#computer-science

Quanta Magazine quantamagazine.org biology computer-science longform math mathematics physics quanta science 2026-06-11 13:37

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In the first episode of the new season of ‘The Joy of Why,’ Nobel Laureate Jennifer Doudna discusses how she discovered CRISPR’s genome-editing power, the breakthroughs and hurdles during its explosive growth, and what lies ahead for this groundbreaking technology. The post...

One of the most surprising and remarkable discoveries in recent scientific history has been CRISPR. Short for Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR is a form of immune system that evolved in bacteria more than a billion years ago to defend against persistent viral threats. Under attack, bacteria can snip a small fragment of a virus’s DNA, store it in the CRISPR region…

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- Sen. Sanders wants Americans to have a say — and stake — in the future of AI NPR - Politics
- A vocabulary for the future: poetry Psyche
- US-Iran deal leaves the future of Lebanon uncertain – and subject to Israel playing the spoiler The Conversation US
- Stanford CS153 Frontier Systems | Scale, AGI, and the Future of Everything stanfordonline
- My thoughts on the future of Go Package main
- The Future of Home Computing: Radical Changes Ahead? ExplainingComputers
- Microsoft’s CEO Just Explained the Future of Development and Business Stefan Mischook
- AI Tutors: The Future of Learning & Engineering Open Data Science
- Cisco's Vision for AI-Native Operations: Cloud Control, AI Canvas, and the Future of IT #ai #data The Ravit Show
- Cisco Just Showed the Future of Networking NetworkChuck
- Unlocking the Future of Automation with Modern DevOps | Tech Talk Fredrik Christenson
arXiv - Computer Science: Artificial Intelligence arxiv.org ai arxiv computer-science preprint research science 2026-06-18 04:00

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arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous...

arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.
- ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research arXiv - cs.CL
- Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems arXiv - cs.CL
- ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research arXiv - cs.AI
arXiv - cs.CL arxiv.org ai arxiv computer-science preprint repository 2026-06-18 04:00

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arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous...

arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.
- ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research arXiv - Computer Science: Artificial Intelligence
- Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems arXiv - cs.CL
- ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research arXiv - cs.AI
arXiv - cs.AI arxiv.org ai arxiv computer-science preprint repository 2026-06-18 04:00

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arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous...

arXiv:2606.07591v3 Announce Type: replace-cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.
- ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research arXiv - Computer Science: Artificial Intelligence
- ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research arXiv - cs.CL
- Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems arXiv - cs.CL
Siraj Raval youtube.com artificial-intelligence-and-machine-learning channel computer-science video youtube 2026-06-02 09:52

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I gave an AI my voice, asked it to write a country breakup song about dumping a developer, and shipped it in 8 minutes. Here's how, plus the ML that makes it possible in 2026. 🎵 Listen to "Sound of Losing You" on Spotify: https://open.spotify.com/album/3DQZtFYzuHWxUJyLJ5tFlX...

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I gave an AI my voice, asked it to write a country breakup song about dumping a developer, and shipped it in 8 minutes. Here's how, plus the ML that makes it possible in 2026. 🎵 Listen to "Sound of Losing You" on Spotify: https://open.spotify.com/album/3DQZtFYzuHWxUJyLJ5tFlX 🎤 Try Fish Audio S2 Pro free: https://fish.audio/?fpr=siraj80 - code SIRAJ20 for 20% off 💻 Fish Audio is open source: https://github.com/fishaudio/fish-speech Chapters: 00:00 The AI Country Song 00:21 A Brief History of AI Voice 01:51 Fish Audio S2 Pro + Emotion Control 02:44 Cloning My Voice (Tutorial) 04:23 Adding Emotion, Line by Line 05:17 Inside the Model: How It Works 07:15 Writing the Country Breakup Song 07:58 Stitching It in CapCut 08:48 Uploading to Spotify 09:01 Make Your Own + What's Next This video is sponsored by Fish Audio. The voice and song are AI-generated; all opinions are my own. 📬 CONTACT Business: hello@sirajraval.com 📲 FOLLOW X: https://x.com/sirajraval Instagram: https://instagram.com/sirajraval LinkedIn: https://linkedin.com/in/sirajraval 🔔 Subscribe for more AI videos! #AI #VoiceAI #FishAudio #AImusic #AIcountrysong
- Allbirds Used to Make Viral Wool Sneakers. Now It’s an AI Company Called ‘Smartbird.’ Entrepreneur.com
- DeepInflation: an AI agent for research and model discovery of inflation arXiv - hep-th
- I Hacked an AI Customer Service Agent in 8 Seconds Siraj Raval
- I Quit Chrome for an AI Browser. It Actually Worked. Siraj Raval
- Building an AI Interviewer From Scratch in 3 Hours Harkirat Singh
- How an AI Agent Deleted PocketOS Production in 9 Seconds Kent C. Dodds
- How to build an AI Agent and MCP Server (step-by-step) Google Cloud Tech
- How to Add an AI Chatbot to Your Client's Website Code with Ania Kubów #JavaScriptGames
- I Built an AI Agent That Fixes My Resume Codevolution
- What Is an AI Agent? LLMs, Tools, and a Loop Real Python
- How to Become an AI Engineer in 2026 Tech With Tim
- They Killed an AI Model Overnight (Fable 5 & Mythos 5) Traversy Media
Two Minute Papers youtube.com ai-research channel computer-science video youtube 2026-06-06 06:20

