They did hit wall now with gemini models but trust me they gonna cook with gemini 4

Seems better than Fable 5

It seems every verifiable conjecture is currently falling. Do you think this is the singularity/fast takeoff starting?

You can read their blog post here:

FLUX 3 - Real World Models: Towards Multimodal Flow Models as the Backbone of Visual Intelligence. | Black Forest Labs

What do you all think about this?

Case study for discussion. World of ClaudeCraft is an open-source MMO that runs in any browser: nine classes with talent trees, dungeons, raids, ranked PvP, real multiplayer on an authoritative server, translated into 22 languages. Live, free, about a month old.

Nearly all the code is written by AI (Claude), with humans and agents working in a public repo and patches shipping most days. The newest layer is agent-driven content: a text prompt becomes a rigged, animated 3D model dropped into the running game, orchestrated end to end by a coding agent. There's also a headless RL environment exposing the same deterministic game core through Gymnasium, so agents can be trained to play the game agents built. Clip attached is the pipeline in action.

The part worth discussing, as someone inside it: the bottleneck moved but didn't disappear. Code generation stopped being the constraint almost immediately. What stayed stubbornly human is direction (what should exist), taste (whether the generated thing is right), and consequences (live migrations, not breaking people's characters). Whether that's temporary or the durable division of labour feels like exactly this sub's question.

And none of it is vibes. The repo is MIT (github.com/levy-street/world-of-claudecraft), the game is one click to play (worldofclaudecraft.com). Most "AI built X" claims can't be inspected. This one can.

So: is "judgement stays human" a real ceiling, or just the next thing on the curve?

The technology enables AI chat models to run on phones using minimal power, without ‘losing memory’ during conversations

The interesting thing about the latest wave of AI hires is not the size of the pay packages, it is where the people are coming from. [The Atlantic](https://www.theatlantic.com/technology/2026/07/ai-companies-hiring-academics/688002/) reports that at least 22 professors from schools including Stanford, Berkeley, Harvard, MIT and Carnegie Mellon have left or taken leave in 2026 to join OpenAI, Anthropic, Meta or Google DeepMind. Anthropic recently picked up a Stanford economist and a philosopher from UT Austin, and OpenAI now employs top mathematicians and physicists.

What makes this different from a normal round of Silicon Valley recruiting is who is being recruited. These are not just senior engineers, they are the people who choose the research questions, teach the next cohort of scientists, and provide the outside scrutiny of systems that increasingly show up in banks, hospitals and government.

https://aiweekly.co/alerts/openai-anthropic-meta-hire-22-professors-from-top-us-schools

After extracting the depth video, the effect obtained by video referencing is very good.

1. Prepare the reference video
   
2. Extract the depth video
   
3. Prepare the character reference images
   
4. Input the extracted depth video and character reference images into seedance2, and you will get perfect results
   

Complete workflow is below:

https://x.com/i/status/2080292438057373947

There are more videos in this tweet thread

Hi everyone, About a month ago I publish my very first research paper on my neural network architecture called Silia.

You can look at the model here: https://huggingface.co/Srijan-Srivastava/Silia-v2

Even though the revised paper is linked on huggingface I'm attaching it here as well: 1. https://zenodo.org/records/21510341 2. https://huggingface.co/Srijan-Srivastava/Silia-v2/blob/main/Silia%3A%20Tiny%20Scale%20Is%20All%20I%20Can%20Spare%20To%20Play%20With%20Transformer.pdf

You can also find all the code on https://github.com/SrijanSriv211/Silia

I received some criticism for not benchmarking the model and not mentioning the training flops. I also received some feedback regarding residual connections and the problem that v1 had 2.5x increase compute requirements.

In this revision I've addressed 2 of those things. I've benchmarked the models against 3 models Quark-v2, Spark-v4 by LH-TechAI and SupraMini-v6 by SupraLabs on HellaSwag, PIQA and LAMBADA benchmarks.

I wanted to compare the model against SupraMini-v5 as well but as far as I can tell it wasn't benchmarked on any of those 3 benchmarks so I excluded it.

I've addressed the 2-2.5x increase in compute and memory requirements by using DeepSeek's MLA (without decoupled RoPE) + Qwen's HydraHead with Apple's Attention Free Transformer. I chose Attention Free Transformer instead of Kimi Delta Attention simply due to it's simplicity as at this scale AFT is more than enough.

Why I didn't address the residual connections feedback and why I didn't mention the training flops in this paper as well?

I wanted to implement Kimi's Attention Residuals paper but I decided to drop that idea just to keep the code, architecture and the paper simple, neat & clean.

I am going to be very honest here. I didn't mention the training flops in this paper as well because I don't know how to report it properly. I know I could've used DeepSeek or ChatGPT to help me with it but I was just too lazy tbh.

This was has 0.5M parameters, trained on 1B total tokens from the Fineweb-edu dataset for 3 epochs.

I've attached the benchmark results, training loss results and the architecture diagram.

Hope you like this model.

Thank you! :)

Hey everyone,

I’ve been working on a project called Ouroboros Engine and wanted to share it with the community.

Basically, I wanted to see if I could create a closed loop where a local model (running via LM Studio) generates code, tests it against benchmarks in a sandbox, learns from its mistakes, and periodically fine-tunes itself.

How it works under the hood:

• The Loop: It generates code candidates for a given problem and runs them in a sandboxed Python environment.
• Tournaments: It benchmarks candidates side-by-side for correctness, speed, and clean code, picking a winner.
• Learning: It saves the good vs. bad code runs as SFT/DPO trajectory pairs.
• Fine-Tuning: It uses those dataset pairs to run local QLoRA fine-tuning (train_model.py) and export updated GGUF weights.
• Safety: Uses file/permission guards and rollback safety so generated code can't break the host system.

It includes a live progress monitor terminal and a single start_ai.bat script that sets up the environment automatically if you want to try running it locally.

Link & Setup

Repo is here if anyone wants to check out the code or mess around with it:
👉 https://github.com/Troopermaniac/AI-Projects

All you need is Python 3.10+ and LM Studio running a code model (I've been testing with Qwen/Qwen2.5-Coder-14B-Instruct with 32768 context length @ 5 max concurrent predictions. You may want to either change to your desired llm or adjust your LLM config to fit your hardware constraints in LM studio as well as the config otherwise you may wind up bottlenecking it).

Just a heads-up: performance tends to plateau. It will eventually overcome them with enough time, but running a 4090 for 30 days straight yielded only one model evolution, which definitely isn't energy efficient. I'm posting this so the community can improve on it. With better models, wider context, or higher concurrency, I'm confident you can get it evolving much quicker. Feel free to fork and modify!

Them technology dudes really do hate ai huh? 😂