16 best GitHub repos to build AI engineering projects!(star + bookmark them):The open-source AI ecosystem has 4.3M+ repos now.New repos blow up every month, and the tools developers build with today look nothing like what we had a year ago.I put together a visual covering the 16 repos that make up the modern AI developer toolkit right now.The goal was to cover key layers of the stack:1) OpenClaw↳ Personal AI agent that runs on your devices and connects to 50+ messaging platforms2) AutoGPT↳ Platform for buil ...

RT Avi Chawla (@_avichawla)RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant chunks, but this task needs the full context.Graph RAG solves this.The following visual depicts how it differs from naive RAG.The core idea is to:- Create a graph (entities & relationships) from documents.- Trave ...

RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant chunks, but this task needs the full context.Graph RAG solves this.The following visual depicts how it differs from naive RAG.The core idea is to:- Create a graph (entities & relationships) from documents.- Traverse the graph during retrie ...

Core Viewpoint - The article reviews the evolution of the quantitative investment industry over the past decade, highlighting a shift from localized advantages to systemic cognitive capabilities, driven by the implementation of AI technology [1][4][12]. Industry Trends: From Speed to Cognition - The quantitative industry is transitioning towards Quant 4.0, characterized by a cognitive architecture centered on multi-agent collaboration, moving away from traditional linear models [4][12]. - Leading firms are focusing on building AI-driven mid-frequency prediction platforms, emphasizing the importance of unique high-quality data and sophisticated algorithms for sustainable excess returns [4][9][12]. Information Processing: LLM and RAG's Infrastructure Value - Large Language Models (LLMs) are transforming the processing of alternative data, significantly reducing marginal costs and enhancing the ability to extract key information from complex documents [5][26]. - Retrieval-Augmented Generation (RAG) technology addresses LLM's limitations by ensuring traceability and accuracy in quantitative strategies, enabling the capture of deeper insights [5][29]. Factor Mining: From Data Mining to Logic Generation - LLMs assist in overcoming the limitations of manual factor mining by introducing a Multi-Agent Debate framework, which enhances the quality of factors through logical generation rather than brute-force computation [6][30][36]. Structural Upgrade: From Pipeline to Cognitive Systems - The traditional linear pipeline structure in quantitative research is evolving into a multi-agent system that allows for cognitive division of labor, enhancing collaboration and accountability [7][38][41]. - Multi-Agent systems modularize the research process, improving efficiency and traceability while maintaining rigorous standards [7][41]. LLM Beyond AI Quant: Continuous Innovation - New trends in machine learning models, such as Time Series Foundation Models (TSFM) and Reinforcement Learning (RL), are emerging, emphasizing cross-asset and cross-frequency applications [8][44][46]. - TSFM enhances generalization and transfer learning capabilities, while RL optimizes decision-making in trading execution and dynamic risk management [44][46][47]. Future Outlook: Mid-Frequency as the Main Battlefield - The mid-frequency range (minute to weekly) is expected to become the primary battleground for AI technology, balancing data abundance and latency tolerance [9][50]. - Future quantitative research systems may adopt an upstream-midstream-downstream architecture, integrating real-time knowledge bases with multi-agent debate mechanisms for factor mining and execution [51][52].

Financial Data and Key Metrics Changes - Total revenue for Q3 was $450 million, representing an 18% growth year-over-year and a 16% growth on a constant currency basis [23][24] - Sales-led subscription revenue reached $376 million, growing 21% as reported and 19% on a constant currency basis [24] - Current remaining performance obligations (CRPO) crossed the $1 billion mark for the first time, reaching approximately $1.06 billion, growing 19% as reported and 15% on a constant currency basis [24][25] - Non-GAAP operating margin was 18.6%, with subscription gross margins at 82% and total gross margins at 78% [27][28] Business Line Data and Key Metrics Changes - Sales-led subscription revenue growth was driven by both self-managed and cloud offerings, with strong consumption trends [24][26] - The number of customers with an annual contract value (ACV) of over $100,000 grew to over 1,660, marking a 14% increase [25] - 28% of customers in the greater than $100,000 ACV cohort are utilizing Elastic for AI, indicating significant adoption of AI capabilities [26] Market Data and Key Metrics Changes - The company experienced strong deal momentum across all geographies, with multi-year commitments indicating the platform's strategic value [25] - The demand for Elastic's solutions is being driven by the need for organizations to manage increasing data volumes and leverage AI for innovation and efficiency [9][21] Company Strategy and Development Direction - The company is focused on becoming the essential infrastructure for AI-powered businesses, emphasizing the importance of context in AI applications [5][21] - Elastic aims to bridge the gap between proprietary data and AI models, enhancing the adoption of AI across its customer base [12][13] - The introduction of new features like Agent Builder and Elastic Workflows aims to enhance the platform's capabilities for building intelligent applications [18][20] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the ongoing demand for Elastic's solutions, particularly in the context of AI and data management [23][34] - The company anticipates continued growth in sales-led subscription revenue and adjusted free cash flow, supported by strong customer commitments [33][34] - The outlook for Q4 includes expected total revenue in the range of $445 million to $447 million, representing 15% growth at the midpoint [30][31] Other Important Information - The company has made significant progress on its $500 million share repurchase program, returning approximately $186 million to shareholders in Q3 [29] - The partnership with NVIDIA aims to enhance AI application deployment without straining IT infrastructure [17] Q&A Session Summary Question: Potential for growth acceleration as AI penetration increases - Management noted that as more customers reach the $100,000 ACV mark, there is potential for accelerated growth beyond the current 5% average [36][38] Question: Core components for securing status as a leading provider of context for AI applications - The company emphasized the need for a comprehensive data platform that can handle both structured and unstructured data, along with capabilities for hybrid search and context engineering [40][41] Question: Insights on cloud revenue growth and sequential performance - Management highlighted that the sales-led subscription revenue growth remains the key metric, with strong performance in both self-managed and cloud segments [78] Question: Broader AI use cases and their impact on customer spending - The company is witnessing a diversification of AI use cases beyond vector databases, including security and observability workflows, which is expected to drive increased usage and spending [82]

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):A graph-powered all-in-one RAG system!RAG-Anything is a graph-driven, all-in-one multimodal document processing RAG system built on LightRAG.It supports all content modalities within a single integrated framework.100% open-source. https://t.co/1Kw21DDcA7 ...

