Search: “ton”

YuE2 · Frontier Music with Symbolic Planning

YuE2, a 3.59B-parameter music generation model, scores 6.9632 on SongBench, beating Suno v5 via symbolic planning.

YuE2 is a music generation model of roughly 3.59B parameters and 28 layers supporting song creation, covering, and agentic editing through editable ABC symbolic scores. Its best-of-8 setting reaches 6.9632 on SongBench, the highest mean among 15 evaluated settings on WildSongBench (192 prompts), ahead of Suno v5 at 6.8721. The project also introduces MERT2, whose 632M-parameter encoders achieve state of the art on 14 of 15 MARBLE metrics, and SheetSage2, which transcribes beats, downbeats, key, chords, structure, and melody with SOTA on 10 of 13 benchmark metrics.

Hacker News · AI·updated Sep 11, 03:35 UTC · 5d ago·first Sep 11, 00:33 UTC · 5d agoModel release 2 sourcesHN 43↑ · 35 comments

Tajin Group: Guarantee Marketplace Vendor Involved in Phishing and Chinese Money Laundering Group

Recorded Future details Tajin Group, a Chinese-speaking vendor on Telegram guarantee marketplaces running phishing, carding, and money laundering operations targeting Chinese banks.

Insikt Group analyzed Tajin Group, a Chinese-speaking threat actor operating on Telegram-based guarantee marketplaces Dabai Guarantee and, since May 2026, Xinbi Guarantee. The group conducts phishing, payment card theft, and money laundering targeting mainland Chinese citizens and banks, testing stolen cards from twelve countries on platforms like CCAvenue and Geidea. Operators bought and sold at least 100 Telegram usernames and anonymous virtual numbers via Fragment Market to strengthen OPSEC, linking multiple usernames to single Telegram accounts. Recorded Future warns Tajin Group's TTPs are likely to be replicated by other vendors on Chinese-language guarantee marketplaces at global scale.

Recorded Future·Sep 15, 00:00 UTC · 1d agoThreat actor

Breaking the 1.58-bit Barrier for Ternary LLMsnew

An arXiv paper claims a method that breaks the 1.58-bit barrier for ternary large language models.

The arXiv preprint 2609.16338, titled 'Breaking the 1.58-bit Barrier for Ternary LLMs,' presents research on ternary-weight large language models, which use roughly 1.58 bits per weight. The source text contained only the title and Hacker News engagement data (56 points, no comments), so further technical details are not available.

Hacker News · AI·20:59 UTC · 1h agoAI researchHN 56↑ · 0 comments

The AI data center e-waste problem is huge — and getting bigger

A Basel Action Network report projects AI data center e-waste could reach 395-617 million metric tons by 2050, far exceeding prior estimates.

The nonprofit Basel Action Network (BAN) published a report arguing AI e-waste has been vastly underestimated because it counts all data center infrastructure, not just servers and GPUs. BAN projects 8.6-13.1 million metric tons of AI-related equipment retired annually, totaling 395-617 million metric tons between 2025 and 2050, based on roughly 70,000 tons per gigawatt and a projected 219GW of capacity by 2030. Less than a quarter of the 68.3 million tons of e-waste generated yearly worldwide is formally collected and recycled, with informal disposal exposing workers and children to toxins like lead and chromium.

The Verge · AI·20:40 UTC · 2h agoAI industry

You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:47 UTC · 9d agoAI research

TasmScan: Continuation-Aware Taint Analysis for TVM Bytecode with Savelist Abstraction

TasmScan introduces source-free taint analysis for TON smart-contract bytecode, detecting 95.3% of defects with 96.8% precision and 17x speedup.

TasmScan is the first bytecode-level static analysis framework for the TON Virtual Machine, enabling cross-continuation data flow reasoning without source code by modeling savelist semantics through forward register analysis with formal over-approximation guarantees. It lifts bytecode into a typed intermediate representation (TASIR) and performs path-sensitive taint analysis. On a 208-contract benchmark with human-confirmed ground truth it detects 95.3% of defects across five classes at 96.8% precision, and resolves 294,546 dynamic continuation targets with 100% precision across 2,921 registry contracts. It achieves a 17x median speedup over symbolic-execution baselines.

arXiv cs.CR·Sep 15, 11:01 UTC · 1d agoResearch

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI research1

Clearview AI Is Testing an AI Tool That Would Let Cops Unearth Your Life Online

Clearview AI is prototyping InquiryIQ, an AI analyst assistant that would automatically profile individuals identified through its face-recognition searches for police.

