u/Best_Cup_8326

Agentic AI is changing the way users get work done. Following the success of OpenClaw, the community is embracing new open source agentic frameworks. The latest is Hermes Agent, which crossed 140,000 GitHub stars in under three months and, as of last week, is the most used agent in the world according to OpenRouter.

Developed by Nous Research, Hermes is designed for reliability and self-improvement — two qualities that have historically been hard to achieve with agents. It’s provider- and model-agnostic by design, and optimized for always-on local use, making NVIDIA RTX PCs, NVIDIA RTX PRO workstations and NVIDIA DGX Spark the ideal hardware to run it at full speed, around the clock.

Qwen 3.6, a new series of high-performance, open weight large language models (LLMs) from Alibaba, are ideal for running local agents like Hermes. The Qwen 3.6 27B and 35B parameter models are outperforming their previous-generation 120B and 400B parameter model counterparts and run on NVIDIA RTX and DGX Spark for accelerated agentic AI.

While Psyche uses solar arrays to power a xenon-fueled ion engine, NASA’s new lithium-fed MPD thruster is designed to be part of a nuclear electric propulsion system. Ultimately, the space agency thinks this experimental combination could provide the power necessary for shorter transit times, enabling crewed missions to Mars.

Unlike traditional ion thrusters, which use electrostatic fields to accelerate individual ions, or charged atoms (typically in the form of xenon) out through a nozzle, MPD engines combine high currents with a magnetic field to electromagnetically accelerate lithium plasma. To be precise, NASA’s new model runs on lithium metal vapor.

Posting not for the trailer itself, but for the absolutely unhinged anti comments in the comment section.

Scientists in recent years have sought to efficiently draw moisture from ambient air and condense it into potable water using materials made of salt and absorbent polymers. But these materials, known as hydrogels, until now have degraded too quickly to be practical or cost-effective.

Researchers have now discovered a way to harvest water from air using solar power and a hydrogel that lasts for eight months or more. Attached to metal coated to prevent corrosion, the long-lasting material can produce water at low cost almost anywhere.

"There are a lot of people who don't have access to water or have to walk hundreds of hours per year to procure water," said Carlos Diaz-Marin, an assistant professor of energy science and engineering in the Stanford Doerr School of Sustainability and co-lead author of the research published May 7 in Nature Communications. "There are also very water-intensive industries like semiconductor manufacturing and data centers that are putting even more pressure on water systems. We believe this could potentially be a way to provide additional water resources."

"These new hydrogels are exceptionally exciting because they give us a way to produce potable drinking water in really extreme conditions," said co-lead author Chad Wilson, who worked on the hydrogel as a graduate student at the Massachusetts Institute of Technology.

https://youtu.be/VUthq-JuxxE?si=ly4yacka936iWfjF

Using a new technique that can create vacancies at any site across a material and then shrink it to about 1/2,000 of its original volume, MIT researchers have designed nanotechnology devices that could be used for optical computing and other applications involving the manipulation of visible light.

The new fabrication technique, known as “implosion carving,” allows researchers to imprint features throughout a hydrogel using photopatterning. If patterned with a resolution of about 800 nanometers, these features can then be shrunk to less than 100 nanometers.

Because that resolution is smaller than the wavelength of light, the devices can bend light in specific ways that allow them to perform optical computations.

We have been building toward this moment, and now it’s finally arrived. Anthropic has formally launched ‘Claude For Legal’, a comprehensive offering that could reshape the legal tech world and places the LLM-maker at the heart of the market. (See below Artificial Lawyer interview with Mark Pike, Anthropic Associate General Counsel.)

Legal tech companies from Thomson Reuters and LexisNexis, to Harvey and Legora, are all participants in one way or another, in what is a bold strategic move that changes the legal tech market in ways that would have been unimaginable just a few years ago. (Plus, see comments from Harvey and TR below.) And of course, Freshfields has already gone all-in with Claude, while other major firms are also deeply exploring what it can do.

