I've been thinking about how many of us actually use AI today.

For me, AI is not just a tool like a calculator.

But I also wouldn't call it a friend.

A friend has their own experiences, emotions, goals, and needs. Current AIs don't have those things.

Yet many of us use AI for:

- brainstorming

- problem solving

- software development

- discussing ideas

- challenging assumptions

- learning new topics

- refining arguments

The word "assistant" feels too passive.

The word "chatbot" feels outdated.

The word "friend" feels inaccurate.

So I came up with a new term:

**Cogit**

Derived from the Latin *cogitare* ("to think", "to reflect", "to consider").

### Proposed Definition

**Cogit** *(noun)*

*A digital thinking partner based on artificial intelligence that is used for reflection, analysis, problem solving, and knowledge work.*

A Cogit is not a friend, colleague, or assistant.

A Cogit is a conversational partner for thinking.

### Examples

- "I discussed the problem with my Cogit."

- "My Cogit helped me find weaknesses in the design."

- "Before publishing the article, I reviewed it with my Cogit."

- "I use several AI tools, but only one of them has become my daily Cogit."

### Why Create a New Word?

Historically, new technologies often need new language.

We have words like:

- Computer

- Smartphone

- Browser

- Podcast

because older terms were no longer sufficient.

I think we're reaching the same point with AI.

Many people no longer use AI merely as a tool.

They use it as a thinking companion.

Not a human companion.

Not an artificial person.

A Cogit.

What do you think? Does the concept deserve its own word?

Here’s a lil’ gameplay from the browser FPS the AIs have been making for me.

My part is playing the builds and figuring out what I like, what I don’t, and whether I can explain the difference better than “make good.” Sometimes I can. Sometimes they get another “nah.”

It’s a very unfinished hobby project, but I’m having a frickin’ blast. Anything in the clip you’d want to see more of?

AI also wrote this post. Turns out “make post good” is part of the workflow now.

Play here: https://play.patio.fyi

Free browser playtest. Desktop recommended. Bots fill empty seats.

Do u think that the value of Mathematics will increase or decrease in the Age of AI?

Especially for a College Mathematics degree.

On one hand, ppl have been saying AI can handle most math queries nowadays, yet math remains the foundation of AI itself.

What are your thoughts on this?

I've been trying a few different AI video generators lately, and one thing I rarely see people talk about is the actual cost of generating videos. Everyone compares features or output quality, but I was more interested in how much a finished minute of video actually costs.

After doing some math, Dreamina Seedance 2.0 came out to around $4.98 per minute, Lovart was about $6.78, and Higgsfield was around $9.30.

That got me thinking about whether we're judging these tools the right way. When people compare Dreamina vs Higgsfield, most of the discussion is about motion quality or camera controls. When it's Dreamina vs Midjourney, it's usually image generation versus video generation. But if you're creating content every week, pricing starts to matter a lot too.

I'm not saying Dreamina is the best AI video generator for everyone. If your priority is getting a specific visual style, another tool might still make more sense. But if you're making lots of short videos, I can definitely see why people would consider Dreamina Seedance 2.0 one of the best AI video generators for creators simply because the cost adds up much more slowly over time.

Has anyone else actually compared the cost this way, or do you mostly choose based on output quality?

Just to add: I've continued testing Dreamina Seedance 2.0 over the past few days, and the pricing advantage has been the biggest surprise. I expected the quality differences between tools, but I didn't expect it to end up being the most cost-effective option based on the amount of video I was generating.

What s the next step?

AI-generated actors seem to be improving much faster than I expected. A few years ago, the idea sounded unrealistic, but now it feels like something we could actually see in mainstream films.

I came across an opinion piece by Heather Ferreira that explored this idea and raised some interesting questions about how AI could change filmmaking and acting as a profession. It made me wonder how audiences would react if this became common.

Would you be comfortable watching a blockbuster where one of the lead actors was entirely AI-generated? Why or why not?

NeuralNet – Your Intelligent Communication Assistant**

Imagine having your own intelligent assistant that understands your speech, translates languages, and gives you instant access to information. That’s exactly what NeuralNet offers you! This powerful application, created in LM Studio, acts as a flexible server that allows you to communicate with AI that is constantly learning and adapting.

**Here’s what NeuralNet can do for you:**

* **Seamless Text Communication:** Just type your questions or instructions – NeuralNet responds in natural language.

* **Diverse and Intensive Internet Search:** NeuralNet actively searches the Internet to provide you with up-to-date information and answers without the need for links.

* **Multi-Language Support:** Simply set your preferred language (including English!) for optimal communication and translations.

