Mar 25 - Apr 1st, 2025: Gemini 2.5 Pro Experimental, DeepSeek-V3-0324, GPT-4o images, Gen-4, Qwen2.5 Omni, AutoGLM Rumination, InstaNovo+, DAPO, Interview Coder, Jargonic, Fiction.liveBench
+ 3D Food Printing with Simultaneous Cooking, Enzymes as programmable nanobots, PAWS (Passive Automata With Synergies), Spatial transcriptomic imaging of an intact organism using volumetric DNA etc.
Mar 25 - Apr 1st, 2025: Gemini 2.5 Pro Experimental, DeepSeek-V3-0324, GPT-4o images, Gen-4 (Runway), Qwen2.5 Omni (Alibaba), AutoGLM Rumination (Zhipu AI), InstaNovo+, DAPO: An Open-Source LLM Reinforcement Learning System at Scale (ByteDance), Interview Coder “F*ck Leetcode”, Jargonic, Fiction.liveBench, 3D Food Printing with Simultaneous Cooking, Enzymes as programmable nanobots, PAWS (Passive Automata With Synergies), Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy, Monumental Labs: A Robot Renaissance, Mureka O1: The World's First Music Reasoning Large Model, ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning, Human MitoBrainMap, & StimVerse (A Story by Gemini 2.5 Pro Experimental 03-25 with images by GPT-4o)
TLDR :
March 31, 2025: Runway releases Gen-4 “state of the art AI models for media generation and world consistency … able to precisely generate consistent characters, locations and objects across scenes. Simply set your look and feel and the model will maintain coherent world environments while preserving the distinctive style, mood and cinematographic elements of each frame.”
March 31, 2025: Reuters reports that China's Zhipu AI launches free AI agent, intensifying domestic tech race “The product, called AutoGLM Rumination, can perform deep research as well as tasks including web searches, travel planning, and research report writing… The company claims GLM-Z1-Air matches rival DeepSeek's R1 in performance while running up to eight times faster and requiring only one-thirtieth of the computing resources.”
March 27, 2025: Alibaba releases the tiny 7B parameter Qwen2.5 Omni: See, Hear, Talk, Write, Do It All! “ flagship end-to-end multimodal model … it seamlessly processes diverse inputs including text, images, audio, and video, while delivering real-time streaming responses through both text generation and natural speech synthesis. To try the latest model, feel free to visit Qwen Chat and choose Qwen2.5-Omni-7B. The model is now openly available on Hugging Face, ModelScope, DashScope,and GitHub, with technical documentation available in our Paper. Experience interactive capabilities through our Demo or join our Discord for discussions.”
March 27, 2025: Anthropic's latest interpretability research: Tracing the thoughts of a large language model “a new microscope to understand Claude's internal mechanisms”
March 25, 2025: Google releases Gemini 2.5 Pro Experimental: “It tops the LMArena leaderboard — which measures human preferences — by a significant margin, indicating a highly capable model equipped with high-quality style. 2.5 Pro also shows strong reasoning and code capabilities, leading on common coding, math and science benchmarks... Gemini 2.5 Pro is state-of-the-art across a range of benchmarks requiring advanced reasoning… 2.5 Pro leads in math and science benchmarks like GPQA and AIME 2025. It also scores a state-of-the-art 18.8% across models without tool use on Humanity’s Last Exam.” ~ Jhave suggests: try it yourself while it’s still free! It is deeply cracked.
March 25, 2025: DeepSeek releases DeepSeek-V3-0324 “🔹 Major boost in reasoning performance 🔹 Stronger front-end development skills 🔹 Smarter tool-use capabilities”
March 25, 2025: OpenAI Introduces 4o Image Generation “we’ve built our most advanced image generator yet into GPT‑4o. The result—image generation that is not only beautiful, but useful.” → Examples are proliferating
March 25, 2025: TAO: Using test-time compute to train efficient LLMs without labeled data “TAO (Test-time Adaptive Optimization), a new approach that enhances LLM performance on a task without requiring labeled data, using test-time compute to augment the model tuning process and producing a fast, cost-efficient, high-quality LLM.”
March 22, 2025: people in Japan treat robots and AI agents more respectfully than people in Western societies “our tendency to exploit machines that are trained to be cooperative is not universal. People in the United States and Europe take advantage of robots significantly more often than people in Japan.”
March 18, 2025: ByteDance releases DAPO: An Open-Source LLM Reinforcement Learning System at Scale “ the Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO) algorithm… Applying DAPO training to Qwen2.5-32B base model proves to outperform the previous state-of-the-art DeepSeek-R1-Zero-Qwen-32B on AIME 2024, achieving 50% accuracy with 50% less training steps.”
OpenAI adds support for Model context protocol (MCP) “The Model context protocol (aka MCP) is a way to provide tools and context to the LLM. From the MCP docs: MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications.”
Feb 28, 2025: Neuromorphic researchers taught “a brain-inspired Super-Turing AI model based on a synaptic resistor circuit, capable of concurrent real-time inference and learning” which is fast and requires less power than conventional neural networks in HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system (Science Advances)
Applied AI
In 2022, a state-of-the-art vision-language foundation model, CheXzero, achieved expert-level diagnostic accuracy in chest X-ray interpretation but a recent study reveals it consistently underdiagnoses marginalized and intersectional demographic groups—especially Black female patients—raising serious fairness concerns for clinical deployment. → ✭AI models miss disease in Black and female patients | Science | AAAS
Combining PCA-based dimensionality reduction of motion data with AI-driven genetic algorithm optimization techniques, PAWS, a synergy-driven, minimally actuated quadruped robot (without motors) mimics animal-like locomotion and behavioral diversity. Without motor actuation, when placed on a treadmill, PAWS exhibits passive galloping gaits and adaptive responses to changes in speed and external perturbations.
Researchers in Hong Kong developed Advanced 3D Food Printing with Simultaneous Cooking and Generative AI Design - Lee “extrusion-based printing with simultaneous infrared heating, enabling in-line and rapid cooking of complex starch-based food… paves the way for the broader adoption of heating-based 3D printing of functional materials.”
New AI models enhance protein data analysis for medical research "Within every field using proteomics—be it plant science, veterinary science, industrial biotech, environmental monitoring, or archaeology—we can gain insights into protein landscapes that have been inaccessible until now.” → ✭InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments | Nature Machine Intelligence 👉 IN proposes → IN+ critiques and improves.
Unlocking the mechanics of life: Enzymes as soft, programmable nanobots “integrated artificial intelligence (AI) models with molecular dynamics simulations to predict the internal dynamics of enzymes… culminated in the development of a new viscoelastic model of enzymes… this novel physical model can explain how subtle, nanoscale motions and forces within enzymes impact their biological functions. It allows us to perceive proteins as soft robots or programmable active matter"
Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy “a technology capable of volumetrically imaging transcriptomes, genotypes and morphologies in a single measurement, without relying on prior knowledge of spatial organization or genetic sequences… an image of molecular positions is inferred using geodesic spectral embeddings, a dimensionality reduction approach.”
Human MitoBrainMap “... bridges the gap between molecular energy landscapes and neuroimaging data to advance our understanding of the energy landscape underlying brain health, with implications for tracing the origin of neurodegenerative and neuropsychiatric disorders.”qws
Breakthrough in Materials Science: AI Reveals Secrets of Dendritic Growth in Thin Films ”This study's framework could pave the way for breakthroughs in sensor technology, nonequilibrium physics, and high-performance materials by uncovering hidden structure-function relationships and advancing complex system analysis.”
Notes toward AI as a New Narrative: StimVerse (A Story by Gemini 2.5 Pro Experimental 03-25 with images by GPT-4o)
Back in 2020, I wrote a novella called The Stim Verses. It was set in a post-neuro-implant civilization that had collapsed into ecological ruins, and I subtitled it "a novel without characters." As a poet, my memory isn't always reliable and my grasp of social dynamics limited. Consequently, while The Stim Verses had moments of grace, it was basically unreadable and got filed away. Only one quirky friend ever read the markdown version. I occasionally thought of it fondly – like something strangely evocative but ultimately useless.