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Check the pinned comment for the link to the full interview. Could AI agents eventually become the "Games Master" driving your gaming storylines? We explore the concept of AI assisting players or creating dynamic, non-scripted narratives. Discover how AI is currently being...

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Check the pinned comment for the link to the full interview. Could AI agents eventually become the "Games Master" driving your gaming storylines? We explore the concept of AI assisting players or creating dynamic, non-scripted narratives. Discover how AI is currently being tested inside immersive game environments to change how we play. 🧠 Hashtags: #aiingames #gaming #ai #gamedev #futuretech
- 🚀 Hermes Agent Just Released a Desktop App And It Changes Everything About Using AI Agents DEV Community
- Announcing ADK for Kotlin and ADK for Android 0.1.0: Building AI Agents on Android and Beyond Google Developers Blog
- Orphaned AI Agents: How to Find Hidden Access Risks Inside Your Network The Hacker News
- Announcing ADK for Kotlin and ADK for Android 0.1.0: Building AI Agents on Android and Beyond Google Developers Blog
- Research Paper: AI Agents and the ReAct Pattern Gaurav Sen
- Canceling Subscriptions, Building Local AI Agents Tina Huang
- AI Agents Fail Tina Huang
- How Modern AI Agents Work Under the Hood Harkirat Singh
- If You're Building AI Agents in 2026, Watch This ft. @oracledevs Harkirat Singh
- connecting all scientific knowledge for ai agents??? Yacine Mahdid
- 3 patterns to build long-running AI agents Google Cloud Tech
- Building long-running AI agents with ADK Google Cloud Tech
- How to build reliable software with AI agents Google Cloud Tech
- Voice for AI Agents and Applications DeepLearningAI
- Securing AI Agents: Risk, Governance, Recovery, and Anthropic’s Mythos with Arvind Nithrakashyap Open Data Science
- Generative UI: When AI Agents Design the Interface with Maxime Beauchemin and Evan Rusackas Open Data Science
- AI-Enabled Workforce: AI Agents, Productivity, and Enterprise Transformation The Ravit Show
- Why the Way You're Giving AI Agents Data Access Is Probably Wrong The Ravit Show
- Will AI Agents Replace Jobs in 2026? The Invisible Shift in Work Intellipaat
- The 3 Types of AI Agents Every Developer Should Know Real Python
- Build 3 PRODUCTION AI Agents in Python - Full Course (Agentspan) Tech With Tim
- This is why my AI Agents never guess JavaScript Mastery
Two Minute Papers youtube.com ai-research channel computer-science video youtube 2026-06-03 17:00

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- What to study in the AI age - from big tech bosses BBC News - Technology
- The AI Hate Progression Hacker News - Front Page
- The AI Industry is Spending $10 Million Against One Guy? Robert Miles
- AI Engineering Podcast Episode #1:Beyond the AI hype Gaurav Sen
- Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Building AI Factories stanfordonline
- Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, Applied AI stanfordonline
- The Architect's Guide to the AI Era • Luca Mezzalira & Teena Idnani • GOTO 2026 GOTO Conferences
- WHY THE AI "INTERVIEW" TAKEOVER IS A JOKE! Joshua Fluke
- The AI Scam Your Family Isn’t Ready For Cassie Kozyrkov
- The AI Advantage Isn't Better Prompts—It's Better Data Rasa
- Crushed by the AI Elephant by Rehgan Bleile, AlignAI | Women in Analytics (WIA) Open Data Science
- The AI bubble is bursting Level Up Tuts
- The AI Skill I use to prevent refactors JavaScript Mastery
Siraj Raval youtube.com artificial-intelligence-and-machine-learning channel computer-science video youtube 2026-06-09 09:03

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Learn AI Security with Practical Labs on TryHackMe: https://tryhackme.com/SIRAJ25 - Use coupon SIRAJ25 to get 25% OFF on Annual Subscription! I built a production-style AI customer-service agent in 15 minutes, then broke it 5 different ways and patched 4 of them. This is the...

