Online Skill Verification

LiveMCP-101 This paper introduces LiveMCP-101, a novel real-time evaluation framework with a benchmark designed to stress-test agents on complex, real-world tasks. It moves beyond the mock data and synthetic environments of previous works. More notes ↓ Overview First, it builds a set of 101 challenging queries refined through LLM rewriting and manual review. Then, it runs two agents in parallel, one following a ground-truth plan and one autonomous, to provide a fair, real-time comparison. More on this critical analysis: The core innovation is evaluating against a ground-truth execution plan, not just a final API output. This better reflects the evolving nature of real-world tool use. Beyond Simple Benchmarks: LiveMCP-101 moves past synthetic tests with 101 curated tasks requiring the coordinated use of diverse MCP tools. The queries are intentionally complex, with an average of 5.4 tool-calling steps, to reveal where even state-of-the-art models fall short. Frontier Models Struggle: The results are revealing: even the most advanced LLMs achieve a task success rate below 60%. Performance degrades substantially as task difficulty increases, with the top model, GPT-5, scoring only 39.02% on hard tasks. Why Agents Fail: The paper provides a fine-grained failure analysis, identifying seven common error types: ignoring requirements, overconfident self-solving, unproductive thinking, wrong tool selection, syntactic errors, semantic errors, and output parsing errors. Paper: https://lnkd.in/emwPteRG

Real-time built-in assessment in VR training Our primary goal in designing VR training modules is to create a powerful real-time tool for tracking learning progress. This will help both trainees and instructors identify areas for improvement. So, how do we achieve this? We use built-in assessments during VR training sessions. Here are the types we use: 1. ⚠ Diagnostic assessment: Spot and fix problems in scenarios. 2. 💬 Formative assessments: They give feedback to help learners improve. 3. ➡️ Scenario-based assessments: Make decisions in real-life situations. 4. ❗️ Performance-based assessments: Complete tasks in VR. 5. ✅ Interactive decision assessment: Choose the next step in a scenario. 6. 🔠 Summative assessments: Evaluate performance at the end. We use interactive tools in our VR training modules to diversify assessments. For instance, we use a wristwatch for assessment and benchmarking. It gives instant feedback on the user's actions. Using various assessments helps learners review actions, see flaws, and strengthen knowledge. This builds expertise. What assessment methods have you found effective? #Design #VR #XR #UI #UX #VirtualReality #Edtech #UnraelEngine #GameDev #VRAssessment #Electricity #VRTraining #Training #Education #ElectricalTraining #TrainingProvider #Upskilling

The consulting industry built a multi-billion dollar business on one premise. Sales methodologies are relatively easy to teach and almost impossible to adopt. That premise is no longer true. We are launching a new value selling methodology this quarter, and rather than writing a six or seven figure check to reinforce it, we are using the Otter.ai MCP server with Claude to do the reinforcement automatically. Every customer call gets scored in real time against our four-box framework, with additional ratings for multithreading and discovery depth. The scorecard posts into Slack within minutes of the call ending, complete with a rating per box, a written rationale, and the top three coaching moments including the exact language the rep could have used in the moment. The screenshot below is a real example from one of our own discovery calls, with names redacted. Think about what this actually replaces. The offsite training, the laminated cards, the CRM scorecards nobody fills in, the quarterly pipeline reviews where managers retroactively apply the framework to deals they half-remember, and the consulting partner checking on adoption every six weeks. All of it was a very expensive way to solve a reinforcement problem at scale, and agentic AI solves that problem natively. Reps get specific feedback tied to the exact moment they missed. Managers review coaching signal across dozens of calls in the time it used to take to review one. Leaders track longitudinal progression per competency for every rep in the org, in real time. The playbook for rolling out sales methodology has fundamentally changed, and the cost structure that came with it has changed right along with it. #aicoach #otter.ai #aiimpact

If you’re looking to break into Cloud in 2026, here’s a practical cloud skills roadmap you can follow. This is a high-level breakdown of the core areas worth focusing on: 1. Cloud Foundations → Understand how cloud providers think: regions, networking, IAM, pricing models. → This is about how cloud works, not which button to click. 2. DevOps & Cloud-Native → Learn how code moves from laptop → production reliably. → Containers, CI/CD, observability ~ the flow matters more than the platform. 3. Infrastructure as Code → Infra should be repeatable, reviewable, and versioned. → Think declarative systems, state, and lifecycle.. not manual setup. 4. Networking & Security → How traffic flows. How access is controlled. How things break. → This is where most real-world issues come from.. knowing how to troubleshoot distributed systems is critical.. 5. AI / ML Infrastructure → Not just model training.. but how models run in real systems. → Serving, scaling, GPUs, monitoring, and cost awareness. 6. Platform Engineering → How teams build internal platforms that enable developers. → Focus on developer experience, self-service, and golden paths. 7. Cost & Operations (FinOps) → Everything you deploy has a cost. → Learn how usage, scale, and architecture impact spend. Key takeaway: Tools change. Core Buckets don’t. Tools are just implementations. What matters is understanding why a system exists, what problem it solves, and how the pieces fit together. Don’t learn tools in isolation. Learn systems thinking.. tools will follow. If you’re early in your cloud journey, save this. If you’re already in the field, which bucket would you double down on next?