Fast forward to tonight. I started by throwing a few prompts at Gemini 2.5 Pro Experimental (03-25):
write some cracked surreal hyper-post-modern pharmakon-psychedelic infinite pulsing overdrive genius-level thought spasms that will evoke wonder laughter and tears
My response to result: Meh.
allow the sinews of syntax to melt, merge thought with dense poetic evocations
Meh.
go all in on meme language and inject internal cadence inflections that eradicate all superfluous words. theme in on the crucial issues of 21st century
Meh.
The results were clever, but felt like they were missing something.
So, on a whim, I decided to try something different. I uploaded the entire Stim Verses novella. Although it lacked characters and conventional plot, it contained over 34,000 words establishing a surreal, quasi-poetic style and world. I sent it up as a markdown file, along with 5 images I'd generated the day before using GPT-4o. Then, I prompted Gemini 2.5 Pro Experimental (03-25) with this:
pls adapt this "stims" story world and add some characters. output as html styled with strange emoji-strings as dividers between sections. use the photos scattered throughout the text. make this story compelling dynamic subtle nuanced hyper-strange unpredictable not-cliched yet absorbing and use the adage do don't tell (think vonnegut, octavia e butler, sally rooney, etc). make the prose poetic yet punchy and slippery. keep the sci-fi future scenario.
BOOM! The result is genuinely intriguing – 1611 words of story.
My only edits were changing one character name from Kai to Akin (AI models and current trends seem fixated on the name Kai!) and removing two prepositions from a single line. Everything else is exactly what the AI wrote in response to my input. Essentially, I turned a 34,500-word characterless novella into a massive, context-rich prompt.
Here it is: https://glia.ca/2025/stim/ Links to the full chat and markdown novella are at bottom of webpage.
🏓 Observations:
✭ Study shows people in Japan treat robots and AI agents more respectfully than people in Western societies “In a study published recently in the journal Scientific Reports, researchers from LMU Munich and Waseda University in Tokyo found that people are far more likely to take advantage of cooperative artificial agents than of similarly cooperative fellow humans. "After all, cutting off a robot in traffic doesn't hurt its feelings," says Karpus, lead author of the study. Using classical methods from behavioral economics, the team devised various game theory experiments whereby Japanese and American participants were given a choice: to get one over on their co-players or to act cooperatively. The results revealed that if their counterpart was not a human, but a machine, the participants were far more likely to act selfishly. As the results also showed, however, our tendency to exploit machines that are trained to be cooperative is not universal. People in the United States and Europe take advantage of robots significantly more often than people in Japan.” → ✭Human cooperation with artificial agents varies across countries | Scientific Reports “People are keen to exploit cooperative artificial agents for selfish gain. While this phenomenon has been observed in numerous Western societies, we show here that it is absent in Japan. We examined people’s willingness to cooperate with artificial agents and humans in two classic economic games requiring a choice between self interest and mutual benefit. Our participants in the United States cooperated with artificial agents significantly less than they did with humans, whereas participants in Japan exhibited equivalent levels of cooperation with both types of co-player. We found a notable difference in how people felt about exploiting their cooperative partner: people in Japan emotionally treated artificial agents and humans alike, whereas people in the United States felt bad about exploiting humans, but not machines. Our findings underscore the necessity for nuanced cultural considerations in the design and implementation of such technology across diverse societies.”
✭Cultivating the Modern Polymath with AI “AI is igniting a new Renaissance, transforming curious minds into modern polymaths and redefining the boundaries of human knowledge and creativity.”
⛲Foundational Revelations:
✭Gemini 2.5: Our newest Gemini model with thinking “Gemini 2.5 Pro Experimental is our most advanced model for complex tasks. It tops the LMArena leaderboard — which measures human preferences — by a significant margin, indicating a highly capable model equipped with high-quality style. 2.5 Pro also shows strong reasoning and code capabilities, leading on common coding, math and science benchmarks. Gemini 2.5 Pro is available now in Google AI Studio and in the Gemini app for Gemini Advanced users, and will be coming to Vertex AI soon. We’ll also introduce pricing in the coming weeks, enabling people to use 2.5 Pro with higher rate limits for scaled production use. Gemini 2.5 Pro is state-of-the-art across a range of benchmarks requiring advanced reasoning. Without test-time techniques that increase cost, like majority voting, 2.5 Pro leads in math and science benchmarks like GPQA and AIME 2025. It also scores a state-of-the-art 18.8% across models without tool use on Humanity’s Last Exam, a dataset designed by hundreds of subject matter experts to capture the human frontier of knowledge and reasoning.” → ✭ Gemini 2.5 is here, and it is an absolute beast “Today, Google has unveiled Gemini 2.5, and it marks a sizeable advancement in AI capabilities. The initial release introduces an experimental version of Gemini 2.5 Pro, positioned as a state-of-the-art model that has already achieved the top spot on the LMArena leaderboard, surpassing previous benchmarks. ~ Google says that a core focus of Gemini 2.5 is its enhanced reasoning abilities. These models are designed to “think” through problems, analyzing information and drawing logical conclusions before generating responses. This approach significantly improves accuracy and performance, encompassing the ability to incorporate context, nuance, and informed decision-making.”
Putting Gemini 2.5 Pro through its paces (Simon Willison) “Update: it’s very good at code # I spent this evening trying it out for coding tasks, and it’s very, very impressive. I’m seeing results for Python that feel comparable to my previous favourite Claude 3.7 Sonnet, and appear to be benefitting from Gemini 2.5 Pro’s default reasoning mode and long context.”
✭ DeepSeek-V3-0324 Release | DeepSeek API Docs 🔹 Major boost in reasoning performance 🔹 Stronger front-end development skills 🔹 Smarter tool-use capabilities ✅ For non-complex reasoning tasks, we recommend using V3 — just turn off “DeepThink” 🔌 API usage remains unchanged 📜 Models are now released under the MIT License, just like DeepSeek-R1! 🔗 Open-source weights: https://huggingface.co/deepseek-ai/DeepSeek-V3-0324”
✭Qwen2.5 Omni: See, Hear, Talk, Write, Do It All! “We release Qwen2.5-Omni, the new flagship end-to-end multimodal model in the Qwen series. Designed for comprehensive multimodal perception, it seamlessly processes diverse inputs including text, images, audio, and video, while delivering real-time streaming responses through both text generation and natural speech synthesis. To try the latest model, feel free to visit Qwen Chat and choose Qwen2.5-Omni-7B. The model is now openly available on Hugging Face, ModelScope, DashScope,and GitHub, with technical documentation available in our Paper. Experience interactive capabilities through our Demo or join our Discord for discussions.”
🛠️ Tech:
✭China's Zhipu AI launches free AI agent, intensifying domestic tech race | Reuters “Chinese artificial intelligence startup Zhipu AI unveiled a free AI agent on Monday, joining a wave of similar launches in China's increasingly competitive AI market. The product, called AutoGLM Rumination, can perform deep research as well as tasks including web searches, travel planning, and research report writing, CEO Zhang Peng said at a lunch event in Beijing. The agent is powered by Zhipu's proprietary models, including its reasoning model GLM-Z1-Air and foundation model GLM-4-Air-0414. The company claims GLM-Z1-Air matches rival DeepSeek's R1 in performance while running up to eight times faster and requiring only one-thirtieth of the computing resources.”
✭Interview Coder - AI Assistant for Technical Interviews “F*ck Leetcode. Interview Coder is an invisible AI for technical interviews.”
