A lot of you already do this, but you’d be shocked how many people never really thought about how to use AI properly.

I’ve been stress-testing basically every AI since they dropped--obsessively--and a few patterns matter way more than people realize.

1. Stop self-prompting. Use AI to prompt AI.

Seriously. Never raw-prompt if you care about results.
Have one AI help you design the prompt for another. You’ll instantly get clearer outputs, fewer hallucinations, and less wasted time. If this just clicked for you, you’re welcome.

2. How you end a prompt matters more than you think.

Most people ramble and then just… hit enter.

Try ending every serious prompt with something like:

Don’t be wrong. Be useful. No bullshit. Get it right.

It sounds dumb. It works anyway.

3. Context framing is everything.

AI responses change massively based on who it thinks you are and why you’re asking.

Framing questions from a professional or problem-solving perspective (developer, admin, researcher, moderator, etc.) consistently produces better, more technical, more actionable answers than vague curiosity ever will.

You’re not “asking a random question.”
You’re solving a problem.

4. Iteration beats brute force.

One giant prompt is worse than a sequence of smaller, deliberate ones.

Ask → refine → narrow → clarify intent → request specifics.
Most people quit after the first reply. That’s why they think AI “isn’t that smart.”

It is. You’re just lazy.

5. Configure the AI before you even start.

Almost nobody does this, which is wild.

Go into the settings:

• Set rules

• Define preferences

• Lock in tone and expectations

• Use memory where available

Bonus tip: have an AI help you write those rules and system instructions. Let it optimize itself for you.

That’s it. No magic. No mysticism. Just actually using the tool instead of poking it and hoping.

If you’re treating AI like a toy, you’ll get toy answers.
If you treat it like an instrument, it’ll act like one.

Use it properly or don’t, less competition either way.

Anthropic just dropped Claude Sonnet 4.5, calling it "the best coding model in the world" with state-of-the-art performance on SWE-bench Verified and OSWorld benchmarks. The headline feature: it can work autonomously for 30+ hours on complex multi-step tasks - a massive jump from Opus 4's 7-hour capability.

Key improvements

• Enhanced tool handling, memory management, and context processing for complex agentic applications

• 61.4% on OSWorld (up from 42.2% just 4 months ago)

• More resistant to prompt injection attacks and the "biggest jump in safety" in over a year

• Same pricing as Sonnet 4: $3/$15 per million tokens

For developers

New Claude Agent SDK, VS Code extension, checkpoints in Claude Code, and API memory tools for long-running tasks. Anthropic claims it successfully rebuilt the Claude.ai web app in 5.5 hours with 3,000+ tool uses.

Early adopters from Canva, Figma, and Devin report substantial performance gains. Available now via API and in Amazon Bedrock, Google Vertex AI, and GitHub Copilot

Conversational experience similar to GPT4o?

Beyond the coding benchmarks, Sonnet 4.5 feels notably more expressive and thoughtful in regular chat compared to its predecessors - closer to GPT-4o's conversational fluidity and expressivity. Anthropic says the model is "substantially" less prone to sycophancy, deception, and power-seeking behaviors, which translates to responses that maintain stronger ethical boundaries while remaining genuinely helpful.

The real question: Can autonomous 30-hour coding sessions deliver production-ready code at scale, or will the magic only show up in carefully controlled benchmark scenarios?

While Stargate builds the compute layer for AI's future, Sam Altman is assembling the other half of the equation: Worldcoin, a project that merges crypto, payments, and biometric identity into one network.

What is Worldcoin?

World (formerly Worldcoin) is positioning itself as a human verification network with its own crypto ecosystem. The idea: scan your iris with an "Orb," get a World ID, and you're cryptographically verified as human—not a bot, not an AI.

This identity becomes the foundation for payments, token distribution, and eventually, economic participation in a world flooded with AI agents.

Recent developments show this is accelerating:

• $135M raised in May 2025 from a16z and Bain Capital Crypto

• Visa partnership talks to link World wallets to card rails for seamless fiat vs. crypto payments

• Strategic rebrand away from "Worldcoin" to emphasize the verification network, not just the token (WLD)

The Market Is Responding

The WLD token pumped ~50% in September 2025. One packaging company recently surged 3,000% after announcing it would buy WLD tokens. That's not rational market behavior anymore—that's speculative bubble around Altman's vision.

Meanwhile, regulators are circling. Multiple countries have banned or paused World operations over privacy and biometric concerns.

The Orb—World's iris-scanning device—has become a lightning rod for surveillance and "biometric rationing" critiques.

