Committee

Researchers at Stanford University and Lambda Labs, have published the research paper for OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. The open-weight models configured through OpenJarvis land within 3.2 percentage points of the best cloud model on average, at roughly 800× lower marginal API cost per query and roughly 4× lower latency under the research’s benchmark protocol. This research work builds on the research team’s earlier Intelligence Per Watt study, which reported that local models already handle 88.7% of single-turn chat and reasoning queries at interactive latency, with intelligence efficiency improving 5.3× from 2023 to 2025. Model Overview & Access OpenJarvis is not a single model. It is a framework that composes any supported model with a configurable agent stack, evaluated across 11 local models from four families. Property Value License Apache 2.0 Framework release March 12, 2026 Paper arXiv:2605.17172 (posted May 16, 2026) Repository github.com/open-jarvis/OpenJarvis Stars / forks ~5.4k / ~1.2k (June 2026) Languages Python (~83%), Rust (~9%), TypeScript (~7%) Evaluated models 11 local models across 4 families: Qwen3.5, Gemma4, Nemotron, Granite Cloud baselines Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro Supported engines Ollama, vLLM, SGLang, llama.cpp, Apple Foundation Models, Exo (among others) Context window Model-dependent Installation Single command; ~3 minutes on broadband Hardware Tested on 7 platforms, from Mac Mini M4 to NVIDIA DGX Spark Architecture: Five Primitives and a Spec OpenJarvis decomposes a personal AI system into five typed primitives, composed through a single declarative configuration object called a spec. Intelligence — the model, weights, generation parameters, and quantization format. Engine — the inference runtime (Ollama, vLLM, SGLang, etc.), batching, KV-cache settings, and hardware path. Agents — the reasoning loop (ReAct or CodeAct), system prompts, tool-use policy, and turn limits. Tools & Memory — external interfaces, retrieval backends, 25+ data connectors, and 32+ messaging channels, with native MCP support and interchangeable memory backends. Learning — the optimizer that updates the spec from traces. This slot accepts LoRA, DSPy, GEPA, or LLM-guided spec search. Each primitive is independently swappable, and a spec serializes all five into a TOML file. Two specs can share the same agent and tool configuration and differ only in model and engine, so the same behavior runs on a Mac Mini and a workstation without rewriting prompts. LLM-guided spec search is the second contribution. It is a local–cloud collaboration: a frontier cloud model acts as a teacher at search time, reading traces, diagnosing failure clusters, and proposing edits across Intelligence, Engine, Agents, and Tools & Memory. An edit is accepted only if it improves the target failure cluster without causing meaningful regressions elsewhere — the research team calls this the gate (default tolerance 1%). The optimized spec then runs entirely on-device at inference time, with zero cloud calls. The teacher is used only at search time; at 100 queries per day, the amortized teacher cost falls below $0.001 per query within six months. Prior work (GEPA, DSPy, LoRA) optimizes one primitive at a time, and prompt optimizers alone recover only about 5 pp of the cloud–local gap. LLM-guided spec search recovers 13–32 pp because it edits across primitives jointly, at 7–11× lower optimization cost than single-primitive baselines. The four-primitive move space contributes 5.5–16.5 pp, and the LLM proposer adds about 10 pp on average over an evolutionary search at the same move space. https://arxiv.org/pdf/2605.17172v1 Capabilities & Performance OpenJarvis was evaluated across 8 benchmarks spanning 508 tasks: tool calling (ToolCall-15), agentic workflows (PinchBench), coding (LiveCodeBench), customer service (τ-Bench V2, τ²-Bench Telecom), general assistance (GAIA), and deep research (LiveResearchBench, DeepResearchBench). The swap test: Replacing the intended cloud model with Qwen3.5-9B in existing frameworks (OpenClaw, Hermes Agent) drops accuracy by 25–39 pp. With the same model under an OpenJarvis spec, the residual drop shrinks to 5.6–16.5 pp — recovering 56–77% of the portability loss. The accuracy frontier: The best single local model, Qwen3.5-122B, reaches 80.3% average accuracy versus Claude Opus 4.6 at 83.5% — a 3.2 pp gap. Local specs match or exceed cloud on 4 of 8 benchmarks: ToolCall-15, PinchBench, LiveCodeBench, and τ-Bench V2. Cost and latency: Local configurations form the accuracy–efficiency frontier. Qwen3.5-122B delivers its 80.3% at roughly a thousandth of a cent per query, versus $0.009 per query for Claude Opus 4.6 — an approximately 