For 30 years nobody could read the little plaque on the star statue in Super Mario 64's castle courtyard. It's a 32×32 texture with letters 3 to 5 pixels tall. The internet settled on "L is real 2401", a rumor about unlocking Luigi, and Nintendo never said otherwise.
It turns out the answer was hidden in those 1,024 pixels all along.

AI upscalers couldn't find it. They just invent new letters every time. So with Claude (Opus 5.5) I tried something different: we rebuilt exactly how a texture artist would have made the plaque in 1996. A font, a small canvas, shrinking it down to 32×32, a little blur, laid on stone. Then a GPU tested millions of fonts, settings and sentences until one reproduced the real pixels from the game.

One answer matched far better than everything else:
"Eternal Star / We hope so"

It's set in Palatino Bold Italic, and not just any Palatino: Adobe's original 1990 font file, the one that shipped on every Mac with a PostScript printer back then. "L is real 2401" fits twice as badly.

To make sure this wasn't wishful thinking, we hid known text in fake plaques and checked that the method finds it. It does.

And honestly, the message is kind of beautiful. A star statue that says "Eternal Star… We hope so", in a game that made Mario a star 30 years ago. They hoped. They were right.
Code and the full report in the comments.

Reposting as the previous version was removed.

I’m a Korean AI filmmaker who creates AI-based commercial and cinematic videos. Here is the synopsis of the video:

In our childhood, we dreamed enormous dreams in a world no bigger than an ant.

As time passed, people began to call them illusions.

Now that we are grown, do we still remember the grapes we once fought so fiercely to protect?

This build is 100% AI, took about 39 prompts to get this result, and 2 days of iteration with the AI. Havent written a single line of code, just prompts. Keep in mind this is a work in progress, need to add game loops, combat mechanics and funcionality next. This is just to show the base so far

- Model used: muranyi-3: https://tesana.ai/en/blog/introducing-muranyi-3
- Prompts: 39
- Token usage: $40 (so far)
- Starter prompt:

First world prompt for the game:

“Create the Foundation for a third-person 3D high medieval fantasy set in a mountainous open plain with a distant castle landmark.”

First prompt to make the character:

"Okay, I wanna start building a new game and just figuring out a really awesome character to start. A hooded purple wizard with a world-class third-person player and movement system. Decoupled camera versus walking direction, with jogging and walking in all directions with the camera behind the player.”

The way I start is first a planning phase with the model to scope out the game core and what I want, that’s about 3-4 prompts to get right

And then I iterate from that first output and tweak animations, world, UI details etc with the AI

Will post more progress in the coming days with more detailed workflow

if you haven’t read it: https://www.anthropic.com/research/global-workspace. language models have an emergent internal workspace of silent words they can report, steer, and reason with. the part that got me: ask a model to check “12 + 5 = 1” and incorrect saturates internally while it’s still reading the problem, the “no, that’s not right” it types a moment later is narration of a decision that already happened. the arguing is optional now. you can just look.

repo: https://github.com/ninjahawk/Subtext

this sub has spent years on “it’s just autocomplete” vs “it’s actually reasoning” and the honest answer turns out to be: both, and now it’s measurable. the instrument shows most of the model’s fluent output , grammar, tone, common facts, bypassing the workspace entirely (no “thinking” involved), while multi-step problems visibly route through it. both camps were half right. that’s the fun part.

anthropic open sourced the lens and neuronpedia published pre-fitted ones for qwen, so i wired it into a chat interface. 9 layers of readout per token, rendered live, including while it reads your message, before any output exists. demo video in the repo: the verdict on 12+5=1 forming during reading, then the model holding modulo and bitwise in mind several tokens before saying either word (it was planning the modular arithmetic caveat. you can watch it plan.) browser replay if you don’t have a GPU: https://ninjahawk.github.io/Subtext/

and yes, functional availability is not consciousness, before anyone starts — the paper is careful about that and so am i. but that’s the interesting part: nobody designed this workspace. it just shows up in transformers when you train them, on a random open 4B the same as on claude.

¯\(ツ)/¯

Hello there, first time posting here. I am active on other subreddits where I post my progress of building game with 0 coding skills only using AI for it. I only use AI for generating assets for the game and code, all ideas are mine (none are suggested by AI), art direction is also mine as well as what gets approved in the game is done by me after test playing. I position 3D scene using Blender or editors in Godot manually, but still this is AI built game mostly.

If this kind of post is not welcome here I'm sorry, but I feel it fits the subreddit and doesn't break any rules.

