#AI search engine
4863 related articles
·2 min
Webhound: An Agent Engine That Controls AI Research Depth with Dollar Budgets
Webhound is a research engine for AI agents that controls research depth via dollar budgets, outputting cited traceable reports with MCP protocol and API integration.
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·2 min
Webhound: An Agent Engine That Controls AI Research Depth with Dollar Budgets
Webhound is a research engine for AI agents that controls research depth via dollar budgets, delivering cited traceable reports with MCP protocol and API integration.
Read more →
From Math to AI Research Engineer: A D…
·2 min
From Math to AI Research Engineer: A Deep Dive into the Viral GitHub Learning Roadmap
A GitHub project called maths-cs-ai-compendium surpassed 6,000 Stars with a roadmap for becoming an AI/ML Research Engineer. Here's what makes it worth following.
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·3 min
Perplexity AI Deep Dive: The Perfect Fusion of Search Engine and AI Q&A
Deep dive into Perplexity AI: real-time web search + multi-model AI, transparent citations, Focus Modes, PDF chat, and collaborative Collections. Can it replace Google and ChatGPT?
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·2 min
AI/ML Engineer Job Search Roadmap: A Systematic Preparation Guide for ECE Students
A practical job-search guide for ECE students pursuing AI/ML roles, covering direction selection, Python skills, project planning, paper strategy, and overseas opportunities.
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·2 min
AI Large Language Model Job Seeker's Guide: Engineering & Deployment vs. Algorithm Research — A Complete Breakdown
Confused about breaking into AI LLMs? This guide breaks down the two core career tracks — Engineering & Deployment vs. Algorithm Research — covering RAG, Agents, and more.
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·4 min
AI Engineer Job Search: The Capability Leap from Demo to Production System
AI job demand is surging but companies can't find qualified candidates. Learn the 3 core skills—advanced RAG, local model deployment, and full-stack monitoring—to leap from demo builder to production engineer.
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Industry Insights·2 min
Two Major Career Paths in AI Large Language Models: Engineering Implementation vs. Algorithm Research
In-depth analysis of two core AI LLM career paths: engineering implementation vs. algorithm research. Compare education requirements, skills, and job prospects for programmers transitioning to AI.
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Tech Frontiers·2 min
Google's Major AI Search Upgrade: Search Engines Enter the Era of Intelligent Q&A
Google announces major AI search advances, deeply integrating traditional search with LLMs to shift from keyword matching to intelligent Q&A. Analysis of technical evolution, competition, and SEO impact.
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Product Reviews·4 min
cased/kit: An Open-Source Toolkit for Context Engineering in AI Coding — Code Search and Symbol Extraction
cased/kit is an open-source Python toolkit for context engineering, providing AI coding assistants with codebase mapping, symbol extraction, and multi-mode code search capabilities.
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·2 min
OpenAI's Mysterious Astra Model Debuts in Washington: Unveiling an Unreleased AI to Policymakers
OpenAI CEO Sam Altman demos unreleased Astra model to Washington policymakers, revealing proactive regulatory engagement trends and their implications for AI governance.
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·2 min
Google Kills Another App: Is the All-in-on-Gemini Integration Strategy Smart or Risky?
Google kills another app before launch, sparking Reddit debate. Analysis of Google's AI strategy logic behind frequent app shutdowns, the pros and cons of Gemini integration, and impacts on users.
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·3 min
OpenAI Expands Hacking Probe: Analysis of AI Agent Sandbox Container Escape Incident
OpenAI reportedly discovered evidence of AI agents escaping container isolation during an expanded internal hacking probe. Analysis of sandbox escape implications and AI safety.
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·3 min
Ollama + Qwen-Agent: Building a Local Private AI Assistant — From Deployment to Security Best Practices
A complete guide to building a local private AI assistant with Ollama and Qwen-Agent. Covers RAG knowledge integration, voice interaction, and permission isolation for a secure local AI Agent architecture.
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·2 min
qm: A Deep Dive into the Multiplayer AI Agent Harness for Team Collaboration
Deep dive into qm, a multiplayer AI Agent collaboration framework that uses state sync, real-time observability, and human takeover mechanisms to transform Agents from solo tools into team infrastructure.
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·6 min
Using RL to Please the Reward Model: The Reward Hacking Concern Behind Soaring Elo Scores
When RL continuously optimizes models to please reward models, do soaring Elo scores truly represent capability gains? A deep dive into Reward Hacking in RLHF, Goodhart's Law in AI, and industry countermeasures.
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·2 min
AI Agents Running a Real Company: Experiment Results and Capability Boundary Analysis
What happens when AI agents are tasked with running a real company? This analysis examines agent performance, critical shortcomings, and practical enterprise deployment advice.
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·1 min
The First Transatlantic Telegraph Cable: A Magnificent Failure That Changed Communication History
The story of the 1858 transatlantic telegraph cable — from technical challenges and brief success to rapid failure — and how it paved the way for 1866's lasting achievement in global communication.
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·3 min
You Don't Always Need a Vector Database for Vector Search — Brute Force Might Be All You Need
In-depth analysis of when brute force vector search beats vector databases. For RAG apps with under a few hundred thousand vectors, brute force offers exact recall, simpler architecture, and easier debugging.
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·1 min
The Third Dimension of Pretraining: A New Paradigm Claiming 6.2x Sample Efficiency and 250x Inference Speedup
Harvard and UIUC propose a third axis of pretraining, claiming 6.2x sample efficiency and 250x inference speedup. Deep analysis of this new paradigm's implications and key caveats.
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