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Arcee's new, open source Trinity-Large-Thinking is the rare, powerful U.S.-made AI model that enterprises can download and customize
The baton of open source AI models has been passed on between several companies over the years since ChatGPT debuted in late 2022, from Meta with its Llama family to Chinese labs like Qwen and z.ai. But lately, Chinese companies have started pivoting back towards proprietary models even as some U.S. labs like Cursor and Nvidia release their own variants of the Chinese models, leaving a question mark about who will originate this branch of technology going forward. One answer: Arcee, a San Francisco based lab, which this week released AI Trinity-Large-Thinking—a 399-billion parameter text-only reasoning model released under the uncompromisingly open Apache 2.0 license, allowing for full customizability and commercial usage by anyone from indie developers to large enterprises. The release represents more than just a new set of weights on AI code sharing community Hugging Face; it is a strategic bet that "American Open Weights" can provide a sovereign alternative to the increasingly clo
OpenAI Acquires Tech Talk Show ‘TBPN’—and Buys Itself Some Positive News
OpenAI acquires TBPN, the buzzy founder-led business talk show
Models & Gen AI
Google releases Gemma 4 under Apache 2.0 — and that license change may matter more than benchmarks
For the past two years, enterprises evaluating open-weight models have faced an awkward trade-off. Google's Gemma line consistently delivered strong performance, but its custom license — with usage restrictions and terms Google could update at will — pushed many teams toward Mistral or Alibaba's Qwen instead. Legal review added friction. Compliance teams flagged edge cases. And capable as Gemma 3 was, "open" with asterisks isn't the same as open. Gemma 4 eliminates that friction entirely. Google DeepMind's newest open model family ships under a standard Apache 2.0 license — the same permissive terms used by Qwen, Mistral, Arcee, and most of the open-weight ecosystem. No custom clauses, no "Harmful Use" carve-outs that required legal interpretation, no restrictions on redistribution or commercial deployment. For enterprise teams that had been waiting for Google to play on the same licensing terms as the rest of the field, the wait is over. The timing is notable. As some Chinese AI lab
Cursor Launches a New AI Agent Experience to Take On Claude Code and Codex
As Cursor launches the next generation of its product, the AI coding startup has to compete with OpenAI and Anthropic more directly than ever.
When a reasoning LLM chooses, which comes first: thought or decision?
Article URL: https://arxiv.org/abs/2604.01202 Comments URL: https://news.ycombinator.com/item?id=47622971 Points: 1 # Comments: 0
Human Pose Estimation in Trampoline Gymnastics: Improving Performance Using a New Synthetic Dataset
arXiv:2604.01322v1 Announce Type: new Abstract: Trampoline gymnastics involves extreme human poses and uncommon viewpoints, on which state-of-the art pose estimation models tend to under-perform. We demonstrate that this problem can be addressed by fine-tuning a pose estimation model on a dataset of synthetic trampoline poses (STP). STP is generated from motion capture recordings of trampoline routines. We develop a pipeline to fit noisy motion capture data to a parametric human model, then generate multiview realistic images. We use this data to fine-tune a ViTPose model, and test it on real multi-view trampoline images. The resulting model exhibits accuracy improvements in 2D which translates to improved 3D triangulation. In 2D, we obtain state-of-the-art results on such challenging data, bridging the performance gap between common and extreme poses. In 3D, we reduce the MPJPE by 12.5 mm with our best model, which represents an improvement of 19.6% compared to the pretrained ViTPose
AllyHub – AI agent that builds reusable skills from every task it runs
Article URL: https://allyhub.com Comments URL: https://news.ycombinator.com/item?id=47624809 Points: 2 # Comments: 0
Q1 2026 Timelines Update
We’re mostly focused on research and writing for our next big scenario, but we’re also continuing to think about AI timelines and takeoff speeds, monitoring the evidence as it comes in, and adjusting our expectations accordingly. We’re tentatively planning on making quarterly updates to our timelines and takeoff forecasts. Since we published the AI Futures Model 3 months ago, we’ve updated towards shorter timelines. Daniel’s Automated Coder (AC) median has moved from late 2029 to mid 2028, and Eli’s forecast has moved a similar amount. The AC milestone is the point at which an AGI company would rather lay off all of their human software engineers than stop using AIs for software engineering. The reasons behind this change include:1 We switched to METR Time Horizon version 1.1. We included data from newly evaluated models (Gemini 3, GPT-5.2, and Claude Opus 4.6). Daniel and Eli revised their estimates for the present doubling time of the METR time horizon to be faster, from a
The end of 'shadow AI' at enterprises? Kilo launches KiloClaw for Organizations to enable secure AI agents at scale
As generative AI matures from a novelty into a workplace staple, a new friction point has emerged: the "shadow AI" or "Bring Your Own AI (BYOAI)" crisis. Much like the unsanctioned use of personal devices in years past, developers and knowledge workers are increasingly deploying autonomous agents on personal infrastructure to manage their professional workflows. "Our journey with Kilo Claw has been to make it easier and easier and more accessible to folks," says Kilo co-founder Scott Breitenother. Today, the company dedicated to providing a portable, multi-model, cloud-based AI coding environment is moving to formalize this "shadow AI" layer: it's launching KiloClaw for Organizations and KiloClaw Chat, a suite of tools designed to provide enterprise-grade governance over personal AI agents. The announcement comes at a period of high velocity for the company. Since making its securely hosted, one-click OpenClaw product for individuals, KiloClaw, generally available last month, more than
Mercor, a $10 billion AI startup that works with companies including OpenAI and Anthropic, confirms major data breach
Mercor confirmed it was hit by a supply-chain attack targeting LiteLLM, a widely used AI developer tool. Extortion gang Lapsus$ claims to have walked away with four terabytes of data.
