Resources - A free visual guidebook to learn Agents from scratch is available [1] - The guidebook includes 12 projects [1] External Link - A repository link is provided for further information: https://t.co/E6GJTTT50q [1]

AI发展趋势 - Artificial intelligence can now act, not just generate text, due to the development of "agents": software that equips large language models with tools to perform tasks [1]

RT Avi Chawla (@_avichawla)AWS did it again!They have introduced a novel way for developers to build Agents.Today, when you build an Agent, you start with a simple goal, then end up juggling prompts, routing logic, error handling, tool orchestration, and fallback flows.One unexpected user input and the whole thing collapses.Strands Agents framework by AWS approaches Agent building differently.It takes a model-driven approach that lets the LLM decide how to plan, choose tools, and adapt to edge cases on its ...

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. https://t.co/np057bqlC3Avi Chawla (@_avichawla):AWS did it again!They have introduced a novel way for developers to build Agents.Today, when you build an Agent, you start with a simple goal, then end up juggling prompts, routing logic, error handling, tool orchestration, and fallback flows.One unexpected user input and https://t.co/KPS3aKAer9 ...

AWS did it again!They have introduced a novel way for developers to build Agents.Today, when you build an Agent, you start with a simple goal, then end up juggling prompts, routing logic, error handling, tool orchestration, and fallback flows.One unexpected user input and the whole thing collapses.Strands Agents framework by AWS approaches Agent building differently.It takes a model-driven approach that lets the LLM decide how to plan, choose tools, and adapt to edge cases on its own.You provide the capabil ...

AI Trends and Predictions - The AI industry is shifting towards specialization, with enterprises focusing on fine-tuning and specializing models for specific domains [6][8][82] - Open source technologies are driving transparency and adoption of AI agents, giving more power to enterprises and consumers [8][10] - The next wave of innovation is expected in world models, which are extremely data-intensive and will be the base layer for robotic opportunities [69][72] Challenges in AI Adoption - Agent memory is an unsolved problem, requiring agents to have persistent memory of both the user and itself [13][14][15] - Seamless communication between AI agents requires open source communication protocols [23][56] - AI security is crucial, with the potential need for a high ratio of security agents to cognitive intelligence agents [24][26] - Evaluating AI performance requires moving from academic benchmarks to real-world evaluations and reinforcement learning environments [34][38][39] Investment and Innovation - Capital investments are shifting from the model space to the agent space, driven by the focus on people and applications [58][59] - Enterprises seek AI solutions with high accuracy, small footprint, and data privacy [49][50] - Distillation, which involves making large models more efficient and smaller, is becoming important for cost-effectiveness [51][52] Enterprise Adoption Strategies - Enterprises should view model development as a software development platform, focusing on MVP and optimization over time [53][54][55] - Enterprises are adopting generative AI slower due to legacy systems and data locked in those systems [80][81] - Enterprises should focus on systems of smaller, specialized models rather than one model to solve all problems [83] Stochastic Mindset and Evaluation - AI compute is becoming more stochastic, requiring a shift in how we interface with and evaluate computers [30][32] - Verification of AI in specialized domains is challenging due to the difficulty and expense of expert verification [41] The Role of Open Source - Open source is critical for base models and communication protocols, enabling enterprises to build and compete with their own workflows [11][57] - A \$2 billion investment was raised with Nvidia's participation to support the US open source development ecosystem [11] Iteration and Mindset - Companies should iterate quickly, inspired by gradient descent algorithms, to gather information and find new opportunities [75][77] - Founders should pick a starting place that is exciting, big, and challenging enough to be worth the effort [79]

AI Development & Application at Los Alamos National Laboratory - Los Alamos National Laboratory has been applying AI and ML for almost 70 years, evolving from early applications like Los Alamos chess to modern generative AI agents [2][3] - The laboratory is leveraging generative AI and agentic approaches to accelerate scientific discovery, particularly in areas like inertial confinement fusion (ICF) capsule design [4][5] - The laboratory emphasizes the importance of writing its own models and pushing the science of AI, while also recognizing the need for partnerships with commercial industry and academia [9][10] - Los Alamos National Laboratory is using AI to enhance its mission, including national security, by improving speed and efficiency in various tasks [8] Partnerships & Collaboration - Los Alamos National Laboratory actively seeks partnerships with commercial industry and academia to advance AI development and application [9][10] - The laboratory has established partnerships with the UC family of schools and frontier labs like OpenAI for collaborative research and development [12] - The laboratory emphasizes the importance of trust and shared responsibility in partnerships, particularly regarding the security and governance of AI tools and services [14][15] Governance & Security Considerations - The US government, including Los Alamos National Laboratory, is developing strategies and plans for AI implementation and governance, guided by OMB memorandums like M-25-21 and M-25-22 [15][26] - The laboratory highlights the critical need for robust cybersecurity measures, including compliance with standards like NIST 800-53 and FedRAMP, to protect sensitive data [21][22] - The laboratory emphasizes the importance of building AI systems with explanability, isolation, and governance in mind, particularly for applications with real-world impacts [31][32][35] - The laboratory requires software bills of materials and detailed information on open-source dependencies and patching plans from its service providers [36] Key Requirements for AI Service Providers - AI service providers should prioritize building for explanability to ensure transparency and accountability in decision-making [31] - AI service providers should build for isolation, considering deployment in environments with limited services, such as DoD Impact Level 5 [33][34] - AI service providers should build for governance, providing software bills of materials and detailed information on open-source dependencies and patching plans [35][36] - AI service providers should maintain speed in updating their federal offerings to align with commercial releases, addressing export compliance laws [37][38]