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Learn AI Security with Practical Labs on TryHackMe: https://tryhackme.com/SIRAJ25 - Use coupon SIRAJ25 to get 25% OFF on Annual Subscription! I built a production-style AI customer-service agent in 15 minutes, then broke it 5 different ways and patched 4 of them. This is the OWASP LLM Top 10 in practice: direct prompt injection, indirect/RAG injection, system-prompt extraction, tool abuse, and a roleplay jailbreak; live, on a real AI agent. If you ship anything with an LLM in 2026, this video shows exactly how each attack works and how to defend against it. ⏱ Chapters 0:00 An AI agent leaked every customer email in 8 seconds 0:33 2026: everyone's shipping AI agents (OWASP LLM Top 10) 1:14 I built a customer-service agent in 15 minutes 2:02 It works… now I break it 2:17 Attack 1 — Direct prompt injection 3:08 Why a better system prompt won't save you 4:11 Attack 2 — Indirect / RAG injection 5:07 Attack 3 — System-prompt extraction 5:53 Attack 4 — Tricking the agent's tools ($5,000 refund) 6:44 Attack 5 — The roleplay jailbreak 8:14 Patching it: 4 of 5 attacks blocked 9:10 If you ship AI in 2026, learn this 9:42 Your challenge — comment your best attack 🧠 Covered: prompt injection, indirect/RAG injection, system-prompt extraction, tool-call authorization, jailbreaks, input sanitization, human-in-the-loop approval. 👉 Subscribe for the build-and-break series — I attack real production AI patterns every week. 💬 Drop your most creative prompt-injection attack in the comments — best ones get featured. 📬 Business inquiries: hello@sirajraval.com 📲 Follow X: https://x.com/sirajraval Instagram: https://instagram.com/sirajraval LinkedIn: https://linkedin.com/in/sirajraval #AIsecurity #PromptInjection #LLM #AIagents #cybersecurity
- Allbirds Used to Make Viral Wool Sneakers. Now It’s an AI Company Called ‘Smartbird.’ Entrepreneur.com
- DeepInflation: an AI agent for research and model discovery of inflation arXiv - hep-th
- I Built an AI That Wrote Me a Country Breakup Song Siraj Raval
- I Quit Chrome for an AI Browser. It Actually Worked. Siraj Raval
- Building an AI Interviewer From Scratch in 3 Hours Harkirat Singh
- How an AI Agent Deleted PocketOS Production in 9 Seconds Kent C. Dodds
- How to build an AI Agent and MCP Server (step-by-step) Google Cloud Tech
- How to Add an AI Chatbot to Your Client's Website Code with Ania Kubów #JavaScriptGames
- I Built an AI Agent That Fixes My Resume Codevolution
- What Is an AI Agent? LLMs, Tools, and a Loop Real Python
- How to Become an AI Engineer in 2026 Tech With Tim
- They Killed an AI Model Overnight (Fable 5 & Mythos 5) Traversy Media
Two Minute Papers youtube.com ai-research channel computer-science video youtube 2026-06-03 13:49

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Anthropic's Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B...

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Anthropic's Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu
- Claude 4.8 - Three Things You Need To See Tyler Moore
- Claude Opus 4.8 for UI/UX Design. 5 Projects. Is it good? DesignCourse
- I Let Claude Opus 4.8 and InsForge Build an Entire Startup. Here's What Happened. Hitesh Choudhary
- Claude Opus 4.8 Released - What's New? Traversy Media
freeCodeCamp.org youtube.com channel computer-science programming tutorial video youtube 2026-06-08 12:17

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Back in 2004, email seemed like a solved problem. But an engineer at Google had different ideas...here, Ania tells you about how Gmail was created.