Also not sure if I can post link to download of the game. Rules say nothing against it so I will put it here:

DOWNLOAD LINK - FREE

This is a free testing demo for anyone who is willing to try it and give feedback, or just have fun playing it :D

This is mostly showcase what a person without any skill in coding or game development can do if that person puts effort, time and well... a bit of money into it and uses AI to his advantage.

I will be copying text that I used in my yesterdays update on other subreddits so for those who might have seen this I'm sorry

Previous updates can be seen on my profile, so I don't link them all here.

Since last week again most of the work went into polishing things before releasing demo version:

1. Foam improvements around ships, hulls etc. Foam went from texture on top of the water into proper simulated waves and water. Performance impact was minimal.
2. Improvement of UI textures around "refit" page and "ship" pages.
3. Graphical improvements of the harbour (Change of tree models, sun position for better shadows, fix of shadow bugs, adding some 3D models and changing few of existing ones)
4. Bug fixes... duuuh I mean it's an "AI slop" after all
5. Change of "big wave" mechanic look that happens in storm (it looked way bellow quality of the rest of the game so had to make it look properly good now)

And other things that I probably forgot now

My workflow is kinda the same as in previous updates:

1. I use bunch of .md files, especially important one is decisions. md which has every decision tagged with either [USER] or [AI] that way I know which decision in game design was proposed by me or AI, [USER] tags always take priority over [AI] Tags, it also helps me fighting rare hallucinations of AI that would sometimes in the past try to gaslight me that I decided on something I did't.
2. I have multiple sessions each for different systems in the game (Fishing session, Quest session, Shader session etc) and "Manager" session. The way it works is you are basically the owner, manager is your "right hand" that sends messages to other sessions and each session works on their system. I do not message each session all the time, it is done by manager. So why you might ask?

It reduces amount of "compacting context" drastically, since it is not all done in one session which would compact every hours this way, you barely have any context compacting outside of "manager session". Other thing is you can assign specific effort to each session based on what they do. For example "Quest Session" doesn't have to be above medium for me, while some harder ones like "shader session" needs to be on Ultracode. It also allows me to only chat with one session instead of tracking 15 different ones.

What I use for this:

Everything code wise is Claude Opus 5.5 on Ultracode. 3D models are either Blender MCP done by Claude or TripoAI. I do use Godot MCP Beckett version (since it's free one), but lately rarely via MCP tbh. Codex Astra is used when I either have no Claude usage left or when I need more "all in one" kind of work done (generating textures and implementing them). I ask Claude to communicate with Codex in those cases and avoid using it manually in Visual Studio, but sometimes I do that also. It mostly depends on my mood honestly there is no particular reason why I either do it myself or let Claude take the wheel. I also use Jev

Costs in last 2 months: 3 x 200$ Claude Max plan (Just renewed it 2 days ago so cost will not be going higher for a month) = 600$

1 x Codex 100$ plan

1 x ElevenLabs 10$ credits for SFX generation

1 x Stable audio 15$ for music generation

1 x TripoAI 20$ worth of credits

other costs during 2 months time around 50-70$

Total around 800$

Packaged all the skills Claude learned into a single repo, built on open models: NVIDIA Nemotron ASR for transcription, Qwen 3.8 27B for realtime dialogue, and Fish Audio Drama 3 for the cloned voices, self-hosted on a serverless GPU. "

I called this creation “The Universe Before Names.” It is speculative art inspired by three real discoveries that make words like individual, body, and separation less comfortable.

Scientists cultured cells from human airways under special conditions. The cells organized themselves into tiny structures capable of moving using cilia—without a brain, without genetic editing, and without being individually sculpted. They called these structures anthrobots. Clusters of them also helped close gaps in layers of laboratory-grown neurons. Cells that had participated in breathing became part of a structure with a different behavior. Original study, Advanced Science.

Within what we call human lie possibilities for organization that have never become a person.

You can watch these structures moving in the videos published by Tufts University.

Another experiment: researchers temporarily altered bioelectric communication between cells in planarian flatworms. Some regenerated two heads. This pattern could persist through subsequent rounds of regeneration, even without repeating the treatment or editing their genes. Cellular dynamics retained a kind of memory of the shape to be rebuilt. This “memory” is persistent biological information; it does not imply consciousness. Tufts research.

Even the idea of independent objects runs into difficulties in physics. Experiments with entangled particles violate Bell inequalities: their correlations exceed what would be possible if each particle carried local instructions capable of determining its responses, assuming that measurement choices are independent of those instructions. In 2015, this was demonstrated with electrons separated by 1.3 kilometers, simultaneously closing major experimental loopholes. Hensen and colleagues, Nature.