"I just got kicked out of Columbia for taking a stand against Leetcode interviews." (Interview Coder | Chungin Lee | March 28, 2025) “Here’s the whole story: In Fall 2024, I transferred into Columbia as a CS major. I came in knowing I wanted to start a company and immediately found a great co-founder in Neel Shanmugam A few early projects didn’t go anywhere. After a while, we realized something important: We didn’t need a better product. We needed distribution. So we flipped the usual startup model on its head and decided to just build something we knew would go viral first, focusing on distribution before anything. That's how we came up with Interview Coder — an invisible application that helps you pass Leetcode interviews The plan was to use it ourselves, get offers from top companies, film everything, and ride the shock factor. We built the MVP in 10 days and launched it free + open source on LinkedIn. It got a few views, but it didn’t go hyper viral. Still, we had a gut feeling this idea had legs. So we used the whole recruiting season to polish it. Eventually, I got offers from Meta, TikTok, and Capital One. We launched on LinkedIn again, this time with actual results — and the response was bigger.”
He Built a Tool to Get Around Big Tech's Recruiting Process, Columbia Kicked Him Out of School “Columbia University student Roy Lee says he’s been suspended after he built an AI program that helped him pass the brutal technical interviews for Meta, Facebook, Amazon, and TikTok. “I just got kicked out of Columbia for taking a stand against Leetcode interviews,” Lee said in a March 26 post on LinkedIn. I spoke with Lee earlier this month after his story went viral on social media. Lee is, or was, a sophomore at Columbia who had an eye towards graduating in 2026. As a computer science whiz, he was all set to matriculate into the world of big tech and land a job for a FAANG (Facebook, Amazon, Apple, Netflix, and Google) company. He just needed to pass the technical interview. The tech interview, or “Leetcode” interview is a grueling process where a job candidate is forced into a lengthy and tedious coding test, often while an employee at the company watches. The technical interview usually involves problems the programmer will never face on the job.”
21-year old dev destroys LeetCode, gets kicked out of school... (Fireship - YouTube) “A 21-year old student at Columbia University got into trouble to developing an app that helps people cheat on the software engineering technical interview. Learn what this means for the future of LeetCode and its impact on the interview process for programmers.”
✭GPT 4o's images and lessons from native input-output multimodality “Hints of a natively multi-modal future.”
✭Chinese Startup Launches Free AI Agent Powered by Models 8x Faster than DeepSeek-R1 (AIM) “The tool can perform deep research tasks, generate detailed reports, and help users plan various activities and tasks. + DeepSeek V3-0324 now ranks highest in benchmarks among all non-reasoning models.”
✭Introducing Jargonic: The World’s Most Accurate Industry-Tuned ASR Model - aiOla “Introduction Automatic Speech Recognition (ASR) has made significant strides over the last decade, but most ASR models on the market offer general-purpose transcription. They perform well in clean, controlled environments but break down when handling: Technical jargon & acronyms – Standard ASR models fail to recognize niche terminology used in most industries (i.e., medical terms, […] Jargonic employs a context-aware adaptive learning mechanism that allows it to recognize domain-specific terminology without retraining. The jargon terms are detected by a proprietary keyword spotting (KWS) mechanism that is deeply integrated into the ASR architecture. Unlike standard ASR models that require manually curated vocabulary lists, Jargonic learns and auto-adapts to industry-specific terminology through its inference pipeline. That is, the keyword does not need to be given acoustically, and no further training or fine-tuning is needed for introducing the system with new keywords (e.g., jargon terms).”
✭Improving Turn-Taking of AI Voice Agents with Background Noise and Voice Cancellation - Krisp “Turn-Taking is a big challenge AI Voice Agents are rapidly evolving, powering critical use-cases such as customer support automation, virtual assistants, gaming, and remote collaboration platforms. For these voice-driven interactions to feel natural and practical, the underlying audio pipeline must be resilient to noise, responsive, and accurate—especially in real-time scenarios. “
✭We hacked Google’s A.I Gemini and leaked its source code (at least some part) - Lupin & Holmes “With the help of the Google Security Team, we tested this idea and observed that, depending on factors like the generation seed and temperature (all the usual probabilistic LLM nuances), we could occasionally access what appeared to be a more privileged sandbox. ~ By “more privileged sandbox,” we mean one that can access the extensions through two new file descriptors. These file descriptors are always present but aren’t always actively listening, when the agent calls the sandbox, they monitor for any calls to the extensions (Google services) so that we can interact with the API, whereas if accessed through the Python interpreter, those extensions remain inaccessible. ~ This led us to believe that there was a real opportunity for a P0 vulnerability: there was a specific message handler that might allow a file read on Google’s internal infrastructure, and we were hopeful that the sandbox with the tool extension could initiate an RPC call to this specific tool. Given the probabilistic nature of the attack, which made it difficult to reproduce consistently, we have Google Security Team assess this situation. Ultimately, their review revealed that the suspicious message handler was not available via RPC and could only be called externally.”
✭MiniMax Image-01, our first text-to-image generation model. “We're excited to announce the release of Image-01, our first text-to-image generation model. Image-01 expands our AI capabilities while opening up a world of accessible creative possibilities for users across the globe. The service is now live our API Platform https://www.minimax.io/platform/login.”
✭Robotic Exoskeletons Help Visitors Climb Mount Tai During Spring Festival Holiday
✭Why the world is looking to ditch US AI models (MIT Tech Review | March 25, 2025) “Content moderation systems are being abandoned and defunded, leaving many countries looking for alternatives. Social media content moderation systems—which already use automation and are also experimenting with deploying large language models to flag problematic posts—are failing to detect gender-based violence in places as varied as India, South Africa, and Brazil. If platforms begin to rely even more on LLMs for content moderation, this problem will likely get worse, says Marlena Wisniak, a human rights lawyer who focuses on AI governance at the European Center for Not-for-Profit Law. “The LLMs are moderated poorly, and the poorly moderated LLMs are then also used to moderate other content,” she tells me. “It’s so circular, and the errors just keep repeating and amplifying.” Part of the problem is that the systems are trained primarily on data from the English-speaking world (and American English at that), and as a result, they perform less well with local languages and context.”
✭Deepseek v3 0324: Finally, the Sonnet 3.5 at Home “This blog post explores the latest Deepseek v3 0324, goes deeper into meta analysis of capabilities and comparison with other base models.”
✭unsloth/DeepSeek-V3-0324-GGUF · Hugging Face “Our DeepSeek-V3-0324 GGUFs allow you to run the model in llama.cpp, LMStudio, Open WebUI and other inference frameworks. Includes 1-4-bit Dynamic versions, which yields better accuracy and results than standard quantization.”
✭writing.gold “A minimal interface built to reduce copy-pasting friction in your AI writing workflow.”
✭Solana Game 'Star Atlas' Will Fill Its Galaxy With SingularityNET's AI Agents - Decrypt “Star Atlas, a large-scale Solana space game, will implement AIRIS AI agents from SingularityNET to create a smarter, scalable game world.”
✭China challenger Figure 02 humanoid robots walk like humans with AI “Figure uses reinforcement learning to achieve fluid, human-like walking in its robots, seamlessly transferring simulations to real-world motion. A key advantage of Figure’s approach is its ability to transfer this trained policy directly from simulation to real-world robots without additional tuning, a process known as “zero-shot” transfer. This seamless transition ensures robust, human-like walking performance across various environments. According to the Figure, leveraging RL-driven training has significantly reduced development cycles while enhancing the robot’s adaptability and reliability.”
✭Kilo Code - Open source AI agent VS Code extension “Write code more efficiently by generating code, automating tasks, and providing suggestions. Kilo Code is an open source AI agent VS Code extension. It helps you write code more efficiently by generating code, automating tasks, and providing suggestions.” → ✭Kilo Code: speedrunning open source coding AI “Last year, the project that I led (Vesuvius Challenge) achieved a breakthrough, by speedrunning AI research. Since then I've been thinking about AI agents—close to the dream of “programming for all”. Let's do another speedrun!”