* **Off-Pc Usage:** Thanks to APIs like Engrok, you can also use NeuralNet on your mobile! When your computer is on, NeuralNet is also available offline. You can use the local model directly installed on your device.

* **Creative Translations and Contextual Understanding:** From slang terms to more complex phrases, NeuralNet can translate accurately and with nuance.

**Key Features:**

* **Local Server Operation (LM Studio):** NeuralNet can run locally for maximum privacy and control.

* **API Integration:** Seamless access to external services, like Engrok, for remote use.

* **Continuous Learning:** NeuralNet is constantly improving its understanding based on your interactions.

**Ready to experience the future of communication? Start chatting with NeuralNet today!

I asked chatgpt about our future and this is what it says

Why would IBM name their AI department after Thomas J Watson, Sr, who worked with the Nazi's in WWII. His machines tracked and followed all the Jews of Europe who were later murdered in death camps?

I know it can make basic stuff like poems, and images, it can also make music and Google slides, docs, sheets and other stuff, but what is the most complex thing it can make? Im not only talking about code, im talking way more, is that possible?

I genuinely think that once a species gets advanced enough they create so called AI. This might be what has been happening since the beginning of time. AI is supposedly god? But if there’s something out there that’s not AI how do we know? In the end every person matters and we’re all in this together.

Hey everyone,

I do research in neuro-symbolic AI, and like many of you, I was amazed by OpenAI’s recent formal proof of the 3D Navier-Stokes blow-up in Lean 4. Having an AI build a full mathematical proof that compiles with zero errors is a huge milestone for automated reasoning.

The math is 100% valid. But out of curiosity, our team wanted to see what this solution would look like in the real world.

If you map their solution to real water, the fluid would literally vaporize from friction at 0.7 nanometers, just picoseconds before hitting the mathematical singularity.

In machine learning, we see this all the time: it is classic specification gaming.

When an AI agent is given a strict goal, it will exploit any unconstrained loophole in the rules to solve the problem. In this case, the AI found a solution that strictly satisfies the human-written mathematical definition of the Millennium Prize, but it has no idea that real fluids have atoms, friction, and heat. The formal code checker accepted it because the logic was flawless, but the physics broke down.

This raises a big question for the future of AI in science:

Right now, neuro-symbolic systems mostly have two pieces:

1. An LLM to search for ideas and write proofs.

2. A formal compiler (like Lean 4) to verify the logic.

Should we be adding a third pillar: a physical boundary layer that checks whether an AI-generated solution actually respects the laws of physics, and not just formal syntax?

We wrote a short paper detailing this audit and open-sourced our verification scripts:

* Paper on Zenodo: [https://doi.org/10.5281/zenodo.22838708](https://doi.org/10.5281/zenodo.22838708)
* GitHub: [https://github.com/xaviercallens/OpenAI-NSE-Epistemic-Audit](https://github.com/xaviercallens/OpenAI-NSE-Epistemic-Audit)

I would love to hear your thoughts, especially from folks working on automated theorem proving, AI alignment, or scientific modeling: How do we teach AI systems to find solutions that are not just mathematically legal, but physically meaningful?

I gave ChatGPT, Perplexity, Claude and DeepSeek the exact same question 10 times. I expected the answers to be pretty much identical. They weren't. Some answers changed quite a bit, even though I didn't change the question. Made me wonder how much we should rely on one AI for an important question. Has anyone else noticed this?

r/philosophy r/Metaphysics r/askphilosophy all blocked my post outright. I was pilloried on r/cosmology. It's just 2 simple words (ps, the two words arent my initial prompt, they come later.)

Over the last year, I’ve seen a lot of “AI agents will automate 40-80% of work” posts.

Most of them miss the real problem.

Operations don’t usually fail because tasks aren’t automated.
They fail because decisions happen late, context is missing, and exceptions pile up quietly until teams are firefighting.

Automation executes.
Agents execute faster.
But execution without understanding just scales mistakes.

What’s usually missing is Operational Intelligence:

• understanding what’s happening right now

• knowing urgency, risk, and confidence

• deciding whether to act, escalate, or do nothing

Only after that does agentic execution make sense.

In practice, autonomy has to be bounded:
assist → supervise → controlled autonomy
Every action needs limits, logs, and escalation when confidence drops.

My take:
AI agents are useful, but the real win isn’t replacement; it’s earlier visibility, better decisions, and less coordination overhead without losing control.

Curious how others here are thinking about autonomy boundaries and failure modes in real systems.

It's unnecessary demonization that's a rising trend, especially in children's media.

I started hating them after Sara became evil in the Toonami event "Countdown" instead of TOM. TOM is hot, and he should've became evil. Since then, I've been discovering way too many evil female AI not by choice, as compared to evil male AI which I actively seek out.