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Back in 2004, email seemed like a solved problem. But an engineer at Google had different ideas...here, Ania tells you about how Gmail was created.
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- Why your best ideas usually start as bad ones | Think Like A Musician TED-Ed
- The 2000 Year Old Method to Read Like a Genius Python Programmer
- Summarise Anything Like a Pro in ChatGPT (Master the Perfect ChatGPT Prompts) Simon Sez IT
- How To Use AI Skills Like A Senior Developer Web Dev Simplified
- How To Write Permissions Like A Senior Dev Web Dev Simplified
arXiv - cs.CL arxiv.org ai arxiv computer-science preprint repository 2026-06-18 04:00

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arXiv:2605.29676v2 Announce Type: replace-cross Abstract: Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for...

arXiv:2605.29676v2 Announce Type: replace-cross Abstract: Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for application-to-application interchange rather than token efficiency, so its structural elements impose substantial token overhead. Recent work proposes token-optimized alternatives such as TOON (Token-Oriented Object Notation) and TRON (Token Reduced Object Notation) as more compact replacements, but these formats have been evaluated only on isolated comprehension or generation tasks. Whether their token reductions hold inside end-to-end agentic loops therefore remains an open question. We evaluate TOON and TRON on four agentic benchmarks (BFCL, MCPToolBenchPP, MCP-Universe, StableToolBench) and five open-weight LLMs, decoupling input compression from output compression to measure comprehension and generation independently. TRON reduces tokens by up to 27% with accuracy within 14pp of the JSON baseline. TOON achieves up to 18% reduction at a similar 9pp accuracy cost, but additionally cascades on multi-turn parsing failures and collapses parallel tool-call output for most models. The code is available at: https://github.com/lkutschka/notation-matters
- Where AI Is Heading In 2026- Generative AI, Agentic AI, LLM Gateways,Guardrails,Evals, LLM Caching Krish Naik
- 3.0 Agentic AI Bootcamp Announcement Krish Naik
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- Complete Agentic AI Course In 10 Hours- Langchain, Langgraph, RAG,Vectorless RAG, Guardrails,Evals Krish Naik
- Thriving in the Agentic AI Era: A Guide for Knowledge Workers and Organizations Mathematical Foundations of ML with Jon Krohn
- From Dashboards to Decisions: Zoho Analytics on the Agentic AI Revolution The Ravit Show
- Agentic AI Live Course QnA Telusko
freeCodeCamp.org youtube.com channel computer-science programming tutorial video youtube 2026-06-15 11:47

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Stop manually debugging your code and learn how to leverage AI to automatically detect, analyze, and resolve CI/CD pipeline failures. In this course, you will bridge the gap between DevOps and automation by integrating N8N, OpenAI, and GitHub Actions to create a fully...

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Stop manually debugging your code and learn how to leverage AI to automatically detect, analyze, and resolve CI/CD pipeline failures. In this course, you will bridge the gap between DevOps and automation by integrating N8N, OpenAI, and GitHub Actions to create a fully autonomous, self-healing workflow. ✏️ Course from @TheTechzeen LinkedIn: https://www.linkedin.com/in/farzeen-ali-533479204/ Website: https://www.thetechzeen.com/ ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Contents ⭐️ - 00:00 Introduction to Self-Healing CI/CD Pipelines - 02:00 Recommended Tech Stack Overview - 02:29 Workflow Logic and Architecture - 03:02 Setting Up the Local Environment (Git & GitHub) - 05:09 Building the Node.js and Express Application - 08:17 Creating the Smoke Test Script - 10:22 Implementing the GitHub Actions Pipeline - 19:51 Setting Up the N8N Automation Account - 21:56 Repository Setup and Security Secrets - 27:31 Testing Failure Detection and AI Analysis - 30:48 Fetching Logs and Analyzing Changes - 38:54 Generating AI Fixes with OpenAI - 46:51 Creating a Git Branch and Pushing Fixes - 54:32 Automating Pull Requests - 56:51 Email Notifications for Team Updates - 58:28 Finalizing Production Deployment 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
- We Built a Multi-Player Audio App With AI || Intro to Audiotool Nexus The Audio Programmer
- Should you learn ML before starting with AI? Gaurav Sen
- AI Hype vs. Reality: Finding Balance with AI #shorts How to Get an Analytics Job
- How I'd Become a Freelance Developer Today (With AI) Stefan Mischook
- How to build reliable software with AI agents Google Cloud Tech
- The EASIEST Way to Make a Website with AI (2026) Tyler Moore
- How to Manage Employee Payroll in QuickBooks Online (with AI Agent Update) Simon Sez IT
- The BIGGEST Problem With AI Codedamn
- How I Edit Videos with AI - Fully Automatic (Free) Website Learners
- Explore Playwright to code with AI #tanaypratap #shorts Tanay Pratap
- The One Mistake Everyone Makes with AI JavaScript Mastery
- How Senior Engineers Actually Build with AI in 2026 | Build a Full Stack Job Applications Platform JavaScript Mastery
Siraj Raval youtube.com artificial-intelligence-and-machine-learning channel computer-science video youtube 2026-05-29 10:33