800× marginal API-cost advantage. End-to-end latency drops by roughly 4× on the agentic workloads, though the paper notes single-shot prompts can favor cloud serving. Search gains: LLM-guided spec search improves the Qwen3.5-9B student to 100% on PinchBench, 83% on LiveCodeBench, and 91% on LiveResearchBench. Across the full eight-benchmark suite, average gains per student model range from 13.1 to 31.5 pp. The authors report that these gains survive their robustness checks (reward-weight variants, search-seed variance, and random restarts). How to Use it Installation is one command. On macOS, Linux, or WSL2: Copy CodeCopiedUse a different Browser curl -fsSL https://open-jarvis.github.io/OpenJarvis/install.sh | bash Windows users run an equivalent PowerShell script (irm … | iex). The installer provisions uv, a Python virtual environment, Ollama, and a starter model in about three minutes on broadband. A desktop GUI ships as a .dmg, .exe, .deb, .rpm, or .AppImage from the releases page. After install, jarvis starts a chat session. Starter presets cover common workflows: Copy CodeCopiedUse a different Browser jarvis init –preset morning-digest-mac # daily briefing with TTS jarvis init –preset deep-research # multi-hop research with citations jarvis init –preset code-assistant # agent with code execution and shell access jarvis init –preset scheduled-monitor # stateful agent on a schedule The framework ships with eight built-in agents across three execution modes — on-demand, scheduled, and continuous. It connects to 25+ data sources (Gmail, Calendar, iMessage, Notion, Obsidian, Slack, GitHub, and others) and exposes agents over 32+ messaging channels (WhatsApp, Telegram, Discord, iMessage, Signal, and others). Skills can be imported from external catalogs — about 150 from Hermes Agent and about 13,700 community skills from OpenClaw — all following the agentskills.io specification. A jarvis optimize skills –policy dspy command refines them from local trace history. Marktechpost’s Visual Explainer OpenJarvis ·

Miso Labs has released MisoTTS, an open-weights 8-billion-parameter text-to-speech model. It generates expressive speech from both text and audio context. The model uses residual vector quantization (RVQ) to widen its sonic range. This avoids scaling a single flat vocabulary while keeping parameter count fixed. What is MisoTTS MisoTTS is an 8B-parameter text-to-dialogue RVQ Transformer. It is inspired by the Sesame CSM architecture. It pairs a Llama 3.2-style backbone with a smaller audio decoder. It generates Mimi audio codes from text and optional audio context. The model conditions on both text and prior audio. That second input lets it respond to the speaker’s tone. The text vocabulary is 128,256 tokens, and there are 32 audio codebooks. Mimi is the audio tokenizer, and max sequence length is 2,048. Default inference runs in torch.bfloat16. Miso Labs claims 110ms latency. It lists ElevenLabs at 700ms and Sesame at 300ms. The Vocabulary Size Problem Standard transformers generate from a fixed vocabulary of discrete tokens. That works when a small vocabulary covers the target space. Human speech does not fit that assumption. It varies across pitch, rhythm, emphasis, emotion, and accent. Expanding the audio vocabulary is the obvious fix. But larger vocabularies need more parameters in a standard transformer. Each token must be represented and predicted by the model. Miso Labs calls this the vocabulary size problem. The second issue is conditioning. Most TTS models condition only on text. They ignore the interlocutor’s tone. Miso Labs argues this contributes to the “uncanny valley” effect. Residual Vector Quantization: The Core Idea MisoTTS addresses both problems with residual vector quantization (RVQ). Miso Labs traces RVQ to image-generation research and to Sesame’s CSM for audio. Instead of one token index, the model emits a vector of indices. Each audio token is 32 codebook indices over 2048-way codebooks. The model keeps a separate codebook for each position in the vector. To recover the sound, it sums the looked-up vectors. Each codebook adds another refinement to the signal. This is what makes the scaling work. Addressable vocabulary equals codebook size raised to the depth. Growing the depth adds no parameters to the model. So MisoTTS reaches about 204832, or roughly 10105 addressable tokens. Miso Labs notes naive scaling would require a far larger network. https://www.misolabs.ai/blog/miso-tts-8b The Two-Transformer Architecture The model splits into a backbone and a decoder. The backbone is a 7.7B-parameter transformer, autoregressive over time. It predicts the first codebook index and a final hidden state. A 300M-parameter decoder then runs autoregressively over depth. It