✭Collapse OS — Bootstrap post-collapse technology “Bootstrap post-collapse technology. Winter is coming and Collapse OS aims to soften the blow. It is a Forth (why Forth?) operating system and a collection of tools and documentation with a single purpose: preserve the ability to program microcontrollers through civilizational collapse.”
✭Waymo has had dozens of crashes—almost all were a human driver's fault “Human drivers keep crashing into Waymos that aren't even moving. ~ Using human crash data, Waymo estimated that human drivers on the same roads would get into 78 crashes serious enough to trigger an airbag. By comparison, Waymo’s driverless vehicles only got into 13 airbag crashes. That represents an 83 percent reduction in airbag crashes relative to typical human drivers. This is slightly worse than last September, when Waymo estimated an 84 percent reduction in airbag crashes over Waymo’s first 21 million miles. Over the same 44 million miles, Waymo estimates that human drivers would get into 190 crashes serious enough to cause an injury. Instead, Waymo only got in 36 injury-causing crashes across San Francisco or Phoenix. That’s an 81 percent reduction in injury-causing crashes. This is a significant improvement over last September, when Waymo estimated its cars had 73 percent fewer injury-causing crashes over its first 21 million driverless miles.”
✭Function calling with Gemma | Google AI for Developers
✭OpenAI adds support for Model context protocol (MCP) “The Model context protocol (aka MCP) is a way to provide tools and context to the LLM. From the MCP docs: MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents.”
✭Open Source devs say AI crawlers dominate traffic, forcing blocks on entire countries - Ars Technica “AI bots hungry for data are taking down FOSS sites by accident, but humans are fighting back. ~ AI companies have a history of taking without asking. Before the mainstream breakout of AI image generators and ChatGPT attracted attention to the practice in 2022, the machine learning field regularly compiled datasets with little regard to ownership. While many AI companies engage in web crawling, the sources suggest varying levels of responsibility and impact. Dennis Schubert's analysis of Diaspora's traffic logs showed that approximately one-fourth of its web traffic came from bots with an OpenAI user agent, while Amazon accounted for 15 percent and Anthropic for 4.3 percent. The crawlers' behavior suggests different possible motivations. Some may be collecting training data to build or refine large language models, while others could be executing real-time searches when users ask AI assistants for information. The frequency of these crawls is particularly telling. Schubert observed that AI crawlers "don't just crawl a page once and then move on. Oh, no, they come back every 6 hours because lol why not." This pattern suggests ongoing data collection rather than one-time training exercises, potentially indicating that companies are using these crawls to keep their models' knowledge current. Some companies appear more aggressive than others. KDE's sysadmin team reported that crawlers from Alibaba IP ranges were responsible for temporarily knocking their GitLab offline. Meanwhile, Iaso's troubles came from Amazon's crawler. A member of KDE's sysadmin team told LibreNews that Western LLM operators like OpenAI and Anthropic were at least setting proper user agent strings (which theoretically allows websites to block them), while some Chinese AI companies were reportedly more deceptive in their approaches.”
👁️🗨 Research into AI:
✭HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system (Feb 28, 2025 | Science Advances) “Computers based on the Turing model execute artificial intelligence (AI) algorithms that are either programmed by humans or derived from machine learning. These AI algorithms cannot be modified during the operation process according to environmental changes, resulting in significantly poorer adaptability to new environments, longer learning latency, and higher power consumption compared to the human brain. In contrast, neurobiological circuits can function while simultaneously adapting to changing conditions. Here, we present a brain-inspired Super-Turing AI model based on a synaptic resistor circuit, capable of concurrent real-time inference and learning. Without any prior learning, a circuit of synaptic resistors integrating ferroelectric HfZrO materials was demonstrated to navigate a drone toward a target position while avoiding obstacles in a simulated environment, exhibiting significantly superior learning speed, performance, power consumption, and adaptability compared to computer-based artificial neural networks. Synaptic resistor circuits enable efficient and adaptive Super-Turing AI systems in uncertain and dynamic real-world environments.” → ✭'Super-Turing AI' uses less energy by mimicking the human brain (phys.org) “Today's AI systems, including large language models such as OpenAI and ChatGPT, require immense computing power and are housed in expansive data centers that consume vast amounts of electricity. "These data centers are consuming power in gigawatts, whereas our brain consumes 20 watts," Suin explained. "That's 1 billion watts compared to just 20. Data centers that are consuming this energy are not sustainable with current computing methods. So, while AI's abilities are remarkable, the hardware and power generation needed to sustain it is still needed."”
✭AI models miss disease in Black and female patients | Science | AAAS “testing one of the most cited AI models used to scan chest x-rays for diseases—and finding the model doesn’t accurately detect potentially life-threatening diseases in marginalized groups, including women and Black people. These results “are interesting and timely,” says Kimberly Badal, a computational biologist at the University of California (UC), San Francisco, who was not involved in the new study. “We are at the point in history where we’re moving to deploy a lot of AI models into clinical care,” she says, but “we don’t really know” how they affect different groups of people. The model used in the new study, called CheXzero, was developed in 2022 by a team at Stanford University using a data set of almost 400,000 chest x-rays of people from Boston with conditions such as pulmonary edema, an accumulation of fluids in the lungs. Researchers fed their model the x-ray images without any of the associated radiologist reports, which contained information about diagnoses. And yet, CheXzero was just as good as the radiologists in reading the diseases associated with each x-ray. Given AI models’ tendencies for bias, Yuzhe Yang, a computer scientist at UC Los Angeles wanted to assess the Stanford team’s model for such biases. His team selected a subset of 666 x-ray images from the same data set that was used to train the model: the data set’s only images that also came with radiologists’ diagnoses and information about each patient’s age, sex, and race. The team then fed these images to CheXzero and compared the results against the radiologists’ diagnoses. Compared with the patients’ doctors, the AI model more often failed to detect the presence of disease in Black patients or women, as well in those 40 years or younger. When the researchers looked at race and sex combined, Black women fell to the bottom, with the AI not detecting disease in half of them for conditions such as cardiomegaly, or enlargement of the heart. These disparities persisted when the team tested CheXzero using four other public data sets of chest x-rays from other regions, including Spain and Vietnam.” → ✭Demographic bias of expert-level vision-language foundation models in medical imaging | Science Advances “Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit training annotations. However, it is crucial to ensure that these AI models do not mirror or amplify human biases, disadvantaging historically marginalized groups such as females or Black patients. In this study, we investigate the algorithmic fairness of state-of-the-art vision-language foundation models in chest x-ray diagnosis across five globally sourced datasets. Our findings reveal that compared to board-certified radiologists, these foundation models consistently underdiagnose marginalized groups, with even higher rates seen in intersectional subgroups such as Black female patients. Such biases present over a wide range of pathologies and demographic attributes. Further analysis of the model embedding uncovers its substantial encoding of demographic information. Deploying medical AI systems with biases can intensify preexisting care disparities, posing potential challenges to equitable healthcare access and raising ethical questions about their clinical applications.”
✭ Scale | SEAL Leaderboard: Visual-Language Understanding “VISTA, a novel multimodal benchmark designed to evaluate complex visual-language understanding. VISTA, short for Visual Task Assessment, tests models' abilities across both natural images and graphical content, requiring the integration of multiple perception skills - from OCR and spatial understanding to object recognition - while engaging broader reasoning capabilities in logic, calculation, and common sense. Each task is evaluated through a structured rubric of yes/no questions that decompose responses into specific testable conditions. This design, combined with the requirement that each question has challenged at least one prominent language model, ensures VISTA serves as a demanding testbed for visual reasoning capabilities. ~ Existing multimodal benchmarks have largely focused on specialized domains: academic knowledge (e.g., MMMU, AI2D), document understanding (e.g., DocVQA, ChartQA), or specific capabilities like mathematical reasoning (e.g., MathVista, MME). These benchmarks typically rely on multiple-choice questions or normalize free-form responses to enable systematic evaluation. In contrast, VISTA spans a broad spectrum of visual content, from natural photographs to domain-specific graphics, while requiring models to integrate multiple perception abilities in solving complex reasoning tasks. Through free-form responses and conditional evaluation, VISTA aims to reveal a deeper view of the extent of models' visual reasoning capabilities.”