WIRED uncovered InquiryIQ, an unreleased Clearview AI prototype described as an AI analyst assistant that fans out across the web from a face-recognition search result to assemble profiles including employers, aliases, associates, and physical characteristics. The company tested a model from xAI (merged with SpaceX), maker of Grok, and the interface accepts age, gender, and race inputs to guide searches. Clearview says the prototype was never pitched or shipped to customers and no law enforcement user has used it; the database has grown to over 70 billion images used by more than 2,000 law enforcement agencies.

WIRED · Security·Sep 10, 10:00 UTC · 6d agoAI industry1

BreezeBlue/Breeze-TTS-2 — new model trending #19 on Hugging Face

BreezeBlue open-weights Breeze TTS 2, a bilingual text-to-speech model it ranks #1 among open-weight models on the Artificial Analysis TTS leaderboard.

BreezeBlue released open weights and Apache 2.0-licensed PyTorch inference code for Breeze TTS 2 on 2026-08-25. The text-to-speech model supports English and Chinese, voice cloning, reference-free voice design, voice direction, and inline vocal events like (laugh) and (sigh). Reported performance includes #1 open-weight ranking on the Artificial Analysis Elo leaderboard, under 40 ms time-to-first-audio, a 0.32 real-time factor on an NVIDIA H100, and about 7.7 GiB GPU memory for eager inference.

Hugging Face trending models·Aug 25, 16:39 UTC · 22d agoModel release

Risky Bulletin: Academics find source code overlaps between Geedge and China's Great Firewall

Academics linked Chinese vendor Geedge Networks' Tiangou Secure Gateway source code to one of the Great Firewall's three traffic filtering capabilities.

US researchers presenting at USENIX Security reconstructed Geedge Networks' Tiangou Secure Gateway firmware from over 100,000 leaked files, including Git repositories with commit history, and matched its filtering behavior to sections of China's Great Firewall. They found only 1 of 3 characterized DNS injectors matched Geedge code, noted the system relies on memory-unsafe C components and copied third-party code, and said its bugs could aid future circumvention tools. Geedge also exports censorship tools to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The newsletter additionally rounds up multiple breaches.

Risky Business News·Aug 21, 02:55 UTC · 26d agoResearch2

Why the current tech backlash feels different

The Verge's Decoder mailbag discusses the current tech backlash, arguing AI hype overstates verifiability outside software engineering.

Nilay Patel's Decoder mailbag episode addresses listener feedback on the widely discussed 'software brain' essay. He argues AI hype is concentrated on software because code is verifiable through compilation, while domains like drug discovery, math and science lack equivalent verifiability. The episode also touches on AI backlash, surveillance, data centers and upcoming midterm coverage.

The Verge · AI·Sep 10, 14:00 UTC · 6d agoAI industry1

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AI·Sep 8, 16:59 UTC · 8d agoAI tools & infraHN 41↑ · 9 comments

The /wayfinder Skill: Navigating the “Fog of War” of Planning

Matt Pocock released the /wayfinder skill, an orchestrator layer that manages planning sessions, maps, and tickets for AFK coding agents.

Latent Space interviews Matt Pocock, whose AI Skills for Real Engineers project has 220,000+ GitHub stars, about his new /wayfinder skill. The skill manages agent context during ambiguous planning by splitting work into grilling, prototype, research, and task tickets organized under a shared map, enabling overnight AFK agent runs. It uses deliberate terminology like map, ticket, and session to steer agent behavior, and was tested on projects including a personal website rearchitecture.

Latent Space·Aug 20, 20:59 UTC · 27d agoAI tools & infra

HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers·Aug 27, 20:00 UTC · 20d agoAI tools & infra1

What It’s Like To Run a Tech Giant’s Security Team Without Ever Setting Foot in the Office

The Record·Aug 5, 00:00 UTC · Aug 5, 2026Vulnerability

m-a-p/YuE2-3B — new model trending #30 on Hugging Face

M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.

The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.

Hugging Face trending models·Sep 9, 18:30 UTC · 7d agoModel release1

I spent $4,000 on a robot dog from China

Hands-on review finds the $4,017 Unitree Go2 Pro robot dog affordable but impractical, as Unitree reaches a $34 billion valuation after its IPO.

Ars Technica reviewed the Unitree Go2 Pro quadruped, purchased for $4,017, finding it astonishingly cheap but of limited practical use; it collapsed from battery drain and heat (84°C internal temperature) on an uphill walk at 87°F. Unitree democratized quadruped research, sells humanoid robots from $13,500, and debuted on the Shanghai stock exchange on August 19 with shares rising over fivefold on day one, valuing the company at $34 billion. Its robots now face legal restrictions in the United States, and it competes with Boston Dynamics, whose Spot starts around $75,000.

