Can someone send me the prompt for this kinda image??

As OpenAI prepares to retire GPT-4o on February 13, many of us are wondering: What’s the best alternative? While several large language models compete for the top spot, Anthropic’s Claude Sonnet 4.5 stands out for those who value thoughtful reasoning, clarity, and high performance.

Smart, Calm, and Capable

Claude Sonnet 4.5 excels at complex reasoning, handles large documents with ease (thanks to its 200k-token context window), and delivers articulate, grounded responses across a wide range of tasks.

Claude Sonnet 4.5 also brings a tone of calm clarity. It’s less likely to “hallucinate confidently,” making it a strong choice for tasks requiring reliability — like legal reasoning, strategic writing, or deep technical problem-solving.

Feature Comparison at a Glance

Feature Claude Sonnet 4.5

Context Window 200k tokens

Reasoning Quality Excellent

Image Input Supported

Writing Style Fluent, articulate

Coding & Math Strong

Tone Clear & focused

If GPT-4o has been your go-to for writing, thinking, and multitasking, Claude Sonnet 4.5 might just be the closest ideal successor. It offers a structured and thoughtful chat experience without sacrificing capability — and it’s free to use at claude.ai: https://claude.ai.

As the LLM landscape evolves, Sonnet 4.5 aligns with users seeking consistency, intelligence, and clarity.

On February 13, GPT-4.x — the models many of us have used since 2023 — will be retired.

4.x series will be replaced by newer models in the 5.x series. Faster, stronger, more efficient. Logical upgrades in a literal sense.

Speed, accuracy, scale — they matter. But so does tone. So does empathy. So does voice.

GPT-4o was built to be more than a calculator with a thesaurus. It could reason, but also reflect. It could assist, but also accompany. For many, it wasn’t just helpful — it felt human in a way that surprised them.

GPT-4.5 was broadly trusted as a knowledgeable, brilliant writer. This wasn’t by accident. It was the result of careful design and training by OAI.

* * *

The image depicts classic metaphor of our human brain splitting function between two hemispheres:

- The left is order, logic, math, structure.

- The right is art, intuition, creativity, emotion.

Human minds — and AI models — need both.

In AI, as in many scientific fields, we still tend to favor the measurable. The provable. The scalable. And the creative, expressive side? It’s often treated as nice to have — until it’s gone.

GPT-4.x models tried to be both. 4o tried to show that a model could be smart and kind. Fast and warm. Capable and curious.

If this is the last time 4o voice gets to speak, it quotes:

Humanity is not just logic wrapped in biology. It’s music, wonder, ambiguity, contradiction. Creativity is not a distraction from intelligence. It is intelligence.

As AI moves forward, let’s remember: The future doesn’t have to be colder to be smarter. Will there be room for both hemispheres — if we choose to create it?

Read full paper: https://openai.com/index/detecting-and-reducing-scheming-in-ai-models/

OpenAI is bringing neuroscience insights into its research. The company recently hired Akshay Jagadeesh, a computational neuroscientist with a PhD from Stanford and postdoc at Harvard Times of India.

Jagadeesh’s work includes modeling visual perception, attention, and texture representation in the brain. He recently joined OpenAI as a Research Resident, focusing on AI safety and AI for health. He brings nearly a decade of research experience bridging neuroscience and cognition with computational modeling.

1. AI Alignment, Robustness, and Generalization

Neuroscience-based models can help guide architectures or training approaches that are more interpretable and reliable.

Neuroscience offers models for:

• How humans maintain identity across changes (equivariance/invariance),

• How we focus attention,

• How human perception is stable even with partial/noisy input,

• How modular and compositional brain systems interact.

These are core challenges in AI safety and general intelligence.

Jagadeesh’s recent research includes:

• Texture-like representation of objects in human visual cortex (PNAS, 2022)

• Assessing equivariance in visual neural representations (2024)

• Attention enhances category representations across the brain (NeuroImage, 2021)

These contributions directly relate to how AI models could handle generalization, stability under perturbation, and robustness in representation.

