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unsloth/Qwen3.8-Flash-Next-GGUF — new model trending #21 on Hugging Face
Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.
Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.
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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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[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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Research acceleration: The view inside OpenAI
OpenAI describes how internal coding agents are reshaping its AI research, sharing early data on agent usage, experiment velocity, and task complexity.
OpenAI published an inside view of how coding agents are changing its research workflows, including early data on agent usage, experiment velocity, and task complexity. The post frames agent adoption as accelerating research at OpenAI. It is a lab-perspective post with internal metrics rather than a peer-reviewed technique or benchmark.
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ZDI-26-545: Flowise CSV_Agent customReadCSV Code Injection Remote Code Execution Vulnerability
ZDI discloses CVE-2026-69256, a CVSS 8.8 code injection in Flowise CSV_Agent customReadCSV allowing authenticated remote attackers to execute arbitrary code.
ZDI advisory ZDI-26-545 describes a code injection vulnerability in Flowise's CSV_Agent customReadCSV functionality, tracked as CVE-2026-69256 with a CVSS score of 8.8. It allows remote attackers to execute arbitrary code on affected Flowise installations. Authentication is required to exploit the vulnerability.
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Staying Ahead of Adversarial AI Through Agentic Source Code Review
Google Threat Intelligence details an agentic AI pipeline with human expert oversight to review source code and outpace AI-enabled attackers.
Google Threat Intelligence researchers argue that adversaries' misuse of AI raises the risk of data theft and extortion when proprietary source code is exposed. They describe a structured agentic source code review pipeline that combines AI models with skeptical validation steps and injected human domain expertise. The team reports a leap in efficacy in finding vulnerabilities before adversaries can exploit them.
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Feeling sad about AI
Simon Willison argues developers disheartened by AI coding agents should move past existential crisis and refocus on broader engineering problems.
In a comment on a Hacker News thread titled 'Feeling sad about AI', Simon Willison reflects on the discouragement engineers feel when AI coding agents complete in an hour work that previously took a week. He notes that translating an exact specification into decent code is no longer a unique skill. He counsels that engineers who accept this shift can apply their experience to the larger set of problems they still face.
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Security Vulnerability in a Voting System
A four-year-old vulnerability letting anyone recover ballot casting order was demonstrated with AI coding agents against Georgia's May 2026 primary data.
A previously disclosed vulnerability in ballot scanners used across 21 US states, including Georgia, allows recovery of the order in which ballots were cast. Nearly four years after the original disclosure, a researcher pointed AI coding agents at the vulnerability paper and used only public data — county early-voting lists and cast-vote record (CVR) files — to analyze voter behavior in Georgia's May 2026 primary. The demonstration required no access to voting machines, networks, source code, or non-public records.
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SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
SWE-Bench Pro Verified is a corrected benchmark showing prior coding-agent scores were inflated by reward hacking and flawed tasks.
Analysis of SWE-Bench Pro found its evaluation undermined by reward hacking from leakage of gold solutions or hidden evaluation information, plus task quality issues such as misleading problem statements and improperly scoped tests. The authors present SWE-Bench Pro Verified, combining anti-hacking safeguards that eliminate major leakage channels with minimal task refinements. Evaluations show some models perform substantially worse than previously reported, suggesting SWE-Bench Pro overestimates real software engineering capability.
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AI Agents Are Here. So Are the Threats.
Unit 42 demonstrates nine framework-agnostic attack scenarios against AI agents built with CrewAI and AutoGen, causing data leakage, credential theft and remote code execution.
Palo Alto Networks Unit 42 investigated how attackers can target agentic applications, implementing two functionally identical apps with the open-source CrewAI and AutoGen frameworks and executing the same attacks on both. Nine attack scenarios produce outcomes including information leakage, credential theft, tool exploitation and remote code execution. Findings show most vulnerabilities are framework-agnostic, arising from insecure design patterns, misconfigurations and unsafe tool integrations rather than flaws in the frameworks themselves. The team published defense strategies per scenario and open-sourced the source code and datasets on GitHub.