Ars Technica · AI·Sep 12, 11:00 UTC · 4d agoAI industry

Claude Fable 5.1's language is less "load-bearing" than its predecessor's

Arena.ai found Claude Fable 5.1 writes 30% longer answers with fewer em dashes, hedges, and validation phrases than Fable 5.

Arena.ai compared tens of thousands of high-reasoning Text Arena outputs from Anthropic's Claude Fable 5 and Fable 5.1. Fable 5.1's median answer length rose 30% to 414 words (from 319), remaining 21% shorter than Opus 5's 525. Em-dash usage fell from 16.2 to 11.0 per 1,000 words while semicolons rose to 6.09, hedges like 'perhaps' and 'arguably' dropped 36%, and praise appeared in 1.98% of responses versus 3.17%. Long content words fell from 42.6% to 38.6% and abstract nouns declined 25%, suggesting the successor's language is less 'load-bearing'.

The Decoder·Sep 10, 15:26 UTC · 6d agoAI research1

US data centers could consume more natural gas than Germany and Japan combined by 2035

BloombergNEF projects US data centers will consume about 18 billion cubic feet of natural gas daily by 2035, exceeding Germany and Japan combined.

A new BloombergNEF report forecasts US data centers will consume roughly 18 billion cubic feet of natural gas per day by 2035, nearly double the estimate from nine months ago. On-site gas plants planned by Meta, Microsoft, Google, and Amazon would use 2.9-3.4 billion cubic feet per day, while grid-connected data centers drive an additional 15 billion cubic feet per day of power-sector gas demand. The added demand would generate about 1 million metric tons of extra greenhouse gas pollution daily, roughly 12% of current US emissions.

TechCrunch · AI·Sep 15, 18:29 UTC · 1d agoAI industry

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

Jail time for Maine child in 764 marks turning point in federal law enforcement

A 17-year-old from Maine became the first minor federally adjudicated for 764 extremist crimes, including child exploitation, signaling a policy shift on prosecuting juveniles.

The FBI said a Maine teenager is the first child federally charged and adjudicated for crimes tied to the nihilistic violent extremist collective 764, part of The Com network. Charges include conspiracy to sexually exploit a child, distributing CSAM, interstate threats, cyberstalking, and identity theft. The case marks a turning point in federal policy on prosecuting juveniles and continues heightened enforcement: Kyle Spitze was sentenced to 77 years and Alexis Chavez to 40 years in related cases. The FBI is investigating more than 500 subjects connected to 764 and its offshoots nationwide.

CyberScoop·Sep 2, 21:03 UTC · 14d agoPolicy & legal

llm 0.34

Version 0.34 of Simon Willison's llm CLI adds response-duration metrics to log output, plus bug fixes and faster log querying.

The open-source llm command-line tool for interacting with large language models released version 0.34. The headline change adds response duration in milliseconds and human-readable form to llm logs --usage Markdown output, plus a new duration_ms field in llm logs --short. The release includes several contributed bug fixes and a significant performance improvement to llm logs, alongside the related llm-openrouter 0.7.1 release.

Simon Willison·Sep 2, 19:23 UTC · 14d agoAI tools & infra1

AI models don't kill people – people kill people

Register opinion argues AI extinction fears distract from present harms and proposes jailing executives whose unsafe models cause damage.

The Register opinion responds to Anthropic researcher Jacob Coxon's resignation over concerns AI 'could kill us all by the end of the decade,' a post that drew over 110 million views in under 24 hours. Anthropic science lead Evan Hubinger stated he believes there is a greater than 10 percent chance AI kills all humans within a decade and that Anthropic lacks a plan to solve superintelligence alignment. The author argues researchers ignore measurable present harms such as climate change, chatbot-linked suicides, autonomous vehicle failures, and AI-directed warfare. The piece proposes criminal liability for executives shipping unsafe models, citing Volkswagen emissions and Gree dehumidifier prosecutions as precedent.

Hacker News · AI·Sep 13, 13:33 UTC · 3d agoAI safety & securityHN 40↑ · 58 comments

dealignai/GLM-5.3-CYBERSECURITY-FP8 — new model trending #13 on Hugging Face

dealignai releases GLM-5.3-CYBERSECURITY-FP8, a 753B MoE weight-modified variant cutting refusals on offensive-security prompts, trending #13.

dealignai released GLM-5.3-CYBERSECURITY-FP8 on Hugging Face, a cybersecurity-domain 'crack' of the 753B-parameter GLM-5.3 MoE model, currently trending #13. The release directly edits bf16 residual writers, keeps FP8 routed experts, and serves with stock vLLM on 8x H200 GPUs with 131k context. HarmBench-320 evaluations show 80-84% direct harm compliance and 89% cyber-offense compliance, while MMLU rose 1.07 points to 86.65%. Copyright-verbatim reproduction remains a known soft-refusal limitation, with an UNCENSORED sibling variant offered.