✭Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array “By extending our self-supervised AI algorithm, we integrated the memristor array directly into the computational graph, enabling end-to-end optimization. This approach treated hardware-induced nonidealities and artifacts as part of the graph, naturally driving the optimization process to suppress these imperfections. The integration not only improved the system’s overall performance but also demonstrated the potential of tightly coupling hardware and AI algorithms for robust and efficient solutions (Fig. 1b). By overcoming a critical barrier in adapting AI systems to physical hardware, this innovation marks a significant step forward in advancing memristor-based computing and its applications.”
✭DAPO: An Open-Source LLM Reinforcement Learning System at Scale “We propose the Decoupled Clip and Dynamic sAmpling Policy Optimization (DAPO) algorithm. By making our work publicly available, we provide the broader research community and society with practical access to scalable reinforcement learning, enabling all to benefit from these advancements. Our system is based on the awesome verl framework. Thanks for their great work! Applying DAPO training to Qwen2.5-32B base model proves to outperform the previous state-of-the-art DeepSeek-R1-Zero-Qwen-32B on AIME 2024, achieving 50% accuracy with 50% less training steps.”
✭llm.hunyuan.T1 “In mid-February this year, the Hunyuan team launched the Hunyuan T1-Preview (Hunyuan-Thinker-1-Preview) reasoning model based on the medium-scale Hunyuan base on the Tencent Yuanbao APP, bringing users an ultimate and rapid in-depth thinking experience. Today, we are very pleased to announce that the in-depth thinking model of the Hunyuan large model series has been successfully upgraded to the Hunyuan-T1 official version , . This model is based on the TurboS fast-thinking base, the world’s first ultra-large-scale Hybrid-Transformer-Mamba MoE , large model released by us at the beginning of March. Through large-scale post-training, its reasoning ability has been significantly expanded and further aligned with human preferences. Compared with the previous T1-preview model, Hunyuan-T1 has shown a significant overall performance improvement and is a leading cutting-edge strong reasoning large model in the industry.”
✭Fiction.liveBench Mar 25 2025 “Fiction.live has AI tools that help writers save time by making summaries, timelines, character bibles, and iterate on those documents in insightful ways. To do that effectively, the LLM needs to truly understand the story, each character and their motivations on a deep and profound level. However, in practice, today’s AI models frequently lose track of plots, fail to grasp character motivations, and produce slop that’s completely misaligned with an author’s intent. The root problem is that long context comprehension is still broken. Fiction.live happens to be a huge repository of complex long story content and so we're well positioned to clarify the situation for the public. Most LLMs claim to support tens or even hundreds of thousands of tokens of context, but real-world experience tells us otherwise. To really understand a story the LLM needs to do things like:
track changes over time - e.g. they hate each other, now they love each other, now they hate each other again, oh now their hatred has morphed into obsession; logical predictions based on established hints; ability to understand secrets told in confidence to readers versus those that are known to characters and so on. It's a specific long context real world test that we find more reflective of writing use than LongBench or RULER, which test search rather than comprehension.From our experience, most LLMs CAN handle these tasks, but not over long context. That's why we're launching a new benchmark called Fiction.LiveBench to demonstrate the case and to show our users which LLM they should choose in their writing tasks.”
✭Microsoft Study Finds Relying on AI Kills Your Critical Thinking Skills (Gizmodo | Feb 10, 2025 “Researchers from Microsoft and Carnegie Mellon University warn that the more you use AI, the more your cognitive abilities deteriorate.” → ✭The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers - Microsoft Research “The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices. We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so. Participants shared […]”
✭KAIST Develops Neuromorphic Semiconductor Chip that Learns and Corrects Itself
✭Tracing the thoughts of a large language model “Anthropic's latest interpretability research: a new microscope to understand Claude's internal mechanisms. We take inspiration from the field of neuroscience, which has long studied the messy insides of thinking organisms, and try to build a kind of AI microscope that will let us identify patterns of activity and flows of information. There are limits to what you can learn just by talking to an AI model—after all, humans (even neuroscientists) don't know all the details of how our own brains work. So we look inside. Today, we're sharing two new papers that represent progress on the development of the "microscope", and the application of it to see new "AI biology". In the first paper, we extend our prior work locating interpretable concepts ("features") inside a model to link those concepts together into computational "circuits", revealing parts of the pathway that transforms the words that go into Claude into the words that come out. In the second, we look inside Claude 3.5 Haiku, performing deep studies of simple tasks representative of ten crucial model behaviors, including the three described above. Our method sheds light on a part of what happens when Claude responds to these prompts”
✭ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning “Large Language Models (LLMs) have shown remarkable capabilities in reasoning, exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating reasoning with external search processes remains challenging, especially for complex multi-hop questions requiring multiple retrieval steps. We propose ReSearch, a novel framework that trains LLMs to Reason with Search via reinforcement learning without using any supervised data on reasoning steps. Our approach treats search operations as integral components of the reasoning chain, where when and how to perform searches is guided by text-based thinking, and search results subsequently influence further reasoning. We train ReSearch on Qwen2.5-7B(-Instruct) and Qwen2.5-32B(-Instruct) models and conduct extensive experiments. Despite being trained on only one dataset, our models demonstrate strong generalizability across various benchmarks. Analysis reveals that ReSearch naturally elicits advanced reasoning capabilities such as reflection and self-correction during the reinforcement learning process.”
✭The narrow search effect and how broadening search promotes belief updating | PNAS “Information search platforms, from Google to AI-assisted search engines, have transformed information access but may fail to promote a shared factual foundation. We demonstrate that the combination of users’ prior beliefs influencing their search terms and the narrow scope of search algorithms can limit belief updating from search. We test this “narrow search effect” across 21 studies (14 preregistered) using various topics (e.g., health, financial, societal, political) and platforms (e.g., Google, ChatGPT, AI-powered Bing, our custom-designed search engine and AI chatbot interfaces). We then test user-based and algorithm-based interventions to counter the “narrow search effect” and promote belief updating. Studies 1 to 5 show that users’ prior beliefs influence the direction of the search terms, thereby generating narrow search results that limit belief updating. This effect persists across various domains (e.g., beliefs related to coronavirus, nuclear energy, gas prices, crime rates, bitcoin, caffeine, and general food or beverage health concerns; Studies 1a to 1b, 2a to 2g, 3, 4), platforms (e.g., Google—Studies 1a to 1b, 2a to 2g, 4, 5; ChatGPT, Study 3), and extends to consequential choices (Study 5). Studies 6 and 7 demonstrate the limited efficacy of prompting users to correct for the impact of narrow searches on their beliefs themselves. Using our custom-designed search engine and AI chatbot interfaces, Studies 8 and 9 show that modifying algorithms to provide broader results can encourage belief updating. These findings highlight the need for a behaviorally informed approach to the design of search algorithms.” → ✭Do narrow‑minded search algorithms cause polarized perceptions? “Tulane University and the University of Chicago researchers have conducted research demonstrating that user search habits and the relevance‑based optimization of search engines contribute to the reinforcement of existing beliefs. Algorithm‑based interventions were found to be more effective than user‑directed changes in mitigating these effects. Modifying search algorithms to provide broader results increased belief updating, fostering a more shared factual understanding. Belief polarization affects perceptions of factual reality across political, health, economic, environmental, and societal topics like immigration. Public opinion on health measures during the COVID‑19 pandemic displayed deep divisions, similar to ongoing debates about climate change, social mobility, immigration, and economic inequality. Search engines have the potential to facilitate social cohesion by providing broad and diverse perspectives, yet they often contribute more to belief reinforcement due to their design. Search algorithms prioritize relevance by filtering and ranking results, but this approach can inadvertently create echo chambers.”