predicts the remaining codebook indices, one position at a time. Each prediction conditions on the indices already chosen in the frame. The same 300M parameters are reused for every position. Embeddings follow the same logic. Text tokens use a single lookup. An audio token’s embedding is the sum of per-position codebook lookups. Interleaving text and audio lets the backbone use conversation history. That is how it carries context across turns. Strengths and Challenges Strengths: Open weights on day one, under a modified MIT license. RVQ scales the sonic range without scaling parameter count. Conditions on audio context, not text alone. Local deployment keeps sensitive audio data in-house. The architecture and math are documented in a public blog post. Challenges: Half-duplex only, with no turn-taking yet. The large model needs a capable CUDA GPU. API access is announced but not yet available. Latency and quality claims still need third-party testing. Marktechpost’s Visual Explainer Marktechpost · Model Brief 01 / 09 Open-Weights Release · June 3, 2026 MisoTTS An 8B emotive text-to-speech model from Miso Labs, built on residual vector quantization and conditioned on both text and audio. 8B params RVQ Transformer Mimi codes modified MIT What MisoTTS Is A text-to-dialogue RVQ Transformer An 8B-parameter model inspired by the Sesame CSM architecture. Pairs a Llama 3.2-style backbone with a smaller audio decoder. Generates Mimi audio codes from text and optional audio context. Conditions on prior audio, so output responds to speaker tone. At a Glance Published specifications Parameters 8B (7.7B + 300M) Architecture RVQ Transformer Audio codebooks 32 (2048-way) Audio tokenizer Mimi Text vocabulary 128,256 Max sequence length 2,048 Default precision torch.bfloat16 License modified MIT The Motivation The vocabulary size problem Transformers generate from a fixed vocabulary of discrete tokens. Speech varies in pitch, rhythm, emphasis, emotion, and accent. A bigger audio vocabulary needs more parameters in a standard transformer. Most TTS condition only on text, ignoring tone — the “uncanny valley” effect. The Core Idea Residual vector quantization The model emits a vector of indices, not a single token index. Each token is 32 codebook indices over 2048-way codebooks. Summing the looked-up vectors reconstructs the sound. Depth scales addressable vocabulary to ~204832 (≈10105) with no added parameters. Architecture Two transformers, one vector token Backbone (7.7B) — autoregressive over time; predicts codebook index k₁ and hidden state h₀. Decoder (300M) — autoregressive over depth; predicts k₂ through k₃₂. The same 300M parameters are reused for every position. Interleaved text and audio let the backbone use conversation history. Run It Locally Inference in a few lines from generator import load_miso_8b import torchaudio gen = load_miso_8b(device=”cuda”, model_path_or_repo_id=”MisoLabs/MisoTTS”) audio = gen.generate( text=”Hello from Miso.”, speaker=0, context=[], max_audio_length_ms=10_000) torchaudio.save(“miso.wav”, audio.unsqueeze(0).cpu(), gen.sample_rate) Setup uses uv with Python 3.10. Weights download from Hugging Face. Audio is watermarked by default via SilentCipher. One-shot voice cloning works from a ~10-second clip. Limitations Where it stops, for now Handles individual turns only; no turn-taking yet. Generates half-duplex audio — it cannot speak while the other party speaks. Miso Labs frames full-duplex and turn-taking as future work. API access is announced but not yet available. Key Takeaways The short version Open-weights 8B TTS under a modified MIT license. Conditions on text and audio, so output tracks speaker tone. RVQ scales vocabulary to ~204832 without adding parameters. 7.7B backbone over time, 300M decoder over depth. Half-duplex and single-turn today; API access pending. Prev Next Decoded by Marktechpost — AI research, model briefs, and developer tools for practitioners. marktechpost.com Key Takeaways Miso Labs open-sourced MisoTTS, an 8B text-to-speech

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How courts are coping with a flood of AI-generated lawsuits Most days in her chambers, Judge Maritza Braswell, a federal magistrate judge in Colorado, sifts through stacks of documents written by people without a lawyer. The number of these filings has more than doubled compared to before 2023. She puts that jump down to AI.  