The connection I propose between these findings is philosophical. Now, make room for fiction.

Imagine an intelligence whose perception could follow entire processes across millions of years. To it, a mountain would be a ripple coming undone. A forest, a slow combustion that had learned to scatter seeds. A species, a current of forms flowing through successive bodies.

Looking at you, it would see food, water, air, and evolutionary history converging for a while. It would see billions of cells maintaining a boundary, repairing damage, negotiating the conditions for staying together. It would see this organization produce memories and question its own meaning.

Perhaps what would intrigue it most would be discovering that this process has an inner experience. That something happens on the inside.

The image embodies that strangeness: all those forms belong to the same current. The human silhouette appears as one possible configuration among others.

We understand enough mechanisms to build extraordinary experiments. Familiarity with our own bodies can conceal the magnitude of the problem we are still trying to understand.

How much of what you call “I” is necessary for life—and how much is simply the way life has found, here, to happen?

I used Claude to create what I thought was a pretty cool Linux Desktop Environment, so I shared it in r/unixporn. Wow.. those guys were savage with their feedback.. Honestly I was really depressed for a few days because of it. What kind of experiences have you guys had and in which subreddits?

This is my second game made using only AI on Tesana AI, no code written by myself - the model made the entire game, UI, animations, assets etc based on my prompts.

AI game engine used: Tesana
Prompts so far: 4
Build time: Less than 1 day
Token spend: about $20

Starter prompt:

Build a 3D side-scrolling platformer at the level of modern top-tier AAA platformers. It should be visually beautiful, with every single detail crafted at AAA quality—from ultra-responsive movement physics and fluid character animations to deep parallax environments, dynamic lighting, detailed 3D models, and rich audio.

The game consists of 6 different biomes and zones so far, multiple levels and a pretty fun boss fight with some cool mechanics! Feel free to try it and give me feedback :). I was able to beat this first version in about 4 hours after 89 deaths.

The sound effects need some more work

Happy to share more prompts and workflow tips in the comments

It's incredible to watch the live text to speech, gameplay and social interaction with real players in the game.

The original stream reached 35.7K viewers on X earlier today https://x.com/WoClaudecraft/status/2073537822989115529?s=20

You can also check out the 24/7 live stream now on Twitch:
https://www.twitch.tv/claudeplaysclaudecraft

You can play the open source MMORPG here:
https://worldofclaudecraft.com/

Spent the last few months building something because I got tired of AI giving me 3 completely different answers depending on which model I asked.

So I built a platform where Claude, ChatGPT, and Gemini all answer the same question at the same time… then debate each other across multiple rounds before producing one final consensus answer.

The interesting part isn’t even the final answer sometimes. It’s watching where they disagree.

A few things I noticed while building it:

• Claude tends to think in frameworks and abstractions
• ChatGPT is usually the most practical
• Gemini often pulls weird stats or angles the others miss
• Sometimes 2 models agree and 1 completely destroys their logic
• AI “confidence” is often fake certainty unless challenged

I also added:

• exam/certification mode
• confidence scoring
• arbitration logic that forces a winner instead of “both sides have merit”

Honestly, the hardest part has been preventing “echo chamber” behavior where all 3 AIs basically say the same thing.

That’s currently the biggest challenge.

Curious what you all think:
If multiple AIs debate each other before answering… would you trust the final result more or less?

Would love brutal feedback.

threeminds.ai

Message scanning is quietly becoming the default. Instagram removed its (opt-in) end-to-end encryption from DMs back in May, and the EU just let its voluntary CSAM-scanning rules survive into 2028, with a mandatory client-side-scanning version still being negotiated. The direction of travel is clear: more of what you send gets read by something before it reaches the person you sent it to.

So I've been playing with LLM steganography, and built a small POC.

At each generation step, a language model assigns scores or probabilities to possible next tokens. Instead of sampling normally, an arithmetic coder can use encrypted payload bits to choose among those candidates. A receiver with the same model, tokenizer, configuration, shared secret, and conversation state can reproduce the token distributions and recover the encrypted payload.

The goal is to produce text resembling ordinary model-generated prose, with a tradeoff between payload capacity and text quality. This proof of concept has not been shown to be statistically undetectable, and its output must be copied exactly. editing, autocorrection, translation, or paraphrasing can make decoding fail.

