AI Monthly: AI’s green thumb raises bigger questions for agriculture | articles - ING THINK economic and financial analysis | ING THINK
AI Monthly: AI’s green thumb raises bigger questions for agriculture | articles ING THINK economic and financial analysis | ING THINK
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Gemma 4 E4B + E2B Uncensored (Aggressive) — GGUF + K_P Quants (Multimodal: Vision, Video, Audio)
My first Gemma 4 uncensors are out. Two models dropping today, the E4B (4B) and E2B (2B). Both Aggressive variants, both fully multimodal. Aggressive means no refusals. I don't do any personality changes or alterations. The ORIGINAL Google release, just uncensored. Gemma 4 E4B (4B): https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive Gemma 4 E2B (2B): https://huggingface.co/HauhauCS/Gemma-4-E2B-Uncensored-HauhauCS-Aggressive 0/465 refusals * on both. Fully unlocked with zero capability loss. These are natively multimodal so text, image, video, and audio all in one model. The mmproj file is included for vision/audio support. What's included: E4B: Q8_K_P, Q6_K_P, Q5_K_P, Q5_K_M, Q4_K_P, Q4_K_M, IQ4_XS, Q3_K_P, Q3_K_M, IQ3_M, Q2_K_P + mmproj E2B: Q8_K_P, Q6_K_P, Q5_K_P,
I Broke My Multi-Agent Pipeline on Purpose. All 3 Failures Were Silent.
78% of enterprises have at least one AI agent pilot running. Only 14% have successfully scaled one to production. I used to think the gap was about model quality — smarter models, better prompts, more capable agents. After today's experiment, I think the gap is about something much more mundane: what happens between agents. I deliberately broke the handoffs in my 4-agent Content Factory three different ways. Every single failure was silent. The System Quick context: I run a 4-agent content pipeline built with Claude Code: Architect (experiments) → Writer (articles) → Critic (scoring) → Distributor (publishing) Each agent passes structured data to the next: Architect → Writer: a JSON seed file with theme , experiment , results , surprise , learnings Writer → Critic: a markdown article with
Can AI Predict the Next Stock Market Crash? Unpacking the Hype and Reality for Global Investors
Can AI Predict the Next Stock Market Crash? Unpacking the Hype and Reality for Global Investors While AI can identify complex patterns and anomalies that might precede market downturns, it cannot definitively predict the exact timing or magnitude of a stock market crash with 100% accuracy. The reality for global investors NOW is that AI serves as a powerful tool for risk management and early warning systems, analyzing vast datasets including inflation trends, interest rate movements, and geopolitical risks, but it remains susceptible to 'black swan' events and the inherent unpredictability of human behavior and unforeseen global shocks. Understanding AI's Role in Market Prediction AI's role in market prediction stems from its ability to process and analyze immense volumes of data far beyon
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