But while AI appears to be expanding access to justice, it doesn’t seem to be improving people’s chances of winning. Judges are starting to question what rights and duties chatbots should have as they stand in for lawyers. Lawmakers, meanwhile, are grappling with who should pay the price when chatbots produce bad legal advice. Read the full story on how AI is reshaping access to the law. —Michelle Kim How virtual power plants could provide energy for data centers Would you take a payment to ramp down your electricity use? Would it change anything if you were doing so to help power a local data center? A new project backed by Google will put those questions to the test. The company has signed a deal to fund a virtual power plant in the largest power grid in the US. The system will group together devices like electric vehicles and smart thermostats, paying customers to adjust their usage when the grid is stretched. The project could free up capacity for Google’s data centers—but there’s a catch: people might not play along. Find out what the future holds for these virtual power plants. —Casey Crownhart This story is from The Spark, our weekly newsletter giving you the inside track on all things climate. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The EU has proposed new legislation to end its Big Tech dependenceThe laws aim to boost domestic ​cloud, AI and semiconductors. (CNBC)+ US firms would be blocked from critical public tenders. (Reuters $)+ It also wants to make sure non-EU actors cannot disrupt tech services with a “kill switch.” (The Guardian)+ But the proposal needs to be negotiated with EU member states. (Politico $) 2 Intelligence agencies warn Chinese spies are recruiting on LinkedInThe Five Eyes alliance said Beijing is using job platforms for espionage. (BBC)+ The spies are allegedly recruiting government and military staff. (Politico $)+ The Chinese embassy in the UK condemned the accusations. (Bloomberg $)+ Meet the man hunting the spies in your smartphone. (MIT Technology Review) 3 AI CEOs have called for a law protecting against biological weaponsThey warn that synthetic DNA could be used for bioweapons. (Wired $)+ Sam Altman, Dario Amodei, and Demis Hassabis joined the call. (WSJ $)+ No one’s sure if synthetic mirror life will kill us all. (MIT Technology Review) 4 Firms are using Reddit to manipulate ChatGPT and Google AI searchThey’re spamming subreddits to get posts scraped by chatbots. (404 Media)+ What we’ve been getting wrong about AI’s truth crisis. (MIT Technology Review) 5 Meta keeps delaying the launch of its new AI modelThe new Muse Spark ‌AI model API still has no release date. (WSJ $)+ Which is hampering Meta’s plans to monetize its AI investments. (Reuters $) 6 For the first time, a US city has voted to permanently ban data centersMonterey Park, California, voted in favor of the move. (LA Times)+ Should we be moving data centers to space? (MIT Technology Review) 7 China is betting on household chore training to advance roboticsData harvested in homes and factories provides a scaling edge. (Rest of World)+ Gig workers are training humanoids at home. (MIT Technology Review) 8 Sam Altman will urge US lawmakers not to require AI model approvalsHe’s advocating against proposals for new AI rules. (Reuters $)+ His move comes after President Trump signed a new AI order. (Wired $) 9 Quantinuum raised $1.68 billion in an IPO as quantum computing rises Investors flocked to one of the fast-growing sector’s leaders. (Reuters $) 10 Someone finally wants to hire philosophers: Silicon ValleyBig tech hopes they will help build better machines. (The Atlantic $) Quote of the day “Historically, these companies have been very willing to play Russian roulette—and they’re playing another round.” —Connor Leahy, an AI researcher, former hacker and US director of ControlAI, tells the Financial Times why he’s concerned about Anthropic’s relentless race to the top. One More Thing HENRY HORENSTEIN/GETTY What an octopus’s mind can teach us about AI’s ultimate mystery Emily Bender, a linguist at the University of Washington, has developed a thought experiment she calls the octopus test. It involves an octopus learning to copy patterns in human writing and produce squiggles in response. But does the animal actually understand the language or are we merely projecting meaning onto it? Bender’s octopus is a stand-in for AI systems like ChatGPT. The intelligence we see in these machines is also projected on them by us. The same applies to consciousness: we may claim to see it, but it remains unclear whether it is really there. Read the full story on the debate over machines with minds. —Will Douglas Heaven We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Discover where iconic sound effects actually came from in this fabulous audio history.+ Need a serotonin boost? Then tune into this live puppy cam from Denali National Park.+ Linux lovers can try 570 extinct operating systems at a new virtual museum.+ Beethoven’s “Moonlight Sonata” becomes something entirely different in this lightning-fast bass guitar performance.