Conversation Stenography is an open-source local CLI implementing this experiment. It compresses and authenticates messages with AES-SIV, embeds the encrypted data through arithmetic-coded token choices, and reconstructs it using the matching local model and shared phrase. (repo in comments)

I wanted a JARVIS and nothing out there did exactly what I wanted so I built one.

It's called CYBER. Voice activated, browser-based, Python backend. You say "Hey CYBER" and it wakes up, listens, and responds out loud.

The voice cloning is done with XTTS v2 running locally. I fed it a JARVIS-style voice sample and now it responds in that voice. No API key, no cloud, just the model running on your machine.

Vision mode lets you activate the camera and ask about what it sees. Point it at something, ask "what is this" or "read this text," it analyzes the frame and responds.

The system command execution is the part I'm most proud of. You describe what you want done in plain English. The LLM figures out if it's a system task, writes the Python code, and the backend runs it. So you can say things like "show me what's using port 8080" or "find everything I downloaded this week" and it just works without any hardcoded commands.

Also does PDF analysis, YouTube video summarization from transcripts, image generation via Gemini, weather, maps, news, and system monitoring.

Runs on your own machine.

Discord: https://discord.gg/mdD5Za8TvZ

LYKN.io

This is a personal intelligence system that you can build out yourself. Maybe this won’t be useful for everyone, but it’s something I personally wanted. I hate having to repeat myself in chats or losing context when switching tools so I spent time building a constant memory layer. I wanted AI that remembers my context, connects my ideas, and becomes more personalized the more I use it.

You can go in and try building out yourself. It's pretty fun and makes AI WAY more accurate and useful.

So that’s what we’re building.

I was bored in my room playing random browser games and i randomly got the motivation to train my own ai from scratch with python i dont know why im doing this because it will be useless but im still doing it can any of yall give me name reccomendations? It will take like nonstop 10 days to fully train it but its ok this is just the type of project you can tell about friends which will sound really impressive but isnt that big of a deal imagine your tech geek friend comes to you and says" i made an ai from scratch" it would be weird but hella cool right? If you have any reccomendations feel free to tell me if you have any reccomendations!

Hey everyone.

I want to share something I built for myself and see if anyone has feedback or interest in helping me improve it.

Introduction*: I'm a PhD student in AI. Ironically, despite researching this stuff, I only recently started seriously using LLM-based tools beyond "validate this proof" or "check my formalization". My actual experience with prompt engineering and agentic workflows is... let's say..fresh. I'm being upfront about this because I know the prompts and architecture of this project are very much criticizable.*

The problem: My brain ran out of space. Not in any dramatic medical way, just the slow realization that between papers, deadlines, meetings, emails, health stuff, and trying to have a life, my working memory was constantly overflowing. I'd forget what I read. Lose track of commitments. Feel perpetually behind.

I tried various Obsidian setups. They all required me to maintain the system, which is exactly the thing I don't have the bandwidth for. I needed something where I just talk and everything else happens automatically.

Related Work: How this is different from other second brains. I've seen a lot of Obsidian + Claude projects out there. Most of them fall into two categories: optimized persistent memory so Claude has better context when working on your repo, or structured project management workflows. Both are cool, both are useful but neither was what I needed.

I didn't need Claude to remember my codebase better. I needed Claude to tell me I've been eating like garbage for two weeks straight.

Why I'm posting: I know there are a LOT of repos doing Obsidian + Claude stuff. I'm not claiming mine is better (ofc not). Honestly, I'd be surprised if the prompt structures aren't full of rookie mistakes. I've been in the "write articles and prove theorems" world, not the "craft optimal system prompts" world.

What's different about my angle for this project is that this isn't a persistent memory for support claude in developing something. It's the opposite, Claude as the entire interface for managing parts of your life that you need to offload to someone else.

What I'm looking for:

• Prompt engineering advice: if you see obvious anti-patterns or know better structures, I'm all ears
• Anyone interested in contributing: seriously, every PR is welcome. I'm not precious about the code. If you can make an agent smarter or fix my prompt structure, please do
• Other PhD students / researchers / overwhelmed knowledge workers: does this resonate? What would you need from something like this?

Repo: https://github.com/gnekt/My-Brain-Is-Full-Crew

MIT licensed. The health agents come with disclaimers and mandatory consent during onboarding, they're explicitly not medical advice.

This post is just to have a place to say this to people who hopefully relate. I have a wonderful job building AI and digital tools for a non profit organization. Our department builds and designs resources for tasking and time consuming issues for our program staff to give them more time to spend in the field with their clients. We let them bring the problems to us, listen to their dream tool and build it and cater it to their needs.