Summary of Key Points from the Conference Call Company and Industry Overview - The conference primarily focuses on Amazon Web Services (AWS), a leading cloud computing platform, which has grown to a $132 billion business, with a year-over-year growth rate of 20% [1][2][3] - AWS is recognized for its extensive infrastructure, including the largest private network and a global network of data centers spanning 38 regions and 120 availability zones [3][4] Core Insights and Arguments - AWS's growth is attributed to various services, including S3, which handles over 500 trillion objects and hundreds of exabytes of data, and the increasing adoption of AI technologies [2][3] - The introduction of Bedrock, a platform for deploying generative AI applications, has seen significant uptake, with over 50 customers processing more than 1 trillion tokens each [30][31] - AWS's AI infrastructure is highlighted as the most scalable and powerful, with a focus on NVIDIA GPUs and the launch of new Trainium chips designed for AI workloads [14][20][21] - The company emphasizes the importance of security and compliance, particularly in sectors like healthcare and finance, where AWS has established partnerships with major organizations [5][18] Innovations and Developments - AWS has launched several new AI models and services, including Nova 2, which offers cost-optimized low-latency models, and Nova Forge, allowing customers to blend proprietary data with AWS's training datasets [47][49] - The introduction of AI Factories enables customers to deploy dedicated AI infrastructure in their own data centers, enhancing security and compliance [19] - The Trainium 3 Ultra servers, featuring the first 3-nanometer AI chip, promise significant improvements in compute performance and efficiency for AI workloads [22][23] Customer Success Stories - Companies like Eli Lilly are leveraging AWS's infrastructure to create AI Science Factories, enabling autonomous hypothesis generation and experimentation [27][28] - Sony's partnership with AWS has transformed its operations, enhancing its ability to deliver engaging customer experiences through data insights and AI capabilities [51][56] Additional Important Points - The conference highlighted the shift towards AI agents, which are expected to revolutionize business operations by automating tasks and improving efficiency [11][12][59] - AWS's commitment to supporting startups is evident, with a significant percentage of AI startups being built on its platform [6][41] - The importance of integrating proprietary data into AI models to enhance their effectiveness and relevance to specific business needs was emphasized [42][45] This summary encapsulates the key points discussed during the conference, focusing on AWS's growth, innovations, customer success stories, and the future of AI in business.

Pantic AI Products & Features - Pantic AI supports temporal and other durable execution frameworks, with ongoing efforts to integrate more workflow orchestration backends [1] - Pantic AI offers tools for building AI agents, including the ability to perform web searches and analyze data [11][41] - Pantic AI's temporal agent handles the IO needed to call an LLM, including tool calls, by turning them into activities [16] - Pantic AI is developing a gateway for buying inference from various models, including observability features [61] Temporal & Durable Execution - Temporal is highlighted as a leading solution for durable execution, crucial for long-running workflows where progress preservation is essential [2] - Temporal records every activity and its inputs/outputs, enabling rerun from any point by plugging in the answers [15] - Temporal enables the resumption of workflows without adding resume code to the agent code [29] - Temporal's retry logic handles runtime errors and ensures continuous operation [22][25] Deep Research & Agent Architecture - Deep research is presented as analogous to a 20 questions game, with web search or RAG as intermediate steps [11] - The company is shifting towards viewing agents as micro-tasks that form larger autonomous task completion systems [40] - A deep research agent can be composed of multiple specialized agents, such as a plan agent, a search agent, and an analysis agent [41] Evaluation & Performance - Pantic AI evals are used to compare the performance of different models, considering factors like cost, speed, and accuracy [33] - Gemini was initially found to be faster and cheaper, but later discovered to sometimes invent incorrect answers [33][35]

RT Claude (@claudeai)Introducing Claude Opus 4.5: the best model in the world for coding, agents, and computer use.Opus 4.5 is a step forward in what AI systems can do, and a preview of larger changes to how work gets done. https://t.co/mid2Z1qzIf ...