This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.

NVIDIA AI team have released Cosmos 3. It is a family of omnimodal world models for physical AI. The models combine physical reasoning, world generation, and action generation. All three capabilities live inside one open model. NVIDIA open sourced the checkpoints, training scripts, deployment tools, and datasets. The Cosmos 3 release targets robotics, autonomous vehicles, and warehouse monitoring teams. NVIDIA Cosmos 3 Physical AI systems must understand the world before acting in it. Robots and vehicles need to perceive, predict, and then act. Earlier Cosmos releases split these jobs across separate models. Cosmos 3 unifies them with a Mixture-of-Transformers (MoT) architecture. The architecture is built around two towers. The reasoner tower is a vision-language model (VLM). It interprets images, videos, and text using an autoregressive architecture. It understands motion, object interactions, and other physical context. NVIDIA team describes this tower as the model’s brain. The generator tower produces future observations and action sequences. It uses a diffusion-based process for physics-aware video and actions. These outputs are conditioned on the reasoner tower’s understanding. Information flows one way, from reasoner to generator. The reasoner can run alone. The generator always activates both towers for guided generation. A single model can therefore handle reasoning and generation together. https://developer.nvidia.com/blog/develop-physical-ai-reasoning-world-and-action-models-with-nvidia-cosmos-3 The Model Family NVIDIA team describes three model scales: Edge, Nano, and Super. Each uses the dual-tower Mixture-of-Transformers design. The two towers are initialized from pre-trained Qwen3-VL weights. That roughly doubles the parameter count of the backbone transformer. Cosmos3-Nano is a 16B model built on a dense 8B transformer. It adapts the Qwen3-VL 8B architecture. Nano targets efficient inference on workstation GPUs. It runs on hardware like the NVIDIA RTX PRO 6000. That suits real-time robotics and on-device physical AI. Cosmos3-Super is a 64B model built on a dense 32B transformer. It adapts the Qwen3-VL 32B architecture. Super targets datacenter GPUs, including NVIDIA Hopper and Blackwell. It fits large-scale synthetic data generation and advanced reasoning. This release ships Nano and Super, along with task-specific variants. These include Super Text2Image, Super Image2Video, and Nano-Policy-DROID. How the Unified Design Works Both towers share one transformer architecture and a joint attention operator. They use a 3D multimodal rotary position embedding (mRoPE). mRoPE aligns video, audio, and action tokens on one temporal axis. In Reasoner Mode, tokens pass through causal self-attention. This enables next-token prediction for perception, planning, and reasoning. In Generator Mode, noisy tokens are denoised through full attention. The autoregressive tokens are never updated by the diffusion tokens. The model treats action as a core modality with dedicated action tokens. Supported inputs include text, image, video, and JSON action arrays. Outputs include images, video, synchronized sound, action states, and text. The reasoner follows Qwen3-VL-compatible message conventions for vision inputs. Generation supports 256p, 480p, and 720p resolution tiers. Frame counts range from 5 to 300, defaulting to 189. That equals about 7.9 seconds of video at 24 FPS. Sound is generated as stereo AAC at 48 kHz. Action conditioning spans camera, vehicle, egocentric, single-arm, dual-arm, and humanoid embodiments. Each embodiment uses a fixed action dimension, such as 9D for cameras. The Benchmark Case NVIDIA team evaluated Cosmos 3 across reasoning and generation suites. On reasoning, Super and Nano lead VANTAGE-Bench at their respective tiers. VANTAGE-Bench tests VLMs on real-world fixed-camera footage. It covers warehouses, transportation, and smart spaces. Cosmos 3 also tops the Traffic Anomaly Reasoning (TAR) leaderboard. TAR is the official leaderboard for AI City Challenge 2026 Track 3. On generation, NVIDIA reports open-source state-of-the-art results. Cosmos 3 is the