1,568 Matching Annotations
  1. Aug 2025
  2. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  3. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  4. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  5. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  6. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Developed a full-stack web application using with Flask serving a REST API with React as the frontend

      Remove 'using with' for clarity. Add impact metrics, such as user adoption rates or performance improvements.

  7. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Created LLM extension tools to help translate complex internal wikipedia pages to hyperlinked code snippets to help internal customers use the project at low-level logic, increasing efficiency by 300%.

      Provide context on what 'efficiency' means here. What specific tasks were made easier or faster?

    2. Automated robust CI/CD by building custom pipelines to unit, load, and integration test the code with 100% code coverage, enhancing safety in deployment into production waves.

      Specify how this automation improved deployment frequency or reduced errors in production.

    3. Designed a highly efficient system flow in integration and canary testing, decreasing latency by 70% and cost per API invocation by 2000%.

      Clarify the baseline metrics for latency and cost to provide context for the improvements made.

    4. Streamlined session management across internal teams by consolidating different types of sessions into a single master session, simplifying workflows between upstream and downstream callers.

      Quantify the efficiency gained or time saved through this consolidation to better illustrate the impact.

    5. Developed portable Model Context Protocol (MCP) servers for the team, extending knowledge for AI tools such as Amazon Q and Kiro IDE to study internal data and automate self-service tools, saving $240,000 every year.

      Explain how the $240,000 savings was calculated and what specific processes were improved to achieve this.

    6. Engineered solutions to operational problems involving cache validations and cyclic calls to raise the business availability to 99.998% and lower latency in customer federation by 60% in the busiest availability zones.

      Break down the specific operational problems solved and how they directly impacted user experience or system reliability.

    7. Addressed security challenges in serving device authentication and authorization flows to extremely reduce the chance of phishing attacks for customers.

      Quantify the reduction in phishing incidents or security breaches to highlight the effectiveness of your solutions.

    8. Led the creation of user background sessions to enable AI services such as AWS SageMaker run long-running tasks without user interactivity, creating a new paradigm in model training on AWS.

      Clarify how this paradigm shift benefited AWS users or reduced costs. Provide measurable outcomes.

    9. Took ownership of maintaining OIDC and SAML services for customer federation and integration with native and third-party applications across AWS.

      Specify the impact of maintaining these services. How did it improve customer experience or system performance?

  8. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  9. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  10. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. driving fast and iterative improvements and integrating AI-powered feedback directly within Discord.

      Provide specific outcomes from the feedback integration, such as user adoption rates or satisfaction scores.

  11. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Developed a full-stack web application to help students locate nearby study spots, track study sessions, and create study groups.

      Add metrics on user engagement or feedback to showcase the app's impact on student productivity.

    2. Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.

      Highlight any specific contributions or outcomes from these meetings to show leadership or initiative.

    3. eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.

      Quantify the time saved for executives or any decision-making improvements resulting from this change.

  12. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Developing an AI agent that monitors stablecoin flows in real time and infers intent behind large movements such as panic selling or emerging depeg risks, triggering proactive alerts and automated treasury actions for DAOs and crypto funds.

      Consider shortening for clarity; e.g., 'Developing an AI agent to monitor stablecoin flows and trigger alerts for large movements.'

    2. Implemented in-line PDF annotations through integration with Hypothes.is and AWS S3, automated change detection for resume updates, and version tracking with DynamoDB.

      Break into two sentences for clarity; consider rephrasing 'automated change detection' to 'automated detection of changes'.

    3. Built a Discord bot to streamline collaborative resume reviews, driving fast and iterative resume improvements for a community of 2000+ students.

      Specify 'driving fast and iterative improvements' with measurable outcomes, e.g., 'resulting in 30% faster review times'.

    4. Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.

      Use active voice: 'Collaborated in daily scrum meetings with a team of 5 developers...' for a stronger impact.

    5. Redesigned layout and fixed critical responsiveness issues on 10+ web pages using Bootstrap, restoring broken mobile views and ensuring consistent, functional interfaces across devices.

