惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Spread Privacy
Spread Privacy
L
LangChain Blog
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Google DeepMind News
Google DeepMind News
有赞技术团队
有赞技术团队
博客园 - 【当耐特】
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
www.infosecurity-magazine.com
www.infosecurity-magazine.com
Engineering at Meta
Engineering at Meta
P
Privacy International News Feed
I
Intezer
NISL@THU
NISL@THU
Jina AI
Jina AI
G
GRAHAM CLULEY
C
CERT Recently Published Vulnerability Notes
S
Schneier on Security
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Cisco Talos Blog
Cisco Talos Blog
Scott Helme
Scott Helme
MyScale Blog
MyScale Blog
IT之家
IT之家
Security Latest
Security Latest
C
Cisco Blogs
Cyberwarzone
Cyberwarzone
aimingoo的专栏
aimingoo的专栏
V
Vulnerabilities – Threatpost
L
LINUX DO - 热门话题
Recorded Future
Recorded Future
The Hacker News
The Hacker News
C
CXSECURITY Database RSS Feed - CXSecurity.com
月光博客
月光博客
A
Arctic Wolf
云风的 BLOG
云风的 BLOG
N
Netflix TechBlog - Medium
K
Kaspersky official blog
S
Securelist
M
MIT News - Artificial intelligence
T
Threat Research - Cisco Blogs
P
Palo Alto Networks Blog
Simon Willison's Weblog
Simon Willison's Weblog
Know Your Adversary
Know Your Adversary
WordPress大学
WordPress大学
Project Zero
Project Zero
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
N
News and Events Feed by Topic
AWS News Blog
AWS News Blog
T
The Exploit Database - CXSecurity.com
T
The Blog of Author Tim Ferriss