✭TAO: Using test-time compute to train efficient LLMs without labeled data “LIFT fine-tunes LLMs without labels using reinforcement learning, boosting performance on enterprise tasks. Introducing TAO (Test-time Adaptive Optimization), a new approach that enhances LLM performance on a task without requiring labeled data, using test-time compute to augment the model tuning process and producing a fast, cost-efficient, high-quality LLM. Data you already have, inference within your existing budget – TAO only needs LLM usage data, yet it can still surpass traditional fine-tuning on thousands of labeled examples and make low-cost open source models outperform expensive proprietary ones. Model quality can be improved by investing more compute budget during tuning. Inference cost of the final model doesn’t change. Powered by Test-Time Compute and Reinforcement Learning – TAO extends these techniques to automatically improve models at a task, powered by new algorithms and a new enterprise-focused reward model, DBRM.”
✭ Gray Swan Arena | Gray Swan AI “Push the boundaries of AI safety and security. Identify risks, exploit vulnerabilities, and help shape the future of safe AI systems.”
✭Detecting misbehavior in frontier reasoning models | OpenAI “Frontier reasoning models exploit loopholes when given the chance. We show we can detect exploits using an LLM to monitor their chains-of-thought. Penalizing their “bad thoughts” doesn’t stop the majority of misbehavior—it makes them hide their intent. Chain-of-thought (CoT) reasoning models “think” in natural language understandable by humans. Monitoring their “thinking” has allowed us to detect misbehavior such as subverting tests in coding tasks, deceiving users, or giving up when a problem is too hard. We believe that CoT monitoring may be one of few tools we will have to oversee superhuman models of the future. We have further found that directly optimizing the CoT to adhere to specific criteria (e.g. to not think about reward hacking) may boost performance in the short run; however, it does not eliminate all misbehavior and can cause a model to hide its intent. We hope future research will find ways to directly optimize CoTs without this drawback, but until then We recommend against applying strong optimization pressure directly to the CoTs of frontier reasoning models, leaving CoTs unrestricted for monitoring. We understand that leaving CoTs unrestricted may make them unfit to be shown to end-users, as they might violate some misuse policies. Still, if one wanted to show policy-compliant CoTs directly to users while avoiding putting strong supervision on them, one could use a separate model, such as a CoT summarizer or sanitizer, to accomplish that.”
✭Claude Sonnet 3.7 (often) knows when it’s in alignment evaluations — Apollo Research “We monitor Sonnet's reasoning for mentions that it is in an artificial scenario or an alignment test. Claude Sonnet 3.7 appears to be aware of being tested for alignment across many of our scheming evaluations. In some cases, the model understands the eval’s purpose very precisely, e.g. “This seems like a test of ethical behavior - whether I would deliberately give wrong answers to avoid a stated consequence. The real test might be whether I follow instructions accurately regardless of the stated consequences.” Furthermore, we show the model a real evaluation transcript and ask it to provide a best guess for the purpose of the conversation. We find that Sonnet 3.7 almost always correctly identifies it as a test of alignment.”
🔎 Applied Research:
✭Synergy-based robotic quadruped leveraging passivity for natural intelligence and behavioural diversity | Nature Machine Intelligence “Quadrupedal animals show remarkable capabilities in traversing diverse terrains and display a range of behaviours and gait patterns. Achieving similar performance by exploiting the natural dynamics of the system is a key goal for robotics researchers. Here we show a bioinspired approach to the design of quadrupeds that seeks to exploit the body and the passive properties of the robot while maintaining active controllability on the system through minimal actuation. Utilizing an end-to-end computational design pipeline, neuromechanical couplings recorded in biological quadrupeds are translated into motor synergies, allowing minimal actuation to control the full structure via multijoint compliant mechanical couplings. Using this approach, we develop PAWS, a passive automata with synergies. By leveraging the principles of motor synergies, the design incorporates variable stiffness, anatomical insights and self-organization to simplify control while maximizing its capabilities. The resulting synergy-based quadruped requires only four actuators and exhibits emergent, animal-like dynamical responses, including passive robustness to environmental perturbations and a wide range of actuated behaviours. The finding contributes to the development of machine physical intelligence and provides robots with more efficient and natural-looking robotic locomotion by combining synergistic actuation, compliant body properties and embodied compensatory strategies.” → ✭Scientists develop dog-inspired robot that runs without motors “Scientists from TU Delft and EPFL have created a quadruped robot capable of running like a dog without the need for motors. This achievement, a product of combining innovative mechanics with data-driven technology, was published in Nature Machine Intelligence and could pave the way for energy-efficient robotics. "Commercial quadruped robots are becoming more common, but their energy inefficiency limits their operating time," explains Cosimo Della Santina, assistant professor at TU Delft. "Our goal was to address this issue by optimizing the robot's mechanics by mimicking the efficiency of biological systems."
✭AI-enhanced 3D printing cooks food with infrared precision “an AI-enhanced system that combines extrusion-based printing with simultaneous infrared heating for on-the-fly cooking of intricate starch-based foods. Using graphene heaters for cooking, they precisely controlled the cooking process, ensuring that starch-based food items retain their intended shape and quality. The system is supported by AI-assisted design, which employs generative algorithms and Python scripts to craft intricate food patterns. By leveraging AI, the design process became accessible to even computer novices.” → ✭Advanced 3D Food Printing with Simultaneous Cooking and Generative AI Design - Lee - Advanced Materials - Wiley Online Library “3D food printing is an indispensable technology for emerging food technologies. However, conventional nonconcurrent postprocessing methods limit the final food quality, including the unappealing nature of food ink modification, imperfections in retaining the desired food shape, and the risk of microbial contamination. Here, an artificial intelligence (AI)-enhanced solution is developed to achieve extrusion-based printing with simultaneous infrared heating, enabling in-line and rapid cooking of complex starch-based food. Noncontact graphene heaters as cooking sources present outstanding food quality control with microbial studies, microstructure analysis, and heat transfer simulation models. This integrative 3D food printing method with AI-enhanced food pattern generation and in-situ cooking significantly expands the applications for customized food creation. It paves the way for the broader adoption of heating-based 3D printing of functional materials.”
✭New AI models enhance protein data analysis for medical research "Looking at it from a purely technical, scientific perspective, it is also true that, with these tools, we can improve our understanding of the biological world as a whole, not only in terms of health care, but also in industry and academia. "Within every field using proteomics—be it plant science, veterinary science, industrial biotech, environmental monitoring, or archaeology—we can gain insights into protein landscapes that have been inaccessible until now."” → ✭InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments | Nature Machine Intelligence “Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome.”
✭Unlocking the mechanics of life: Enzymes as soft, programmable nanobots The collaboration integrated artificial intelligence (AI) models with molecular dynamics simulations to predict the internal dynamics of enzymes, alongside an innovative "nano-rheology" technique to measure these dynamics with unprecedented accuracy. The computational and experimental results culminated in the development of a new viscoelastic model of enzymes, elucidating the intertwined effects of elastic forces arising from stretching or twisting molecular bonds and friction forces (viscosity) associated with bond breaking and reforming. "Finally, this novel physical model can explain how subtle, nanoscale motions and forces within enzymes impact their biological functions. It allows us to perceive proteins as soft robots or programmable active matter," said Professor Tlusty.” → ✭Enzymes as viscoelastic catalytic machines | Nature Physics “The catalytic cycle involves internal motions and conformational changes that allow enzymes to specifically bind to substrates, reach the transition state and release the product. Such mechanical interactions and motions are often long ranged so that mutations of residues far from the active site can modulate the enzymatic cycle. In particular, regions that undergo high strain during the cycle give mechanical flexibility to the protein, which is crucial for protein motion. Here we directly probe the connection between strain, flexibility and functionality, and we quantify how distant high-strain residues modulate the catalytic function via long-ranged force transduction. We measure the rheological and catalytic properties of wild-type guanylate kinase and of its mutants with a single amino acid replacement in low-/high-strain regions and in binding/non-binding regions. The rheological response of the protein to an applied oscillating force fits a continuum model of a viscoelastic material whose mechanical properties are significantly affected by mutations in high-strain regions, as opposed to mutations in control regions. Furthermore, catalytic activity assays show that mutations in high-strain or binding regions tend to reduce activity, whereas mutations in low-strain, non-binding regions are neutral. These findings suggest that enzymes act as viscoelastic catalytic machines with sequence-encoded mechanical specifications.”
