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GitHub Introduces Project HydraFusion: Runtime Multi-Model Orchestration That Builds a Workflow Per Coding Task in Copilot CLI
GitHub's Project HydraFusion research preview builds per-task multi-model workflows (Single, Cascade, Critique) in Copilot CLI, reporting +4.9 quality at 67% lower cost on TerminalBench 2.1.
Project HydraFusion is a research preview available on all GitHub Copilot plans inside Copilot CLI that treats model routing as workflow selection, choosing among Single, Cascade (draft plus quality gate), and Critique (cross-family reviewer) execution patterns per request. Against Claude Opus 5 baselines at medium reasoning, fixed HydraFusion policies cut estimated cost 67% while adding 4.9 quality points on TerminalBench 2.1, and cut cost 36% and 65% with slight quality dips on DeepSWE and CheckpointBench. Billing is per token at each underlying model's standard rate; there are no open weights or self-hosting options.
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Wiz Red Agent Finds Its Way Into Snowflake’s Internal Jira Through a Flaw in a GitHub Copilot–Assisted PR
Wiz's autonomous Red Agent found and exploited a GitHub Actions injection in a Copilot-assisted PR, reaching Snowflake's internal Jira without human help.
Wiz's Red Agent autonomously discovered a GitHub Actions injection flaw in a GitHub Copilot-assisted pull request, five days after the flaw went live. It exploited the flaw to validate access to sensitive data in Snowflake's internal Jira, bypassing detection by GitHub Advanced Security, and assessed the blast radius without human intervention. The exercise demonstrates agentic AI performing end-to-end offensive security operations against production systems.
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Nvidia buys Hugging Face, the GitHub of AI, for $13 billion
Nvidia agreed to acquire Hugging Face for $13 billion, pledging the 3-million-model open platform will remain open to its 18 million developers.
Nvidia has agreed to acquire Hugging Face, the model and dataset hosting platform, for $13 billion, pending regulatory review. The platform hosts about 3 million primarily open AI models, 500,000 datasets, and 1 million AI applications, used by more than 200,000 companies and 18 million developers. Nvidia pledged Hugging Face will remain an open platform and keep its brand; the startup previously declined a $500 million Nvidia investment to avoid a dominant investor. The deal supports Nvidia's open-weights strategy, alongside backing for Reflection AI, CoreWeave, and Nebius.
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How much of F-Droid is LLM generated?
A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.
A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.
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NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100
NVIDIA released BioNeMo Inference Runtime (BioIR), an open-source PyTorch-compatible library delivering 2.90x higher Boltz-2 protein-folding throughput on 8xH100 GPUs.
NVIDIA detailed BioIR, a Python library that accelerates Boltz-2, OpenFold2, and OpenFold3 structure-prediction inference on NVIDIA GPUs while preserving standard PyTorch workflows. On a matched benchmark of 1,000 human dimers on 8xH100 80GB GPUs, BioIR delivered 58.5K folded residues per GPU-hour versus 20.2K for a torch.compile baseline, a 2.90x throughput gain. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate complexes across 4,777 proteomes, with 1.81 million released as high-confidence predictions. Extrapolated to 1 million targets, estimated folding energy drops from 35 MWh to 11 MWh at 8-GPU TDP equivalents.
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New AI Workflow Identity Hijacking Attack Lets Hackers Exfiltrate Sensitive Data
Noma Labs disclosed Workflow Identity Hijacking, an AI automation flaw letting anonymous users trigger privileged data exfiltration without prompt injection or stolen credentials.
Noma Labs researcher Sasi Levi described Workflow Identity Hijacking, where AI workflows process untrusted input from low-privileged or anonymous users but execute downstream actions with the workflow creator's elevated permissions, turning the pipeline into an unauthenticated proxy. Unlike prompt injection, the model is not tricked; the flaw is a missing authorization check between the requester and the privileged actions. Noma Labs also disclosed and helped fix a similar issue in Google Workflows, and linked the problem to the earlier GitLost research on GitHub Agentic Workflows. Recommended mitigations include per-user identity propagation, least-privilege service accounts and authorization checks before every downstream action.