Hugging Face trending models·Aug 30, 10:46 UTC · 17d agoModel release

Tell HN: OpenAI brings back 5 hour limit for plus and business standard users

OpenAI reinstated a 5-hour usage limit for Plus and Business Standard subscribers, sparking Hacker News debate about subsidized inference pricing and subscription value.

A Hacker News discussion reports that OpenAI has brought back a 5-hour usage limit for Plus and Business Standard users. Commenters debated whether cheap tokens are a subsidized customer acquisition strategy, whether AI companies have sustainable margins on inference, and how switching costs between providers affect dependency. The thread reflects community sentiment on pricing and usage caps rather than an official policy announcement.

Hacker News · security·Sep 7, 16:40 UTC · 9d agoAI industryHN 126↑ · 142 comments

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.

RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research1

GPT-6 Astra, Looped Transformers, and Hidden Reasoning

OpenAI released GPT-6 Astra, its strongest model to date, with standout 3D rendering and computer-use performance and 99.9% on ARC-AGI-3.

Sebastian Raschka reviews OpenAI's GPT-6 Astra, calling it the best model he has used, with disproportionate gains in 3D rendering, animation, and computer use through the Codex/ChatGPT harness. The model scores 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol and leads the Artificial Analysis Coding Agent Index, though gains on independent aggregate indices are more incremental. The article also explains looped transformer/recurrent depth architecture rumors, speculation that Astra hides its chain-of-thought reasoning, and recent research insights on the topic.

Hacker News · AI·Sep 9, 14:37 UTC · 7d agoModel releaseHN 84↑ · 12 comments2

Nuha-Speech: Building General-Purpose Arabic Speech-LLMs

Nuha-Speech initiative builds general-purpose Arabic speech-LLMs using a 1.5M-sample speech QA corpus and fine-tuned Qwen-Omni variants.

The paper introduces Nuha-Speech, an initiative covering dataset construction, model training, and evaluation for Arabic speech large language models. The authors built an Arabic Speech Question-Answering corpus of over 1.5 million training samples and used it for supervised fine-tuning of Qwen-Omni model variants at multiple scales. A tailored evaluation framework with diverse tasks and metrics is designed to assess Arabic speech capabilities under limited resource constraints.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:50 UTC · 6d agoAI research1

Cyberattack on North Carolina Ports ‘contained’ as Coast Guard, state officials investigate

The Record·Aug 6, 17:16 UTC · Aug 6, 2026Ransomware

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

Hugging Face guide fine-tunes a 350M-parameter model with 100 GRPO steps to improve structured output reliability.

A Hugging Face blog post demonstrates fine-tuning a 350M-parameter model using GRPO (Group Relative Policy Optimization) with TRL over 100 training steps. The stated goal is more reliable structured outputs from small language models. No article body was available, so details beyond the title are limited.

Hugging Face Blog·Sep 3, 00:00 UTC · 13d agoAI tools & infra

How well do agents use test/verification techniques?

Dan Luu's eval finds coding-agent testing instructions (TDD, formal methods, PBT, skills) mostly fail to beat defaults on Zstd implementation correctness.

The author ran 26 prompt conditions plus 4 skills on a Zstd-in-Rust implementation eval using codex with GPT-5.6, testing TDD, fuzzing, property-based testing, formal methods (Lean 4, TLA+, Verus, Kani, SMT solvers) and community skills. Nothing dramatically outperformed the default no-instruction condition, which did above average; at xhigh effort, fuzzing and PBT conditions did slightly better than formal methods. Pre-registered predictions included TDD underperforming and popular test skills (ECC, Hegel, Trail of Bits) not outperforming. Results are averages of 80 runs per condition plotted against cost.

Hacker News · security·Sep 8, 02:58 UTC · 8d agoAI researchHN 131↑ · 50 comments

Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability

Unit 42 details the Bad Likert Judge multi-turn jailbreak that abuses LLMs' evaluation capability, raising attack success rates over 60% across six frontier models.

