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Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome
DeepMind's AlphaGenome Atlas precomputes impact predictions for ~9 billion human DNA variants in a 1-petabyte dataset; its AVI score beats CADD in benchmarks
Google DeepMind released the AlphaGenome Atlas, precomputing functional-effect predictions for roughly 9 billion human genome variants (about 27,000 prediction values per variant) in a one-petabyte dataset more than 30 times the size of the AlphaFold database. The accompanying AlphaGenome Variant Impact Score (AVI), a small neural network combining AlphaGenome, AlphaMissense and evolutionary conservation features (18 inputs versus CADD's 150+), outperformed existing tools on clinically classified variants, ranking causal variants in the top 50 candidates for 29.5% of solved GREGoR cases versus 12.5% for CADD. A GREGoR epilepsy case illustrates the impact: AVI elevated a previously unclear DNM1 splice variant that lab experiments confirmed as likely disease-causing. The atlas is available for noncommercial use via web portal, API and a Google Antigravity skill, with a commercial version planned through Google Cloud.
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Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
Google DeepMind releases Gemini 3.8 Flash and 3.8 Flash Cyber with improved reasoning, coding, and cybersecurity vulnerability detection and automated patching.
Google DeepMind introduced Gemini 3.8 Flash, its strongest reasoning and coding model, priced at $0.75 per million input and $3.75 per million output tokens, alongside Gemini 3.8 Flash Cyber, a cybersecurity-specialized variant offered to trusted defenders via the Fairwind Program. The Cyber variant shows frontier-level autonomous vulnerability discovery on CyberGym, exceeds 70% success on an internal benchmark spanning 20 programming languages, and scores 47.2% pass@1 on the CWE-Bench patching benchmark. Google reports it produced 2.6x more correct Chrome vulnerability patches than larger commercial models and found a critical foundational bug in under 2 hours.
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Google’s Gemini 3.8 Flash takes on bigger AI models at a lower cost
Google released Gemini 3.8 Flash with a security-focused Cyber variant that produces 2.6x more correct patches and found a critical vulnerability in under two hours.
Google launched Gemini 3.8 Flash for developers and a gated Gemini 3.8 Flash Cyber model reserved for vetted security teams through the new Fairwind program. The company says the model beats most larger frontier models on the DeepSWE v1.1 engineering benchmark at lower cost, and Chrome Security reports the Cyber variant produced 2.6 times more correct patches than the best commercial models while Google's Cloud Vulnerability Research team found a critical foundational vulnerability in under two hours. The models show significant prompt-injection robustness gains measured by Gray Swan and carry CBRN misuse safeguards, with cyber-offense restrictions on the standard version. Pricing matches Gemini 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.
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Google DeepMind Releases AlphaGenome Atlas With Precomputed Molecular Effect Predictions and AVI Scores for 9 Billion Human DNA Variants
Google DeepMind launched AlphaGenome Atlas, precomputing molecular effect predictions and AVI impact scores for ~9 billion human single-nucleotide variants in a 1-petabyte catalogue.
Google DeepMind released AlphaGenome Atlas, a 1-petabyte catalogue of precomputed molecular effect predictions for roughly 9 billion possible single-nucleotide variants in the human genome. It introduces the AlphaGenome Variant Impact (AVI) score, combining AlphaGenome regulatory predictions with AlphaMissense, plus per-variant feature attributions and over 2,500 recurrent DNA sequence motifs. DeepMind reports best-in-class AVI performance on variant pathogenicity and rare disease benchmarks. Early users at the Broad Institute, University of Exeter, and Stowers Institute demonstrated rare-disease variant reprioritization and 22% more non-coding associations across 54,000+ UK Biobank genomes.
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Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science
Stellar Colosseum, a many-agent harness for long-horizon math and TCS research, solves open problems and reaches 71% on TCS-Bench with Gemini models.
Stellar Colosseum is a model-agnostic harness that allocates inference across long-horizon research in mathematics and theoretical computer science, using strategy exploration, a readiness gate, section-level decomposition, and verifier feedback routing. Integrated into Google Antigravity's Teamwork framework as the Long Proof pattern, it obtains new results on open problems from FOCS and JMLR papers using Gemini 3.1 Pro. On TCS-Bench it achieves 71.0% accuracy with Gemini 3.1 Pro and Gemini 3.7 Flash, and a Codeforces evaluation with Gemini 3.1 Pro solves 218 of 222 problems.
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Google Open-Sources Mantis: A Modular Skills Toolkit That Lets Coding Agents Find, Reproduce and Patch Vulnerabilities
Google open-sourced Mantis, an Apache-2.0 modular skills toolkit that lets AI coding agents find, reproduce, and patch vulnerabilities with sandboxed verification.
Google released Mantis on GitHub under Apache 2.0 as a stack-agnostic set of slash-command skills that chain through the full vulnerability lifecycle: mining version history, building threat models, filtering findings, reproducing bugs in gVisor or network-disabled VMs, assembling exploit chains, patching, and scoring residual risk from 1 to 10. It runs with Gemini CLI, Antigravity CLI, the Google ADK, or comparable agent frameworks, and a supervisor skill (/mantis-meta-agent) can drive the whole loop. Google says the design targets the sub-7 percent true-positive rate of naive AI code scanning, and that its hierarchical summary tree cuts token overhead by over 85 percent. The toolkit is deployable for local and internal evaluation but not yet recommended for production.
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Google’s Atlas of the human genome could pave the way for new treatments
Google DeepMind launches AlphaGenome Atlas, a catalog of predicted molecular effects for roughly nine billion single-letter DNA changes across the human genome.
Google DeepMind unveiled AlphaGenome Atlas, a roughly 1-petabyte dataset predicting how each of about nine billion possible single-nucleotide variants affects molecular biology, including non-coding regions that regulate gene behavior. It builds on the AlphaGenome model released in 2025 and adds a Variant Impact Score (AVI) to help researchers rank variants. The catalog is available for noncommercial research via a web portal, the Antigravity platform, and the AlphaGenome interface, with commercial access on Google Cloud planned.
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Google’s Gemini Spark can now manage your Google Photos library
Google's Gemini Spark agent can now edit images, curate albums, and create calendar events in Google Photos for eligible US subscribers.
Google announced that its personal agent, Gemini Spark, can now manage Google Photos libraries, including editing images, curating albums, creating shared albums, converting concert flyer photos into calendar appointments, and running workflows. Google Photos lead Shimrit Ben-Yair said the capabilities roll out over the next few weeks to eligible Gemini AI Pro and Ultra subscribers in the US, in English, with no international timeline given. The move illustrates the industry's push to demonstrate consumer AI value amid criticism that labs have poorly communicated AI's benefits.
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