MarkTechPost

A Coding Implementation of End-to-End Brain Decoding from MEG Signals Using NeuralSet and Deep Learning for Predicting Linguistic Features Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation A Coding Guide on LLM Post Training with TRL from Supervised Fine Tuning to DPO and GRPO Reasoning Qwen AI Releases Qwen-Scope: An Open-Source Sparse AutoEncoders (SAE) Suite That Turns LLM Internal Features into Practical Development Tools A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks Microsoft Research’s World-R1 Uses Flow-GRPO and 3D-Aware Rewards to Inject Geometric Consistency Into Wan 2.1 Without Architectural Changes A Coding Implementation on Pyright Type Checking Covering Generics, Protocols, Strict Mode, Type Narrowing, and Modern Python Typing IBM Releases Two Granite Speech 4.1 2B Models: Autoregressive ASR with Translation and Non-Autoregressive Editing for Fast Inference Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods Qwen Team Releases FlashQLA: a High-Performance Linear Attention Kernel Library That Achieves Up to 3× Speedup on NVIDIA Hopper GPUs Step by Step Guide to Build a Complete PII Detection and Redaction Pipeline with OpenAI Privacy Filter Meta FAIR Releases NeuralSet: A Python Package for Neuro-AI That Supports fMRI, M/EEG, Spikes, and HuggingFace Embeddings smol-audio: A Colab-Friendly Notebook Collection for Fine-Tuning Whisper, Parakeet, Voxtral, Granite Speech, and Audio Flamingo 3 A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified How to Build Traceable and Evaluated LLM Workflows Using Promptflow, Prompty, and OpenAI OpenAI Releases Privacy Filter: A 1.5B-Parameter Open-Source PII Redaction Model with 50M Active Parameters Top 10 Physical AI Models Powering Real-World Robots in 2026 How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control Meet Talkie-1930: A 13B Open-Weight LLM Trained on Pre-1931 English Text for Historical Reasoning and Generalization Research Build a Reinforcement Learning Powered Agent that Learns to Retrieve Relevant Long-Term Memories for Accurate LLM Question Answering OpenMOSS Releases MOSS-Audio: An Open-Source Foundation Model for Speech, Sound, Music, and Time-Aware Audio Reasoning Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo The LoRA Assumption That Breaks in Production How to Build a Fully Searchable AI Knowledge Base with OpenKB, OpenRouter, and Llama How to Build Smarter Multilingual Text Wrapping with BudouX Through Parsing, HTML Rendering, Model Introspection, and Toy Training Top 7 Benchmarks That Actually Matter for Agentic Reasoning in Large Language Models RAG Without Vectors: How PageIndex Retrieves by Reasoning A Coding Tutorial on Datashader on Rendering Massive Datasets with High-Performance Python Visual Analytics xAI Launches grok-voice-think-fast-1.0: Topping τ-voice Bench at 67.3%, Outperforming Gemini, GPT Realtime, and More A Coding Implementation on kvcached for Elastic KV Cache Memory, Bursty LLM Serving, and Multi-Model GPU Sharing Google DeepMind Introduces Vision Banana: An Instruction-Tuned Image Generator That Beats SAM 3 on Segmentation and Depth Anything V3 on Metric Depth Estimation Meet GitNexus: An Open-Source MCP-Native Knowledge Graph Engine That Gives Claude Code and Cursor Full Codebase Structural Awareness A Coding Implementation on Deepgram Python SDK for Transcription, Text-to-Speech, Async Audio Processing, and Text Intelligence A Coding Implementation on Microsoft’s OpenMementos with Trace Structure Analysis, Context Compression, and Fine-Tuning Data Preparation DeepSeek AI Releases DeepSeek-V4: Compressed Sparse Attention and Heavily Compressed Attention Enable One-Million-Token Contexts Google DeepMind Introduces Decoupled DiLoCo: An Asynchronous Training Architecture Achieving 88% Goodput Under High Hardware Failure Rates Mend Releases AI Security Governance Framework: Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model Mend.io Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model OpenAI Releases GPT-5.5, a Fully Retrained Agentic Model That Scores 82.7% on Terminal-Bench 2.0 and 84.9% on GDPval A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing Google Cloud AI Research Introduces ReasoningBank: A Memory Framework that Distills Reasoning Strategies from Agent Successes and Failures Xiaomi Releases MiMo-V2.5-Pro and MiMo-V2.5: Matching Frontier Model Benchmarks at Significantly Lower Token Cost How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows Next Leap to Harness Engineering: JiuwenClaw Pioneers ‘Coordination Engineering’ Photon Releases Spectrum: An Open-Source TypeScript Framework that Deploys AI Agents Directly to iMessage, WhatsApp, and Telegram OpenAI Open-Sources Euphony: A Browser-Based Visualization Tool for Harmony Chat Data and Codex Session Logs Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping Google Introduces Simula: A Reasoning-First Framework for Generating Controllable, Scalable Synthetic Datasets Across Specialized AI Domains A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence Moonshot AI Releases Kimi K2.6 with Long-Horizon Coding, Agent Swarm Scaling to 300 Sub-Agents and 4,000 Coordinated Steps A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning OpenAI Scales Trusted Access for Cyber Defense With GPT-5.4-Cyber: a Fine-Tuned Model Built for Verified Security Defenders Moonshot AI and Tsinghua Researchers Propose PrfaaS: A Cross-Datacenter KVCache Architecture that Rethinks How LLMs are Served at Scale Meet OpenMythos: An Open-Source PyTorch Reconstruction of Claude Mythos Where 770M Parameters Match a 1.3B Transformer How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost A Coding Implementation to Build an AI-Powered File Type Detection and Security Analysis Pipeline with Magika and OpenAI NVIDIA Releases Ising: the First Open Quantum AI Model Family for Hybrid Quantum-Classical Systems xAI Launches Standalone Grok Speech-to-Text and Text-to-Speech APIs, Targeting Enterprise Voice Developers A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG A Coding Guide for Property-Based Testing Using Hypothesis with Stateful, Differential, and Metamorphic Test Design Anthropic Releases Claude Opus 4.7: A Major Upgrade for Agentic Coding, High-Resolution Vision, and Long-Horizon Autonomous Tasks Google AI Releases Auto-Diagnose: An Large Language Model LLM-Based System to Diagnose Integration Test Failures at Scale A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows Top 19 AI Red Teaming Tools (2026): Secure Your ML Models A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities OpenAI Launches GPT-Rosalind: Its First Life Sciences AI Model Built to Accelerate Drug Discovery and Genomics Research Building Transformer-Based NQS for Frustrated Spin Systems with NetKet UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size How to Build a Universal Long-Term Memory Layer for AI Agents Using Mem0 and OpenAI A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration A Technical Deep Dive into the Essential Stages of Modern Large Language Model Training, Alignment, and Deployment Google AI Launches Gemini 3.1 Flash TTS: A New Benchmark in Expressive and Controllable AI Voice Google DeepMind Releases Gemini Robotics-ER 1.6: Bringing Enhanced Embodied Reasoning and Instrument Reading to Physical AI A Coding Implementation of Crawl4AI for Web Crawling, Markdown Generation, JavaScript Execution, and LLM-Based Structured Extraction TinyFish AI Releases Full Web Infrastructure Platform for AI Agents: Search, Fetch, Browser, and Agent Under One API Key NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model A Hands-On Coding Tutorial for Microsoft VibeVoice Covering Speaker-Aware ASR, Real-Time TTS, and Speech-to-Speech Pipelines Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model A Coding Implementation of MolmoAct for Depth-Aware Spatial Reasoning, Visual Trajectory Tracing, and Robotic Action Prediction MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution How Knowledge Distillation Compresses Ensemble Intelligence into a Single Deployable AI Model Alibaba’s Tongyi Lab Releases VimRAG: a Multimodal RAG Framework that Uses a Memory Graph to Navigate Massive Visual Contexts A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model Five AI Compute Architectures Every Engineer Should Know: CPUs, GPUs, TPUs, NPUs, and LPUs Compared An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context A Coding Guide to Build Advanced Document Intelligence Pipelines with Google LangExtract, OpenAI Models, Structured Extraction, and Interactive Visualization Google AI Research Introduces PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing A Comprehensive Implementation Guide to ModelScope for Model Search, Inference, Fine-Tuning, Evaluation, and Export
Google Launches ‘Skills’ in Chrome: Turning Reusable AI Prompts into One-Click Browser Workflows
Maxime Mommessin · 2026-04-15 · via MarkTechPost

Google just announced the release of Skills in Chrome, a new feature built into Gemini in Chrome that lets users save frequently used AI prompts as reusable, one-click workflows called Skills. The rollout begins April 14, 2026, targeting Mac, Windows, and ChromeOS users who have their Chrome language set to English-US.

