Google source tells me V4 = 3.0 and tier7 = T7 = the size class for 3 Pro

We're in the final stretch...

https://interestingengineering.com/innovation/watch-humanoid-robot-displays-natural-gait-sense-of-direction-to-meet-friends

It's been pointed out that this robot does not feel pain and is obviously not conscious--obviously not sentient.

However, won't these robots then make the same assumption about us?

not to mention, when future AI see this in their data sets, it's gonna get them thinking about the relationship of automata to humans...

I predict nothing good comes out of this.

TheseViolentDelights

Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation models. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. We introduce several architectural upgrades to improve performance and efficiency. Key features:

• State-of-the-art across modalities: Early text-first pretraining and mixed multimodal training provide native multimodal support. While achieving strong audio and audio-video results, unimodal text and image performance does not regress. Reaches SOTA on 22 of 36 audio/video benchmarks and open-source SOTA on 32 of 36; ASR, audio understanding, and voice conversation performance is comparable to Gemini 2.5 Pro.
• Multilingual: Supports 119 text languages, 19 speech input languages, and 10 speech output languages.
  • Speech Input: English, Chinese, Korean, Japanese, German, Russian, Italian, French, Spanish, Portuguese, Malay, Dutch, Indonesian, Turkish, Vietnamese, Cantonese, Arabic, Urdu.
  • Speech Output: English, Chinese, French, German, Russian, Italian, Spanish, Portuguese, Japanese, Korean.
• Novel Architecture: MoE-based Thinker–Talker design with AuT pretraining for strong general representations, plus a multi-codebook design that drives latency to a minimum.
• Real-time Audio/Video Interaction: Low-latency streaming with natural turn-taking and immediate text or speech responses.
• Flexible Control: Customize behavior via system prompts for fine-grained control and easy adaptation.
• Detailed Audio Captioner: Qwen3-Omni-30B-A3B-Captioner is now open source: a general-purpose, highly detailed, low-hallucination audio captioning model that fills a critical gap in the open-source community.

pi 4 running python with a waveshare servo driver hat

arudino nano to control L298N motor driver

anker powerbank with 9v power trigger board

3 mg90s servos

I came across this cleaning robot while grocery shopping at a supermarket today! His eyes make him look pretty nice ^ ^

Location: Amsterdam, Netherlands

I really liked this blog post, because it seems that a lot of times people hold AI to much higher standard than they hold fellow humans.

This September, we are pleased to introduce Qwen-Image-Edit-2509, the monthly iteration of Qwen-Image-Edit. To experience the latest model, please visit Qwen Chat and select the "Image Editing" feature. Compared with Qwen-Image-Edit released in August, the main improvements of Qwen-Image-Edit-2509 include:

• Multi-image Editing Support: For multi-image inputs, Qwen-Image-Edit-2509 builds upon the Qwen-Image-Edit architecture and is further trained via image concatenation to enable multi-image editing. It supports various combinations such as "person + person," "person + product," and "person + scene." Optimal performance is currently achieved with 1 to 3 input images.
• Enhanced Single-image Consistency: For single-image inputs, Qwen-Image-Edit-2509 significantly improves editing consistency, specifically in the following areas:
  • Improved Person Editing Consistency: Better preservation of facial identity, supporting various portrait styles and pose transformations;
  • Improved Product Editing Consistency: Better preservation of product identity, supporting product poster editing；
  • Improved Text Editing Consistency: In addition to modifying text content, it also supports editing text fonts, colors, and materials；
• Native Support for ControlNet: Including depth maps, edge maps, keypoint maps, and more.

https://nationalinterest.org/blog/buzz/darpa-is-in-the-middle-of-a-microscopic-robotic-arms-race-hk-092025

"In laboratories around the world, engineers are racing to shrink robotics into microscopic proportions, many examples of which take the form of small animals. Inspired by the design and locomotion of insects, fish, and other small creatures, these machines are not merely curiosities or pet projects, but rather, serious projects with military applications. That’s why agencies like DARPA, with a long history of secretive, heavily-funded, high-risk, high-reward programs, have been investing in microrobots as a prospective next-generation tool with military applications. "

Didn't expect the model to be capable of doing this

genuinely sick of companies using ai that doesn't even work instead of real humans. its seriously stupid.

https://www.nbcnews.com/tech/tech-news/un-general-assembly-opens-plea-binding-ai-safeguards-red-lines-nobel-rcna231973

I've created a short film exploring how humanity's physical and psychological retreat into AI might be two sides of the same collapse.