      Quantify 'critical responsiveness issues' with specifics to enhance impact; e.g., 'fixed 5 critical responsiveness issues'.

    6. Developed dashboards for an internal portal with .NET Core MVC, eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.

      Consider rephrasing 'eliminating the need for 100+ complex spreadsheets' to 'replacing 100+ complex spreadsheets' for stronger impact.

    7. Led backend unit testing automation for the shift bidding platform using xUnit, SQLite, and Azure Pipelines, contributing 40+ tests, identifying logic errors, and increasing overall coverage by 15%.

      Break into two sentences for clarity; consider rephrasing 'increasing overall coverage by 15%' to 'increasing test coverage by 15%'.

  13. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Built an NLP-powered Telegram Bot that parses natural language commands to allow expense-splitting directly in your group chat

      Specify user engagement metrics or feedback to illustrate the bot's effectiveness and popularity.

    2. Built a Discord bot to streamline collaborative resume reviews, driving fast and iterative resume improvements for a community of 2000+ students.

      Add specific metrics on how many resumes were improved or how quickly to demonstrate impact.

    3. Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.

      Focus on your contributions or outcomes from these meetings to highlight your role more effectively.

    4. eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.

      Clarify how this change improved decision-making or efficiency for the executives.

  14. Jul 2025
  15. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Built an NLP-powered Telegram Bot that parses natural language commands to allow expense-splitting directly in your group chat with fast, secure, on-chain expense records.

      Include user adoption rates or feedback to illustrate the bot's effectiveness and popularity.

    2. Developing an AI agent that monitors stablecoin flows in real time and infers intent behind large movements such as panic selling or emerging depeg risks, triggering proactive alerts and automated treasury actions for DAOs and crypto funds.

      Clarify the potential financial impact or risk reduction achieved through this AI agent's alerts.

    3. Built a Discord bot to streamline collaborative resume reviews, driving fast and iterative resume improvements for a community of 2000+ students.

      Add metrics on how many resumes were improved or user satisfaction ratings to demonstrate impact.

    4. Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.

      Highlight a specific contribution or idea that led to a significant improvement in team performance.

    5. Redesigned layout and fixed critical responsiveness issues on 10+ web pages using Bootstrap, restoring broken mobile views and ensuring consistent, functional interfaces across devices.

      Specify the user engagement metrics or feedback received post-redesign to showcase impact.

    6. Developed dashboards for an internal portal with .NET Core MVC, eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.

      Quantify the decision-making improvements or time saved for executives due to the dashboards.

    7. Built a React/.NET impersonation tool enabling admins to emulate employee sessions for support and troubleshooting, cutting developer testing setup time by 86% by eliminating the need for test accounts.

      Consider rephrasing to emphasize how this tool improved support response times or user experience.

    8. Led backend unit testing automation for the shift bidding platform using xUnit, SQLite, and Azure Pipelines, contributing 40+ tests, identifying logic errors, and increasing overall coverage by 15%.

      Add a specific example of a critical bug found to highlight the importance of your contributions.

    9. Developed an end-to-end shift bid publishing feature using Azure Functions (C#), SQL, and Azure Logic Apps, automating shift imports into the HR system for 700+ employees and saving 50+ hr/month of manual entry.

      Clarify the impact by stating how this improved efficiency or employee satisfaction beyond just time saved.

  16. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  17. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. Developed a full-stack web application to help students locate nearby study spots, track study sessions, and create study groups.

      Mention any user adoption rates or feedback to highlight the application's success and relevance.

    2. Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.

      Highlight any specific contributions or outcomes from these meetings to demonstrate leadership.

    3. eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.

      Quantify the time saved for executives to highlight the efficiency gained through your work.

  18. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  19. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
  20. resu-bot-bucket.s3.ca-central-1.amazonaws.com resu-bot-bucket.s3.ca-central-1.amazonaws.com
    1. AI and RPA are reshaping how businesses operate by combining automation with intelligence. This blog explores their individual roles, synergy, real-world use cases, and how they drive smarter, faster, and more scalable business processes.