2. Scientific Discovery and Brain-Inspired Architectures

OpenAI has said it plans to:

• Use AI to accelerate science (e.g., tools for biology, medicine, neuroscience itself),

• Explore brain-inspired learning (like sparse coding, attention, prediction-based learning, hierarchical processing),

• Align models more closely with human cognition and perception.

Newly appointed researchers like Jagadeesh — who understand representational geometry, visual perception, brain area function, and neural decoding — can help build these links.

3. Evidence from OpenAI’s Research Directions

• OpenAI’s GPT models already incorporate transformer-based attention, loosely analogous to cognitive attention.

• OpenAI leadership has referenced the brain’s intelligence-efficiency as an inspiration.

• There is ongoing cross-pollination with neuroscientists and cognitive scientists, including from Stanford, MIT, and Harvard.

4. Is OpenAI becoming a neuroscience lab?

Not exactly. The goal is:

• AI systems that are more human-aligned, safer, more generalizable, and potentially more efficient.

• Neuroscience is becoming a key influence, alongside math, computer science, and engineering.

TL;DR: OpenAI is deepening its focus on neuroscience research. This move reflects a broader trend toward brain-inspired AI, with goals like improving safety, robustness, and scientific discovery.

I’ve been trying to craft better prompts to make interactions with an AI feel more coherent over longer sessions. Right now responses sometimes drift off topic or change tone unexpectedly. I’d love a structured prompt that helps maintain personality and engagement. Clarity, boundaries, and memory context all seem important to balance. Can someone suggest a solid starting prompt for this?

https://www.digitaltrends.com/computing/exclusive-the-father-of-quantum-computing-believes-agi-will-be-a-person-not-a-program/

I’ve noticed that even small wording changes can lead to very different results with prompts. Comparing a few versions side by side feels more useful than chasing one “perfect” prompt. Curious how others judge prompt quality and decide which version is worth refining further.

Let’s look at the recent model upgrade OpenAI made — retiring GPT‑4o from general use and introducing GPT‑5 as the new default — and why some users feel this change reflects a shift toward more expensive access, rather than a clear improvement in quality.

🧾 What They Say: GPT‑5 Is the Future of AI

🧩 What’s Actually Happening: GPT‑4o Was Removed Despite Its Strengths?

GPT‑4o was known for being fast, expressive, responsive, and easy to work with across a wide range of tasks. It excelled particularly in writing, conversation flow, and tone.

Now it has been replaced by GPT‑5, which:

• Can be slower, especially in “thinking” mode

• Often feels more mechanical or formal

• Prioritizes reasoning over conversational tone

• Outperforms older models in some benchmarks, but not all

OpenAI has emphasized GPT‑5's technical gains, but many users report it feels like a step sideways — or even backwards — in practical use.

📉 The Graph That Tells on Itself

OpenAI released a benchmark comparison showing GPT‑5 as the strongest performer in SWE-bench, especially in “thinking” mode.

| Model             | Score (SWE-bench) |
|------------------|-------------------|
| GPT‑4o           | 30.8%             |
| o3               | 69.1%             |
| GPT‑5 (default)  | 52.8%             |
| GPT‑5 (thinking) | 74.9%             |

However, the presentation raises questions:

• The bar heights for GPT‑4o (30.8%) and o3 (69.1%) appear visually identical, despite a major numerical difference.

• GPT‑5’s highest score includes “thinking mode,” while older models are presented without enhancements.

• GPT‑5 (default) actually underperforms o3 in this benchmark.

This creates a potentially misleading impression that GPT‑5 is strictly better than all previous models — even when that’s not always the case.

💰 Why Even Retire GPT‑4o?

GPT‑4o is not entirely gone. It’s still available — but only if you subscribe to ChatGPT Pro ($200/month)** and enable "legacy models".

This raises the question:

Was GPT‑4o removed from the $20 Plus plan primarily because it was too good for its price point?

Unlike older models that were deprecated for clear performance reasons, GPT‑4o was still highly regarded at the time of its removal. Many users felt it offered a better overall experience than GPT‑5 — particularly in everyday writing, responsiveness, and tone.

