Imagine you've built a perfect agent for your blog writing team. Now your social media team wants to use it but they need different prompts, different models and different tools. But modifying your underlying code for each use case is not only time consuming but also prone to errors.This creates two distinct problems. Developers get stuck in constant code changing cycles that slow down iteration while business teams can't experiment without engineering support. That's where Lang graph assistants come in.Tod ...

RT GG (@ggDecrypt)Ubisoft Adds AI Agents to ‘Captain Laserhawk’ Game That Vote and Govern► https://t.co/84s9J5z4er https://t.co/84s9J5z4er ...

Core Argument - The presentation argues that event-driven architecture (EDA), while seemingly loosely coupled at runtime, is tightly coupled at design time, leading to complexities and challenges in AI agent development [21][22] - It proposes a shift in focus from events to durable execution as the core of AI agent architecture, which simplifies development and handles failures more effectively [26][27] Problems with Event-Driven Architecture - EDA sacrifices clear APIs, as events lack the documentation and structure of traditional APIs [15] - Business logic becomes fragmented and scattered across multiple services, making debugging and understanding the system more difficult [16] - Services become ad hoc state machines, leading to potential race conditions and difficult-to-debug issues [18][19] - EDA can lead to reluctance to iterate on architecture due to fear of breaking existing functionality [25] Durable Execution as a Solution - Durable execution is presented as a crash-proof execution environment that automatically preserves application state, virtualizes execution, and is not limited by time or hardware [27][28][29][30][31][32][33][34] - It allows developers to focus on business logic rather than managing events and queues [38] - Temporal provides durable execution as an open-source, MIT-licensed product with SDKs for multiple programming languages [38][39] - Durable execution abstracts away the complexities of events into the software layer [40][43] Temporal's Offering - Temporal's durable execution system offers automatic retries for failures, such as LLM downtime or rate limits [36] - It supports polyglot programming, allowing functions written in different languages to be called seamlessly [39] - Temporal is available for demonstration and further discussion at the company's booth and Slack channel [44][45]

Finally! You can reliably test AI Agents without humans:- One Agent asks questions.- The Agent being tested responds.- Another Agent Judges.Here’s a complete breakdown (with code): https://t.co/wDm2a4zN0bAvi Chawla (@_avichawla):How Agents test Agents, clearly explained (with code): ...

Agents are all the rage in 2025, and every single b2b SaaS startup/incumbent promises AI agents that can "automate work" in some way. But how do you actually build this? The answer is two fold: 1. really really good tools 2. carefully tailored agent reasoning over these tools that range from assistant-to-automation based UXs. The main goal of this talk is to a practical overview of agent architectures that can automate real-world work, with a focus on document-centric tasks. Learn the core building blocks o ...

10 real-world projects for AI engineers that cover:- Memory- Video RAG- Voice Agent- Corrective RAG- and much more!Find them in the explainer thread below. https://t.co/RucVrx4Xt0Avi Chawla (@_avichawla):AI Engineering Hub is about to cross 10k GitHub stars!It’s 100% open-source and hosts 70+ free hands-on demos.Here are 10 MCP, RAG, and AI Agents projects for AI engineers: https://t.co/Zxe2K6DEKg ...