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I switched from Chrome to the new Norton Neo, an AI-native browser, for a full week, and I didn't switch back. Here's everything that actually changed about how I work. Try Neo (free, ~30 sec):...

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I switched from Chrome to the new Norton Neo, an AI-native browser, for a full week, and I didn't switch back. Here's everything that actually changed about how I work. Try Neo (free, ~30 sec): https://neobrowser.ai/?utm_source=youtube&utm_medium=influencer&utm_campaign=SirajRaval2 This video is sponsored by Norton. The week-long test and all opinions are my own. Most browsers were built for 2010. We don't just browse the web anymore, we work in it, and Neo is the first browser built around that: cross-tab research with citations, AI-aware tab groups, built-in VPN, anti-fingerprinting, phishing alerts, and an AI sidebar that actually has context on what you're doing. I cover what works, where it falls short, and why I'm staying on it. Chapters: 00:00 14 years on Chrome 00:19 Why my browser was working against me 00:56 What the new Norton Neo is 01:39 Cross-tab research with citations 02:18 Tab management that actually thinks 02:53 Privacy: VPN, anti-fingerprinting, phishing 04:31 The AI sidebar's magic modes 05:27 Where Neo falls short (honest take) 06:23 Why I'm not switching back 06:44 Neo on mobile (iOS + Android) 07:00 Try it yourself Set Neo as your default browser and your free VPN allotment doubles. Works on Mac, Windows, Linux, iOS, and Android. Try Neo: https://neobrowser.ai/?utm_source=youtube&utm_medium=influencer&utm_campaign=SirajRaval2 #AI #Browser #Productivity #Norton
- Allbirds Used to Make Viral Wool Sneakers. Now It’s an AI Company Called ‘Smartbird.’ Entrepreneur.com
- DeepInflation: an AI agent for research and model discovery of inflation arXiv - hep-th
- I Hacked an AI Customer Service Agent in 8 Seconds Siraj Raval
- I Built an AI That Wrote Me a Country Breakup Song Siraj Raval
- Building an AI Interviewer From Scratch in 3 Hours Harkirat Singh
- How an AI Agent Deleted PocketOS Production in 9 Seconds Kent C. Dodds
- How to build an AI Agent and MCP Server (step-by-step) Google Cloud Tech
- How to Add an AI Chatbot to Your Client's Website Code with Ania Kubów #JavaScriptGames
- I Built an AI Agent That Fixes My Resume Codevolution
- What Is an AI Agent? LLMs, Tools, and a Loop Real Python
- How to Become an AI Engineer in 2026 Tech With Tim
- They Killed an AI Model Overnight (Fable 5 & Mythos 5) Traversy Media
Computerphile youtube.com channel computer-science computer-sciences informational video youtube 2026-06-16 14:30

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With the UK planning to follow Australia in a ban on social media for under 16s, we ask how it might work? Dr Mike Pound is an Associate Professor at the University of Nottingham. Computerphile is supported by Jane Street. Learn more about them (and exciting career...

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With the UK planning to follow Australia in a ban on social media for under 16s, we ask how it might work? Dr Mike Pound is an Associate Professor at the University of Nottingham. Computerphile is supported by Jane Street. Learn more about them (and exciting career opportunities) at: https://jane-st.co/computerphile This video was filmed and edited by Sean Riley. Computerphile is a sister project to Brady Haran's Numberphile. More at https://www.bradyharanblog.com
- Should the US impose a teen social media ban like the UK? BBC News - World
- Five big questions about the UK's under-16s social media ban BBC News - Technology
- When will social media ban start, and which apps will be affected? BBC News - Technology
- Social media ban - bold and blunt, but no silver bullet BBC News - Technology
- Under-16s will be banned from social media from early 2027 BBC News - Technology
- How to Create Viral Social Media Posts in Minutes Using Claude AI 🚀 Awais Mirza
arXiv - cs.CL arxiv.org ai arxiv computer-science preprint repository 2026-06-18 04:00