Google Home’s latest update makes Gemini better at understanding your commands
Google is launching another update to its Home app, which is supposed to make controlling your smart home with its Gemini AI assistant "more natural and reliable," according to this week's release notes. With the update, you can describe the type of lighting you want, such as "the color of the ocean," and Gemini will pick the color based on your prompt. You can also use more natural and precise language when asking Gemini to control your appliances or climate. That means you can now tell Gemini to "preheat the smart oven to 350 degrees" or set specific humidity levels. Google has improved Gemini's ability to identify your devices, too - lik … Read the full story at The Verge.
Are they human? Detecting large language models by probing human memory constraints
arXiv:2604.00016v1 Announce Type: new Abstract: The validity of online behavioral research relies on study participants being human rather than machine. In the past, it was possible to detect machines by posing simple challenges that were easily solved by humans but not by machines. General-purpose agents based on large language models (LLMs) can now solve many of these challenges, threatening the validity of online behavioral research. Here we explore the idea of detecting humanness by using tasks that machines can solve too well to be human. Specifically, we probe for the existence of an established human cognitive constraint: limited working memory capacity. We show that cognitive modeling on a standard serial recall task can be used to distinguish online participants from LLMs even when the latter are specifically instructed to mimic human working memory constraints. Our results demonstrate that it is viable to use well-established cognitive phenomena to distinguish LLMs from huma
Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models
arXiv:2604.00006v1 Announce Type: new Abstract: AI-powered recruitment tools are increasingly adopted in personnel selection, yet they struggle to capture the requisition (req)-specific personal competencies (PCs) that distinguish successful candidates beyond job categories. We propose a large language model (LLM)-based approach to identify and prioritize req-specific PCs from reqs. Our approach integrates dynamic few-shot prompting, reflection-based self-improvement, similarity-based filtering, and multi-stage validation. Applied to a dataset of Program Manager reqs, our approach correctly identifies the highest-priority req-specific PCs with an average accuracy of 0.76, approaching human expert inter-rater reliability, and maintains a low out-of-scope rate of 0.07.
Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
arXiv:2604.00007v1 Announce Type: new Abstract: We present Dynin-Omni, the first masked-diffusion-based omnimodal foundation model that unifies text, image, and speech understanding and generation, together with video understanding, within a single architecture. Unlike autoregressive unified models that serialize heterogeneous modalities, or compositional unified models that require orchestration with external modality-specific decoders, Dynin-Omni natively formulates omnimodal modeling as masked diffusion over a shared discrete token space, enabling iterative refinement under bidirectional context. Dynin-Omni adopts a multi-stage training strategy with model-merging-based modality expansion and omnimodal alignment. We evaluate Dynin-Omni across 19 multimodal benchmarks spanning language reasoning, image generation and editing, video understanding, and speech recognition and synthesis. Dynin-Omni achieves 87.6 on GSM8K, 1733.6 on MME-P, 61.4 on VideoMME, 0.87 on GenEval, and 2.1 WER o
New ways to balance cost and reliability in the Gemini API
Google is introducing two new inference tiers to the Gemini API, Flex and Priority, to balance cost and latency.
Anthropic Says That Claude Contains Its Own Kind of Emotions
Researchers at the company found representations inside of Claude that perform functions similar to human feelings.
AI news from 200+ sources
Get Started FreeGeneral AI
Microsoft takes on AI rivals with three new foundational models
MAI released models that can transcribe voice into text as well as generate audio and images after the group's formation six months ago.
Google announces Gemma 4 open AI models, switches to Apache 2.0 license
Gemma 4 brings the first major update to Google's open models in a year.