open-source SOTA on R-Bench. It also leads PAI-Bench, Physics-IQ, and RoboLab on public leaderboards. On Artificial Analysis, it leads two open-source leaderboards. These cover text-to-image and image-to-video without audio. NVIDIA team also introduced its Cosmos Human Evaluation framework, called HUE. HUE decomposes each generated video into yes/no fact questions. It scores four dimensions across seven physical AI domains. The dimensions are semantic alignment, physical laws, geometric reasoning, and visual integrity. A VLM pipeline drafts the questions, and human experts refine them. Marktechpost’s Visual Explainer marktechpost@guide ~ /nvidia/cosmos-3 01 / 09 DEVELOPER GUIDE · PHYSICAL AI NVIDIA Cosmos 3 Open omnimodal world models for physical AI. Released May 31, 2026. One model for physical reasoning, world generation, and action generation. Mixture-of-Transformers Open weights OpenMDW-1.1 Use ← → or swipe to navigate 01 · WHAT IT IS A unified model for understanding and generation Cosmos 3 is a family of omnimodal world models for physical AI. Earlier Cosmos releases split jobs across separate models. Cosmos 3 unifies them in a single open model. Physical reasoning over images, video, and text. World generation of physics-aware video and sound. Action generation for robots and autonomous systems. Subsumes VLMs, video generators, world simulators, and world-action models. 02 · ARCHITECTURE Two towers, one transformer REASONER TOWER An autoregressive vision-language model (VLM). It interprets motion, object interactions, and physical context. NVIDIA calls it the model’s brain. GENERATOR TOWER A diffusion-based path for physics-aware video and actions. It is conditioned on the reasoner’s understanding. Information flows one way, reasoner → generator. Both towers share a 3D multimodal RoPE (mRoPE). 03 · MODEL FAMILY Pick a size for your hardware Cosmos3-Nano 16B total (dense 8B, Qwen3-VL 8B). Workstation GPUs like RTX PRO 6000. Real-time robotics. Cosmos3-Super 64B total (dense 32B, Qwen3-VL 32B). Datacenter Hopper and Blackwell GPUs. Large-scale SDG. Cosmos3-Edge 4B total (dense 2B). On-device scale. Planned for a later release. Plus variants: Super-Text2Image, Super-Image2Video, and Nano-Policy-DROID. 04 · MODALITIES Inputs, outputs, and generation settings Inputs: text, image, video, and JSON action arrays. Outputs: image, video, synchronized sound, action states, text. Resolution: 256p, 480p, 720p. Sound: stereo AAC at 48 kHz. Length: 5 to 300 frames; default 189 (about 7.9s at 24 FPS). Embodiments: camera, vehicle, egocentric, single-arm, dual-arm, humanoid. 05 · BENCHMARKS What NVIDIA reports REASONING Nano and Super lead VANTAGE-Bench at their tiers. Cosmos 3 tops TAR, the AI City Challenge 2026 Track 3 leaderboard. GENERATION Open-source SOTA on R-Bench. Leads PAI-Bench, Physics-IQ, and RoboLab. Top open-source on Artificial Analysis text-to-image and image-to-video.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. 5 key points in Trump’s new AI order Less than two weeks after scrapping an executive order on AI, President Donald Trump signed a new one on Tuesday. Promising to promote innovation and security, the policy represents a turning point in the White House’s AI governance—but is likely to attract criticism from both opponents and supporters of stricter regulation. Here are five key points from the order: 1. It’s created a voluntary review system: tech companies will be asked to share frontier models with the government for review 30 days before they plan to release them.2. There’s no mandatory licensing: the government will not require permits before software can be deployed.3. It establishes a dedicated AI cybersecurity clearinghouse: the new hub will coordinate security checks with the private sector.4. It’s a watered-down version of the order Trump shelved last month: the earlier version requested models 90 days before their release.5. But it’s still a move towards stronger AI oversight: the policy marks a clear departure from the White House’s previous hands-off approach. Plus: here’s why a previous Trump administration’s AI policy was a distraction and how AI is already making online crimes easier.  