Claude for Legal will manifest itself across four main paths and builds on work that has already been developed:

‘New Legal Plugins: Practice-area-specific plugins (Commercial, Employment, Privacy, Product, Corporate, AI Governance), building on February’s Cowork and legal plugin launch.

New MCP Connectors: DocuSign, Ironclad, iManage, NetDocuments, LexisNexis, Thomson Reuters, Box, Everlaw, LSuite – the tools lawyers and legal ops teams already run on.

Open-source Ecosystem: Partner-contributed skills and plugins from Harvey, Legora, and others building on Claude.

Plus, Free Law Project & Justice Technology Association Partnerships: Expanding access to justice for underserved communities and individuals who would otherwise go without counsel.’

There is also a free webinar this Friday about it, and in the brief for that it mentions the company wants to explore ‘beyond contract review into research, eDiscovery, matter management, and more’.

This guy gets it - not a decel, not an accel, just a trucker wondering where his next meal comes from.

Soon this is everybody.

So it begins.

Abstract

Information flow through synapses in the central nervous system is regulated by both rapid electrochemical activity and slower structural remodeling. While technological advances allow precise manipulation of synaptic activity, methods for structural remodeling remain limited. Here, we present SynTrogo (Synthetic Trogocytosis), a synthetic molecular approach for modulating synaptic connections.

By engineering complementary ligand and receptor proteins, we enable physical interaction between two defined cell populations in culture, leading to a trogocytosis-like process in which receptor-expressing cells internalize membrane fragments and adjacent cytosolic material from ligand-expressing cells.

Applying SynTrogo to hippocampal CA3 neurons and CA1 astrocytes in adult male mice results in ultrastructural changes at axon-astrocyte interfaces, accompanied by significantly reduced synaptic connectivity. The remaining synapses exhibit coordinated pre- and post-synaptic structural changes and reorganization of synaptic components and organelles, and are associated with enhanced synaptic plasticity and memory performance. These findings suggest that neural circuits can undergo adaptive reshaping under conditions of synaptic reduction and may provide a foundation for editing synaptic architecture with therapeutic potential for connectopathies.

In October 2025, we launched Petri, an open-source toolbox of alignment tests that can be applied to any large language model. Petri, which was developed as part of our Anthropic Fellows program, can be used to rapidly and easily test AI models for concerning tendencies like deception, sycophancy, and cooperation with harmful requests. It’s part of our efforts to develop alignment tools that are open and useful for the whole AI development community.

Petri has been part of our alignment assessment for every Claude model since Claude Sonnet 4.5. It compares how the new model behaves across a range of alignment-relevant scenarios that are simulated by a separate “auditor” model. A further “judge” model then scores the resulting transcripts for misaligned behaviors.

The developer of Firefox says it has “completely bought in” on AI-assisted bug discovery.

The disbelief was palpable when Mozilla’s CTO last month declared that AI-assisted vulnerability detection meant “zero-days are numbered” and “defenders finally have a chance to win, decisively.” After all, it looked like part of an all-too familiar pattern: Cherry pick a handful of impressive AI-achieved results, leave out any of the fine print that might paint a more nuanced picture, and let the hype train roll on.

Mindful of the skepticism, Mozilla on Thursday provided a behind-the-scenes look into its use of Anthropic Mythos—an AI model for identifying software vulnerabilities—to ferret out 271 Firefox security flaws over two months. In a post, Mozilla engineers said the finally ready-for-prime-time breakthrough they achieved was primarily the result of two things: (1) improvement in the models themselves and (2) Mozilla’s development of a custom “harness” that supported Mythos as it analyzed Firefox source code.

“Almost no false positives” The engineers said their earlier brushes with AI-assisted vulnerability detection were fraught with “unwanted slop.” Typically, someone would prompt a model to analyze a block of code. The model would then produce plausible-reading bug reports, and often at unprecedented scales. Invariably, however, when human developers further investigated, they’d find a large percentage of the details had been hallucinated. The humans would then need to invest significant work handling the vulnerability reports the old-fashioned way.