Show HN: Apfel – The free AI already on your Mac
Article URL: https://apfel.franzai.com Comments URL: https://news.ycombinator.com/item?id=47624645 Points: 3 # Comments: 2
OpenConnect–Native Android app for controlling your local codex AI coding server
Article URL: https://github.com/sunlin-xiaonai/openconnect Comments URL: https://news.ycombinator.com/item?id=47624041 Points: 2 # Comments: 0
Why AI lies, cheats and steals
Article URL: https://www.computerworld.com/article/4153919/why-ai-lies-cheats-and-steals.html Comments URL: https://news.ycombinator.com/item?id=47624289 Points: 1 # Comments: 1
Microsoft’s new ‘superintelligence’ game plan is all about business
Mustafa Suleyman has been preparing for his new job description for a long time. Suleyman is Microsoft's inaugural CEO of AI, but after the company underwent a large-scale restructuring in mid-March, he's handed off some duties and shifted focus to chasing superintelligence. Though the news was only made public last month, he tells The Verge, he'd been preparing for the transition for as many as nine months - and though renegotiating Microsoft's contract with OpenAI is the thing that officially "unlocked [Microsoft's] ability to pursue superintelligence," he'd been planning even before the ink was dry. "This has been a long-held plan," he … Read the full story at The Verge.
Microsoft launches 3 new AI models in direct shot at OpenAI and Google
Microsoft on Wednesday launched three new foundational AI models it built entirely in-house — a state-of-the-art speech transcription system, a voice generation engine, and an upgraded image creator — marking the most concrete evidence yet that the $3 trillion software giant intends to compete directly with OpenAI, Google, and other frontier labs on model development, not just distribution. The trio of models — MAI-Transcribe-1, MAI-Voice-1, and MAI-Image-2 — are available immediately through Microsoft Foundry and a new MAI Playground. They span three of the most commercially valuable modalities in enterprise AI: converting speech to text, generating realistic human voice, and creating images. Together, they represent the opening salvo from Microsoft's superintelligence team, which Suleyman formed just six months ago to pursue what he calls "AI self-sufficiency." "I'm very excited that we've now got the first models out, which are the very best in the world for transcription," Suleyman
OpenAI acquires TBPN
OpenAI acquires TBPN to accelerate global conversations around AI and support independent media, expanding dialogue with builders, businesses, and the broader tech community.
UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
arXiv:2604.01305v1 Announce Type: new Abstract: Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED) architecture is a recent state-of-the-art architecture that reconstructs high-quality spatial domain from hyper-sparse sensor measurement streams. An important limitation of SHRED is that in complex, data-scarce, high-frequency, or stochastic systems, portions of the spatiotemporal field must be modeled with valid uncertainty estimation. We introduce UQ-SHRED, a distributional learning framework for sparse sensing problems that provides uncertainty quantification through a neural network-based distributional regression called engression. UQ-SHRED models the uncertainty by learning the predictive distribution of the spatial state conditioned on the sensor history. By injecting stochastic noise into sensor inputs and training with an energy score loss, UQ-SHRED p
Google now lets you direct avatars through prompts in its Vids app
Google is adding a way to customize and instruct avatars for video creation in the Vids app.
PSA: Anyone with a link can view your Granola notes by default
If you use the AI-powered note-taking app Granola, you might want to double-check your privacy settings. Though Granola says your notes are "private by default," it makes them viewable to anyone with a link, and also uses them for internal AI training unless you opt out. Granola describes itself as an "AI notepad for people in back-to-back meetings." It integrates with your calendar to capture audio from your meetings, and then uses AI to generate a bulleted list of what you've heard, which it calls a "note." You can edit the AI-generated notes, invite other collaborators to view them, and use Granola's AI assistant to ask questions about y … Read the full story at The Verge.
OpenAI just bought TBPN
OpenAI has purchased TBPN, an online talk show that often interviews AI executives and other tech leaders. The show goes live every weekday at 2PM PT, often for a three-hour duration, counting OpenAI CEO Sam Altman, as well as executives from Meta, Microsoft, Palantir, and Andreessen Horowitz, among its past guests, and Bloomberg, CNBC, and Fox Business as its competitors. TBPN's livestream is primarily available on X and YouTube, but many users watch it on X. OpenAI's purchase comes as a lawsuit between Altman and Elon Musk, who was a co-founder of OpenAI before splitting from the project and now owns X, is headed to trial later this mont … Read the full story at The Verge.
Research
It’s not easy to get depression-detecting AI through the FDA
For the past seven years, the California-based startup Kintsugi has been developing AI designed to detect signs of depression and anxiety from a person's speech. But after failing to secure FDA clearance in time, the company is shutting down and releasing most of its technology as open-source. Some elements may even find a second life beyond healthcare, like detecting deepfake audio. Mental health assessments still largely rely on patient questionnaires and clinical interviews, rather than the lab tests or scans common in physical medicine. Instead of focusing on what someone is saying, Kintsugi's software analyzes how it is being said. Th … Read the full story at The Verge.