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TL;DR Here’s a concise summary of the Harvard Business Review article **" Why Agentic AI Projects Fail—and How to Set Yours Up for Success "** (October 21, 2025): ---  **Key Points**  ** The Promise and Pitfalls of Agentic AI* * - **Agentic AI** is not just advanced automation or generative AI; it autonomously manages complex tasks, optimizes processes, and identifies opportunities or risks with minimal human oversight. - The technology has sparked significant interest and investment, with a 265% increase in venture capital funding between Q4 2024 and Q1 2025. - ** Gartner predicts ** that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. - Many vendors engage in “agent washing,” rebranding existing products as agentic AI, but only about 130 out of thousands of vendors deliver genuine agentic solutions.  **Why Most Projects Fail** - ** Misalignment:** Projects ofte...

TL;DR Link:  https://venturebeat.com/ai/researchers-find-adding-this-one-simple-sentence-to-prompts-makes-ai-models

TL;DR Link:  https://www.pcmag.com/news/claude-code-creator-we-didnt-mean-to-build-it-but-its-changed-everything

TL;DR Link:  https://evidencenetwork.ca/germany-is-making-the-biggest-power-play-in-its-energy-history-investing-385-million-in-laser-driven-inertial-confinement-nuclear-fusion/

TL;DR Link: https://unionrayo.com/en/spruce-gold-finland/

TL;DR Link: https://unionrayo.com/en/fossil-fuels-green-hydrogen-climate-change/

From Malware Analysis to Money Laundering: How AI is Transforming Threat Intelligence Artificial intelligence is rapidly reshaping the cybersecurity landscape, and nowhere is this more evident than in threat intelligence. Thomas Rochia, a Senior Threat Researcher at Microsoft, is at the forefront of this transformation, developing open-source tools and techniques that demonstrate both the promise and challenges of integrating AI into security workflows. The Evolution of Threat Intelligence Traditionally, threat intelligence has been a manual, time-intensive process. Analysts would painstakingly reverse engineer malware, track threat actors, and piece together attack patterns from disparate data sources. But AI is changing this paradigm in fundamental ways. Rochia's work spans multiple domains within threat intelligence, from malware analysis to cryptocurrency money laundering detection. His approach reveals an important insight: not every security process ne...

TL;DR Link: https://venturebeat.com/ai/new-markovian-thinking-technique-unlocks-a-path-to-million-token-ai

TL;DR Link:  https://www.nature.com/articles/s41467-025-63804-5

TL;DR Link:  https://www.zmescience.com/ecology/japan-first-osmotic-power-plant/

TL;DR Link:  https://www.marktechpost.com/2025/08/28/memory-r1-how-reinforcement-learning-supercharges-llm-memory-agents/

TL;DR Link:  https://www.infoworld.com/article/2256990/13-clever-apis-for-capturing-every-kind-of-data.html

TL;DR Link:  https://www.infoq.com/news/2025/10/data-commons-mcp-server/

TL;DR Link:  https://www.creativeboom.com/tips/the-creativepreneur-starter-pack-for-2026/

TL;DR Link:  https://www.zdnet.com/article/the-linux-distro-makes-it-super-easy-for-me-to-create-edit-and-stream-heres-how/

TL;DR Key Concepts & Takeaways  ** 1. Why Normalize Tensors?** - ** Problem :** In large neural networks, tensors (weights, activations, gradients) can become too large or too small, causing numerical instability and training difficulties. - ** Solution :** Normalization (e.g., layer norm, gradient normalization) ensures tensors stay within a healthy range, improving training stability and predictability. - ** Weight Normalization :** Less common but beneficial—it prevents weight norms from exploding, simplifies hyperparameter tuning, and can enforce Lipschitz guarantees for robustness. * *2. Manifold Optimization ** - ** Core Idea :** Constrain weight tensors to geometric manifolds (e.g., hyperspheres, Stiefel manifolds) to control their size and behavior. - ** Process :**   1. * *Tangent Space :** Optimization steps are taken in the tangent space of the manifold.   2. ** Retraction:** After each step, weights are projected back onto the manifold.   ...

TL;DR Link:  https://blog.cloudflare.com/code-mode/

TL;DR Link:  https://www.xda-developers.com/open-source-app-like-notion-but-better/

TL;DR Link:  https://www.marktechpost.com/2025/09/23/cloudflare-ai-team-just-open-sourced-vibesdk-that-lets-anyone-build-and-deploy-a-full-ai-vibe-coding-platform-with-a-single-click/ GitHub:  https://github.com/cloudflare/vibesdk?tab=readme-ov-file

TL;DR Link: https://realpython.com/python-mcp/

TL;DR Link:  https://www.energy-reporters.com/storage/scientists-unveil-groundbreaking-doping-that-transforms-sodium-batteries-60-capacity-retained-after-300-cycles/

TL;DR Link: https://www.popularmechanics.com/science/a67947951/scientists-discover-pathway-element-120-holy-grail-of-chemistry/

TL;DR Link: https://developer.nvidia.com/blog/build-a-log-analysis-multi-agent-self-corrective-rag-system-with-nvidia-nemotron/

TL;DR Link:  https://mashable.com/article/how-to-start-vibe-coding

TL;DR Link:  https://towardsdatascience.com/tabular-foundation-models/