OpenAI has released GPT-Image 2.5, the successor to GPT-Image 2. Higgsfield has already integrated the new model as well. What changes according to the announcement: → Light looks more natural → Changes are applied more precisely: only the requested area is adjusted, the rest stays untouched → More complex layouts, transparent backgrounds and real-world content are rendered more reliably → Speed: compared to GPT-Image 2.0, latency is now up to 50 percent lower ChatGPT is also getting a sketch feature: your own drawing becomes the template for the image, plus templates for common formats, comments directly on the image and shareable prompts. The API offers two variants: Flare for speed and high volume, Sunburst for more control in premium workflows, at the cost of longer generation time. The rollout covers all ChatGPT and Codex tiers; C2PA labeling and watermarks remain mandatory. Anyone who works with AI images daily will probably care most about consistency across multiple edit rounds: until now, an image often lost quality with every additional step. Whether that holds up now will only show in your own testing. Has anyone already tried GPT-Image 2.5, does a subject actually stay stable across multiple editing rounds? 👇

Let's get to know each other! Comment below sharing where you are in the world, a career goal you have, and something you like to do for fun. 😊

Project: weekly-niche-report — an automation that researches profitable affiliate niches weekly, analyzes them, and emails a summary report, with no laptop required to keep it running. I have an automation running in Trigger.dev's production environment that you can see in the dashboard, and it doesn't require your laptop to be open. Confirmed: the task is deployed, scheduled, and has successfully completed a real production test run — from data research through to sending the email — entirely independent of any local machine.The automation was built, deployed to Trigger.dev, and connected to GitHub so that new code changes deploy automatically. All required production credentials (AI, email, and search API keys) are configured in Trigger.dev. The weekly schedule is active and set to the correct local time zone. A full production test run completed successfully, confirming the entire pipeline works end-to-end and a real report email was delivered. Every Monday morning, Trigger.dev will run the task on its own and send a fresh report by email. Any future code changes pushed to the repository will redeploy automatically.During initial testing, one of the API integrations (SerpAPI / Resend / Anthropic) didn't behave as expected, most likely due to a version mismatch between the Trigger.dev CLI and the @trigger.dev/* SDK packages — these need to stay in sync, or calls can fail unpredictably. Claude Code identified the mismatch and aligned the package versions, after which the task ran cleanly end-to-end.

Honestly been putting this off for a while always "later, when I have more time." But later doesn't really come, so I just started. Not gonna pretend I know where this goes yet. But I like being a beginner again, that's actually the fun part. If anyone else in here just started too, say hi. Would be cool to go through this together instead of alone.

Today combined the Day 3 theory (skill anatomy, progressive loading) with a full hands-on build: a real, working Claude Code skill for a client project, built from an empty folder to a tested, functioning tool. What was built: agency-infographic Brand system (reference.md, sourced from the real platanusdesign-brand skill): PlatanusDesign's actual colors (#F47D20 orange accent, #341390 indigo, etc.), Poppins/Open Sans typography, real logo usage rules, and a "card grid" layout pattern refined through iteration — solid black background, large topic-specific icons in orange, orange card headings, white body text. Outcome: A fully working, tested skill: confirmed to research, draft, gate on user confirmation, render on-brand output, and generalize correctly across unrelated topics/industries — built and debugged hands-on rather than handed over per-made.