Mozilla’s work with Mythos was different, Mozilla Distinguished Engineer Brian Grinstead said in an interview. The biggest differentiating factor was use of an agent harness, a piece of code that wraps around an LLM to guide it through a series of specific tasks. For such a harness to be useful, it requires significant resources to customize it to the project-specific semantics, tooling, and processes it will be used for.

Grinstead described the harness his team built as “the code that drives the LLM in order to accomplish a goal. It gives the model instructions (e.g., ‘find a bug in this file’), provides it tools (e.g., allowing it to read/write files and evaluate test cases), then runs it in a loop until completion.” The harness gave Mythos access to the same tools and pipeline human Mozilla developers use, including the special Firefox build they use for testing.

He elaborated:

>With these harnesses, so long as you can define a deterministic and clear success signal or task verification signal, you can just keep telling it to keep working. In our case when we’re looking for memory safety issues we have our sanitizer build of Firefox and if you make it crash you win. We point that agent off to a source file and say: “we know there’s an issue in this file, please go find it.” It will craft test cases. We have our existing fuzzing systems and tools to be able to run those tests. It will say: “I think there’s an issue here if I craft the HTML exactly so.” It sends it off to a tool, the tool says yes or no. If the tool says yes then there’s some additional verification.

The additional verification comes in the form of a second LLM that grades the output from the first LLM. A high score gives developers the same confidence they have when viewing reports generated through more traditional discovery methods.

“In terms of the bugs coming out on the other side, there are almost no false positives,” he said.

Thursday’s behind-the-scenes view includes the unhiding of full Bugzilla reports for 12 of the 271 vulnerabilities Mozilla discovered using Mythos and to a lesser extent Claude Opus 4.6. The test cases—meaning the HTML or other code that triggers an unsafe memory condition—are provided in each one and meet the same criteria Mozilla requires for all bugs to be considered security vulnerabilities in Firefox. At least one researcher said Thursday that a cursory look at the reports showed they were “pretty impressive.”

Unlike previous vulnerability disclosure slop, Grinstead said, the details provided by its harness-guided Mythos analysis, and confirmed by the second LLM, and ultimately included in the reports, provide a level of confidence his team didn’t have before.

“That’s the key thing that has unlocked our ability to operate at the scale we’ve been operating at now,” he said. “It gives the engineer a crank they can pull that says: ‘yep this has the problem,’ and then you can iterate on the code and know clearly when you’ve fixed it and eventually land the test case in the tree such that you don’t regress it.”

As noted earlier, Mozilla’s characterization of AI-assisted vulnerability discovery as a game changer has been greeted with massive and vocal amounts of skepticism in many quarters. Critics initially scoffed when Mozilla didn’t obtain CVE designations for any of the 271 vulnerabilities. Like many developers, however, Mozilla doesn’t obtain CVE listings for internally discovered security bugs. Instead they are bundled into a single patch. Normally Bugzilla reports detailing these “rollups” are hidden for several months after being fixed to protect those who are slow to patch. Now that Mozilla has revealed a dozen of them, the same critics will surely claim they too were cherry picked and conceal less accurate results.

Of the 271 bugs found using Mythos, 180 were sec-high, Mozilla’s highest designation for internally reported vulnerabilities. These types of vulnerabilities can be exploited through normal user behavior, such as browsing to a web page. (The only higher rating, sec-critical, is reserved for zerodays.) Another 80 were sec-moderate, and 11 were sec-low.

The critics are right to keep pushing back. Hype is a key method for inflating the already high puffed-up valuations of AI companies. Given the extensive praise Mozilla has given to Mythos, it’s easy for even more trusting people to wonder: What’s it getting in return? Far from settling the debate, Thursday’s elaborations are likely to only further stoke the controversy.

To hear Grinstead tell it, however, the details are clear evidence of the usefulness of AI-assisted discovery and Mozilla’s motivation is simple.

“People are a bit burned from the last year of these slop commits so we felt it was important to show some of our work, open up some of the bugs, and talk about it in a little more detail as a way to hopefully spur some action or continue the conversation,” he said. “There’s no sort of marketing angle here. Our team has completely bought in on this approach. We are trying to get a message out about this technique in general and not any specific model provider, company, or anything like that.”