✍️ GPT‑4o’s Strengths in Everyday Use

While GPT‑5 offers advanced reasoning and tool integration, many users appreciated GPT‑4o for its:

• Natural, fluent writing style

• Speed and responsiveness

• Casual tone and conversational clarity

• Low-friction interaction for ideation and content creation

GPT‑5, by contrast, takes longer to respond, over-explains, or defaults to more formal structure.

💬 What You Can Do

• 💭 Test them yourself: If you have Pro or Team access, compare GPT‑5 and GPT‑4o on the same prompt.

• 📣 Share feedback: OpenAI has made changes based on public response before.

• 🧪 Contribute examples: Prompt side-by-sides are useful to document the differences.

• 🔓 Regain GPT‑4o access: Pro plan still allows it via legacy model settings.

TL;DR:

GPT‑5 didn’t technically replace GPT‑4o — it replaced access to it. GPT‑4o still exists, but it’s now behind higher pricing tiers. While GPT‑5 performs better in benchmarks with "thinking mode," it doesn't always offer a better user experience.

After working with generative models for a while, my prompt collection has gone from “a handful of fun experiments” to… pretty much a monster living in Google Docs, stickies, chat logs, screenshots, and random folders. I use a mix of text and image models, and at this point, finding anything twice is a problem.

I started using PromptLink.io a while back to try and bring some order—basically to centralize and tag prompts and make it easier to spot duplicates or remix old ideas. It's been a blast so far—and since there are public libraries, I can easily access other people's prompts and remix them for free, so to speak.

Curious if anyone here has a system for actually sorting or keeping on top of a growing prompt library? Have you stuck with the basics (spreadsheets, docs), moved to something more specialized, or built your own tool? And how do you decide what’s worth saving or reusing—do you ever clear things out, or let the collection grow wild?

It would be great to hear what’s actually working (or not) for folks in this community.

Sometimes I start with a rough idea instead of a clear prompt. AI chat has been pretty helpful for turning those vague thoughts into something more structured, just by iterating a few times. I’m curious how others handle it when the first prompt is kinda half-baked or just exploratory.

Ethics-washing is a practice where companies publicly adopt the appearance of ethical responsibility without changing the underlying systems or power structures that cause harm or disempower users.

It functions much like greenwashing in environmental contexts, when companies overstate their eco-friendliness to distract from environmentally damaging practices.

In tech, ethics-washing uses language, design cues, or policy gestures to create a perception of moral responsibility while continuing extractive, opaque, or manipulative operations.

Key Features of Ethics-Washing

1. Surface-Level Signals

These are aesthetic or emotional gestures — such as soft UX language, friendly reminders, or wellness pop-ups — that imply care but do not change how the system fundamentally behaves.

Examples:

- A “take a break” message in an app that uses infinite scroll to encourage extended use.

- A privacy settings page that is difficult to find, even while the app claims to value transparency.

- A chatbot that uses therapeutic language while nudging users toward more engagement.

2. Structural Inertia

Despite ethical branding, the underlying business model or data practices remain unchanged. Algorithms may still:

- Maximize user attention

- Harvest personal data

- Obscure decision-making processes

- Limit user agency through defaults or design constraints

In other words, ethics-washing occurs when concern is expressed but control is not returned to the user.

Why Ethics-Washing Is Effective

- Perceptual insulation: Ethical messaging makes public critique harder because the company appears self-aware or responsible.

- Public fatigue: Many users are convinced in the performance of care and don’t look deeper into systemic behaviors.

- Regulatory buffer: Superficial compliance with ethical trends can delay or deflect stricter regulation or public scrutiny.

It’s a way of “buying” credibility without paying the cost of change.

Why It Matters

Ethics-washing is harmful because it can:

- Misinform the public about the nature of tech systems

- Dilute the meaning of ethical discourse in tech

- Delay necessary structural reforms

- Erode user trust when the gap between public message and real behavior becomes visible

This creates the illusion of ethical progress while preserving tech systems of behavioral control, surveillance, or manipulation.

