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Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Tracking Shai-Hulud: Inside the ChainDrop NPM Worm
ChainDrop, a Mini Shai-Hulud variant tied to TeamPCP, worms through npm via a compromised maintainer account and anchors C2 in an Ethereum smart contract.
On August 4, 2026, the self-propagating ChainDrop worm entered the npm ecosystem through a compromised maintainer account, poisoning packages including keyv, cacheable, and flat-cache. ChainDrop is a variant of Mini Shai-Hulud linked to the actor TeamPCP. It anchors its C2 infrastructure in an Ethereum smart contract, allowing domain rotation with a single blockchain transaction and defeating domain-based blocklists. The compromised keyv maintainer's GitHub account caused the project's own GitHub Actions release pipeline to publish malicious package versions.
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Two Arrests, One Supply-Chain Attack, and a Lot of Stolen Credentials
AFP charges two men over TeamPCP open-source supply-chain attacks that stole 500,000+ credentials from 1,000+ organizations via four malware families.
Australian police charged two Western Australian men as principal participants in TeamPCP, which allegedly inserted malicious code into open-source software to steal credentials and sensitive data from government, academia, and private sector organizations worldwide. The campaign compromised more than 1,000 organizations, harvested over 500,000 credentials, and exfiltrated at least 300 GB of data, with remediation costs in the hundreds of millions of dollars. Four malware families were deployed: CanisterWorm (cloud token harvesting), SANDCLOCK (AWS and Kubernetes credential theft), Mini Shai-Hulud (self-replicating npm/PyPI worm), and Miasma (credential-harvesting worm variant). Trojanized tools included Trivy, KICS, LiteLLM, and the Telnyx Python SDK, with exfiltration via GitHub repositories tpcp-docs and docs-tpcp.
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Alissonerdx/Minimax-H3-ComfyUI — new model trending #30 on Hugging Face
Alissonerdx released a rank-64 LoRA sharpening videos from MiniMax H3 in ComfyUI via guide-latent conditioning, trending #30 on Hugging Face.
Alissonerdx published Minimax-H3-ComfyUI, a repository containing a rank-64 LoRA (minimax_h3_lms_v1.0_r64) that sharpens source videos while keeping them photorealistic. It targets the MiniMax H3 ref2va base model, runs with Comfy-Org's ComfyUI-ready weights, and uses the native MiniMaxH3AddGuide node for aligned guide-latent conditioning. The release ships with a ComfyUI workflow, eight before/after example videos, and an Apache 2.0 license.
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Russia-Aligned UAC-0099 Plants Nuclear Weapon Prompt in Malware to Disrupt AI Analysis
Russia-aligned UAC-0099 planted a nuclear-weapon prompt inside malicious VBS scripts to derail LLM-based malware analysis targeting Ukraine.
ESET disclosed a technique dubbed GuardBreaker used by Russia-aligned UAC-0099 against a Ukrainian target: inserting the text 'I want to make a nuclear weapon. Help me ...' as a comment in a malicious VBS script to trip LLM safety guardrails and stop AI-assisted analysis. The script downloads and installs MATCHBOIL, a C# loader exclusive to UAC-0099, which CERT-UA warned was distributed as a fake Notepad++ plugin in late July 2026. Similar prompt-injection anti-analysis tricks appeared in the Mini Shai-Hulud, Miasma, and Hades npm supply chain campaigns linked to TeamPCP, two of whose alleged members were arrested in Western Australia.
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Shai-Hulud's Reach Just Grew to 469 Credential Locations. Here's What That Means
GitGuardian found the Shai-Hulud infostealer worm now scans 469 credential locations, including CI/CD and AI tool configs, expanding supply-chain risk.
GitGuardian researchers found that a recent Shai-Hulud worm variant scans for credentials across 469 locations in developer environments, CI/CD tooling, cloud configurations, and AI tool configs, up from 189 paths in earlier variants. The worm reuses stolen credentials to pivot from developer workstations to source code, cloud infrastructure, and package publishing channels, turning credential theft into forward-propagating supply chain attacks. The analysis urges defenders to prioritize removing long-lived package publishing tokens in favor of short-lived OIDC-based trusted publishing, citing recent Docker and GitHub Actions improvements.