      Discover how AI and RPA revolutionize business operations by automating workflows, reducing costs, enhancing accuracy, and driving digital transformation across industries.

    1. Oh yeah. If you're generating text that could burn anywhere from 0.17 watt hours to 2 watt hours, equal to running this grill for about four seconds. Generating an image add 1.7 watt hours. All that, less than 10 seconds on the grill. But short videos can use far more power. In tests of various open source models, videos took anywhere between 20 watt hours and 110 watt hours. At 110 watt hours, one steamed electric grill steak, about equal to one video generation. I wouldn't eat it, but my dog would. At 220 watt hours, it was looking much more edible. So two video generations equals one pretty good looking steak.

      Comparisons of text versus image versus video generation

    1. Navigating Failures in Pods With Devices

      Summary: Navigating Failures in Pods With Devices

      This article examines the unique challenges Kubernetes faces in managing specialized hardware (e.g., GPUs, accelerators) within AI/ML workloads, and explores current pain points, DIY solutions, and the future roadmap for more robust device failure handling.

      Why AI/ML Workloads Are Different

      • Heavy Dependence on Specialized Hardware: AI/ML jobs require devices like GPUs, with hardware failures causing significant disruptions.
      • Complex Scheduling: Tasks may consume entire machines or need coordinated scheduling across nodes due to device interconnects.
      • High Running Costs: Specialized nodes are expensive; idle time is wasteful.
      • Non-Traditional Failure Models: Standard Kubernetes assumptions (like treating nodes as fungible, or pods as easily replaceable) don’t apply well; failures can trigger large-scale restarts or job aborts.

      Major Failure Modes in Kubernetes With Devices

      1. Kubernetes Infrastructure Failures

        • Multiple actors (device plugin, kubelet, scheduler) must work together; failures can occur at any stage.
        • Issues include pods failing admission, poor scheduling, or pods unable to run despite healthy hardware.
        • Best Practices: Early restarts, close monitoring, canary deployments, use of verified device plugins and drivers.
      2. Device Failures

        • Kubernetes has limited built-in ability to handle device failures—unhealthy devices simply reduce the allocatable count.
        • Lacks correlation between device failure and pod/container failure.
        • DIY Solutions:
          • Node Health Controllers: Restart nodes if device capacity drops, but these can be slow and blunt.
          • Pod Failure Policies: Pods exit with special codes for device errors, but support is limited and mostly for batch jobs.
          • Custom Pod Watchers: Scripts or controllers watch pod/device status, forcibly delete pods attached to failed devices, prompting rescheduling.
      3. Container Code Failures

        • Kubernetes can only restart containers or reschedule pods, with limited expressiveness about what counts as failure.
        • For large AI/ML jobs: Orchestration wrappers restart failed main executables, aiming to avoid expensive full job restart cycles.
      4. Device Degradation

        • Not all device issues result in outright failure; degraded performance now occurs more frequently (e.g., one slow GPU dragging down training).
        • Detection and remediation are largely DIY; Kubernetes does not yet natively express "degraded" status.

      Current Workarounds & Limitations

      • Most device-failure strategies are manual or require high privileges.
      • Workarounds are often fragile, costly, or disruptive.
      • Kubernetes lacks standardized abstractions for device health and device importance at pod or cluster level.

      Roadmap: What’s Next for Kubernetes

      SIG Node and Kubernetes community are focusing on:

      • Improving core reliability: Ensuring kubelet, device manager, and plugins handle failures gracefully.
      • Making Failure Signals Visible: Initiatives like KEP 4680 aim to expose device health at pod status level.
      • Integration With Pod Failure Policies: Plans to recognize device failures as first-class events for triggering recovery.
      • Pod Descheduling: Enabling pods to be rescheduled off failed/unhealthy devices, even with restartPolicy: Always.
      • Better Handling for Large-Scale AI/ML Workloads: More granular recovery, fast in-place restarts, state snapshotting.
      • Device Degradation Signals: Early discussions on tracking performance degradation, but no mature standard yet.

