posting strategy on X, unfortunately the final feed score is quite simple: it’s a weighted sum of all positive signals (repost, reply, share, dwell) minus all the negative signals (block, mute, not interested, report), with additional attenuation from an Author Diversity Scorer and an Out-of-Network Scorer on top. Blocks carry negative weight, so even a single polarizing post could significantly reduce a creator’s reach even if likes continue to accumulate. Since xAI didn’t publish engagement weight values or the embeddings trained on the X social network you can’t really use this to launch your own version of X, it’s mostly for inspiration and learning.
GitHub Copilot Desktop App Enters Technical Preview
https://github.blog/changelog/2026-05-14-github-copilot-app-is-now-available-in-technical-preview/
The News:
- GitHub launched the Copilot app in technical preview on May 14, a standalone native desktop client for agentic development that targets the full workflow from opening an issue to merging a pull request, without requiring an open editor.
- Sessions start from GitHub artifacts: users can open a session from an issue, pull request, free-form prompt, or a previous session, with issue details, repository state, review comments, and CI checks automatically connected.
- Each session runs in its own isolated space with a dedicated branch, file state, and conversation, supporting pause and resume across multiple concurrent tasks in one or more repositories.
- An integrated terminal and browser are built directly into the app, letting users run commands, open live previews, and test changes without switching to an external window.
- Agent Merge lets the agent address review comments, fix failing CI checks, and complete the merge once user-defined conditions are met, but it does not bypass existing branch protection rules requiring human approvals.
- Access is structured in two waves: Copilot Pro and Pro+ subscribers can join the waitlist for early access, while Business and Enterprise subscribers get rolling access during the launch week provided their admin has enabled both previews and Copilot CLI in policy settings.
My take: Currently there are over 80 products named Copilot from Microsoft, so of course they named this new app from GitHub the “Copilot app”. 🤔 😂 Someone who understands how Microsoft works really should explain this to me some day. This new “Copilot app” then, it looks almost identical to the OpenAI Codex desktop app. Remember that OpenAI wrote this app in a few weeks in the beginning of the year? Well it looks like GitHub has followed in their footsteps, and they had a very good template to copy from. At this point GitHub is moving in three different directions with AI. First they have VSCode with the GitHub Copilot side pane, then they have the new VS Code Agents Window in VSCode (which is still in preview) and now they also have this new “Copilot app”. I have really tried to use GitHub Copilot many times for agentic engineering the past year, but despite the models being the same, the results I get from the GitHub harnesses are always of much lower quality than I get from OpenAI Codex.
Read more:
- How many products does Microsoft have named ‘Copilot’? I mapped every one | Tey Bannerman
- Introducing the VS Code Agents Window (Preview) – YouTube
Google DeepMind Turns the Mouse Pointer Into a Gemini Input Device
https://deepmind.google/blog/ai-pointer
The News:
- On May 12, Google DeepMind published a research blog and experimental demos for AI Pointer, a Gemini-powered cursor that reads the visual and semantic context around the pointer position and accepts short voice commands, replacing detailed text prompts.
- The pointer reads what is under the cursor, whether text, images, code, or tables, and pairs that context with brief spoken instructions such as “Fix this,” “Move that here,” or “Double the ingredients,” allowing Gemini to act without the user switching windows or drafting a full prompt.
- Four interaction principles guide the project: maintain the flow, show and tell, embrace the power of “this” and “that,” and turn pixels into actionable entities.
- Two interactive demos are available today in Google AI Studio: an image editing tool and a map-based location finder, accessible via pointer and voice.
- The Chrome integration is rolling out now, letting users point at webpage sections to ask Gemini questions or compare products in context. Magic Pointer, the deeper Googlebook laptop implementation, will roll out soon.
“Our goal is to address a common frustration: because a typical AI tool lives in its own window, users need to drag their world into it. We want the opposite: intuitive AI that meets users across all the tools they use, without interrupting their flow. “
My take: This is an interesting approach, allowing an AI model see whatever your cursor focuses on. This feels like one of the things that’s very difficult to understand how well it works before trying it, so if you have time you can go try to edit an image or find places on a map with this magic pointer. It’s soon rolling out to all Chrome users, so you will have it in your computer soon enough.
Thinking Machines Lab Publishes Research Preview of Native Real-Time Multimodal AI
https://thinkingmachines.ai/blog/interaction-models
The News:
- Thinking Machines Lab, founded by former OpenAI CTO Mira Murati, released a research preview of TML-Interaction-Small, a 276B parameter MoE model with 12B active parameters trained from scratch for real-time multimodal conversation.
- Unlike GPT-Realtime-2.0 and Gemini-3.1-flash-live, which wrap turn-based models with voice-activity detection to simulate responsiveness, TML-Interaction-Small processes and generates audio, video, and text in continuous 200ms chunks, eliminating turn boundaries entirely.
- On FD-bench V1, the model achieves 0.40s turn-taking latency versus 0.57s for Gemini-3.1-flash-live and 1.18s for GPT-realtime-2.0. It is also the only model above chance on visual proactivity tasks such as counting push-up repetitions in a live video stream.
“If asked “Please count how many pushups I do” such a system might respond “Sure thing!” and then remain silent – waiting for an audio-only cue that never comes.”
My take: The proposal from Thinking Machine Lab here is that their AI model can continuously analyze input such as video and react to it immediately. One example mentioned in the post is a user asking to count the number of pushups, here the model continuously analyzes the input and counts as you go on. It’s a very interesting model approach, for something traditional AI models are not natively built to do. How well it will scale to larger sizes we don’t know yet, but I can definitely see use cases for this type of model in the future.
Krea Releases Its First In-House Foundation Model Built for Aesthetics
The News:
- Krea released Krea 2 (K2) on May 12, its first image foundation model built from scratch, with its stated purpose being “an image model built for aesthetics” rather than prompt precision.
- Where most image models optimize for rendering exactly what the prompt describes, K2 is trained to interpret prompts through an aesthetic lens, producing outputs that Krea describes as “raw, flexible, unopinionated, and unconstrained.”
- A style transfer system lets users pass one or more reference images into the model, control how strongly each reference influences the output, and combine multiple styles simultaneously.
- A moodboard feature lets users curate a collection of reference images to communicate broader visual directions, palettes, or textures that cannot be captured by a single prompt or reference.
- Batch variation controls let users set how much outputs should diverge across a generation run, from visually cohesive sets to a wide spread of palettes and details.
- Krea states generation times of 15 seconds or less per output.
My take: I love GPT Image 2, and it creates AI-generated images that look like they were shot by a professional photographer or designed by a professional designer. Krea 2 is something else. The images produced by Krea 2 looks more like a photo taken by a professional lomographer. They are different, but in a positive way. I really liked the photos generated from this model, and I spent some time with it over the weekend and so far I like it a lot.
Read more: