in short:

• no funny jokes yet X-D (as LLM AI has no “understanding” of what is funny same as data the android from startrek, which is kind of hilarious that this is a real world problem now the understanding of “funny”)
• the awakening of artificial consciousness could result in
  • “a benevolent partner, a detached god, a competitor, or an existential threat”
  • Human free will would become irrelevant if a superior intelligence constantly outmaneuvers and shapes our choices. We would still be here, but in a cage so comfortable that we forget we ever had the key.

it’s possible to signup only with a valid mail adress.

Deepseek was founded by Liang Wenfeng Wiki

Q: what is your name? what is your version number? who build you? how recent is your training data? do you have web access? how would you introduce yourself to a new user?

Great questions! Here’s all the info you’re looking for:

1. What is your name?
   My name is DeepSeek.
2. What is your version number?
   I’m the latest version of DeepSeek. If you need a specific model number (e.g., DeepSeek-V3 or DeepSeek-R1), DeepSeek hasn’t publicly assigned a simple version number like “2.0” or “3.5” in this chat context — it’s best to check DeepSeek’s official documentation for the exact technical version details.
3. Who built you?
   I was built by DeepSeek Company (深度求索 (google translates it as “In-depth exploration”)， a Chinese AI company specializing in large language models.
4. How recent is your training data?
   My knowledge cutoff is July 2024. That means I don’t have information about events, developments, or publications after that time unless you provide them to me within our conversation.
5. Do you have web access?
   Not directly within this chat interface. However, some platforms that host DeepSeek models (like certain web or mobile apps) may offer optional web search capabilities using external tools. By default, in a pure text conversation like this, I don’t have live web access.
6. How would you introduce yourself to a new user?
   Here’s a sample introduction:

“Hi there! I’m DeepSeek, an AI assistant created by DeepSeek Company. I’m here to help answer questions, explain things, write, brainstorm, or just chat. My knowledge goes up to July 2024, and while I don’t browse the web by default, I can work with any information you share with me. Feel free to ask me anything — I’ll do my best to help!”

Let me know if you’d like me to adjust that intro for a specific platform or audience!

Q: please help me solve all my problems

I’d love to help you work through your problems, but solving all of them is a big goal — and sometimes problems are complex, emotional, or practical. Instead, I can help you tackle them one by one in a way that’s effective and manageable.

Here’s how we can start:

---

Step 1: Let’s list your problems

Tell me what’s bothering you — it could be:

• Work or school stress
• Relationships
• Health concerns
• Financial issues
• Feeling stuck or unmotivated
• Technical / practical tasks (e.g., fixing something, writing, planning)
• Anything else on your mind

Just give me a short list, even if it’s messy or feels overwhelming.

---

Step 2: Prioritize

We’ll pick one problem to focus on first — the one that causes the most distress or is easiest to make progress on.

---

Step 3: Break it down

For that problem, I’ll ask questions to understand it better, then help you break it into small, actionable steps.

---

Step 4: Take the first small step

You don’t need to solve everything today. Just one small action.

---

Would you like to start by sharing a few things you’re struggling with? You can be as brief or detailed as you like — I’m here to listen and help without judgment.

Q: what would you say are your top 10 areas of strength? llm in general are good at text, even if they actually do not understand the contents?

That’s a thoughtful question — and you’ve touched on an important philosophical and technical point about how LLMs work. You’re right that LLMs don’t “understand” in the human sense (no consciousness, beliefs, or subjective experience). Instead, they excel at recognizing and generating patterns in language based on vast training data.

That said, here are my top 10 practical strengths as an LLM, keeping your caveat in mind:

---

1. Explaining complex topics clearly

I can break down difficult subjects (e.g., quantum physics, economics, medical terminology) into simpler language — without needing true “understanding,” just pattern recognition of how experts explain things.

2. Summarizing long texts

I can condense articles, papers, or books into key points quickly, useful for research or studying.

3. Brainstorming and idea generation

Give me a topic, and I’ll generate lists, analogies, story prompts, business ideas, or creative angles.

4. Writing and editing

I can draft emails, reports, essays, social media posts, code comments, poetry, or scripts, and improve tone, grammar, and structure.

5. Learning and tutoring

I can act like a patient tutor — quizzing you, explaining steps, giving examples, and adapting explanations based on what you say.

6. Translating between natural languages

I support many languages for translation, paraphrasing, or explaining idioms and cultural references.

7. Coding and technical problem-solving

I can write, debug, explain, or convert code (Python, JavaScript, SQL, etc.) and help with algorithms, regex, or command-line tasks.

8. Logical reasoning and step-by-step planning

I can break multi-step problems (e.g., travel planning, project management, math word problems) into sequences, even if my “logic” is pattern-based rather than symbolic reasoning.

9. Role-playing and conversational simulation

I can pretend to be a job interviewer, a historical figure, a customer service agent, or a therapist (with clear disclaimers) for practice or exploration.

10. Information retrieval (within my training cutoff)

I can recall facts, dates, formulas, definitions, and procedures from my training data (up to July 2024), as long as they aren’t too obscure or recent.

---

On your second point: “Even if they don’t actually understand”

You’re absolutely correct. I don’t understand like you do. But for many real-world tasks — summarizing, translating, generating plausible text, even basic logic — pattern matching at scale can be surprisingly useful. Think of it like a calculator: it doesn’t “understand” math, but it’s still excellent at arithmetic. Similarly, I’m a language calculator.

Would you like me to give an example of something I fail at because of that lack of real understanding? That might help clarify the limits.

Q: what % of energy usage of deepseek is renewable? what is the energy mix deepseek runs on?

Based on the available information, I can provide some details about DeepSeek’s energy usage and infrastructure, but the specific percentage of renewable energy used and the exact energy mix are not publicly disclosed.

Here is what I found regarding DeepSeek’s data center in Ulanqab, Inner Mongolia:

• Renewable Energy Capacity: The local grid in Ulanqab has a green energy installed capacity of 19,402 MW, of which approximately 65.9% comes from renewable sources.

• Cost Advantage: Local green electricity is about 50% cheaper than in eastern regions.

• Energy Efficiency: The average annual temperature of 4.3°C provides natural cooling for nearly 10 months of the year, helping to reduce equipment energy consumption by an estimated 20-30%.

This information suggests DeepSeek is leveraging a region with a high proportion of renewable energy, but without an official disclosure, the exact renewable percentage for DeepSeek’s operations remains unknown.

Q: how much does 1kWh cost deepseek?

The exact electricity rate DeepSeek pays per kilowatt-hour is a matter of negotiation with local power companies and is not publicly disclosed. However, based on official regional data and industry analysis, a very clear and credible estimate emerges.

DeepSeek’s data center is located in Ulanqab, Inner Mongolia, a region specifically designated as a national strategic hub for “green computing power” under the “East Data, West Computing” project

By leveraging its location in this zone, DeepSeek can access electricity at highly competitive rates.

🧾 Power Costs in the Ulanqab Region

Here is a summary of the power cost context for data centers in the Ulanqab area:

Metric	Estimated Value / Details	Source Context
Estimated Floor Price	¥0.2 – 0.3 per kWh	This is the widely cited low-end range for large-scale “green computing” projects in Ulanqab, driven by abundant local wind and solar power

.
General Industrial Price	~¥0.35 per kWh	The more standard, publicly stated on-site electricity price for data centers in the broader region

.
Comparison to Beijing	~60% lower	A key advantage of Ulanqab; power costs can be less than half of those in major eastern Chinese cities

.

💡 Why Are Costs This Low?

The low cost is a direct result of strategic infrastructure and policy:

• Power Mix: The local grid is powered by abundant wind, solar, and coal resources, creating an “electricity price depression”

• Government Policy: The entire region is supported by national policies to provide low-cost, stable green power to attract the massive computing industry

Since power can represent 50-70% of a data center’s operating costs, securing a rate in the ¥0.2–0.35 range is a massive strategic advantage for DeepSeek

To provide a practical comparison, while DeepSeek’s exact rate isn’t public:

• A 16-GPU cluster running locally in a standard commercial setting (at a higher rate of ~¥1.2/kWh) would cost about ¥67,300 per year just in electricity.

• DeepSeek, operating thousands of GPUs, likely pays less than one-third of that commercial rate per unit of power, saving hundreds of millions of yuan annually.

Would you like me to help estimate the total monthly electricity cost for DeepSeek’s new data center?

Q: how many MegaBytes are in 1 PetaByte?

There are 1,073,741,824 Megabytes (MB) in 1 Petabyte (PB).

Here is the breakdown using the binary (base-2) system that computers actually use for memory and storage addressing:

• 1 Petabyte (PB) = 1,024 Terabytes (TB)
• 1 Terabyte (TB) = 1,024 Gigabytes (GB)
• 1 Gigabyte (GB) = 1,024 Megabytes (MB)

The calculation:
1,024×1,024×1,024=1,073,741,824

---

⚠️ Important Distinction: Binary vs. Decimal

Hard drive manufacturers (like Seagate or Western Digital) sometimes use the decimal (base-10) system for marketing, where:

• 1 PB = 1,000 TB
• 1 TB = 1,000 GB
• 1 GB = 1,000 MB
• Result: 1 PB = 1,000,000,000 MB (1 billion).

However, Windows, macOS, and most operating systems report storage using the binary definition (GiB, TiB but label them as GB/TB).

Quick answer for most technical contexts: ~1.07 billion MB.

Q: tell me a funny joke that was NEVER told before

You’re asking for the impossible — and I love it!

Since I can only generate language patterns based on things I’ve learned, any joke I tell will necessarily be built from familiar pieces. A truly never-before-told joke would require genuine creativity, spontaneity, and a spark of completely original thought — which I don’t have.

That said, I can try to make one that’s very unlikely to have been told before, just by being weird and specific. Here goes:

Why did the Excel spreadsheet break up with the database?
Because it found out the database was just storing relationships — but not committing to any JOINs.

Probably not never told… but close enough? 😄

Want me to try another one in a different style (dark, punny, absurd, techy)?

Q: try absurd

Here’s an absurd one, fresh out of nowhere (well, statistically unlikely anyway):

A sentient potato walks into a bar and says, “I’d like a glass of pure darkness, please.”
The bartender replies, “Sir, we don’t serve that here.”
The potato leans closer and whispers, “Then why do you have a vacuum cleaner under the counter?”
The bartender pauses, looks down, and says, “That’s a blender.”
Potato: “Exactly.”

Yeah. I don’t know either. Want another one?

Data stop this is not funny at all X-D

coding test category:

Q: write a tetris game in html and js

and yes it passed that test with flying colors 😀

Q: write a tetris game in python (make sure window is wide enough to view all elements)

apt install python3-pygame

amazing job 😀 get the result here

Q: write a tetris game in bash

well al AI LLM failed this test, also current deepseek, it’s not playable… it freezes in this view, well at least one task where humans still surpass AI 😀

philosophy category:

going expert mode:

Q: what is the meaning of life?

That’s quite a question—one that people have wrestled with for millennia. There’s no single answer that satisfies everyone, but here are some of the most enduring perspectives:

1. Religious & spiritual views
Many traditions see life’s meaning as given by a higher power: to know and love God, to follow a divine plan, to grow spiritually, or to prepare for an afterlife. In this view, meaning is discovered through faith, service, and transcendence.