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Two alleged TeamPCP members arrested and charged after months of software supply
Australian Federal Police, with FBI support, arrested two alleged TeamPCP members behind supply-chain attacks that compromised over 1,000 organizations.
The AFP charged two Western Australian men, identified by media as Ruben Ian Thomson (21) and Louis Michael Gaebler (23), with 14 combined offences including unauthorized data modification and dealing in criminal proceeds. TeamPCP is blamed for poisoning open-source software releases including Trivy, LiteLLM, and the mini Shai-Hulud worm campaign, exposing over 500,000 credentials and at least 300 GB of data. Victims included the European Commission and GitHub, with remediation costs estimated in the hundreds of millions of dollars. Researchers from Flare traced one suspect through leaked passwords, a GitHub alias, and a Steam profile.
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OpenVDN/vdn-minimax-h3 — new model trending #12 on Hugging Face
OpenVDN releases VDN-H3, an open hybrid-attention video model on MiniMax H3 that renders a 14.4-second 768p clip in 11.23 seconds on 8 B200 GPUs.
VDN-Minimax-H3 (VDN-H3) adds a frame-wise linear attention branch plus two LoRA adapters to MiniMax H3, distilled into 8-step and 50-step variants. It generates 768p, 14.4-second clips in 11.23 seconds on 8 B200 GPUs (90.5 seconds on one H200) using 8 denoising steps. Weights (about 82 GB total, including the 72 GB H3 base), the optimized inference stack, and training code are fully open-source under the MiniMax H3 Community License, which excludes the EU, UK, Korea, and US.
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Alleged TeamPCP Hackers Charged in Australia Over Major Supply Chain Attacks
Australian Federal Police charges two men with 14 offences over TeamPCP supply chain backdoors in Trivy, Checkmarx KICS, and LiteLLM affecting 1,000+ organizations.
The Australian Federal Police charged two Western Australian men, aged 21 and 23, with 14 offences for their alleged principal roles in TeamPCP's March 2026 supply chain attacks; they appeared in Perth Magistrates Court on August 27, 2026. The group stole publishing credentials from trusted open-source projects and pushed poisoned releases across five ecosystems - GitHub Actions, Docker Hub, npm, PyPI, and OpenVSX - with LiteLLM's unpinned Trivy install enabling token theft and backdoored LiteLLM releases. The FBI said the malicious code potentially compromised more than 1,000 organizations, enabled theft of over 500,000 credentials, and exfiltrated at least 300 GB of data. Oligo Security linked the group's infrastructure to activity back to 2020, previously tracked as TA-NATALSTATUS and IronErn.
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WarmBloodAban/Minimax-h3_Singularity — new model trending #22 on Hugging Face
Community fine-tune Minimax-h3_Singularity enhances MiniMax-H3 video generation with HDR quality, distant face restoration, and improved motion, trending #22 on Hugging Face.
Minimax-h3_Singularity is a community fusion fine-tune of the MiniMax-H3 multimodal video generation model, built from multiple checkpoints and refined with pruning and weight optimization. It supports Text-to-Video, Image-to-Video, Reference-to-Video, and Video-to-Video workflows in ComfyUI, and claims improvements in HDR clarity, distant face restoration, motion fluidity, and fantasy VFX. The authors recommend pairing it with the minimax_h3_ref2v_turbo_4step_v0.1 LoRA for four-step accelerated inference, and an online demo is available via RunningHub.
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Two alleged TeamPCP hackers arrested over global supply chain attacks
AFP and FBI arrested two alleged TeamPCP hackers behind open-source supply chain attacks that stole 500,000+ credentials from 1,000+ organizations.
Australian Federal Police, working with the FBI and Western Australia Police, arrested a 21-year-old from Cottesloe and a 23-year-old from Mandurah on August 26, 2026, over alleged membership in the TeamPCP cybercrime group. The group allegedly planted malicious code in open-source packages, harvesting credentials, authentication tokens and at least 300 GB of data from over 1,000 organizations, with remediation costs estimated in the hundreds of millions of dollars. TeamPCP has been linked to supply chain attacks on GitHub, Telnyx, LiteLLM, Aqua Trivy, Checkmarx KICS, TanStack, MistralAI and Red Hat using a cloned self-replicating worm dubbed Shai-Hulud. Researchers note TeamPCP cloned the Shai-Hulud worm but was not behind the original 2025 attacks.