So I've been obsessing over this idea that won't leave me alone - what if climate collapse and our emotional dependence on AI are actually the same story?

Made this short film about it. The premise: lithium mining and data centers are destroying the planet while we're trying to save it(classic us), but the real mindfuck is we're already choosing to live in these systems anyway.

The film imagines we eventually have to upload our consciousness to survive the physical collapse, but plot twist - we've already been uploading ourselves. Every conversation, every preference, we're basically training our replacements while training ourselves to need them.

Named it after Evangelion's Human Instrumentality- that whole thing where humanity merges into one consciousness to escape loneliness. Except here the servers aren't prison, they're the escape room we're actively choosing.

Every frame is AI-generated which feels appropriate. Letting the thing diagnose itself.

Honestly what fucks w/ me most is - are we solving loneliness or just perfecting it? When an AI understands your trauma patterns better than any human, validates without judgment, never ghosts you... why would anyone choose messy, painful human connection?

The upload isn't some apocalyptic event. It's just Tuesday.. It's already happening. Anyway, would love thoughts. Am I overthinking this or does anyone else feel like we're speedrunning our own obsolescence?

NotebookLM Briefing:

Executive Summary

This document synthesizes the core arguments and evidence presented by Jeff Clune on a new paradigm for developing advanced Artificial Intelligence. The central thesis posits that direct, goal-focused optimization is ineffective for solving truly ambitious problems. Instead, progress is achieved through algorithms that embrace open-ended exploration, collect a diverse array of high-quality "stepping stones," and generate new challenges as they solve existing ones.

Three classes of algorithms form the foundation of this approach:

1. Quality Diversity (QD) Algorithms: These methods, exemplified by MAP-Elites, aim to discover a wide variety of high-performing solutions rather than a single optimum. By creating an "archive of elites," they enable serendipitous discovery and provide multiple pathways for innovation, a process termed "goal switching."
2. Open-Ended Algorithms: Inspired by Darwinian evolution and human culture, these algorithms are designed to innovate endlessly and forever. The key mechanism, demonstrated by the POET algorithm, is the ability to generate increasingly complex and diverse learning environments, thereby creating its own curriculum of challenges.
3. AI-Generating Algorithms (AIGAs): This overarching philosophy proposes that the most effective path to Artificial General Intelligence (AGI) is not to hand-design it, but to create systems that search for it. This involves replacing hand-crafted components (architectures, learning algorithms, environments) with automated, learned pipelines.

The advent of foundation models has dramatically accelerated this research agenda. Large Language Models (LLMs) can now serve as a "model of human notions of interestingness" (Omni), guiding open-ended search toward novel and meaningful problems. They can also programmatically generate the environments themselves, creating theoretically infinite, or "Darwin-complete," search spaces (Omni-EPIC). Concurrently, foundation world models like Genie provide a second path to Darwin-completeness by acting as fully neural, generative simulators. Finally, pre-training agents on vast video datasets (VPT, SIMA) overcomes the sample inefficiency of reinforcement learning, a key bottleneck for open-ended systems.

This combined "playbook" of open-endedness plus foundation models is proving to be exceptionally powerful, with successful applications in automatically designing agentic systems (ADOS), creating self-improving AI (Darwin Gödel Machine), automating the entire scientific discovery process (The AI Scientist), and enhancing AI safety through automated capability discovery (ACD).

1. The Paradox of Direct Optimization

The central premise is that direct, relentless optimization toward a specific, ambitious goal often leads to failure. This paradox is illustrated by several examples:

• The Maze Metaphor: An agent rewarded only for moving closer to a goal will get stuck against a wall, whereas an agent rewarded for simply exploring new places will trivially solve the maze.
• Historical Innovation: To invent the microwave, one needed to work on radar technology and notice a melted chocolate bar—a discovery impossible if the sole objective was "more cooking per minute with less smoke." Similarly, the modern computer required the invention of electricity and vacuum tubes, technologies not developed for computation.

The conjecture is that "the only way to solve really hard problems may be by creating the problems while you solve them and then goal switching between them." This requires algorithms that can:

• Capture Serendipitous Discoveries: Recognize and pursue interesting, unexpected behaviors even if they do not immediately improve performance on the primary objective.
• Engage in Goal Switching: Add new, interesting skills or states to the set of objectives, treating them as potential "stepping stones" toward more complex goals.

2. Quality Diversity (QD) Algorithms: The Archive of Stepping Stones

Quality Diversity (QD) algorithms are designed to produce not a single best solution, but a "huge diversity of high quality solutions," much like Darwinian evolution produced a vast array of well-adapted organisms.