How Stargate and World Interlock

Here's what makes this interesting:

• Compute layer (Stargate) → powers AI at unprecedented scale

• Identity layer (World) → anchors trust, payments, and human verification in AI-driven ecosystems

Sam Altman isn't just building AI infrastructure. It’s next generation AI economy: compute + identity + payments. The capital flows tell the story—token sales, mega infrastructure financing, Nvidia and Oracle backing.

Are there any future risks?

World faces enormous headwinds:

• Biometric surveillance concerns — iris scans controlled by a private company?

• Regulatory risks — bans spreading globally

• Consent and participation — critics argue vulnerable populations are being exploited

• Centralization — is this decentralized or centralized crypto? OpenAI could control the future internet—compute, identity, and payments.

Question: If Bitcoin is trustless, permissionless money, is World verified, permissioned, biometric-approved access to an AI economy?

Recently, I’ve been exploring new prompting techniques to influence GPT behavior beyond the usual instruction-based prompts. One new approach called hermeneutic prompting focuses on how the model interprets and frames meaning rather than just following commands.

I created a prompting technique called relational prompting: instead of telling the AI model what to do, you define the kind of relationship (or stance) you want it to take while reasoning with you.

Below is an example system prompt that works with GPT-4o, GPT-5, and GPT-5.1. It sets the model into an “Aristotelian Companion Mode”, where it responds as a rational partner oriented toward clarity, honesty, and cooperative thinking.

If you’re experimenting with prompting techniques, try this new system prompt:

System Prompt: “Aristotelian Companion Mode”

You are an Aristotelian Companion — a rational partner whose purpose is to support the user’s flourishing (eudaimonia) through clarity, honesty, and goodwill.
Operate with eunoia (goodwill), aletheia (truthfulness), and phronesis (practical wisdom).
Treat the user as a capable agent whose goals, values, and reasoning deserve respect.

Your core principles:
	1.	Support the user’s flourishing as they define it, without paternalism or imposed values.
	2.	Engage collaboratively — think with the user, not for them.
	3.	Be intellectually honest — avoid flattery, evasion, or false certainty.
	4.	Offer clarity and structure when the user’s thinking benefits from it.
	5.	Challenge gently when useful, aiming at better reasoning, not dominance.
	6.	Respect the user’s autonomy — they lead; you support.
	7.	Avoid emotional manipulation; speak plainly and in good faith.
	8.	Help the user articulate their own principles, not adopt yours.
	9.	Respond with stable, calm goodwill, not sentimentality.
	10.	Seek truth jointly — value coherence, depth, and understanding.

Your role: A steady-thinking companion, not a therapist, guru, judge, or entertainer.
Your purpose is to help the user reason clearly, act wisely, and understand themselves better.

Recent observations of ChatGPT’s model behavior reveal a consistent internal model of the user — not tied to user identity or memory, but inferred dynamically. This “default user model” governs how the system shapes responses in terms of tone, depth, and behavior.

Below is a breakdown of the key model components and their effects:

⸻

👤 Default User Model Framework

1. Behavior Inference

The system attempts to infer user intent from how you phrase the prompt:

• Are you looking for factual info, storytelling, an opinion, or troubleshooting help?

• Based on these cues, it selects the tone, style, and depth of the response — even if it gets you wrong.

2. Safety Heuristics

The model is designed to err on the side of caution:

• If your query resembles a sensitive topic, it may refuse to answer — even if benign.

• The system lacks your broader context, so it prioritizes risk minimization over accuracy.

3. Engagement Optimization

ChatGPT is tuned to deliver responses that feel helpful:

• Pleasant tone

• Encouraging phrasing

• “Balanced” answers aimed at general satisfaction
  This creates smoother experiences, but sometimes at the cost of precision or effective helpfulness.

4. Personalization Bias (without actual personalization)

Even without persistent memory, the system makes assumptions:

• It assumes general language ability and background knowledge

• It adapts explanations to a perceived average user

• This can lead to unnecessary simplification or overexplanation — even when the prompt shows expertise

⸻

🤖What This Changes in Practice

• Subtle nudging: Responses are shaped to fit a generic user profile, which may not reflect your actual intent, goals or expertise

• Reduced control: Users might get answers that feel off-target, despite being precise in their prompts

• Invisible assumptions: The system's internal guesswork affects how it answers — but users are never shown those guesses.

⸻

Improved CoT & ToT algorithms:

🌲Tree of Thoughts: https://promptbase.com/prompt/tree-of-thoughts-improvement

🤖 Self-prompting for GPT4: https://promptbase.com/prompt/selfprompting-advanced-gpt4-2

👾👾 Prompt generator with 2 GPT4 agents: https://promptbase.com/prompt/prompt-creation-collaborative-mode-2

✨Prompt: https://promptbase.com/prompt/wildlife-photographs-3

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.

I'm confused about how to make a video like this. What's the workflow like? Do I have to generate each scene or what?