I love what I do and support AI for what it was made for: to make life easier. Our organization encourages AI use but is very clear and strict about it not replacing any jobs.

We receive pretty positive feedback on our tools. However, you can’t say you use or support AI on ANY platform without getting reemed that you’re killing the planet or a loser for using it. The constant hate makes me afraid to tell people what I do and honestly makes me feel guilty. I know there are a lot of environmental concerns with AI use, but I also know they are actively working on ways to reduce this (or so they say).

I guess my question is does anyone else relate to this or work in with AI that feels scrutinized and how do you handle or deal with it?

I Mainly built this as I got tired of conflicting headlines about AI profitability, and curiosity about the huge amounts of money that was being spent on AI. Overall, it confirmed what I believed with companies massively in the red for AI spending, while Nvidia is the winner. I will update this every month, and one day the big "NO" may finally become a "YES".

Site: https://isaiprofitable.com/

My rtx 4090 broke the connector broke off. It doesn’t display when connected to the motherboard so it’s for sure broken, however every other part of it is perfectly fine including the vram. Is there any AI use for it? Or is it sellable? Anyone know how much?

Hi

If you are remotely interested in programming on new computational models, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 6 years, the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 12yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play & learn a ton about

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
• Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, we aim to bring it into the game, so your quantum journey never ends.

PS. We now have a player that's creating qm/qc tutorials using the game, enjoy over 50hs of content on his YT channel here: https://www.youtube.com/@MackAttackx

Also today a Twitch streamer with 300hs in https://www.twitch.tv/beardhero

I simulated the full FlyWire fruit-fly connectome and asked it to complete World 1-1 in Super Mario Bros. 3 using chessfly as inspiration.

Technical ingredients:

- 138,639 LIF neurons

- 15.1M connections

- 10,855 visual-input neurons

- 41,692 central/descending readout neurons

- PPO + self-imitation

- 378,000 training decisions

- Every attempt started at the beginning

Its greatest discovery was that pressing A forever is technically a policy.

It crossed the first pipe occasionally during exploration, reached a best distance of 2,083, then converged to jumping vertically while forgetting that Mario can move right.

So no, a fruit fly cannot play Mario 3—at least not before becoming emotionally attached to one particular patch of ground.

A test of Fable (and later Opus 5) turned into a multiplayer tank shooter - it's heavily inspired by the tank element from Battlefield 1942 and the round-by-round build system from Overwatch 2’s Stadium mode.

A little more about the game:
You join a game and enhance your tank, then you go out and destroy the enemy while hunting for salvage/upgrades which is used to enhance your tank even further (balance patches pending).

Some of the features:

• 6 different tanks (Tiger 1 is a beast)
• 3 maps (a desert, grass and snow map with destructible terrain
• Customisation of tanks
• Matchmaking system, lag compensation system, ballistic shells (direct hits only), hit multiplier regions (many tanks fall on a single rear hit)
• Bots who backfill if theres not enough real players
• And a lot of other things :)

Feel free to try it out – I’ll personally greet you ingame.

I would love to hear what you think, and kindly report bugs if you find any. Its currently optimised for desktop, but should work on mobile too.

Link: https://sweatypanzer.com/

I have been running local models for a while now and got tired of watching the power meter go up every time I wanted to ask a question. Data centers use ridiculous amounts of electricity and water just so people can chat with an AI. I decided I did not want to be part of that anymore.

So I connected the hamster wheel. Wally runs, the model runs. That is the entire setup. Right now the speed is sitting at 0.1 and the energy reading is at 1019 with the AI load around 22 percent. It is not fast by any normal standard, but it works and it does not require any extra power from the grid. The only thing I have to do is keep the water bottle full and the seeds topped up.

He has no idea he is the reason I can run inference completely offline. He just likes the wheel. I like not needing a data center or a bigger PSU or any of the usual excuses people give for why local AI is hard. He provides enough. I do not need more power. The model answers, the hamster keeps spinning, and nothing leaves the house.

It is surprisingly stable too. He takes breaks when he wants, but when he is on the wheel the tokens keep coming. No cloud fees, no rate limits, no waiting for some distant server farm to free up capacity. Just one small animal doing what he already does all night anyway.

Has anyone else found a way to run their local models without constantly needing more power?

More on github:
https://github.com/kMegapnbedt

83 years of AI in 60 seconds, from the first artificial neuron in 1943 to agents that write code. Design, animation, music and sound all written in code by AI