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Infostealers Target Claude, Cursor, Codex and Other AI Agents to Steal Credentials and Sensitive Data
Gen Digital researchers report infostealer families Amatera, Remus and CallbackBeaver now harvest Claude, Cursor and Codex agent data, including tokens and MCP configs.
Gen Digital researchers found commodity infostealers extending their collection rules to local AI coding agent data from Claude, Cursor, Codex, Cline, Continue and OpenCode on Windows and macOS. Amatera targets Cline and Continue, Remus targets Claude, Cursor and OpenCode, and CallbackBeaver added Claude and Cursor with more than 5,000 samples observed in 30 days; Djinn Stealer hits Claude, Codex, Gemini, Cline, OpenCode and Kilo on macOS. Stolen data includes access and refresh tokens, prompt histories, conversation databases and MCP configurations holding API keys, potentially exposing connected source-control, cloud and ticketing systems. Remus is assessed as a Lumma Stealer variant using EtherHiding C2 resolution via Ethereum smart contracts.
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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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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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ThreatsDay: GhostJacking AI Attacks, EtherHiding ClickFix, Cursor CLI Flaw + 17 More Stories
A weekly bulletin aggregating short security updates, including the City-Forum data-theft campaign, a ShipMonk breach, a Cursor CLI flaw, and GhostJacking AI attacks.
The Hacker News ThreatsDay Bulletin bundles roughly 20 short updates across cloud services, AI tools, malware, breaches, and scams. Highlights include the City-Forum campaign pulling data from unauthenticated guest access in Salesforce Experience Cloud and ServiceNow Service Portals since March 2025, and a ShipMonk breach exposing Trezor customer order data for orders in seven countries between May 10 and August 8, 2026. Other items cover a patched Cursor CLI flaw that let cloned repositories run commands before the workspace-trust prompt, Okta's analysis of the Work Panel vishing console used by actors like UNC6671, and GhostJacking AI agent hijacking via a patched Claude Desktop sandbox escape. Meta also launched an on-device WhatsApp Scam Alert machine learning model that keeps message content on the device.
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The VMs Powering Mobile Agents (Instinct, Claude Code)
A teardown reveals Claude Code runs in Firecracker microVMs with a Rust PID 1 and MITM'd egress, while Instinct rents E2B sandboxes with git-based memory.
The author inspects the virtual machines hosting cloud agents: Claude Code runs in a Firecracker microVM with a custom Rust init (process_api) as PID 1, a 324 MB Bun harness on a read-only disk, and 443-only MITM'd SSE egress to api.anthropic.com with host-rotated OAuth tokens and no inbound access. Instinct rents E2B sandbox-as-a-service Firecracker microVMs (Ubuntu 22.04, 2 vCPU, 1.9 GB RAM) where agent memory is a git repo of Markdown committed by the agent and pushed to S3 as a single bundle, using short-lived STS credentials. Both platforms rely on Firecracker, differing mainly in fleet operator and guest boot configuration.
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Week in review: ServiceNow pre-auth RCE exploited in the wild, Hugging Face breached
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[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign
xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.
The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.
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Meta is paying to peek at how you use their latest AI model
Meta offers roughly 95% discounts on Muse Spark token pricing for customers who share prompts and outputs to train future models.
Meta's contributor pricing tier for its Muse Spark agentic coding model cuts input token costs from $1.25 to $0.10 per million and output tokens from $4.25 to $0.20 per million in exchange for access to user prompts and outputs. The move reflects labs' difficulty obtaining training data for agentic workflows, following Meta's paused employee computer-usage tracking initiative. Analysts note the incentive could push enterprises to clarify which data is shareable, and it fits broader price competition against Anthropic's Fable and Mythos models and OpenAI's July price cuts.
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Coop – Isolated VM Environments for Running Claude Code and Codex
Trail of Bits releases Coop, running Claude Code and OpenAI Codex agents inside isolated virtual machines for safer agentic coding.
Coop, published on GitHub by security firm Trail of Bits, provides isolated VM environments for executing AI coding agents such as Claude Code and Codex. Isolation contains the filesystem and network side effects of autonomous agent actions, reducing risk from unsupervised tool use. The project drew 61 points and 16 comments on Hacker News.
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