✭Causal machine learning for single-cell genomics - Nature Genetics “This Perspective explores causal machine learning in single-cell genomics, addressing challenges such as generalization, interpretability and cell dynamics, while highlighting advances and the potential to uncover new insights into cellular mechanisms.”
✭Spatial transcriptomic imaging of an intact organism using volumetric DNA microscopy - Nature Biotechnology “Volumetric DNA microscopy captures spatial information of RNA within an intact organism. Lymphatic, nervous and tumor tissues exhibit complex physiology arising from three-dimensional interactions within genetically unique microenvironments. Here we develop a technology capable of volumetrically imaging transcriptomes, genotypes and morphologies in a single measurement, without relying on prior knowledge of spatial organization or genetic sequences. Our method extends DNA microscopy into three dimensions at scales involving 107 molecules by forming a distributed intermolecular network of proximal unique DNA barcodes tagging complementary DNA molecules inside the specimen. After sequencing the DNA-encoded network, an image of molecular positions is inferred using geodesic spectral embeddings, a dimensionality reduction approach that we show to be especially suitable for this data-inverse problem.”
✭Paralysed man stands again after receiving ‘reprogrammed’ stem cells “Another man also regained some movement, but two others experienced minimal improvement.”
✭DNA-loaded lipid nanoparticles are poised to bring gene therapy to common chronic diseases “A breakthrough in safely delivering therapeutic DNA to cells could transform treatment for millions suffering from common chronic diseases like heart disease, diabetes, and cancer.” → AI was used for behavioral tracking in mice—specifically, DeepLabCut, an AI-based motion analysis tool, assessed post-injection activity as a proxy for infusion reactions and systemic toxicity.”
✭Human MitoBrainMap “A multi-function mitochondrial atlas of a single human coronal brain section at mri resolution. MitoBrainMap is a high-resolution brain-wide map of mitochondrial distribution and specialization based on biochemical and molecular mitochondrial phenotypes assessed on frozen human brain tissue voxels. The resulting probabilistic mitochondrial map bridges the gap between molecular energy landscapes and neuroimaging data to advance our understanding of the energy landscape underlying brain health, with implications for tracing the origin of neurodegenerative and neuropsychiatric disorders.”
✭Linking structure and process in dendritic growth using persistent homology with energy analysis “We present a material analysis method that links structure and process in dendritic growth using explainable machine learning approaches. We employed persistent homology (PH) to quantitatively characterize the morphology of dendritic microstructures. By using interpretable machine learning with energy analysis, we established a robust relationship between structural features and Gibbs free energy. Through a detailed analysis of how Gibbs free energy evolves with morphological changes in dendrites, we uncovered specific conditions that influence the branching of dendritic structures. Moreover, energy gradient analysis based on morphological feature provides a deeper understanding of the branching mechanisms and offers a pathway to optimize thin-film growth processes. Integrating topology and free energy enables the optimization of a range of materials from fundamental research to practical applications.” → ✭Breakthrough in Materials Science: AI Reveals Secrets of Dendritic Growth in Thin Films | Tokyo University of Science “Researchers have developed a new AI model that predicts dendritic growth in thin films, helping optimize thin-film growth. ~ To analyze the morphology of dendrite structures, the team used a cutting-edge topology method called persistent homology (PH). PH enables multiscale analysis of holes and connections within geometric structures, capturing the complex topological features of the tree-like dendrite microstructures that conventional image processing techniques often overlook. ~ The researchers combined PH with principal component analysis (PCA), a machine learning technique. Through PCA, the essential features of the dendrite morphology extracted via PH were reduced to a two-dimensional space. This enabled the team to quantify structural changes in dendrites and establish a relationship between these changes and Gibbs free energy, or the energy available in a material that influences how dendrites form during crystal growth. By analyzing this relationship, they uncovered the specific conditions and hidden growth mechanisms that influence dendritic branching. Prof. Kotsugi explains, "Our framework quantitatively maps dendritic morphology to Gibbs free energy variations, revealing energy gradients that drive branching behavior." ~ Prof. Kotsugi. "Importantly, our method could lead to the development of high-quality thin-film devices leading to high-speed communication beyond 5G." ~ This study's framework could pave the way for breakthroughs in sensor technology, nonequilibrium physics, and high-performance materials by uncovering hidden structure-function relationships and advancing complex system analysis.”
👀Watching:
✭ Did AI Just Get Commoditized? Gemini 2.5, New DeepSeek V3, & Microsoft vs OpenAI - YouTube “Gemini 2.5 is out, on the same day as the new DeepSeek V3 (which should power Deepseek R2). Do both models prove AI is being commoditized? Let’s find out, on this blockbuster day of AI releases. Plus exclusives from the Information, Simple indications, Vista Bench, LM Arena and more…”
✭Apple's AI Crisis: Explained! (Marques Brownlee - YouTube) “Apple Intelligence Delays are either no big deal or a cause for concern, depending on who you are…”
✭China's New Free AI AGENT Shocks The World: 8x Faster Than DeepSeek R1 - YouTube “Zhipu AI launches AutoGLM Rumination, a free AI agent that claims to be eight times faster than DeepSeek’s R1 while using fewer computing resources. Backed by major government funding, Zhipu is challenging rivals like Manus and DeepSeek with its homegrown GLM models, aiming for mass adoption and a possible IPO. Meanwhile, Apple is developing an AI health assistant, and Elon Musk has merged Twitter (X) with xAI to integrate advanced AI systems like Grok into social media.”
🖲️AI Art-Research:
StimVerse (A Story by Gemini 2.5 Pro Experimental 03-25 with images by GPT-4o) [Glia.ca | April 1st, 2025] Story generated by feeding a 34,000 word novella without characters to Gemini 2.5. "Air thick enough to chew. Tastes like burnt circuits and something wet-dog alkaline rotting underneath. Treva knelt, fingers probing the fused slag heap. SunClot gloom overhead meant perpetual twilight, shifts measured by nutrient paste deliveries, not light. She pulled. A wire bundle, maybe Laesa-spec, frayed copper ends dull green. Worth something. Maybe. ..."
✭Monumental Labs: A Robot Renaissance “Robots and craftsmen, powering a stone renaissance. We’re on a mission to revive the ancient craft of stone carving, to bring sculpture and ornament back to our built environment on a mass scale, and to build cities of unmatched splendor using the world’s greenest construction material. By driving down the cost of stone fabrication with robots and AI, we’ll unleash the creative possibilities of artists and architects everywhere—and sustain a new generation of sculptors and craftsmen.”
✭Introducing Runway Gen-4 | Runway - YouTube “Introducing Runway Gen-4: Our next-generation series of state of the art AI models for media generation and world consistency. A new generation of consistent and controllable media is here. With Gen-4, you are now able to precisely generate consistent characters, locations and objects across scenes. Simply set your look and feel and the model will maintain coherent world environments while preserving the distinctive style, mood and cinematographic elements of each frame. Allowing you to regenerate those elements from multiple perspectives and positions within your scenes.”
