📸 ChatGPT images get faster and cleaner

ChatGPT just got a major upgrade to how it creates and edits images. The new experience is faster, more accurate, and makes it easier than ever to tweak photos, whether you’re fixing lighting, refining faces, or adjusting style. Let’s dive in!

In today’s insights:

LATEST DEVELOPMENTS

Evolving AI: ChatGPT rolls out faster image creation with more accurate edits and text handling.

OpenAI released a new ChatGPT Images experience powered by a flagship image model that focuses on accuracy and speed. Users can edit uploaded photos with better detail preservation, from lighting and faces to layout and style. Text rendering is clearer, edits apply more reliably, and generation runs up to four times faster. A new Images space in ChatGPT adds presets and styles, and GPT Image 1.5 brings these gains to the API at lower cost.

Besides the release, there is now a dedicated images space within ChatGPT. This space makes creating and editing visuals feel immediate. You can start from a prompt, upload a photo, or pick from preset styles without writing anything. Edits apply cleanly, keeping lighting, faces, and layout consistent, and text inside images is sharper and easier to control.

This is OpenAI nudging image generation from “fun demo” into something you can drop into real work. If you edit product photos, ad creatives, thumbnails, or decks, the big win is fewer redo loops: edits stick to the request, logos and key visuals hold up, and text in images is more usable, which is where a lot of tools still stumble. Pair that with up to 4× faster output and a ~20% price cut for image inputs and outputs in the API, and suddenly teams can iterate like designers do: quick variants, quick approvals, and faster shipping across ecommerce catalogs and marketing pipelines.

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Evolving AI: Nvidia expands its open AI stack with a Slurm acquisition and new Nemotron models.

Nvidia announced the acquisition of SchedMD, the team behind Slurm, a widely used open source workload manager for AI and high performance computing. Slurm will stay open and independently governed, with Nvidia investing to broaden system support and scale access. Alongside the deal, Nvidia introduced the Nemotron 3 model family, ranging from lightweight Nano models to Ultra models built for advanced agent workflows. The moves reflect Nvidia’s growing focus on open models, open tooling, and physical AI tied to robotics and autonomous systems.

Nvidia is acting on what’s become impossible to ignore: AI isn’t just about bigger models any more, it’s about making them easier and cheaper to run inside real systems, and owning the software that actually runs the work matters just as much as owning the chips. By bringing Slurm into its fold while keeping it open, Nvidia is smoothing out the messy, painful parts of running huge workloads and helping researchers and teams spend less time wrestling clusters and more time building features, and at a time when everyone from cloud operators to robotics startups are trying to scale with tighter budgets, that kind of practical boost changes how fast people can get things done.

Open-source AI is moving fast, and it’s starting to change how real products get built. In this talk, leaders from NVIDIA and Sequoia explain why open models and AI agents may matter more than one giant closed system, and what that means for developers and startups right now. Definitely worth a watch, especially now that NVIDIA is leaning hard into open-source AI.

Evolving AI: The US government will hire 1,000 early career engineers, data scientists, and AI experts for a two-year program.

The US government has announced the US Tech Force, a new early career program aimed at filling technical gaps across federal agencies. The first cohort will include 1,000 software engineers, data scientists, project managers, and AI specialists placed directly inside agencies for two years. Projects range from AI-enabled defense systems to smarter tax platforms and intelligence tools. The program blends public-sector work with private-sector mentorship, offering competitive pay and a pathway back to industry.

This is the US admitting that hiring vendors and writing policy decks is not enough anymore. Real AI progress happens when builders sit inside the system, touching production data, legacy software, and real constraints. By paying close to Big Tech rates and pulling early talent straight into agencies, the government speeds up projects that usually stall for years. It is a test of whether public institutions can finally move at software speed, and whether AI engineers are willing to trade perks for impact, even if only for two years.

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