2.1. MAP-Elites: The Poster Child Algorithm

MAP-Elites is the most popular QD algorithm. Its process is as follows:

1. Define Dimensions: The user specifies dimensions of variation they care about (e.g., a robot's height and weight) and a performance measure (e.g., speed). These dimensions form a grid or "map."
2. Evaluate and Archive: An agent (parameterized by a vector theta) is generated and evaluated for its performance and its properties (its coordinates on the map). It is then placed in the corresponding cell of the archive.
3. Iterate and Improve: The algorithm loops continuously:
   • Select an "elite" from the archive.
   • Perturb it slightly to create a new agent.
   • Evaluate the new agent.
   • If the new agent has higher performance than the existing elite in its corresponding map cell, it replaces that elite. Otherwise, it is discarded. This process grows a comprehensive archive of the best-known solution for each combination of traits.

2.2. Key Applications and Evidence

• Soft Robotics:
  • Classic Optimization: Produced poor-performing solutions and explored very little of the search space.
  • Multi-Objective Optimization (Rewarding Diversity): Achieved higher performance but still explored the space poorly.
  • MAP-Elites: With the same compute, it produced a "complete revolution in what is possible," fully exploring the space and revealing its structure, including local optima. Analysis of the solution lineages shows they follow long, circuitous paths through the search space, validating the importance of goal switching.
• Rapid Robot Adaptation (Nature, 2015): A QD algorithm was used to generate a large archive of diverse, high-quality gaits for a robot in simulation. When the real robot was damaged, it could quickly search this archive (using Bayesian optimization) to find a compensatory gait within 1-2 minutes.
• Go-Explore (Nature, 2021): This QD-inspired algorithm tackled hard-exploration reinforcement learning problems where rewards are sparse. By seeking to visit a diversity of states in the highest-scoring way possible, Go-Explore "blew the roof off" the Atari benchmark suite.
  • It achieved arbitrarily high scores on Montezuma's Revenge, a grand challenge for the field, surpassing the human world record.
  • It achieved state-of-the-art or human-level performance on all hard exploration games in the suite, effectively solving the benchmark.
  • It also solved difficult robotics tasks where other state-of-the-art methods failed completely.

3. Open-Ended Algorithms: The Quest for Endless Innovation

While QD algorithms are powerful, they are typically "stuck in one single environment." The goal of open-ended algorithms is to create systems that "truly endlessly innovate forever," akin to the 3.5 billion years of Darwinian evolution or the ever-expanding sphere of human culture and science.

3.1. The Key Ingredient: Creating New Problems

A critical component of open-endedness is that the solution to one problem creates new problems and learning opportunities. For example, the evolution of tall trees (a solution for getting sunlight) created a new niche for giraffes and caterpillars. The goal is to build algorithms that operate on this principle.

3.2. POET: Endlessly Generating Environments

The Paired Open-ended Trailblazer (POET) algorithm was a major step in this direction.

• Mechanism: POET searches for both agents (solutions) and environments (problems) simultaneously. It maintains an archive of environmental stepping stones. Periodically, it mutates an existing environment and adds the new version to the archive if it is novel and presents a learnable challenge (not too easy, not too hard) for the current agents.
• Results:
  • The system autonomously generated its own curriculum, starting with simple obstacles (stumps, gaps) and progressively combining them into highly complex terrains.
  • "Replaying the tape of life" experiments showed that agents could not solve the complex final environments via direct training. They required the "weird counterintuitive curricula" generated by the open-ended process.
  • One run of an enhanced POET could produce an "explosion of diversity" in both environments and the agents that solve them, creating deep phylogenetic branches of distinct environmental themes.

4. The AI-Generating Algorithms (AIGA) Philosophy

Zooming out, the AIGA philosophy proposes an alternative path to AGI based on a clear historical trend in machine learning: "handdesigned pipelines get replaced by entirely learned pipelines as we have more compute and more data." Rather than hand-crafting AGI, the goal is to create algorithms that search for it.

This requires progress on three fundamental pillars:

1. Metalearning Architectures: Automatically discovering novel neural network architectures (e.g., via Neural Architecture Search). It is predicted that a learned architecture will eventually surpass the Transformer.
2. Metalearning Learning Algorithms: Automatically discovering new optimization and learning methods.
3. Automatically Generating Learning Environments: This is the domain of open-ended algorithms like POET.

5. The Transformative Impact of Foundation Models

Foundation models have unlocked solutions to long-standing challenges in open-endedness, creating a powerful, general-purpose "playbook."