Core Insights - The article highlights the rapid decline in demand for traditional CRUD development roles due to the swift advancement of AI technology, positioning these roles as potentially obsolete in the near future [1] - It emphasizes that 63% of companies are transitioning to AI product development, making AI application development skills a necessity in the current job market [2] - The article points out that positions such as large model application development engineers are in high demand, with a significant talent shortage leading to salary increases of 40-60% for qualified candidates [2] Summary by Sections AI Technology and Job Market - Traditional skills in business coding, API integration, and bug fixing are rapidly depreciating in value in the AI era [2] - Companies are increasingly seeking developers who are proficient in AI technologies, particularly in fine-tuning, Agents, and Retrieval-Augmented Generation (RAG) [2][7] Course Offering - The article promotes a course titled "Large Model Application Development Practice," designed to help developers build complete application development paths from scratch [3][4] - The course includes two live sessions focusing on theoretical knowledge, practical development skills, and demonstrable projects [3] Career Advancement Opportunities - The course offers additional benefits such as internal referral opportunities and direct hiring rights upon completion [5][16] - Participants will receive a collection of large model application case studies and a white paper on AI commercial implementation [5][14] Learning Outcomes - The curriculum covers essential technologies like fine-tuning for specific tasks, RAG for efficient knowledge retrieval and generation, and the development of AI Agents for multi-task collaboration and complex problem-solving [7][14] - The course aims to equip participants with the skills to navigate the evolving job market, particularly in high-demand sectors such as finance, healthcare, and legal [7][14] Market Demand and Job Security - The article stresses the urgency for developers to acquire AI skills to avoid job insecurity, especially for those approaching the age of 35 [11][18] - It notes that many past participants have successfully secured high-paying job offers after completing the course [9][16]

Core Viewpoint - The price increase of NAND flash memory is driven by the rising demand from AI applications, particularly in large-scale inference processes, leading to significant upward revisions in price forecasts by market research firms [1][2][3]. Group 1: Price Forecasts and Market Trends - Samsung Electronics raised NAND flash contract prices by over 100% in January, prompting multiple market research firms to revise their price forecasts upward [1]. - TrendForce increased its first-quarter NAND flash price growth forecast from 33-38% to 55-60%, indicating potential for further upward adjustments [1]. - Counterpoint predicts NAND flash prices will rise by over 40% in the current quarter [1]. Group 2: AI Demand and Storage Architecture - The surge in NAND flash demand is primarily attributed to AI applications, particularly in retrieval-augmented generation (RAG) which enhances the accuracy of large language models [1][2]. - The transition from training to large-scale inference in generative AI has led to increased demand for NAND flash, as systems require high-speed access to vast amounts of data [2][3]. - The need for high-frequency access to context data during inference has resulted in a shift towards a storage architecture that includes HBM, DRAM, and NAND [3]. Group 3: Supply Constraints and Future Outlook - The global NAND flash production capacity is concentrated among a few major players, including Samsung, SK Hynix, and Micron, with investments in NAND lagging behind HBM and advanced DRAM [4][5]. - Morgan Stanley forecasts a 40% year-over-year increase in average NAND sales prices by 2026, with only a slight decline expected in 2027 [5]. - The introduction of High Bandwidth Flash (HBF) aims to address the limitations of traditional NAND SSDs, providing higher bandwidth and capacity suitable for AI inference applications [5][6]. Group 4: Technological Advancements - HBF combines 3D NAND flash with high-bandwidth interface technology, offering 8 to 16 times the capacity of traditional HBM, making it a competitive solution for AI applications [5][6]. - The industry is moving towards a multi-layer architecture of "DRAM cache + HBF acceleration + NAND mass storage," which is expected to alleviate supply-demand imbalances and drive growth [6].

RT Avi Chawla (@_avichawla)The ultimate Full-stack AI Engineering roadmap to go from 0 to 100.This is the exact mapped-out path on what it actually takes to go from Beginner → Full-Stack AI Engineer.> Start with Coding Fundamentals.> Learn Python, Bash, Git, and testing.> Every strong AI engineer starts with fundamentals.> Learn how to interact with models by understanding LLM APIs.> This will teach you structured outputs, caching, system prompts, etc.> APIs are great, but raw LLMs still need the latest inf ...

The ultimate Full-stack AI Engineering roadmap to go from 0 to 100.This is the exact mapped-out path on what it actually takes to go from Beginner → Full-Stack AI Engineer.> Start with Coding Fundamentals.> Learn Python, Bash, Git, and testing.> Every strong AI engineer starts with fundamentals.> Learn how to interact with models by understanding LLM APIs.> This will teach you structured outputs, caching, system prompts, etc.> APIs are great, but raw LLMs still need the latest info to be effective.> Learn h ...