Financial Data and Key Metrics Changes - In Q1 2025, total revenue reached $259 million, up 36% year over year and 9% quarter over quarter [14] - Gross profit increased by 67% year over year to $190 million, with a gross margin of 73.2%, up from 59.2% a year ago [14] - Operating loss narrowed to $27 million from $81 million in the year-ago quarter [15] - Net loss attributable to shareholders was $33 million, reduced from $80 million in the year-ago quarter [15] Business Line Data and Key Metrics Changes - The Internet business saw a 46% increase in revenue year over year, with an operating margin nearly doubling to 15.5% [5][16] - Losses from the AI and Other segment narrowed to $46 million compared to $82 million a year ago [16] Market Data and Key Metrics Changes - The company is focusing on scalable, modernizable use cases in AI and robotics, leveraging open-source models to enhance performance [17] - The total headcount was approximately 850, down from 862 a year ago, indicating cost control measures [18] Company Strategy and Development Direction - The company aims to strengthen its position in both traditional and new business areas, with a focus on AI and robotics [4] - AI is central to the company's strategy, with significant investments in R&D and product upgrades [8] - The company plans to enhance its legacy Internet business while pushing forward with AI initiatives [11] Management's Comments on Operating Environment and Future Outlook - Management expressed optimism about the ongoing positive momentum in revenue growth and loss reduction [5] - The company is cautious about the robotics industry's commercialization timeline, indicating it may take over five years for humanoid robots to achieve significant market presence [52] Other Important Information - The company has over $200 million in cash, providing strategic flexibility for potential acquisitions [66] - The focus is on transforming traditional software into AI-driven applications, emphasizing user-centered payment models [45][46] Q&A Session Summary Question: Will Cheetah's future development focus more on AI tools or robots? - Management believes both areas are complementary, with a short-term focus on AI tools due to rapid development potential [22][24] Question: What are the company's thoughts on data asset construction in robotics? - Management is cautious about the current state of data conversion in robotics and does not plan to provide external data services at this time [26][30] Question: How does the company balance open-source models and self-developed approaches? - The company prioritizes efficiency and will use open-source models when they provide better results [32][33] Question: What is the company's commercialization path for AI tools? - The company is adopting a subscription model for AI tools, which has shown user acceptance and willingness to pay [42][44] Question: Will the company achieve overall break-even in the second half of 2025? - Achieving profitability is a major goal, but it depends on core business progress and market conditions [71][72]

Core Idea - Factory believes software development is transitioning to agent-driven from human-driven [1] - To achieve significant productivity gains (5-20x), a shift from collaborating with AI to delegating tasks entirely to AI is needed [3] - Factory is building a platform for managing and scaling AI agents, integrating various engineering systems [3][4][5] Agentic System Characteristics - Agentic systems require planning to decide future actions [11] - Decision-making is crucial for agents to make calls based on the existing state [13][14] - Environmental grounding is necessary for agents to interact with and adapt to the external environment [14] Human-AI Collaboration - Humans will remain in software development, focusing on the outer loop (reasoning, requirements) [15][16] - Agents will handle the inner loop (coding, testing, code review) [17] - AI UX should blend delegation with control for situations where agents cannot complete tasks [17] Agent Reliability - Clear planning and boundaries are essential for reliable agents [32] - Subtask decomposition, model predictive control, and explicit plan templating can improve planning [19][20] - Control over the tools agents use is the most important differentiator in agent reliability [28] Environmental Interaction - New AI computer interfaces are needed for agents to interact with the world [28] - Processing information from the environment is crucial for complex systems [29][30] - Agents need to ground themselves in the environment to perform full software development work [32] Call to Action - Factory encourages teams not delegating at least 50% of engineering tasks to AI agents to engage with them [34]

Company Overview - Browserbase provides infrastructure connecting large language models and the web, enabling end-to-end workflow automation [1] - Browserbase views itself as the "last-mile" interface between large language models and the web [1] Funding & Investment - Browserbase raised $27.5 million in its first 12 months [1] - The funding includes a $6.5 million seed round and a $21 million Series A [1] - CRV, Kleiner Perkins, and Okta Ventures led the Series A funding [1] Technology & Innovation - The web browser may become the default MCP server for the internet, enabling production AI Agents [1] - Browserbase offers fast, reliable, multi-region headless-browser infrastructure for developers and AI agents [1]

[Music] Bringing a life-saving drug to market requires analyzing massive amounts of complex data. The pharmaceutical industry needs a faster, more automated way to extract meaning and act on it. Ivia is using Agentic AI to do exactly that.Training AI agents to navigate more than a million data streams for clinical, medical, and commercial professionals. Its healthcare grade AI platform combines a growing set of AI agents, each designed to streamline how insights turn into action. Built with NVIDIA Neotron m ...