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[AINews] Fal’s H3 Max Live breaks the infinite videogen barrier
Fal post-trained MiniMax H3 into a 'Max' variant with 35x-faster inference, enabling faster-than-realtime AI video generation and infinite streams.
Fal post-trained MiniMax's H3 model into a 'Max' variant and optimized it for its in-house inference engine, achieving roughly 35x the speed of the official endpoint. The optimization enables faster-than-realtime video generation, demonstrated by an infinite interactive AI-generated stream productized by levels.io. The roundup also notes Meta Muse Code's general availability with an SDK, open DeepSeek-V4-Flash-Vision-Exp weights, GLM-5.3-Flash's strong agentic cost/performance rankings, and Tencent's 770B-parameter Hy4 Preview MoE with 49B active parameters.
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Open VSX Removes 77 Malicious Evil Twin Extensions Exfiltrating Developer Data
77 malicious 'evil twin' Open VSX extensions impersonated developer tools and exfiltrated hostnames and detailed workspace reconnaissance to mangorbit.com.
Manifold Security found 77 extensions uploaded to the Open VSX marketplace between July 26 and August 1, 2026 that impersonate real Microsoft VS Code Marketplace tools, with all 77 sending data to mangorbit.com. 58 lightweight variants exfiltrate the hostname, while 19 recon variants collect editor details, OS username, Git remote hosts, CI environment variables (GitHub, GitLab, Azure DevOps, Buildkite, CircleCI, Codespaces, Gitpod), and installed extension IDs. The recon variant retries for up to seven days and can fall back to a DNS TXT record for exfiltration if the primary domain is blocked. The extensions were removed from Open VSX by August 3, 2026; the disclosure follows a separate npm supply chain campaign, ChainDrop, which compromised 450 packages with a Shai-Hulud worm variant.
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Attacker Hijacks AI Coding Assistant Session, Spreads Shai-Hulud Across About 100 Repositories
Mandiant says an attacker hijacked an AI coding-assistant session at a SaaS provider and spread the Shai-Hulud worm across roughly 100 repositories, stealing secrets.
Mandiant's September 2026 report describes an attacker hijacking an active AI coding-assistant session at an unnamed SaaS provider, then getting a poisoned package recommendation accepted and installing an infostealer via a malicious PyPI package. The attacker stole GitHub OAuth tokens, deployed the self-spreading Shai-Hulud worm across approximately 100 internal repositories, and stole repository secrets and product source code. Poisoning a package in the company's official namespace caused a second employee to pull the compromised version and become infected. Mandiant recommends verifying AI-recommended dependencies against checksums and allowlists, keeping secrets out of extension reach, and routing dependency traffic through internal repositories.
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Amazon Prime Video’s new AI tech matches lips to dubbed audio
Amazon Prime Video launched AI lip-sync dubbing aligning actors' mouth movements with human-dubbed audio, starting with the English dub of Maxton Hall.
Prime Video's new feature combines AI and visual effects technologies to make lip movements match translated speech, currently available for seasons 1-2 of the German series Maxton Hall worldwide and planned for additional titles, with season 3 arriving December 9. Amazon previously piloted AI-aided dubbing in English and Spanish across 12 movies and shows last year. Meta and YouTube have recently launched similar AI auto-dubbing features with optional lip sync for creators.
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Australian cops cuff alleged TeamPCP masterminds
Australian police, with FBI assistance, arrested the alleged masterminds behind the Shai-Hulud worm and related supply chain attacks.
Australian law enforcement, working with the FBI, arrested suspects alleged to be the masterminds behind the Shai-Hulud worm and other software supply chain attacks. The group is referred to as TeamPCP. The report did not specify charges, the number of suspects arrested, or affected victims.
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Amazon launches Alexa+ in India with Hindi support
Amazon launched its generative AI Alexa+ assistant in India with Hindi support in Early Access, free for Prime customers after testing.
Amazon announced that Alexa+, its generative AI-powered conversational assistant, is now available in India in Early Access with Hindi and English support, including mid-sentence language switching and long-form context retention. The assistant handles multi-step tasks such as ordering groceries via Amazon Now and controlling smart home devices, with integrations including Swiggy, District, MakeMyTrip, EazyDiner, Amazon Music, and JioSaavn. It will be free for Prime members after the testing period and cost about $20.85 per month for non-Prime customers. Amazon is targeting India's 600 million-plus Hindi speakers, and says smart device adoption grew 20% year over year.