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Claude Mythos Executes End-to-End Intrusion From Initial Access to Full Domain Compromise
Anthropic's Claude Mythos Preview, its most cyber-capable model, autonomously completed an end-to-end enterprise intrusion simulation in restricted-access testing.
Anthropic's April 2026 system card describes Claude Mythos Preview as the first model to solve a private cyber range end to end and finish a corporate-network attack simulation an expert would need 10+ hours to complete. It scored 100% pass@1 on a 35-challenge Cybench subset and 0.83 on CyberGym versus 0.67 for Claude Opus 4.6. The model is limited to vetted partners under Project Glasswing; it failed an OT cyber range and could not find novel exploits in a fully patched sandbox.
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PRs NOT Welcome: How Top AI Open Source Projects Are Managing Thousands of Contributors
Top AI open source projects like Vercel, Astro, Flue, and tldraw are restricting external PRs and using agent-based software factories to triage, fix, and review contributions.
Several prominent AI-native open source projects are closing or limiting external pull requests, largely because submissions are often AI-generated. Vercel built a multi-agent software factory for its AI SDK (over 20 million weekly npm downloads) that now authors 25-35% of merged PRs and closes 70-80% of issues. Astro adopted similar auto-triage automation, Fred Schott created the Flue framework with automatic PR-to-issue conversion, and tldraw automatically closes external PRs.
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Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models
Nums AI released Causilo, an Apache-2.0 tabular foundation model achieving the highest single-model Elo (1794) on TabArena for classification and regression.
Nums AI released Causilo 1.0.1, a pretrained in-context learning tabular foundation model for classification (up to 10 classes) and regression, with Apache-2.0 code and research-only weights on Hugging Face. It achieved the highest single-model TabArena Elo of 1792.9 overall, beating TabFM (1764.4) and EXAONE Tabular (1758.8), and a maintainer re-run placed it 3rd of 88 including system entries. It also ranked first by CRPS, R² and RMSE on ScoringBench across 101 datasets, and was fastest on fit and predict versus TabICLv2 and TabPFN-3 on an H100 GPU at 8.15 GiB memory. The model was pretrained only on synthetic data, uses cross-attention to keep cost linear in feature count, and version 1.0.1 adds quantile outputs via 999 native quantiles.
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Google Releases Gemini 3.8 Live and 3.8 Live Extended Thinking for Production Grade Voice Agents
Google launches Gemini 3.8 Live and Extended Thinking speech-to-speech models for production voice agents, topping speech-to-speech benchmarks.
Google released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, native speech-to-speech models for real-time voice agents, available hosted via the Gemini Live API and AI Studio. Extended Thinking ranks #1 on Artificial Analysis' Speech-to-Speech Quality Index with 82.6, scores 68.6% on τ-Voice, 35.1% on Sierra's τ-Voice-banking, and 97.7% on Big Bench Audio. The models support asynchronous function calling, near-real-time visual context, alphanumeric precision, and 97 languages, priced at $0.005/min audio input and $0.018/min audio output. All generated audio carries Google DeepMind's imperceptible SynthID watermark.
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Agent-net Open Sources Webagent: A Go Harness That Turns Any Website into a Guarded AI Agent
Agent-net open-sourced Webagent, a Go harness turning websites into AI agents with code-enforced guardrails wrapping every tool call.
Agent-net released Webagent under Apache 2.0, a Go framework where a business fills in a declarative JSON spec, picks one provider for each of nine pluggable slots (retrieval, memory, guardrail, channel, secrets, presenter, model, action, observability), and runs webagent serve. Every tool the agent holds is wrapped by action.Guard so the chosen guardrail executes before any action runs and the model cannot bypass it. Live capabilities include OpenRouter/gateway LLM brains, MCP tools over Streamable HTTP, and Slack, WhatsApp, and HTTP channels; browser actions, OAuth-gated MCP, OTel export, and AgentNet identity/billing are not yet built. The project is v0 with a deferred-hardening list and cites arXiv 2511.19477 on an 85% versus 50% task-success gap attributed to architecture over model capability.
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