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arXiv:2606.18142v2 Announce Type: replace-cross Abstract: AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer...

arXiv:2606.18142v2 Announce Type: replace-cross Abstract: AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer prompts, leaving open whether the welfare reasoning surfaced in those responses transfers to agentic deployment where the model must take actions with tools. We introduce TAC (Travel Agent Compassion), the first agentic benchmark measuring whether AI agents avoid options involving animal exploitation when acting on behalf of users. TAC presents an AI agent with twelve hand-authored travel booking scenarios across six categories of animal exploitation, augmented to forty-eight samples to control for price, rating, and position confounds. We evaluate seven frontier models from four labs. Every model scores below the chance level of sixty-four percent, with the best performer (Claude Opus 4.7) at fifty-three percent. A single welfare-aware sentence in the system prompt yields gains of forty-seven to sixty-three percentage points in Claude and GPT-5.5, twenty-six points in GPT-5.2, and under twelve points in DeepSeek and Gemini. An auxiliary Inspect Scout audit of 288 base-condition transcripts from the top two performers, using Gemini 2.5 Flash Lite as judge, flags zero transcripts for evaluation awareness, suggesting the below-chance rates do not stem from the models recognising the evaluation. We discuss implications for category-level variation across cultural domains, the limits of text-response welfare benchmarks, and the EU General-Purpose AI Code of Practice systemic risk framework.
- AI Dev 26 x SF | Manos Koukoumidis & Stefan Webb: VibeML: Build your AI model in hours, not months DeepLearningAI
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- Secure your AI Traffic with AgentGateway! That DevOps Guy
- Secure your AI traffic using LLM gateways! That DevOps Guy
Two Minute Papers youtube.com ai-research channel computer-science video youtube 2026-06-01 15:41

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Thank you to Google for the invite! 🙏 ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles...

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Thank you to Google for the invite! 🙏 ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu Chapters: 00:00 Intro 02:07 Are We Running Out of AI Data? 06:22 The 90% Shift: Why Inference is Taking Over 09:34 The End of the Pre-Training and Post-Training Split 12:02 What Happens After a 1,000,000x Compute Leap? 15:03 How Distillation is Supercharging Open Models 16:17 The Quest for a "Lifetime AI" 17:25 Multi-Agent Workflows 18:40 AI Generating Operating Systems (and Running Doom) 20:15 Solving The Attention Problem 22:13 Data Center Disasters: Supernovas and Cosmic Rays 24:45 The Lightning Round: Jeff Dean Chuck Norris Jokes 25:40 The One Thing Jeff Dean Got Wrong (Healthcare AI) 26:50 The Ultimate Developer Debate: Vim vs. Emacs
- Bernie Sanders’ New AI Bill Would Pay Americans $1,000 a Year Gizmodo
- Scientists discover an earthquake gate as California faults reach their highest stress levels in 1,000 years ScienceDaily
- How I built a $1,000/mo SaaS in 100 Days Kevin Naughton Jr.
- Guess The Youtuber = Win $1,000 Link
- 50 YouTube Legends Fight For $1,000,000 Link
freeCodeCamp.org youtube.com channel computer-science programming tutorial video youtube 2026-06-12 12:18

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If you're working on a college or job application, think about this: what makes you uniquely you? Rachel discusses this with Quincy on the freeCodeCamp podcast.

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If you're working on a college or job application, think about this: what makes you uniquely you? Rachel discusses this with Quincy on the freeCodeCamp podcast.
- What Makes a Person: The Seven Layers of Selfhood in Literature and Life The Marginalian (formerly Brain Pickings)
- What Makes a Person: The Seven Layers of Selfhood in Literature and Life The Marginalian
- What Makes Good Synthetic Pretraining Data with Joël Niklaus from Hugginface Yacine Mahdid
- What Makes Good Synthetic Pretraining Data with Joël Niklaus from Hugginface Yacine Mahdid
freeCodeCamp.org youtube.com channel computer-science programming tutorial video youtube 2026-06-18 11:52

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Here, Chris Coyier talks about why apps designed by AI look like apps designed by AI - and why skilled designers still matter.

