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 ...

Investment Focus - Notable Capital focuses on founders, providing them with unique experiences and support to build successful companies [2] - The firm is overweight and focused on software infrastructure and cloud infrastructure [4] - AI is central to almost every investment Notable Capital is making [9] - Notable Capital is focused on helping entrepreneurs build global technology companies, particularly in the US market [10] AI and Agent Technology - Agents are impacting companies of all sizes across industries, assisting with software development, sales, customer support, security, HR, and finance [4][5] - Agents require infrastructure for security and monitoring, creating opportunities for companies focused in this area [6][7] - Agents are taking entry-level jobs, but the opportunity lies in embracing AI and becoming proficient users of such technologies [12] Global Strategy - While Silicon Valley is a hub for innovation, Notable Capital is working with founders in Israel, Europe, and Latin America to build global companies [9] - Building a global technology company often starts in the US, with companies focusing on the US market for go-to-market strategies [10] - The next wave of innovative AI companies will be global in nature [13]

Agent Evaluation Challenges - Building agents is difficult, requiring iteration at the prompt, model, and tool call definition levels [2][3] - Systematically tracking the performance of new prompts versus previous ones is challenging [4] - Including product managers or other team members in the iterative evaluation process is difficult [5] - Identifying bottlenecks in applications and pinpointing specific sub-agents or tool calls that create poor responses is hard [6] Evaluation Components - Agent evaluation should include evaluating at the tool call level, considering whether the right tool was called and if the correct arguments were passed [7][11] - Trajectory evaluation is important to determine if tool calls are executed in the correct order across a series of steps [7][20] - Multi-turn conversation evaluation is necessary to assess consistency in tone and context retention across multiple interactions [8][22][23] - Improving evaluation prompts is crucial, as the evals used to identify failure cases are essential for improving the agent [8][27] Arise Product Features - Arise offers a product for tracing and evaluating agent performance, allowing teams to ask questions about application performance and suggest improvements [12][13] - The product provides a high-level view of different paths an agent can take, helping to pinpoint performance bottlenecks [14][15] - Users can drill down into specific traces to evaluate tool call correctness and argument alignment [17][18]

AI Developer Tool Strategy at Uber - Uber's AI developer tool strategy focuses on products that directly improve developer workflow, such as writing tests and reviewing code [6] - The strategy emphasizes building crosscutting primitives, foundational AI technologies applicable across multiple solutions [7] - Intentional tech transfer is a cornerstone, identifying and spinning out reusable components to reduce barriers for future problem-solving [8][9] Key Products and Their Impact - Validator, an IDE experience, flags best practices violations and security issues, resulting in thousands of fixed interactions daily [11][16] - Autocover, a test generation tool, leverages domain expert agents and has increased developer platform coverage by approximately 10%, saving an estimated 21,000 developer hours [17][27][28] - Uber has built an internal custom GPT store where you can build chatbots that are steeped in Uber knowledge [29] Technical Learnings - Building super capable domain expert agents yields outsized results due to better context utilization and reduced hallucination [34] - Composing agents with deterministic sub-agents, like the lint agent in Validator, ensures reliable output [36] - Scaling development efforts is achieved by creating and reusing agents across multiple applications, such as the build system agent [37] Strategic Learnings - Encapsulation through well-defined abstractions like Langraph boosts collaboration and allows for horizontal scaling of development [39] - Graphs model interactions effectively, mirroring developer workflows and improving both AI and non-AI experiences [41]

With the increased use of NHIs and AI agents, especially with machine identities, there is a huge opportunity for Okta. This goes the same for the Total addressable market (TAM). As of now, Okta (NASDAQ: NASDAQ: OKTA ) is amongAnalyst’s Disclosure:I/we have a beneficial long position in the shares of OKTA either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have ...

AI and Automation in Unstructured Data Processing - AI is expected to significantly drive automation, potentially replacing Robotic Process Automation (RPA) [2][56] - The industry is moving towards decentralized, federated AI execution for automation [2][55][56] - Enterprises are exploring AI-driven automation for end-to-end workflows, potentially replacing RPA [62] Challenges and Solutions for Unstructured Data - Unstructured data is defined as anything that cannot be put into a nice database table for SQL queries, such as PDF documents or images [3][4] - Traditional techniques for processing unstructured data, like templates and rule-based systems, are brittle and unreliable [7][8] - A key challenge is ensuring reliability, completeness, and accuracy when using Large Language Models (LLMs) for unstructured data processing, especially in critical decision-making processes [18][19][24][25] - The industry emphasizes the need for complex, explainable, and auditable workflows to guarantee accuracy when using LLMs with unstructured data [24] Enterprise Adoption and Requirements - Enterprises prioritize data safety, security, auditability, and predictability when adopting AI solutions [42][43] - Predictability of errors is more important than achieving 100% accuracy; enterprises need to know which cases require human review [28][30][31] - Enterprises are adapting their acceptance criteria for AI, focusing on improvements over human performance rather than absolute perfection [27] The Role of AI Agents - AI agents can assist during the build or compile time by generating initial drafts of workflows, but runtime execution should remain deterministic and auditable [48][49][50][65][66] - The industry views autonomous agents as a compile-time phenomenon, where they aid in creating artifacts for deterministic runtime execution [49] Transforming Customer Experience - AI is enabling new, conversational customer interactions, such as lending over WhatsApp [36] - AI can transform processes like insurance claims and account openings, making them more interactive and user-friendly [37][38][39]