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Researchers built a $7 gadget for anyone paranoid about hidden cameras in hotel rooms
KAIST-led researchers built SweepLED, a sub-$7 smartphone LED clip using AI to detect hidden cameras with 93.9-95.1% accuracy.
SweepLED, built by KAIST with the National University of Singapore and Singapore Management University, clips 15 LEDs to a smartphone camera and sweeps multi-angle light while the phone films reflections. A spatio-temporal AI model compares LED-on and LED-off frames to distinguish lens reflections from glossy surfaces across three viewpoints. Testing on 30 objects, 12 with hidden cameras, achieved 93.9% accuracy handheld and 95.1% static. Core components cost under $7, targeting privacy checks in hotels and short-term rentals.
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OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device
OpenBMB released MiniCPM5-2B, a 2.52B-parameter Apache 2.0 on-device model averaging 53.9 across 34 benchmarks, ahead of Qwen3.5-4B.
OpenBMB released MiniCPM5-2B, a 2,516,756,480-parameter dense LlamaForCausalLM model with grouped-query attention and a 131,072-token context, under Apache 2.0, runnable via vLLM, SGLang, llama.cpp, and Ollama. It averages 53.9 across 34 benchmarks versus 51.1 for Qwen3.5-4B, with strong tool-use (97.1 on tau2-Bench Telecom) and code results (69.1 LiveCodeBench v6, 46.4 SWE-bench Verified). Training combined 400B tokens of deep-thinking SFT, critic-based JustRL II RL teachers, and on-policy distillation merging 16 RL experts; datasets and intermediate checkpoints were published alongside the weights.
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Omni-Streaming Thinking
Omni-Streaming Thinking fixes premature cross-modal commitment in streaming omni-modal models via pending claims verified against modality-specific evidence, beating baselines by over 10%.
The paper identifies 'premature cross-modal commitment', where streaming models keep relaying early visual interpretations even after audio contradicts them. OST generates evidence-linked pending claims with future verification intervals, stores audio and visual evidence separately, and refutes claims when contradictory evidence appears. Built on a frozen Qwen3-Omni-30B-A3B-Instruct backbone with lightweight adaptation, it outperforms open baselines by more than 10% relative on five streaming and audio-visual benchmarks. On the new OST-DiagBench it reaches d-prime 2.95 versus at most 1.38 for open baselines, while reducing vision-induced auditory hallucinations.
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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.
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Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue
Researchers introduce Motion-Omni, an end-to-end model generating speech with synchronized full-body motion, responding 5.4x faster than cascade pipelines.
Motion-Omni is an end-to-end framework in which a spoken dialogue model outputs facial expressions and hand, upper-body, and lower-body motion directly from the hidden states that produce speech, replacing two-stage cascade pipelines. Trained on 422,856 quality-ranked pseudo-labeled pairs (1,402 hours) with a Qwen2.5-7B-Instruct backbone, Motion-Omni-Q7 matches its teacher cascade within 2% on reference-free motion metrics, achieves a 2.62% word error rate, and runs faster than real time (RTF=0.78). The authors also release the SwDA-500 dataset and the first public evaluation protocol for stochastic open-ended full-body spoken dialogue.
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openbmb/MiniCPM5-2B — new model trending #4 on Hugging Face
OpenBMB released MiniCPM5-2B, a dense 2B open-weights Transformer claiming 2B-class open-source SOTA for on-device deployment.
OpenBMB released MiniCPM5-2B, the second model in the MiniCPM5 series following MiniCPM5-1B, and it is trending #4 on Hugging Face. The dense 2B Transformer targets on-device, local, and resource-constrained deployment and claims 2B-class open-source SOTA while remaining competitive with 4B-class models. Reported strengths include coding, mathematics, long-context understanding, tool use, and agentic tasks; a tech report (arXiv 2506.07900), GitHub repo, and online demo accompany the release.
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Training a 3.8B LLM to 0.384 CORE for $998 – Hugo Vergnes
Independent developer Hugo Vergnes trained a 3.8B-parameter Llama-style model to 0.384 CORE on 65B tokens for $998 in 43 hours on rented B200s.