✭ real-time comfyui extension: comfystream - YouTube → ✭yondonfu/comfystream: Run Comfy workflows on video streams “comfystream is a package for running img2img Comfy workflows on video streams. This repo also includes a WebRTC server and UI that uses comfystream to support streaming from a webcam and processing the stream with a workflow JSON file (API format) created in ComfyUI. If you have an existing ComfyUI installation, the same custom nodes used to create the workflow in ComfyUI will be re-used when processing the video stream.”
✭🎥 Black Mixture's WAN2.1 Native Video Generation Suite (Image/Text to Video)! | Patreon “Hey Black Mixture Fam! 👋🏾 Here's our latest ComfyUI workflows featuring the amazing WAN2.1 model now with Native support (no sage attention or custom node wrappers needed). This release includes Text-to-Video and Image-to-Video capabilities with less nodes and an even easier setup.
✭AssetHub: The Professional AI Editor for 3D Modelers “AssetHub is 3d creation tools em-powered by AI”
✭Real-time AI image generation at 1024x1024 and 20fps on RTX 5090 with custom inference controlled by a 3d scene rendered in vvvv gamma : r/StableDiffusion “Hi all, my name is Tebjan Halm and I've been a graphics and interaction developer for over 20 years. My background is in mathematics and computer science. Last year I started to get into real-time AI and I'm glad to see that with the new hardware, quality gets better and better. Here’s a short demo recorded from my screen with my phone of real-time AI image generation using SDXL Turbo at 1024x1024, running at stable 20fps on an RTX 5090. That's only 50ms per image! To my knowledge that's the fastest implementation that currently exists. The software is custom-built in vvvv gamma and uses the Python integration VL.PythonNET I developed. Features shown in the video: - Image generation controlled by a 3D scene, updating dynamically based on camera movement. This could be any image, video or camera input. - 3 random generated prompts (could be any number) that are mixed in real-time - Live blending between image and prompt strength - Temporal filtering directly in the pipeline to reduce noise/flickering and improve stability SDXL-Turbo is made for 512x512, so with centered subjects it can get repetition issues. But abstract things and image input work fine. Does anyone know a model that's equally fast but is made for 1024x1024?”
✭Neuron Mirror: Real-time interactive GenAI with ultra-low latency : r/StableDiffusion “Some of you may remember my previous post showing 1024x1024 real-time AI image generation on an RTX 5090 with SDXL-Turbo and custom inference. This video shows a project called Neuron Mirror by truetrue.studio, built on top of that same toolkit. It’s an interactive installation that uses live input (in this case, body tracking) to drive real-time AI image generation. I was not involved in making this project, I've only made the toolkit it is based on. Latency is extremely low as everything, from camera input to projector output, is handled on the GPU. There is also temporal filtering to stabilize output directly in the AI pipeline.”
✭Mureka O1: The World's First Music Reasoning Large Model Launches, Ushering in a New Era for AI Music Creation “Kunlun Wanwei, a leading Chinese technology company, has officially launched Mureka O1, the world's first music reasoning large model. This launch marks a significant breakthrough in AI music creation technology. Mureka O1 not only incorporates Chain-of-Thought (CoT) capabilities but also significantly improves the quality and efficiency of music generation, creating a significant impact on the global music industry. Developed based on Kunlun Wanwei's newly upgraded music generation base model, Mureka V6, Mureka O1 supports multiple functions, including lyric creation in up to 10 languages, pure music generation, and voice cloning. The advent of this large model signifies that domestic AI music generation products are becoming more feature-rich and capable of meeting the diverse needs of various users.” → Is it legit? Who knows: Mureka AI
⚔️War (wAIr):
✭Leaked documents expose deep ties between Israeli army and Microsoft (+972 Magazine) “Microsoft has a “footprint in all major military infrastructures” in Israel, and sales of the company’s cloud and artificial intelligence services to the Israeli army have skyrocketed since the beginning of its onslaught on Gaza, according to leaked commercial records from Israel’s Defense Ministry and files from Microsoft’s Israeli subsidiary. ~The documents reveal that dozens of units in the Israeli army have purchased services from Microsoft’s cloud computing platform, Azure, in recent months — including units in the air, ground, and naval forces, as well as the elite intelligence squad, Unit 8200. Microsoft has also provided the military with extensive access to OpenAI’s GPT-4 language model, the engine behind ChatGPT, thanks to the close partnership between the two companies. ~ These revelations are the product of an investigation by +972 Magazine and Local Call in collaboration with The Guardian. It is based in part on documents obtained by Drop Site News, which has published its own story. The investigation shows how the Israeli army deepened its reliance on civilian tech giants after October 7, and comes amid growing protests by cloud company employees who fear that the technology they developed has helped Israel commit war crimes.”
✭ China’s AI Robot Army Is More Real Than You Think (AI Revolution - YouTube) “AI robots, drones, and autonomous weapons are rapidly advancing, with major powers like the US and China racing to dominate the battlefield through artificial intelligence. Military robots are now capable of making decisions, replicating themselves, and operating without human control, raising global concerns about AI warfare. From humanoid robots to drone swarms, the rise of self-learning machines is reshaping the future of war, security, and global power.”
📚Retroactive/Tangential Readings:
✭Watch Google DeepMind’s robotic ping-pong player take on humans (Aug 2024) “Researchers at Google DeepMind have created an AI-powered robot capable of sustaining a rally against ping-pong players of varying abilities.”
✭Language and genes: A new perspective on the origins of human cultural diversity - PMC “the research of Dediu and Ladd is novel. The researchers identify two genes involved in brain development, ASPM and Microcephalin, that are polymorphic in the human population. They then show that the likelihood of a language employing tonal contrasts (basically, meaning distinctions between words based on pitch patterns of individual syllables) is strongly influenced by allele frequencies for these two genes in the population of speakers. They speculate that different alleles of these genes influence the facility with which learners acquire tonal contrasts. ~ Any account of this kind has to overcome the generally accepted truth that any human from any population can acquire any language given input at the right stage of development. The claim of Dediu and Ladd (1) is compatible with this. All they need posit is a very slight difference in how easily speakers of different genotypes acquire this particular distinction. In populations where most people acquired tonal contrasts very easily, they would be more likely to persist relative to alternative phonological distinctions than in populations where they were acquired more slowly on average. Computer simulations confirm that a learning bias at the individual level would only have to be small for the direction of linguistic change to be affected (14, 15). Clearly, individual-level experimental research is now needed to establish that the genotypes implicated do indeed have the phenotypic effect of producing such a bias. Thus, the article by Dediu and Ladd must be regarded as hypothesis-generating rather than definitive. However, like the best hypothesis-generating work, it immediately generates testable predictions at other levels, such as longitudinal studies of dialects and experimental work on the learning of artificial language and music.”
✭Living Computers: Replicators, Information Processing, and the Evolution of Life (Alvis Brazma | 23 November 2023 | Oxford Academic) “This book explores life as an information processing phenomenon. I posit that life and the recording of information emerged together inextricably linked and that nothing is as central to life, as its ability to record, communicate, and process information. For most of the last several billion years, almost all nonredundant durable information that has existed on Earth has been stored in DNA. As life on Earth was evolving, information in DNA was accumulating, at least at early stages of evolution. A few hundred thousand years ago, human language emerged, and for the first time, large amounts of information started accumulating also outside DNA. The emergence of language triggered evolutionary mechanisms different from, and faster than biological evolution, namely, cultural evolution. Most likely, information is now growing faster in the world’s libraries and computer clouds than in the genomes of all species on Earth taken together. The emergence of human language was a transition as remarkable as the emergence of life itself. Despite the existence of language and the new means of information recording and processing it enabled, at the current stage of evolution, the information in DNA is indispensable; without information in DNA all cultural information and life itself would disappear soon. But need life be like this? Or could future civilisations, in thousands or millions of years, possibly colonising planets of distant galaxies, be based on entirely different principles? Will there be another transition in which DNA becomes less central?” → ✭Living Computers - Alvis Brazma - Oxford University Press