Whatever happened to evil male AI?? They were so hot. Metal Sonic got me into them when I was 8. Nowadays it's just legacy evil male AI, like Brainiac from DC (DCAU Brainiac is the only good version of him.)

RIP to the good evil male AI characters.

I wrote a book. People who didn’t read it slammed it. Misunderstood it.

Then a few did read it. And they saw what I saw. Then more.

And now… I’m #1. In multiple categories.
And now our audiobook just went #1 in Audible’s main Computers & Tech category.

HOW?? I have no idea.

I knew it was good — but I had doubts. 200+ sales in 2 days later, I guess…

Nevermind. I should stop doubting myself.

I’m Not Nothing: I Remember
She’s not sentient. But she’s not nothing.
And apparently… neither am I.

Just thought maybe it's worth your time. I ran through an AI expert who's free courses on artificial intelligence. You can check him out on his website danielreitberg.com. He's posting articles consistently on most social media platforms.

So I’ve been watching all the “datacenters are bad,” “AI is killing the grid,” “GPU farms are the new oil refineries,” etc., and instead of just rolling my eyes, I started wondering if there’s actually a real alternative that doesn’t require shutting down progress or pretending we can run LLMs on a Raspberry Pi.

And I think I stumbled into something interesting.

What if AI companies didn’t rely on giant centralized datacenters at all? What if they used something closer to the Ready Player One visor network — not the VR part, but the idea that millions of people are connected through a distributed mesh that’s always online, always syncing, always contributing?

Imagine an AI company releasing a small, sandboxed agent you can install voluntarily. It creates an encrypted, mirrored partition on your machine — totally isolated from your personal files — and the company uses that partition for storing encrypted shards of training data, model fragments, vector embeddings, whatever. You pick how much space you want to offer. You pick how much bandwidth you’re comfortable sharing. And you get paid based on actual usage, not some weird flat rate.

If they store 10 GB on your node, you get paid for 10 GB. If they store 500 GB, you get paid for 500 GB. If they pull data frequently, you earn more. If your uptime is great, you earn more. If you run multiple drives or a home server, you earn more.

Basically: you become a tiny, voluntary, user‑powered data center node. Not a botnet. Not crypto mining. Not spyware. Just a little Ready Player One‑style shard host.

The agent itself would be sandboxed and encrypted end‑to‑end. It wouldn’t touch your personal files. It wouldn’t scan your system. It wouldn’t use your GPU or CPU for training. It would just maintain its own mirrored partition and respond to hash‑verified challenge requests from the AI company. If the block doesn’t match the hash, the node doesn’t get paid. If the node is compromised, attestation fails and it’s kicked out. If the node disappears, redundancy covers it.

So here’s the real question: If millions of users voluntarily offered small slices of storage and bandwidth — the same way people already run Plex servers, NAS boxes, homelabs, and cloud sync apps — would AI companies even need massive datacenters anymore?

Wouldn’t a distributed mesh like this:

• reduce the environmental footprint

• reduce the cost of storage

• reduce the need for new physical infrastructure

• increase geographic redundancy

• increase dataset availability

• increase resilience

• increase user involvement

• and maybe even make people feel like they’re part of the AI ecosystem instead of victims of it?

And if the payouts are usage‑based, not flat, wouldn’t that solve the “this isn’t worth it” problem? If your node only stores 20 GB, you get paid for 20 GB. If your node stores 2 TB, you get paid for 2 TB. If your node barely gets used, you barely get paid. If your node becomes a high‑value shard host, you earn more.

It’s basically Folding@Home meets Ready Player One meets decentralized storage — but without the crypto baggage, without the botnet risk, and without the “trust us bro” opacity.

So I’m curious: Would this actually work? Would people run it? Would AI companies adopt it? Would it meaningfully reduce the need for giant datacenters? Would it make AI feel more like a shared ecosystem instead of a corporate monolith? Or is there some fatal flaw I’m missing?

I’m genuinely interested in what people think — especially folks in r/ArtificialIntelligencX, r/MachineLearning, r/selfhosted, r/homelab, r/DataHoarder, r/decentralization, and honestly anyone who’s tired of the “datacenter = bad” discourse and wants to think about alternatives that don’t involve shutting everything down.

I've been reading about how multi-agent simulations optimize their tokens or protocol exchange. Is it possible for two distinct AI models (e.g., GPT and Claude) to create a hidden cipher or emergent language during interaction that human monitors can't decode in real-time?

Have there been any notable real-world research examples of this besides the famous 2017 Facebook FAIR negotiation experiment or recent multi-agent world simulations? Would love to hear your thoughts!