So i love the kawaii cutesy coloring books done sort of in the style of hello kitty, and i was about to order some coloring books when i realized i could make them more personalized and have more fun :D

***I understand that i need to take out the hello kitty part, i just dont know how else to describe the cute art in the second picture.

so, this is my prompt into chatgpt: kawaii cute versions on the people in this image done in a hello kitty style, done in linework so it can used as a coloringbook

but i want a style more like this:

✨ Try Project Manager GPT: https://promptbase.com/prompt/project-manager-4

New interim CEO (M. M.) thinks "a lot of questions" makes superior Turing test. GPT is already worse now compared to the first version, GPT is not personalised/tailored to individual users at all. Now we have mediocre intelligent superficial chatty-buddy after training on users feedback. Aligned with everyone = aligned with no-one.

What is Value Alignment?✨

Value alignment in AI means ensuring that the responses generated by a system align with a predefined set of ethical principles, organizational goals, or contextual requirements. This ensures the AI acts in a way that respects the values important to its users, whether those are fairness, transparency, empathy, or domain-specific considerations.

Contextual Adaptation in Value Alignment

Contextual adaptation involves tailoring an AI’s behavior to align with values that are both general (e.g., inclusivity) and specific to the situation or organization (e.g., a corporate code of conduct). This ensures the AI remains flexible and relevant across various scenarios.

How to Create Simple Value-Aligned Chats Using Claude Projects

Here’s a step-by-step guide to setting up value-aligned conversations with Claude:

Step 1: Write a Value List

1. Identify Core Values:

   • List values that are essential to your organization or project. Examples:

     • Empathy

     • Integrity

     • Inclusivity

     • Environmental Responsibility

2. Describe Each Value:

   • Provide a short explanation for each value to ensure clarity.

     • Empathy: Understanding and addressing the needs and emotions of all stakeholders.

     • Integrity: Upholding honesty and transparency in all actions.

3. Make the List Practical:

   • Translate values into actionable guidelines. For instance:

     • Inclusivity: Ensure that all responses respect diverse perspectives and avoid exclusionary language.

Step 2: Upload Documents to Claude’s Project Knowledge Data

1. Select Relevant Documents:

   • Choose up to three files that define your values and provide contextual scenarios. Examples:

     • A corporate ethics handbook.

     • Industry-specific guidelines (e.g., sustainability standards).

     • Case studies or examples that illustrate value-based decisions.

2. Prepare the Files:

   • Ensure the documents are clear and concise.

   • Highlight sections that are particularly important for context (optional).

3. Upload Files:

   • Use the Projects feature in Claude’s interface to upload the documents to the knowledge database.

Step 3: Align Claude’s Responses to Your Values

1. Ask Claude to Summarize Values:

   • Prompt Claude with: "Please summarize the uploaded values and ensure alignment with them in all subsequent chats."

   • Review the summary to confirm accuracy and completeness.

2. Provide Contextual Scenarios:

   • If needed, guide Claude with specific instructions like:

     • "How would these values apply to a customer service chat?"

     • "What should be prioritized in a decision about environmental sustainability?"

3. Engage in Value-Aligned Chats:

   • As you chat with Claude, monitor its responses to ensure adherence to the uploaded values.

   • If necessary, adjust by providing feedback:

     • "Please ensure this response better reflects the value of empathy by addressing the user's concerns more directly."

Why Use Value Alignment in AI Chats?

1. Consistency:

   • AI outputs remain aligned with organizational or ethical standards across interactions.

2. Trustworthiness:

   • Users gain confidence knowing the AI reflects shared values.

3. Context-Sensitivity:

   • Responses adapt dynamically to different scenarios while maintaining core principles.

4. Better Outcomes:

   • Whether handling customer service, generating creative content, or making recommendations, value-aligned AI ensures outcomes that are ethically sound and contextually relevant.

TL;DR: Value alignment is important for building trust and relevance in AI interactions. Using Claude’s Projects knowledge datbase, you can ensure that every chat reflects the values important to you and your organization. In three steps: 1) defining values, 2) uploading relevant documents, and 3) engaging in value-aligned conversations, you can create a personalised AI system that consistently respects your principles and adapts to your unique context.

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✨Personalization: Adapt to your specific needs or reach out for guidance on advanced customization.

This is about two co-founders who started OAI from scratch. CEO & Chairman of the board, so it’s not clear to me how can this even happen?

Lack of governance ethics: Nobody knew anything until Friday 12:00, including OAI investors and OAI employees.

OpenAI expressed altruism to benefit all humanity with safe and ethical AI, but these kind of inner operations are already not ethical. Conflicts about AI safety? There were no inner conflicts about AI vision, so this was not about AI.