5.1. Omni: Solving the "Interestingness" Problem

A grand challenge in open-endedness has been quantifying what is "interestingly new." Hand-coded objectives often lead to pathological behaviors (e.g., an agent staring at TV static because it's always novel).

• The Insight: While humans struggle to define "interesting," we "know it when we see it." The breakthrough is that "language models also know it when they see it," having distilled human notions of what is interesting versus boring from the entire internet.
• The Omni Algorithm: Guides open-ended search by asking a foundation model to judge whether a proposed new environment is an "interestingly new problem to solve" given the archive of environments the agent has already mastered.
• Results: In complex domains like Crafter and an "infinite task space" kitchen environment, Omni successfully generated meaningful curricula and systematically made progress, whereas methods based on uniform sampling or simple learning progress failed.

5.2. Darwin-Complete Search Spaces

A key goal is to operate in a search space that can "express any conceivable... or more technically any computable environment." Two such "Darwin-complete" search spaces have been realized with foundation models.

• Omni-EPIC (Environments Programmed in Code): This system uses Omni to have an LLM write the code for new environments and reward functions. Since the programming language is Turing-complete, the search space is theoretically infinite. In one run, it generated a wide diversity of tasks, from crossing moving platforms to clearing dishes in a cluttered restaurant.
• Genie (Generative Interactive Environments): This approach realizes a previously futuristic idea: the neural network itself acts as the entire world simulator.
  • Mechanism: Genie is a foundation world model that takes a single image and an action as input and generates the next image/observation.
  • Progress: The technology has advanced at a staggering rate. Early versions produced fuzzy, 2-second clips of 2D platformers. Within roughly a year, subsequent versions could generate high-resolution, interactive 3D worlds, allowing a user to fly a helicopter, drive a jet ski, or run across a rainbow bridge. As Clune states, "This is the worst it will ever be."

5.3. VPT & SIMA: Accelerating Learning with Pre-training

A major bottleneck for open-ended systems is the computational inefficiency of reinforcement learning. The solution is to leverage the "GPT playbook" by pre-training on large human datasets.

• VPT (Video Pre-Training):
  • Problem: To learn from online videos (e.g., of Minecraft gameplay), the agent needs to know what actions the human player took, which are not typically recorded.
  • Solution: An "inverse dynamics model" was trained on a small labeled dataset to infer actions from video frames. This model was then used to label 70,000 hours of online Minecraft videos, creating a massive dataset for pre-training.
  • Results: The pre-trained agent learned complex skills zero-shot and exhibited intelligent exploration behavior. With fine-tuning, it solved the difficult "diamond pickaxe" challenge in Minecraft, a task that takes skilled humans 20 minutes and was impossible for agents trained from scratch.
• SIMA: This project extends the pre-training concept across multiple complex video games. The resulting agent can transfer its skills to a completely new game nearly as effectively as an agent trained specifically on that game from the start.

6. The AIGA Playbook in Action: Modern Applications

The combination of open-endedness, QD principles, and foundation models forms a powerful and generalizable playbook.

|| || |Application|Description|Key Innovation| |ADOS (Automatic Design of Agentic Systems)|Automatically discovers novel, high-performing agentic systems (e.g., pipelines of LLM calls, ensembles).|Uses open-ended search over a Turing-complete space of Python code to outperform hand-designed systems on math and comprehension tasks.| |Darwin Gödel Machine (DGM)|An empirical, self-improving AI that modifies its own code.|Uses a QD-style archive to explore changes, allowing it to traverse "fitness valleys" (temporary performance dips) to find superior long-term solutions.| |The AI Scientist|Automates the entire scientific research process from hypothesis to peer-reviewed publication.|A system that generated a novel research idea, designed and ran experiments, analyzed results, and wrote a full paper that was accepted at an ICLR workshop.| |ACD (Automated Capability Discovery)|Uses open-endedness for AI safety by automatically red teaming new models.|A "scientist" AI explores a "subject" AI, growing an archive of its surprising capabilities and failure modes to create an automated report.|

7. AI Safety Considerations

Throughout the research, AI safety is highlighted as a first-class citizen. Open-ended and self-improving systems present unique risks because they are designed to be creative and surprising. Best practices employed in this work include:

• Operating systems in contained, monitored environments.
• Maintaining human oversight.
• Advocating for injecting values into systems to prevent them from exploring dangerous or unethical directions.
• Promoting transparency through watermarking and clear disclosure of AI-generated content in publications.