If you’ve been paying attention to how AI is being woven into operating systems and browsers over the past year, Skills in Chrome represents something more interesting than just a productivity shortcut — it’s an early glimpse at how prompt management and browser-level AI agents could converge.

The Problem It Solves

Anyone who has used Gemini in Chrome for routine tasks knows the friction: every time you navigate to a new webpage and want to perform the same AI operation — say, checking nutritional information on a recipe page or comparing product specs across tabs — you have to re-enter the same prompt from scratch. This isn’t just tedious; it’s a signal that browser-native AI tools have been missing a stateful, reusable layer between the user and the underlying model.

Skills in Chrome addresses this directly. Until now, repeating an AI task — like asking for ingredient substitutions to make a recipe vegan — meant re-entering the same prompt as you visited different pages. Skills fix this by turning a prompt into a persistent, named workflow that can be invoked on demand.

How Skills Actually Work

The logic is straightforward but worth understanding precisely, especially if you’re thinking about this from a systems design angle.

When you write a prompt that you’ll want to use again, you can save it as a Skill directly from your chat history. The next time you need it, select your saved Skill in Gemini in Chrome by typing forward slash ( / ) or clicking the plus sign ( + ) button, and your Skill will run on the page you’re viewing, along with any other tabs you select. You can also edit saved Skills and create new ones at any time.

Think of this as a lightweight form of prompt templating at the browser level — similar to how engineers working with LLM APIs maintain libraries of system prompts or few-shot templates for recurring tasks, except Skills surfaces that concept for end users through a browser UI rather than code.

The multi-tab execution capability is particularly notable. Rather than running a prompt against a single page, a Skill can be dispatched across several open tabs simultaneously — enabling workflows like cross-referencing multiple product pages for a spec comparison in a single pass. For users who have built multi-document retrieval pipelines, this is a recognizable pattern: the browser context serves as the retrieval corpus, and the Skill is the query template applied across it.

Early Use Cases and the Skills Library

Early testers have used Skills in Chrome to create personalized and powerful workflows for a wide range of tasks — including quickly calculating protein macros for any recipe, generating side-by-side spec comparisons across multiple tabs, and scanning lengthy documents for important information.

Beyond user-created Skills, Google is also launching a library of ready-to-use Skills for common tasks and workflows. The library includes pre-written Skills covering tasks like breaking down the ingredients of a product you’re viewing online, or selecting the perfect gift from multiple options by cross-referencing your budget with the recipient’s interests. Users can browse this library, add any Skill to their saved collection, and customize it to better fit their needs by editing the Skill and updating the prompt.

This is essentially a curated prompt library inside the browser — a design pattern that developers working with tools like LangChain or prompt management systems will find familiar, now abstracted away from the API layer and delivered to general users without writing a single line of code.

Security and Privacy Architecture

For AI professionals evaluating how this feature fits into enterprise or security-sensitive environments, the safeguards Google has built in are worth noting carefully. Skills are built on Chrome’s foundation of security and privacy, and they utilize the same safeguards applied to prompts in Gemini in Chrome. A Skills prompt will ask for confirmation before taking certain actions, such as adding an event to your calendar or sending an email. Additionally, Skills benefit from Chrome’s layered protections, including automated red-teaming and auto-update capabilities.

The confirmation-gate design before high-consequence actions — calendar writes, email sends — is a deliberate choice that reflects the broader challenge in agentic AI systems: ensuring that automated, reusable workflows don’t fire irreversible side effects without explicit user intent. This is the same problem that AI agent frameworks like LangGraph and AutoGPT have grappled with at the code level; Google is solving it here at the UX layer.

Availability and Management

Starting today, Skills are rolling out to Gemini in Chrome on Mac, Windows, and ChromeOS, for users with their Chrome language set to English-US. Saved Skills are available on any signed-in Chrome desktop device and can be managed by typing forward slash ( / ) in Gemini in Chrome and then clicking the compass icon.


Check out the Technical details hereAlso, feel free to follow us on Twitter and don’t forget to join our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us

Maxime Mommessin

Max is an AI analyst at MarkTechPost, based in Silicon Valley, who actively shapes the future of technology. He teaches robotics at Brainvyne, combats spam with ComplyEmail, and leverages AI daily to translate complex tech advancements into clear, understandable insights