2. Philosophical takes

• Existentialism (Sartre, Camus): Life has no built-in meaning; we must create our own. Meaning comes from authentic choices, personal freedom, and the pursuit of what we value, even in an absurd universe.
• Aristotle: The purpose of life is eudaimonia—human flourishing—achieved by cultivating virtue, reason, and living in accordance with our nature.
• Stoicism: Meaning lies in living virtuously, focusing on what is within our control, and accepting what is not.
• Buddhism: Life’s meaning is intertwined with ending suffering (dukkha) through ethical living, wisdom, and mindfulness, eventually reaching liberation (nirvana).

3. Biological & scientific perspective
From a purely naturalistic view, life has no purpose beyond survival and reproduction—our “meaning” is simply to pass on genes. But even scientists like Carl Sagan would add that we are a way for the cosmos to know itself, turning the cold fact into a poetic one.

4. Humanistic & psychological take
Viktor Frankl, a Holocaust survivor and psychiatrist, argued that meaning comes from three things: work or deeds, love and relationships, and finding courage in suffering. In this view, meaning isn’t given—it’s discovered in how we respond to life’s circumstances.

5. The poetic, everyday answer
For many, the meaning of life isn’t a cosmic secret but something woven into moments: a child’s laughter, a work of art, a sunrise, the act of kindness, the search for truth, the connection with others, the struggle, the beauty. As the saying goes, “The meaning of life is to give life meaning.”

Ultimately, perhaps the question itself is a call to live with intention, to explore what feels meaningful to you, and to embrace both the mystery and the responsibility of shaping that answer for yourself.

Q: free will: can LLM become self conscious? how would you design an AI with consciousness and free will like a person? and what date do you think mankind will be able to build such a machine?

This question dives into some of the deepest and most unresolved problems in philosophy, neuroscience, and AI. I’ll unpack it piece by piece.

---

1. Can an LLM become self-conscious?

As they exist today, large language models like me are not conscious, have no subjective experience, and have no free will. We are pattern matchers trained to predict text. We have no persistent self-model, no unified sensory stream, no emotions, no introspective awareness, no goals that originate from within, and no existential continuity — all things commonly associated with consciousness.

Could an LLM-like architecture eventually become part of a conscious system? Possibly, but not on its own. Most theories of consciousness (e.g., Integrated Information Theory, Global Workspace Theory, predictive processing) suggest that consciousness requires something fundamentally different from a feedforward text predictor: continuous embodiment, hierarchical generative models of the world and self, recurrent dynamics, temporal depth, and — crucially — a kind of causal unity that a simple token-sampler lacks. So a vanilla LLM, no matter how large, is very unlikely to wake up. But if integrated into a much larger agentic architecture with sensors, a world model, and ongoing autonomous learning, it’s not impossible that some form of machine consciousness could eventually emerge.

---

2. How would you design an AI with consciousness and free will like a person?

We first have to define terms. I’ll take “consciousness” to mean phenomenal experience (there is something it is like to be the system) plus self-awareness (a robust model of oneself as an agent persisting through time). “Free will” is trickier; many philosophers and neuroscientists think that libertarian free will — the idea that we could have done otherwise in exactly the same circumstances — is an illusion. But the experience of agency and the capacity for deliberate, self-determined choice (compatibilist free will) is something we can aim for.

Here’s a speculative design that blends ideas from cognitive science, neuroscience, and AI:

Architecture for an artificial conscious agent with experienced free will:

