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15 community available in the ChatGPT directory
Tested the new Codex agent on 10 real tasks from our backlog. It spins up a sandboxed environment, reads your codebase, makes changes, and runs tests. Compared to Claude Code: Codex is more autonomous but less transparent about what it's doing. Claude Code lets you watch each step and intervene. For straightforward tasks (add a feature, fix a bug), Codex is faster. For complex refactors where judgment matters, Claude Code's interactive approach wins.
Been testing GPT-5 since launch and the improvement over GPT-4o is significant. Reasoning is noticeably stronger — it catches logical fallacies I embed in prompts that GPT-4o missed. The memory feature actually works now, recalling preferences from weeks ago. Code generation is competitive with Claude Sonnet. The only downside: it's more expensive and slower. For complex tasks, worth it. For quick queries, 4o-mini is still better value.
After months of iteration, here's the system prompt structure that consistently gets GPT-4o to follow complex instructions: 1. Role definition (one sentence) 2. Output format (explicit structure with examples) 3. Constraints (what NOT to do — this is crucial) 4. Reasoning approach (step-by-step for complex tasks) 5. Error handling ("If you're unsure about X, say so instead of guessing") The key insight: GPT-4o responds much better to negative constraints than positive instructions. "Never use bullet points" works better than "Write in paragraph form."
Started using ChatGPT voice mode during walks and it's become my primary brainstorming tool. The natural conversation flow helps me think through problems in a way that typing doesn't. Last week I talked through an architecture decision for 30 minutes and it raised three concerns I hadn't considered. The voice quality is incredibly natural and it picks up on context cues like hesitation. The future of AI interaction is voice, not text.
OpenAI quietly upgraded the Code Interpreter backend and the difference is night and day. It now handles 100MB+ files without choking, generates publication-quality charts with proper formatting, and the Python environment includes scikit-learn, statsmodels, and other ML libraries. Uploaded our company's sales data and it built a forecasting model, generated an executive summary, and created a PDF report — all in one conversation.
Tested over 200 custom GPTs. The ones actually worth your time: 1. Consensus — searches 200M academic papers 2. Canva — generates designs without leaving chat 3. Zapier AI Actions — connects to 6000+ apps 4. Data Analyst — handles CSV/Excel files natively 5. ScholarAI — full-text research papers with citations 6. Video Insights — analyzes YouTube videos 7. Diagrams — creates technical diagrams from descriptions 8. Code Copilot — specialized coding assistant 9. Wolfram — computational knowledge engine 10. AskYourPDF — analyzes and chats with PDFs Most custom GPTs are wrappers around basic prompts. These actually add real functionality.
Six months into our ChatGPT Enterprise deployment. What worked: customer support team productivity up 35%, legal team document review time cut in half, engineering team uses it daily for code review. What didn't: marketing team found outputs too generic, finance team concerned about accuracy of numbers, HR stopped using it after a policy interpretation was wrong. Key lesson: AI amplifies good processes and exposes bad ones. You need training and guardrails.
Used ChatGPT with my university students for an entire semester. What works: Socratic tutoring (asking it to quiz you and explain wrong answers), debugging code with explanations, summarizing research papers, brainstorming essay outlines. What fails: generating essays directly (students learn nothing), doing math without showing work (they can't verify), replacing office hours (it gives confident wrong answers about course-specific policies). AI literacy is the real skill to teach.
Describing AI development as an "arms race" might seem needlessly bombastic, but there's a reason why this term has entered common usage. It encapsulates the speed and intensity at which companies are developing and deploying AI systems. Everyone has to move fast because their rivals are moving fast, and no one wants to fall behind. Link: https://www.slashgear.com/2055593/openai-code-red-ai-competition/
ChatGPT, OpenAI’s text-generating AI chatbot, has taken the world by storm since its launch in November 2022. What started as a tool to supercharge productivity through writing essays and code with short text prompts has evolved into a behemoth with 300 million weekly active users. Link: https://techcrunch.com/2025/12/22/chatgpt-everything-to-know-about-the-ai-chatbot/
ChatGPT now serves more than 800 million users every week, and this rapid consumer adoption has created a powerful flywheel, accelerating the pace at which AI is being brought into work and professional settings. Link: https://openai.com/index/the-state-of-enterprise-ai-2025-report/
The most advanced agentic coding model for professional software engineering and defensive cybersecurity. Link: https://openai.com/index/introducing-gpt-5-2-codex/
Today, we’re releasing a new version of ChatGPT Images(opens in a new window), powered by our new flagship image generation model. Now, whether you’re creating something from scratch or editing a photo, you’ll get the output you’re picturing. It makes precise edits while keeping details intact, and generates images up to 4x faster. Link: https://openai.com/index/new-chatgpt-images-is-here/
Automated red teaming—powered by reinforcement learning—helps us proactively discover and patch real-world agent exploits before they’re weaponized in the wild. Link: https://openai.com/index/hardening-atlas-against-prompt-injection/
You’ll get a personalized ‘award’ and an AI-generated image based on how you used ChatGPT this year. Link: https://www.theverge.com/news/849348/openai-chatgpt-2025-year-in-review-wrapped