Fiscal Policy & Economic Growth - The analysis suggests tariffs are running at an annual rate between $400 billion and $500 billion, potentially improving the deficit, but real GDP growth is considered the key to significantly reducing the deficit as a percentage of GDP [1] - The report anticipates real GDP growth will surprise on the high side of expectations later in the year and into 2026, driven by innovation platforms like robotics, energy storage, AI, multiomic sequencing, and blockchain technology, all catalyzed by AI [1] - The analysis highlights deregulation, particularly in crypto, AI, and nuclear energy, as a significant factor for economic growth, with tax changes encouraging manufacturing and innovation through accelerated depreciation schedules and full expensing of equipment, R&D, and software [1] Inflation & Monetary Policy - The report indicates that while inflation may seem stuck in the 2% to 3% range, innovation-driven productivity gains could lead to deflation in the coming years [2] - The analysis points out that M2 money supply growth has significantly dropped compared to the COVID boom, and the velocity of money is declining, potentially diffusing inflationary pressures [2] - The yield curve, measured by the two-year Treasury yield relative to the three-month Treasury yield, indicates tight monetary policy, which is expected to have disinflationary or deflationary effects [3] - True inflation CPI is reported at 19%, even with tariffs factored in, and consumer inflation expectations are expected to decline [3] Market Indicators & Investment Strategy - The analysis notes that manufacturing has been contracting for the last three years, and services are not in great shape, signaling potential economic concerns [4] - The report highlights that AI-powered capital spending is increasing, supported by new tax rules, while the trade deficit is being addressed [5] - The analysis observes that pending home sales are deteriorating, and new home inventory is high, potentially leading to price cuts and impacting the CPI [5] - The report suggests that the return on investment in the US is expected to increase due to innovation, tax laws, and deregulation, potentially strengthening the dollar [5] - The analysis notes that corporate profits are healthy, but quality of earnings and harnessing new technologies will be crucial for future growth [5] - The report observes that commodity prices are going nowhere, and gold is breaking out to all-time highs relative to metals, possibly signaling deflationary concerns [5]

RT OpenRouter (@OpenRouterAI)Gemini is dominating our image input rankings for LLMs 👀 https://t.co/Sgy2mNffiq ...

Function calling & MCP for LLMs, clearly explained (with visuals): ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):3 expert ways to use GROUP BY in SQL, clearly explained (with code): ...

The greatest threat to humanity is not population collapse, disease, or superintelligent machinesit's the fact that LLMs disproportionately use reddit as a sourcethe source of all midwits and cancer ...

AI Agent Evolution - AI agents have evolved from simple LLMs to sophisticated systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots processed small chunks of input, exemplified by ChatGPT's initial 4k token context window [1] - LLMs expanded to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs with tools like search APIs and calculators [1] - Multimodal LLMs process text, images, and audio, incorporating memory for persistence across interactions [1] Key Components of Advanced AI Agents - Current AI agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling capabilities, including search, APIs, and actions, are integral to advanced AI agents [1] - Reasoning and ReAct-based decision-making are crucial components of modern AI agents [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):5 levels of evolution of AI Agents.Over the last few years, we’ve gone from simple LLMs → to fully-fledged Agentic systems with reasoning, memory, and tool use.Here’s a step-by-step breakdown.1) Small context window LLMs- Input: Text → LLM → Output: Text- Early https://t.co/DvNTsnXpYT ...

AI Agent Evolution - The industry has progressed from simple LLMs to sophisticated Agentic systems with reasoning, memory, and tool use [1] - Early transformer-based chatbots were limited by small context windows, exemplified by ChatGPT's initial 4k token limit [1] - The industry has seen upgrades to handle thousands of tokens, enabling parsing of larger documents and longer conversations [1] - Retrieval-Augmented Generation (RAG) provided access to fresh and external data, enhancing LLM outputs [1] - Multimodal LLMs can process multiple data types (text, images, audio), with memory introducing persistence across interactions [1] Key Components of Advanced AI Agents - Advanced AI Agents are equipped with short-term, long-term, and episodic memory [1] - Tool calling (search, APIs, actions) is a crucial feature of modern AI Agents [1] - Reasoning and ReAct-based decision-making are integral to the current AI Agent era [1]

RT Avi Chawla (@_avichawla)Temperature in LLMs, clearly explained (with code): ...

LLM Insights - Tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) are shared daily [1] - Temperature in LLMs is clearly explained with code [1] Engagement - The author encourages readers to reshare the content if they found it insightful [1]