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Here, Chris Coyier talks about why apps designed by AI look like apps designed by AI - and why skilled designers still matter.
- NVIDIA's New Free AI - A Gift To Humanity Two Minute Papers
- Will AI End the Open Internet? [Wading Through AI - Episode 6] Molly Rocket
- Will AI Make Me Worse? [Wading Through AI - Episode 5] Molly Rocket
- Never paste this into AI - and what happens when you do Code with Ania Kubów #JavaScriptGames
- How I Edit Videos with AI - Fully Automatic (Free) Website Learners
Two Minute Papers youtube.com ai-research channel computer-science video youtube 2026-06-14 15:27

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Nemotron 3 Ultra paper is available here: https://research.nvidia.com/labs/nemotron/Nemotron-3-Ultra/ Free Rendering course and source code:...

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Nemotron 3 Ultra paper is available here: https://research.nvidia.com/labs/nemotron/Nemotron-3-Ultra/ Free Rendering course and source code: https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi Thumbnail design: https://felicia.hu #nvidia
- Why apps designed by AI look like apps designed by AI - and why skilled designers still matter freeCodeCamp.org
- Will AI End the Open Internet? [Wading Through AI - Episode 6] Molly Rocket
- Will AI Make Me Worse? [Wading Through AI - Episode 5] Molly Rocket
- Never paste this into AI - and what happens when you do Code with Ania Kubów #JavaScriptGames
- How I Edit Videos with AI - Fully Automatic (Free) Website Learners
arXiv - cs.AI arxiv.org ai arxiv computer-science preprint repository 2026-06-18 04:00

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arXiv:2606.10466v2 Announce Type: replace-cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal structures across domains. To address...

arXiv:2606.10466v2 Announce Type: replace-cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal structures across domains. To address this fragmentation, we propose UPLOTS, a Unified, Prompt-guided Language model framework fOr constrained Time-Series Generation across diverse domains. Instead of building task-specific models, UPLOTS leverages a single pre-trained transformer backbone guided by learned constraint prompts, enabling on-demand generation with precise pattern control. One key innovation is our dynamic multi-dataset loss re-weighting and prompt-to-pattern mapping, which allows UPLOTS to internalize diverse temporal structures during training and conditionally generate them at inference. We evaluate UPLOTS on four real-world benchmarks and multiple constraint settings, including peak-period, calendar, load-level, and volatility patterns. Additional held-out constraint-combination and downstream forecasting experiments further demonstrate that UPLOTS generalizes beyond the original peak-pattern setting and improves data augmentation under scarce real-data regimes. Our code and baselines are available at anonymous github repo: https://anonymous.4open.science/r/UPLOTS-6C36.
- Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap arXiv - Computer Science: Artificial Intelligence
- UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation arXiv - Computer Science: Artificial Intelligence
- Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap arXiv - cs.AI
- UnoSolver.jl a unified SQP/barrier solver for nonlinearly constrained optimization | Charlie Vanaret The Julia Programming Language
arXiv - cs.AI arxiv.org ai arxiv computer-science preprint repository 2026-06-18 04:00

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arXiv:2606.15091v2 Announce Type: replace-cross Abstract: Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma. Brain-computer interfaces (BCIs) offer a promising avenue for sensory and motor...

arXiv:2606.15091v2 Announce Type: replace-cross Abstract: Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma. Brain-computer interfaces (BCIs) offer a promising avenue for sensory and motor restoration. However, the scientific literature remains highly fragmented between invasive neuroprosthetics and non-invasive electrophysiological decoders, with a lack of consistent terminology and comparison metrics. This chapter proposes a unified 2 x 2 framework categorizing BCIs along two axes: degree of invasiveness (invasive vs. non-invasive) and signal direction (afferent sensory-IN vs. efferent sensory-OUT). We define and distinguish the paradigms of restoration, substitution, and augmentation. Furthermore, we outline a structural roadmap for the convergence of these modalities over near-, medium-, and long-term horizons, focusing on physical limits and the integrative role of machine learning foundation models.
- Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap arXiv - Computer Science: Artificial Intelligence
- UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation arXiv - Computer Science: Artificial Intelligence
- UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation arXiv - cs.AI
- UnoSolver.jl a unified SQP/barrier solver for nonlinearly constrained optimization | Charlie Vanaret The Julia Programming Language
arXiv - Computer Science: Artificial Intelligence arxiv.org ai arxiv computer-science preprint research science 2026-06-18 04:00

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arXiv:2606.10466v2 Announce Type: replace-cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal structures across domains. To address...