MIT Technology Review Narrated: inside Anduril and Meta’s quest to make smart glasses for warfare The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands. Quay Barnett, who leads the effort at Anduril following a career in the Army’s Special Operations Command, aims to optimize “the human as a weapons system.” His vision is cyborg-inspired: drones and soldiers will see together, share information seamlessly, and make decisions as one. —James O’Donnell This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 President Trump has signed an AI order that expands model oversightThe long-awaited executive order aims to mitigate security threats. (NYT $)+It asks companies to submit models voluntarily for tests before release. (NPR)+ It’s a slimmed-down version of the order Trump shelved in May. (WSJ $)+ And marks a strategic shift in his AI strategy. (Reuters $)+ A war over AI regulation is coming to the US. (MIT Technology Review) 2 SpaceX plans to raise $75 billion in IPO at $135 per shareThe company intends to sell 555.6 million shares. (Reuters $)+ The fixed price breaks from the traditional IPO process. (Bloomberg $)+ Morningstar says the valuation should be nearly 50% lower. (BI) 3 Meta has scaled back plans to track workers’ clicks and keystrokes to train AIAll staff can pause it for 30 minutes, with some fully exempt.(The Information $)+ The changes follow a fierce backlash to the tracking plans. (Reuters $)+ AI is supercharging surveillance. (MIT Technology Review) 4 Microsoft wants to ‘make users addicted’ to its new AI assistantAccording tointernal documents for the “Scout” tool. (404 Media)+ Microsoft launched the assistant on Tuesday. (TechCrunch)  5 Mathematicians fear that AI threatens their fieldA new declaration raises concerns about AI’s trustworthiness. (Ars Technica)+ It arrives a week after OpenAI said it solved a famous math problem. (WSJ $)+ A startup wants to change how mathematicians do math. (MIT Technology Review) 6 Scientists have found a way to supercharge computer worms with AIThe worm could target any known flaw in the world’s computers. (NYT $)+ AI supercharging scams. (MIT Technology Review) 7 Google must let UK publishers opt out of AI search featuresOnline publishers can choose not to appear in the AI Overviews. (BBC)+ Google is now testing features for sites to exit AI search. (Reuters $) 8 America’s data center build-out is falling way behind schedule60% of those planned for completion in 2027 aren’t yet under construction. (WSJ $)+ Nobody wants a data center in their backyard. (MIT Technology Review) 9 EVs are getting cheaper worldwide—except in the USThe US is short on supportive policies and affordable Chinese EVs. (Rest of World) 10 The European Parliament is ditching Google for… QuantThe French search engine is the new default on in-house computers. (Politico)+ The switch comes amid a broader push to wean the EU off US tech. (FT $) Quote of the day “SpaceX’s valuation could be richer than a plate of dauphinoise potatoes.” —Dan Coatsworth, head of markets at AJ Bell, tells CNBC that SpaceX’s IPO price looks overloaded with expectations. One More Thing Marseille’s battle against the surveillance state Heading toward Marseille’s central train station, Eda Nano points out what looks like a streetlamp on the Rue des Abeilles. But this sleek piece of urban furniture is not a lamp. It’s a video camera, with a 360-degree view of the narrow street. Nano, a 39-year-old developer, wants to make Marseille residents more aware that they’re being watched. She’s part of a growing group of activists resisting the rise of policing cameras in their hometown. Find out how the rebellious port city of Marseille is fighting the surveillance state. —Fleur Macdonald We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + These aerial photos of solar farms transform renewable energy into abstract art.+ Open a window over Earth’s water with this hypnotic 4K atmospheric film made from satellite imagery.+ Spend three relaxing hours with David Attenborough narrating this collection of extraordinary wildlife moments.+ Radiohead sounds beautiful on traditional Japanese instruments in this koto performance of “Weird Fishes/Arpeggi”.