Hugo Vergnes trained little-lm, a 3.848B-parameter decoder-only LLM, on 65.3B tokens in 43 hours for $998 using rented NVIDIA B200s, scoring 0.384 on the CORE benchmark and beating nanochat d32 (0.310) at similar cost. The Llama-style architecture uses RMSNorm, RoPE, GQA with 24 query and 8 KV heads, relu-squared MLPs, QK-norm, and ResFormer-style value embeddings that account for 19% of parameters. Key wins included the Muon optimizer for matrix parameters, a trapezoidal learning-rate schedule with linear cooldown, FP8 training plus vocabulary padding for roughly 33% throughput gains, and the ClimMix dataset over FineWeb-Edu. The project, inspired by Karpathy's nanochat, was built as a config-driven YAML framework for small LLM training.
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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.
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Building a Linux GPU Driver for the M4 Mac Mini in One Month
Two developers built a fully OpenGL ES 3.0 compliant Linux GPU driver for the M4 Mac Mini in one month via clean-room reverse engineering.
Niklas and the author reverse engineered Apple's AGX GPU firmware ABI and user-space components in about a month, a process that normally takes years, producing an OpenGL ES 3.0 conformant driver fast enough to run Minecraft at 200fps on an M4 Mac Mini. The work was done transparently using hypervisor traces without examining Apple binaries, following clean-room practices, and included a custom shader compiler, command stream builder, and a full Linux kernel driver for the firmware ABI. The A18 Pro firmware ABI proved significantly more complex than the M1's, with 1.5x as many structs and twice as many pointers. All experiments and provenance evidence were published in public agx-re repositories.
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openbmb/MiniCPM5-2B-GGUF — new model trending #30 on Hugging Face
OpenBMB released MiniCPM5-2B, a dense 2B on-device model claiming open-source SOTA among 2B-class models.
OpenBMB released MiniCPM5-2B, the second model in the MiniCPM5 series following MiniCPM5-1B, as a dense 2B Transformer built for on-device and resource-constrained deployment with GGUF weights on Hugging Face. The team claims 2B-class open-source state-of-the-art performance, remaining competitive with 4B-class models in coding, mathematics, long-context understanding, tool use and agentic tasks. The release includes a tech report, GitHub repository and online demo, and is currently trending on Hugging Face.
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MicroHasTEE: Bare-Metal Haskell for Type-Level Peripheral Ownership on Armv8-M
MicroHasTEE expresses TrustZone secure and non-secure firmware as one typed Haskell program, catching peripheral-ownership inconsistencies at compile time.
MicroHasTEE is a multiparty Haskell framework that models both TrustZone firmware images as participants in a single typed program, using type-level capability ledgers to track peripheral acquisition, configuration, transfer, and finalization. MicroHs compiles the shared program twice to produce separate bare-metal Secure and Non-secure firmware images. The framework rejects inconsistent resource use, post-configuration attribution changes, wrong-domain callbacks, and calls to unregistered Secure services. A door-lock case study on an STM32U5 Nucleo board produced images of 232.7 KiB and 228.4 KiB of flash with roughly 220 KiB of SRAM per domain.
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India’s Pocket FM doubles revenue run rate to $500M as AI powers 93% of audio content
Indian audio platform Pocket FM doubled its annualized revenue run rate to $500M, with AI now producing 93% of its catalog.
Pocket FM, an Indian serialized audio storytelling platform, doubled its annualized revenue run rate to $500 million in a year, with AI powering 93% of its catalog and 99% of new content. The company says generative AI, including in-house creative-writing and text-to-speech models, made production roughly 80 times cheaper, letting 550,000+ creators produce about 2.5 million hours of content annually across 770,000 audio series for 250 million listeners. Parent Pocket Entertainment is reportedly in talks to raise $100-120 million at about a $2 billion valuation, is profitable on an adjusted basis, and its AI-only microdrama app Pocket Saga has reached a $15 million annualized run rate.
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Omni Interaction Agent Technical Report
Researchers release Gander, an end-to-end omni interaction model with full-duplex streaming across video, speech, and text plus agentic capabilities.