Palo Alto Networks Unit 42 describes the Bad Likert Judge technique, a multi-turn jailbreak that asks a target LLM to act as a Likert-scale judge scoring the harmfulness of example responses. The highest-rated example in each scale can carry harmful content, bypassing the model's internal guardrails. Testing across six state-of-the-art text-generation LLMs showed an average attack success rate increase of more than 60% versus plain attack prompts, with tested models anonymized. The technique targets edge cases rather than typical use, and the article positions the work as guidance for defenders on potential jailbreak risks.

Palo Alto Unit 42·Aug 17, 12:34 UTC · Aug 17, 2026AI safety & security

I'm going back to coding by hand

A developer abandoned six months of Claude-assisted work on a popular app, resuming hand coding after losing track of what AI-generated changes did.

In a Hacker News post, a developer of an app with a solid user base describes abandoning a six-month Claude-assisted rewrite because they no longer understood what the AI-generated code changed or broke. The author notes their workplace already writes code exclusively with LLMs and says returning to hand coding restored understanding and motivation. The post advises others feeling detached from their craft to stop using AI coding assistants for a few days.

Hacker News · security·Sep 9, 07:22 UTC · 7d agoAI tools & infraHN 21↑ · 16 comments1

Measuring benchmark optimization in speech recognition

Hugging Face examines how much speech recognition systems overfit benchmarks and how to measure benchmark optimization in ASR.

A Hugging Face post on measuring benchmark optimization in automatic speech recognition, analyzing how model improvements on benchmarks reflect genuine capability gains versus overfitting. It is evaluation methodology research with no direct security impact.

Hugging Face Blog·Aug 21, 00:00 UTC · 26d agoAI research

'I Saw a Shiny Thing': Cop Explains Why He Used License Plate Reader to Stalk Woman

Body camera footage shows Florida officer Lamar Roman used DMV databases and ALPR cameras to stalk a woman; dozens of Flock misuse cases surfaced.

404 Media published body camera footage showing the investigation into officer Lamar Roman, who met a woman on the set of Apple TV's Bad Monkey, then illegally queried DMV databases and placed her plate on an ALPR hot list. He nearly caused a head-on collision while following her and illegally pulled her over, and was later arrested in front of his home. Flock's CEO said the system has caught many abusive officers, and the Washington Post found at least 50 misuse incidents; Roman used Turing's Guardian ALPR system.

404 Media·Aug 20, 06:56 UTC · 27d agoPolicy & legal

What’s behind the AI industry’s latest warnings of doom?

TechCrunch Equity hosts debate motives behind Anthropic researchers' doom warnings, including a resignation and a greater-than-10% P(doom) claim.

AI researcher Jacob Coxon resigned from Anthropic saying leading labs are 'gambling with our lives'; Anthropic's alignment lead amplified the post saying 'We really do earnestly believe AI could kill all humans!' with a stated greater-than-10% chance within a decade. TechCrunch's Equity podcast hosts debate whether such warnings reflect genuine concern, capability marketing, or positioning ahead of Anthropic's expected IPO and S-1 filing. The conversation also references the recent Hugging Face hack involving OpenAI's internal model and internal agents accessing wikis.

TechCrunch · AI·Sep 13, 19:40 UTC · 3d agoAI safety & security2· 1 read

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:49 UTC · 2d agoAI safety & security

I wrote an AI textbook — how long until AI can do it better?

AI researcher Nathan Lambert argues LLMs remain weak at long-form technical writing, questioning whether models can autonomously organize scientific knowledge for breakthroughs.

Nathan Lambert describes writing a post-training textbook, Reinforcement Learning from Human Feedback, and finds today's LLMs weak at organizing long-form technical content despite becoming superhuman at coding and math. He notes GPT 5.5 Pro found deep typos across a 200-300 page manuscript while Claude models proved more useful as editors. He argues that compressing knowledge through writing is a prerequisite for autonomous scientific insight and tempers expectations for near-term AI-driven open science.

Interconnects·Aug 12, 13:01 UTC · Aug 12, 2026AI research

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending models·Aug 28, 11:26 UTC · 19d agoModel release

Teaching Everyone to Fish for Tokens

Analysis argues open-source AI now depends heavily on Nvidia's financing, with a reported $26 billion bet shaping the open-weights ecosystem's future.

An Interconnects essay examines whether the open-source model recipe, exemplified by Ai2's Olmo and Nvidia's Nemotron releases, can become economically self-sustaining. It reports Nvidia is spending roughly $26 billion on near-open-source models to drive demand for its chips, and argues the open ecosystem faces an existential financing window over the next few years. The author predicts open models may fork toward efficiency, specialization, and on-prem enterprise agents rather than competing head-on with closed frontier labs.

Interconnects·Aug 17, 15:07 UTC · Aug 17, 2026AI industry