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The Million-Token Context Window: What Can You Actually Do?
Million-token context windows let you feed entire codebases, legal contracts, and hours of video to an LLM in one pass—but advertised limits routinely overstate practical capability. Here's what the benchmarks, failure modes, and real deployment patterns actually show.
The AI Agent Marketplace: An Economy of Digital Workers Emerges
Perplexity API: Adding Real-Time Search to Your Apps in Minutes
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DeepSeek V3/R1: How Chinese Engineers Matched GPT-4 for $6 Million
DeepSeek's V3 and R1 models match GPT-4-class performance using a fraction of the compute through architectural innovations in Mixture of Experts, attention compression, and reinforcement learning—demonstrating that training efficiency may matter more than raw hardware scale.
Facebook Is Cooked: Inside Social Media's Quality Collapse
Facebook's feeds have been overrun by AI-generated spam, collapsed organic reach, and algorithmically-engineered junk — while Meta's revenue hits record highs. Here's the documented evidence of how the world's largest social network became a content wasteland.
The Fight to Keep Android Open
Google's 2026 developer verification mandate threatens the open-source Android ecosystem. A coalition of 37 organizations—including the EFF and F-Droid—is fighting back, as alternative app stores and privacy-focused Android forks face an existential challenge from Google's tightening grip on the platform.
Gemini 2.0 Pro's 2 Million Token Context: What Can You Actually Do With It?
Google's Gemini 2.0 Pro Experimental ships with a 2 million token context window—the largest among production-accessible models. Here's what practitioners have discovered works, what doesn't, and what the hard limits are.
Google's TimesFM: A Foundation Model for Time Series
TimesFM is Google's pretrained, decoder-only transformer model for zero-shot time-series forecasting, trained on ~100 billion real-world time-points to deliver accurate predictions across domains without retraining.
How AI Agents Remember: Memory Architectures That Work
AI agents use four distinct memory tiers—working, episodic, semantic, and procedural—stored across context windows, vector databases, knowledge graphs, and model weights. Choosing the right architecture determines whether your agent stays coherent across sessions or forgets everything the moment a conversation ends.
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