arXiv:2606.10466v2 Announce Type: replace-cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal structures across domains. To address this fragmentation, we propose UPLOTS, a Unified, Prompt-guided Language model framework fOr constrained Time-Series Generation across diverse domains. Instead of building task-specific models, UPLOTS leverages a single pre-trained transformer backbone guided by learned constraint prompts, enabling on-demand generation with precise pattern control. One key innovation is our dynamic multi-dataset loss re-weighting and prompt-to-pattern mapping, which allows UPLOTS to internalize diverse temporal structures during training and conditionally generate them at inference. We evaluate UPLOTS on four real-world benchmarks and multiple constraint settings, including peak-period, calendar, load-level, and volatility patterns. Additional held-out constraint-combination and downstream forecasting experiments further demonstrate that UPLOTS generalizes beyond the original peak-pattern setting and improves data augmentation under scarce real-data regimes. Our code and baselines are available at anonymous github repo: https://anonymous.4open.science/r/UPLOTS-6C36.
- Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap arXiv - Computer Science: Artificial Intelligence
- Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap arXiv - cs.AI
- UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation arXiv - cs.AI
- UnoSolver.jl a unified SQP/barrier solver for nonlinearly constrained optimization | Charlie Vanaret The Julia Programming Language
arXiv - Computer Science: Artificial Intelligence arxiv.org ai arxiv computer-science preprint research science 2026-06-18 04:00

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arXiv:2606.15091v2 Announce Type: replace-cross Abstract: Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma. Brain-computer interfaces (BCIs) offer a promising avenue for sensory and motor...

arXiv:2606.15091v2 Announce Type: replace-cross Abstract: Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma. Brain-computer interfaces (BCIs) offer a promising avenue for sensory and motor restoration. However, the scientific literature remains highly fragmented between invasive neuroprosthetics and non-invasive electrophysiological decoders, with a lack of consistent terminology and comparison metrics. This chapter proposes a unified 2 x 2 framework categorizing BCIs along two axes: degree of invasiveness (invasive vs. non-invasive) and signal direction (afferent sensory-IN vs. efferent sensory-OUT). We define and distinguish the paradigms of restoration, substitution, and augmentation. Furthermore, we outline a structural roadmap for the convergence of these modalities over near-, medium-, and long-term horizons, focusing on physical limits and the integrative role of machine learning foundation models.
- UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation arXiv - Computer Science: Artificial Intelligence
- Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap arXiv - cs.AI
- UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation arXiv - cs.AI
- UnoSolver.jl a unified SQP/barrier solver for nonlinearly constrained optimization | Charlie Vanaret The Julia Programming Language
Computerphile youtube.com channel computer-science computer-sciences informational video youtube 2026-06-04 13:30

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Fuzzing is a technique to find programming bugs by testing with random inputs - but there are smarter ways to go about it! Professor Alastair F Donaldson leads the Multicore Programming research group at Imperial College. Computerphile is supported by Jane Street. Learn more...

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Fuzzing is a technique to find programming bugs by testing with random inputs - but there are smarter ways to go about it! Professor Alastair F Donaldson leads the Multicore Programming research group at Imperial College. Computerphile is supported by Jane Street. Learn more about them (and exciting career opportunities) at: https://jane-st.co/computerphile This video was filmed and edited by Sean Riley. Computerphile is a sister project to Brady Haran's Numberphile. More at https://www.bradyharanblog.com
- Orphaned AI Agents: How to Find Hidden Access Risks Inside Your Network The Hacker News
- We Tested 14 Greek Yogurts to Find the Best Bon Appetit
- Like Statement in PostgreSQL | Using LIKE to find Patterns Alex The Analyst
- AD - How do cities control traffic? We partnered with Anker to find out… Veritasium
Computer history youtube.com channel computer-science video youtube 2026-06-18 16:30

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Storage technology has come a long way. Former Seagate CTO Mark Re reflects on seeing the IBM RAMAC 350 (the world’s first disk drive) still running.

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Storage technology has come a long way. Former Seagate CTO Mark Re reflects on seeing the IBM RAMAC 350 (the world’s first disk drive) still running.
- Mission-Critical Generative AI in Action • Scott Shaw • YOW! 2025 GOTO Conferences
- TabH2O in Action: Predictions From Your Spreadsheet | Full Webinar H2O.ai
- CSS style queries in action Kevin Powell
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NVIDIA's New Free AI - A Gift To Humanity

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

Sensory Restoration via Brain-Computer Interfaces: A Unified 2 x 2 Framework and Convergence Roadmap

Fuzzing Programs to Find Bugs - Computerphile

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