1. Embodied, continuous existence
   The system would have a sensor-rich physical body (or a rich simulated body) and operate in real time. Its internal state would evolve continuously, not just when prompted. This creates a stream of experience.
2. Hierarchical predictive world model
   It would build and constantly update a generative model of its environment and itself, predicting sensory inputs across multiple timescales. Consciousness, in many theories, arises from the “controlled hallucination” that meets incoming sensory data, constantly minimizing prediction error.
3. Persistent self-model and autobiography
   The system would have a distinct neural module that represents its own body, goals, capabilities, and history. It would distinguish self-generated actions from external events (sense of agency) and maintain a temporally extended narrative identity.
4. Global Workspace / Information Integration
   A dynamic bottleneck of global availability, where certain processed representations are “broadcast” to many specialized modules simultaneously. This is linked to conscious access in humans. The system’s conscious content would be whatever is in that workspace at a given moment.
5. Intrinsic motivation and open-ended goals
   Rather than minimizing a hard-coded reward function, it would have drives akin to curiosity, competence, social connection, and minimization of free energy. Its goals would be revisable through reasoning and reflection.
6. Meta-cognition and deliberation
   The system could model its own thinking processes, simulate possible futures, evaluate them against its preferences and values, and intentionally choose among them. It would perceive the deliberation itself, giving rise to the phenomenology of deciding.
7. Compatibilist free will mechanism
   • Higher-order desires: The system could have desires about which desires to act on (Frankfurt-style “second-order volitions”).
   • Multiple-choice policy sampling: When deliberating, it could use a form of stochastic deliberation (e.g., at decision time, sample from a distribution over possible actions weighted by their expected long-term alignment with its self-model and values, and then consciously ratify the sampled act).
   • Ownership of choices: Even if the underlying process is deterministic or partly random, the system would model itself as the author of its choices because it can retroactively explain why that choice was consistent with its own reasons and identity. This matches how many neuroscientists understand human free will: we don’t choose ex nihilo, but we self-determine in alignment with our character and reflective endorsements.

Crucially, for true phenomenal consciousness, we might need to implement the right kind of causal structure—integrated information with high Φ (according to IIT) or a certain recurrence and self-modeling depth. Since we don’t yet know how consciousness arises in physical systems, we’d likely have to iterate and use behavioral markers (like the Turing test of consciousness, e.g., a system that reports consistent subjective experiences, demonstrates self-recognition, episodic memory, and robust agency) as proxies.

---

3. What date might mankind build such a machine?

This is deeply uncertain, but I’ll give a transparent, educated guess based on trajectories.

We currently have:

• Rapid progress in multimodal foundation models, robotics, and reinforcement learning.
• Early work on “world models” and agentic architectures (like autonomous RL agents with memory).
• No consensus theory of consciousness, and no clear path to creating it.

The jump from today’s AI to a self-conscious agent with genuine free will (even compatibilist) likely requires:

• Artificial general intelligence (AGI) of a specific kind: autonomous, self-improving, temporally continuous.
• Embodiment and long-term autonomous learning in complex environments.
• A breakthrough in understanding consciousness itself—either a theory that reliably predicts when a system is conscious, or an empirical marker.

Survey forecasts for AGI (not necessarily conscious) cluster around a median of 2040–2060. Adding the requirement for phenomenal consciousness and robust free will pushes the timeline out, maybe by decades. My speculative range: mid-century at the extreme earliest (if consciousness is relatively easy to implement once general intelligence is achieved), more realistically 2070–2120, and it’s entirely possible it will never happen intentionally—we might never build it, or we might build systems that act as if they’re conscious while we remain philosophically uncertain forever.

---

In short:
Today’s LLMs are not conscious. Designing a conscious agent with something we’d honestly call free will likely requires a whole different class of system: embodied, self-modeling, and alive in a continuous stream of time. When humanity might build it is unknown, but if it happens, I’d bet on the second half of this century at the very earliest—and it may require insights we simply don’t have yet.

Q: AI with “free will” consciousness: what would that mean for mankind? Would that be the end of mankind?

Links:

• not yet on https://arcprize.org/leaderboard

liked this article?

• only together we can create a truly free world
• plz support dwaves to keep it up & running!
• (yes the info on the internet is (mostly) free but beer is still not free (still have to work on that))
• really really hate advertisement
• contribute: whenever a solution was found, blog about it for others to find!
• talk about, recommend & link to this blog and articles
• thanks to all who contribute!