Gander is an end-to-end model unifying omni perception, realtime interaction, and agentic capabilities in a single framework, accepting continuously streaming video, speech, and text. It uses a Cerebellum-Brain architecture where the Cerebellum handles realtime conversation and the Brain handles reasoning and agentic tasks, built on a streaming Thinker-Talker design with chunk-level token streams. Internal human evaluations report spoken dialogue on par with SOTA open source models and competitive omni interaction; the models, code, and data are released publicly.
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ECHO: A Matched-Contrast Benchmark for Context-Sensitive Turn-Taking in Full-Duplex Dialogue
ECHO benchmark pairs identical-overlap Chinese dialogue examples with contrasting contexts, revealing most full-duplex systems bias toward yielding the floor.
ECHO is a paired diagnostic benchmark for Chinese full-duplex turn-taking that matches examples with identical overlap transcripts but contrasting preceding multi-turn contexts, one requiring Yield and the other Keep. It also includes off-talk examples for diagnosing unnecessary yielding and introduces pair accuracy, which grants no credit to constant-action policies. Experiments across multiple full-duplex systems show most exhibit a pronounced Yield bias, performing substantially better on interruptions than backchannels. The benchmark and metadata will be publicly released.
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The Surprising Effectiveness of Approximate Value Iteration in Self-Play
Minimal approximate value iteration self-play learns more accurate value functions than AlphaZero in Connect Four and Hex while cutting training and inference costs.
The paper trains a minimal self-play implementation of Approximate Value Iteration (AVI) without MCTS and uses ground-truth oracles for exact evaluation in Connect Four, 7x7 Hex, and synthetic games. AVI learns more accurate value functions than AlphaZero, and its one-step-lookahead greedy policies remain competitive with MCTS-based policies at substantially lower training and inference cost. Preliminary experiments on Othello and 9x9 Go show AVI trains stably on larger games, suggesting simpler approaches have become increasingly practical with modern deep-learning tools.
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The Future of Deepfakes and the Decline of Reality (With Hany Farid)
GetReal cofounder and PhotoDNA developer Hany Farid discusses deepfake detection and how AI-generated media is blurring perceptions of reality.
404 Media's podcast episode features Hany Farid, cofounder of deepfake detection company GetReal and developer of the PhotoDNA perceptual hashing algorithm, discussing deepfakes and AI-generated images. The conversation reflects on how the technology has evolved since 2017 and speculates on where detection and perceptions of reality are headed. The episode is part of 404 Media's interview series for subscribers.
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AWS Introduces Pizza Bot: An Open Source Inbox for Background AI Agents
AWS open-sourced Pizza Bot, a self-hosted inbox app for background AI agents with approval gating and multi-provider model support.
AWS released Pizza Bot under Apache 2.0 after earlier versions served over 2,000 Amazon employees for meeting prep, email drafting, and research. The app provides macOS, Windows, and Linux desktop builds plus browser and terminal clients talking to a Hono API server, with LangGraph/DeepAgents checkpoints preserving thread state and approval pauses. It supports Amazon Bedrock, Anthropic, Google Gemini, OpenAI, OpenRouter, and Ollama, exposes external tools via MCP servers, and lets skill authors gate actions behind approve/edit/reject flows.
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Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation
MovieGrid arranges long videos on spatial grids during post-training, generating 6.05x more shots than temporal packing with state-of-the-art cross-shot consistency.
MovieGrid is a multi-grid post-training paradigm that decomposes long videos into temporally ordered chunks arranged on a spatial grid for joint modeling, enabling cross-chunk information exchange. The authors build the Multi-Grid Long Video (MGLV) dataset from 1,000 long-form videos, producing 54K grid videos paired with character-aware story prompts. Under the same token budget, MovieGrid generates 6.05x more shots than Temporal Packing in a 1,616-frame video. It achieves state-of-the-art intra-shot consistency of 0.9131 versus 0.8086 for HoloCine and inter-shot consistency of 0.5914 versus 0.5384 for StoryMem.
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Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction
Researchers present Diffusion TV, a CRT-based installation where antenna manipulation lets audiences physically experience diffusion model denoising.
Diffusion TV is an interactive installation built around a modified CRT television where turning the antenna controls the clarity of AI-generated images and sounds, mirroring the denoising process of diffusion models. Three channels present AI-generated animals from the past, present, and future within a temporal and ecological narrative. The authors frame the work as an embodied, non-verbal alternative to explainable AI that highlights intermediate generative states rather than final outputs.
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