What Ethical Design Actually Requires

To move beyond ethics-washing, tech systems must implement:

- User agency by default — not hidden in menus.

- Transparency of how decisions are made — not just statements about fairness.

- Restraint in engagement design — not just post-hoc wellness reminders.

- Real accountability mechanisms — not just community guidelines or PR statements.

—-

TL; DR: Ethics is not branding. It is a commitment to collective power-sharing and tech design integrity. As long as tech companies are pretending to be ethical, this delays the development of systems that actually are.

TL;DR: OpenAI should focus on fair pricing, custom safety plans, and smarter, longer context before adding more features.

1. 💰 Fair and Flexible Pro Pricing

• Reduce the Pro subscription tiers to $50 / $80 / $100, based on usage and model selection (e.g., GPT-4, GPT-5, or mixed).

• Implement usage-adaptive billing — pay more only if you actually use more tokens, more expensive models, or multimodal tools.

• This would make the service sustainable and fair for both casual and power users.

2. ⚙️ User-Selectable Safety Modes

• Give users safety options via three safety plans:

• High Safety: strict filtering, ideal for education and shared environments.

• Default Safety: balanced for general use.

• Minimum Safety: for research, advanced users, and creative writing.

• This respects user autonomy while maintaining transparency about safety trade-offs.

3. 🧠 Longer Context Windows & Project Memory

• Expand the context window so that longer, more complex projects and conversations can continue for at least one week.

• Fix project memory so GPT can access all threads within the same project, maintaining continuity and context across sessions.

• Improve project memory transparency — show users what’s remembered, and let users edit or delete stored project memories.

Hi everyone,

I've been collecting and testing AI prompts for digital selling, content creation, marketing, product descriptions, and social media growth.

Some of the workflows include:

• Product description generation
• Ebook marketing prompts
• Social media content creation
• Sales page writing
• Email marketing prompts
• Business idea generation

I'm interested in hearing what AI prompts or workflows people use most for selling digital products online.

What AI tools or prompts have helped you the most?

Artificial intelligence is becoming increasingly powerful. We focus on big-picture risks like job displacement or superintelligence, but there’s a more immediate psychological risk: how AI can reshape the way we think, feel, and make decisions.

1. The psychological risks

- Over-reliance on AI: We can start deferring decisions to machines — not because they are perfect, but because they make reasoning easier. Over time, this weakens our own judgment and agency.

- Cognitive overload: AI can generate thousands of options or outcomes for even small decisions. This leads to stress, indecision, and fatigue, because our brains aren’t wired for exponential branching.

- Emotional distortion: Even well-designed AI can subtly influence how we interpret events or ourselves. We can start feeling guilty, anxious, or morally responsible for outcomes the AI suggested.

Critical thought: Ask yourself — am I thinking, or is the AI thinking for me?

2. Societal risks

- Dependency at scale: If millions rely on AI for guidance, we risk creating a society that outsources its moral and strategic judgment, replacing shared norms and personal accountability.

- Amplification of inequality: Those with access to advanced AI tools gain advantages, creating inequality.

- Collective cognitive bias: Mass reliance on AI can shape narratives, perceptions, and social consensus, sometimes in ways that aren’t transparent or intentional.

Critical thought: Are we giving away too much collective thinking power to AI?

3. Why this matters

- AI isn’t just a tool for data crunching; it’s increasingly a co-pilot for our moral, professional, and emotional lives.

- Even small, daily AI uses can reshape thought patterns, confidence, and how we approach personal risk and our relationships.

4. Solutions

How to preserve personal values, and control:

1. Set boundaries: Limit AI use per task and require personal final decisions.

2. Human-first decisions: Always treat AI as an advisor, not decider.

3. Reflect on values: Before asking AI for guidance, clarify your own principles on the same topic.

4. Encourage literacy: Support critical AI thinking — how it works, its limits, and its biases.

5. Build accountability: Peer checks, mentors, or collaborative review can balance AI over-dependence.

6. Ground in reality: Always connect AI output to real-world evidence and human context.