      Key Takeaway

      Kubernetes remains the platform of choice for AI/ML, but device- and hardware-aware failure handling is still evolving. Most robust solutions are still "DIY," but community and upstream investment is underway to standardize and automate recovery and resilience for workloads depending on specialized hardware.

    1. Businesses are rapidly relying on artificial intelligence to enhance user experiences, automate processes, and gain competitive advantages. However, understanding AI app development cost remains one of the biggest challenges for companies planning to create artificial intelligence app solutions.

      Explore the key factors influencing AI app development cost in 2025. Learn how app complexity, features, and tech stack impact your budget for smart AI solutions.

    1. Virtual shopping agent technology combines natural language processing with predictive analytics. This combination allows such systems to understand customer queries in natural languages while predicting future purchasing behaviour. The result is a more intuitive and efficient shopping experience that feels personal and responsive. Intelligent shopping bots represent the most advanced form of these systems. They can handle complex customer interactions, process multiple data points simultaneously, and learn from every interaction.

      Explore how AI agents for online shopping revolutionize with personalization, automation, and smart recommendations reshaping the future of eCommerce.

    1. Automating oral argument

      A Harvard Law graduate who argued before the Supreme Court fed his case briefs into Claude 4 Opus and had it answer the same questions the Justices posed to him. The AI delivered what he called an "outstanding oral argument" with coherent answers and clever responses he hadn't considered, leading him to conclude that AI lawyers could soon outperform even top human advocates at oral argument.

    1. Inter-node communication stalls: high batching is crucial to profitably serve millions of users, and in the context of SOTA reasoning models, many nodes are often required. Inference workloads then resemble more training.

      Oh, so to get the highest throughout, the inference servers also batch operations making it look a bit like training too

    1. In today’s fast-moving, AI-powered era, autonomous agents are playing a bigger role than ever. They are helping businesses run smoother and making decisions affecting millions of lives every day. While these systems are designed to make our lives easier and unlock new opportunities, we can’t get carried away—we need to implement proper AI Agent Evaluation frameworks and best practices to ensure these systems actually work as intended and follow ethical AI principles.

      Explore the key metrics, tools, and frameworks used for AI agent evaluation. Learn how to assess performance, reliability, and efficiency of AI agents in real-world scenarios.

    1. Most businesses are making the jump from traditional, reactive and static applications to intelligent, proactive Flutter applications that understand and analyze user behaviour, and adapt accordingly. Moreover, 71% of consumers show interest in wanting Gen AI integrations for their shopping applications.

      Learn how to integrate AI into Flutter apps to deliver smarter, more intuitive mobile experiences. Discover tools, techniques, and best practices for Flutter AI integration.

  21. Jun 2025
    1. https://web.archive.org/web/20250630134724/https://www.theregister.com/2025/06/29/ai_agents_fail_a_lot/

      'agent washing' Agentic AI underperforms, getting at most 30% tasks right (Gemini 2.5-Pro) but mostly under 10%.

      Article contains examples of what I think we should agentic hallucination, where not finding a solution, it takes steps to alter reality to fit the solution (e.g. renaming a user so it was the right user to send a message to, as the right user could not be found). Meredith Witthaker is mentioned, but from her statement I saw a key element is missing: most of that access will be in clear text, as models can't do encryption. Meaning not just the model, but the fact of access existing is a major vulnerability.

    1. Browser-based applications operate entirely within web browsers using standard technologies like HTML, CSS, and JavaScript. Unlike desktop applications requiring local installation, these applications run through web browsers and access device capabilities through modern web APIs. This approach enables cross-platform compatibility and immediate accessibility from any internet-connected device.

      Learn how to build a browser-based AI application with step-by-step insights on tools, frameworks, and best practices. Explore scalable solutions for real-time AI in the browser.