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ChatGPT Images 2.5 and GPT-Image-2.5 Flare vs Sunburst: A Practical Guide for Creators and API Workflows ChatGPT Images 2.5 and GPT-Image-2.5 introduce a more production-oriented approach to image generation. Instead of treating every request as a one-off prompt, creators can now choose between two visual modes—Flare and Sunburst—based on the type of work they are doing. The difference matters most when an image must be edited repeatedly, match a brand system, support product photography, or move through an API workflow with predictable latency. This guide explains how the modes compare, how Sketch and templates fit into the process, what changes when migrating from Images 2.0, and how to make generated creative more reliable in production. Image: mikemacmarketing / photo on flickr via Wikimedia Commons (CC BY 2.0) Image: Midjourney; prompt suggested by Grok via Wikimedia Commons (Public domain) What changed in ChatGPT Images 2.5? Images 2.5 is best understo...

Production RAG Evaluation in 2026: Retrieval Metrics, Faithfulness Checks, and When to Fine-Tune Instead Retrieval-augmented generation (RAG) systems are easy to prototype and difficult to operate reliably. A demo can answer questions from a handful of documents, but a production system must handle stale content, ambiguous queries, missing permissions, conflicting sources, long documents, and model updates. In 2026, evaluating RAG means measuring more than whether the final answer “sounds right.” Teams need to determine whether the system retrieved the right evidence, used that evidence faithfully, declined when the evidence was insufficient, and improved business outcomes without introducing unacceptable risk. Image: mikemacmarketing / photo on flickr via Wikimedia Commons (CC BY 2.0) Image: Midjourney; prompt suggested by Grok via Wikimedia Commons (Public domain) A useful production evaluation strategy separates the RAG pipeline into distinct stages. Retriev...

Street Interview Tips: 10 Proven Ways to Get Better On-Camera Responses Street interviews are one of the most powerful ways to capture authentic opinions, create engaging content, and connect with a real audience. But anyone who has tried knows it's not as easy as pointing a mic at a stranger. You need preparation, psychology, and a bit of technical know-how. These street interview tips will help you get natural, compelling responses — whether you're a journalist, content creator, or market researcher. 1. Prepare, But Don't Over-Script Know your topic cold and have a clear goal for each interview, but don't write out every question word-for-word. Overly scripted questions sound robotic and make interviewees nervous. Instead, prepare a brief outline of key themes and let the conversation flow naturally. Your best material will often come from unexpected tangents. 2. Choose the Right Location Location dictates the tone and energy of your interview. High-foot-traffic ...

Claude Formalizes Fermat's Last Theorem in Lean: What Anthropic’s 11-Day Multi-Agent Proof Means for Developers and Formal Methods Anthropic’s reported 11-day effort to formalize Fermat’s Last Theorem in Lean offers a useful glimpse into where generative AI is heading: not merely producing plausible mathematical prose, but coordinating software agents that construct, check, debug, and refine machine-verifiable proofs. The headline is impressive, but the practical significance is broader than one famous theorem. The project shows how AI can work inside rigorous developer tools, where every claimed result must pass a trusted compiler and where progress depends on managing thousands of small technical decisions. Image: mikemacmarketing / photo on flickr via Wikimedia Commons (CC BY 2.0) Image: Midjourney; prompt suggested by Grok via Wikimedia Commons (Public domain) Why Fermat’s Last Theorem is a meaningful test Fermat’s Last Theorem states that there are n...

GPT-6 Astra for Developers: A Practical Guide to OpenAI’s Flagship Successor to GPT-5.6 Sol GPT-6 Astra is positioned as OpenAI’s flagship successor to GPT-5.6 Sol, with its biggest developer-facing change being a stronger focus on computer use and autonomous task execution. Rather than limiting an agent to text, code, or browser pages, Astra is designed to interact with desktop applications, inspect visual interfaces, operate tools, and complete multi-step workflows. For developers, the important question is not simply whether Astra is more capable than its predecessor, but how to build reliable systems around those capabilities without surrendering control, security, or observability. Image: mikemacmarketing / photo on flickr via Wikimedia Commons (CC BY 2.0) Image: Midjourney; prompt suggested by Grok via Wikimedia Commons (Public domain) What makes GPT-6 Astra different? Astra combines conventional language-model abilities with computer-use features. An a...

Google Teamwork Multi-Agent Framework with Gemini 3.7 Flash: What Adversarial Agent Tournaments Teach Developers About Production AI Systems Google’s Teamwork multi-agent framework, reportedly available as a preview workflow in Antigravity and powered by Gemini 3.7 Flash, points toward a different way to build capable AI systems. Instead of asking one model to solve a difficult problem from beginning to end, Teamwork divides the work among agents with different responsibilities: proposing solutions, attacking assumptions, checking evidence, running experiments, and assembling a final result. The important idea is not simply “use more agents.” It is to create structured disagreement that makes weak reasoning easier to detect. Image: mikemacmarketing / photo on flickr via Wikimedia Commons (CC BY 2.0) Image: Midjourney; prompt suggested by Grok via Wikimedia Commons (Public domain) That distinction matters for developers working on research assista...

Claude Fable 5.1 for Developers in September 2026: How a 75% Cache-Read Cut Changes Agent Economics Anthropic’s reported 75% reduction in Claude Fable 5.1 cache-read pricing changes the economics of long-running coding agents more than a modest reduction in headline token rates would. The important question is not which model has the lower price per million tokens. It is how much of each agent loop can be served from cached context, how often the agent reaches a correct solution, and how many failed tool calls and retries are required before a task is actually solved. This article treats Claude Fable 5.1 and GPT-6 Astra as comparable September 2026 options with roughly 1 million-token context windows and similar list prices, then focuses on the practical cost per solved task. Image: Steve Jurvetson from Los Altos, USA via Wikimedia Commons (CC BY 2.0) Image: Steve Jurvetson via Wikimedia Commons (CC BY 2.0) For developers building coding agents, the central dis...