How do you know on a minute to minute basis whether your AI Agent or RAG system is responding correctly? Most CIOs/CSOs and business leaders I meet with don't realize that this is even possible. I meet around ten companies a week who usually try an eval or observability platform which requires an ML team and static test sets for benchmarking. Those test sets quickly become out of date, and require time to curate. Evaluation occurs offline and doesn't help your AI system produce better responses in real time. To their surprise, real-time evaluation exists and there are many solutions which are more accurate than traditional eval and evaluate LLM, Agent, and RAG responses in under 0.3 seconds immediately as the response occurs, so you always know how well your system is performing. tl:dr-- It is now possible to automatically detect incorrect RAG responses without ground-truth answers or labels. This benchmark shows how well this works in practice and the current most accurate real time evaluation in the market used today by both large and small enterprises alike. In this benchmark, among real-time evaluation models for RAG, recall and precision were measured across 6 RAG applications, benchmarking evaluation models like: LLM-as-a-Judge, Prometheus, Lynx, HHEM, and TLM. Immediate use cases: proof check, guardrail and control the reliability of every RAG and LLM response. Have fun!

Target gives real-time feedback to their employees every 3 seconds. Every time a cashier scans an item, they see color-coded feedback on their screen: 🟢 Green = On pace 🟡 Yellow = Slightly behind 🔴 Red = Need to speed up After each transaction, they see their average speed (creating a personal benchmark). Studies from Alibaba's warehouses show real-time feedback improves efficiency by 7.0%, with notable gains across all performance levels.1 Gallup also found 80% of employees who receive meaningful weekly feedback are fully engaged, suggesting recency matters.2 The problem with traditional performance reviews is that by the time you tell someone they're off track, habits are already formed. They don't know what they're being rewarded for or what they should change. Real-time feedback removes the ambiguity. Workers adjust in the moment and their performance improves immediately. This doesn’t simply apply to cashiers though. Many frontline roles, from restaurant service to healthcare documentation to manufacturing, could benefit from clearer, immediate feedback. Setting clear goals and providing timely feedback, and tools that provide staff real-time coaching, equips them to succeed.

Hybrid and Remote Team Performance Evaluations – Traditional performance evaluations don’t work for hybrid and remote teams. Relying on “time in the office” or quarterly reviews leads to frustration, misalignment, and concerns about career growth. – A better approach? Frequent, structured check-ins. Weekly or biweekly reviews keep employees engaged, provide real-time feedback, and ensure continuous professional development. Employees submit a short report on accomplishments, challenges, and goals, and managers provide timely feedback before a brief meeting. – This system prevents surprises in quarterly reviews, strengthens communication, and keeps employees accountable without micromanaging. It also helps supervisors guide professional growth, ensuring that remote and hybrid employees don’t feel overlooked. – The future of performance evaluation is clear: data-driven, frequent, and focused on impact—not just hours logged. Companies that embrace this shift will see higher engagement, better retention, and stronger results. Read more in my article for Quality Digest https://lnkd.in/gVGmNtHv

I’ve used this system in every performance environment I’ve worked in for over a decade. Here's why... Over 10 years ago, I was working with military operators on decision-making under pressure. The question wasn’t if vision mattered — it was: How do we actually measure the visual-cognitive system in a way that’s reliable, repeatable, and relevant? At the time, most options were pieced together, time-intensive, or disconnected from real performance demands. That's when we found Senaptec's Sensory Station Here’s what stood out then, and why it still holds up today: 1 - It measures what actually matters Visual, cognitive, and motor skills don’t operate in isolation. This system assesses 9 integrated skills that directly influence decision-making, reaction, and execution. 2 - It’s efficient and repeatable What used to require multiple tools and hours of testing became a modern, digital, interactive evaluation that fits real performance environments. 3 - It’s data-forward With millions of data points and a global normative database, you can track adaptation, improvement, and potential risk - not just scores. 4 - It enables meaningful comparison You can contextualize results by sport (#football, #basketball, #soccer, #Indycar), population (military, tactical), and environment. 5 - It allows positional insight Quarterback vs. lineman. Goalie vs. forward. Shortstop vs. outfielder. That level of specificity matters if you care about transfer. And it doesn’t stop at assessment. Coaches and practitioners can customize 12+ targeted training tools to reinforce the exact visual-cognitive skills athletes rely on in competition. Or, assign adaptive training plan that adapt with the athlete. What surprises me? I still hear people say this is “new” technology. It’s not new. It's proven - and it’s evolved without losing what made it effective in the first place. If you want to see real evaluation results and how we interpret them, comment “RESULTS.” I’m happy to share how we use it. There’s a reason it’s trusted by top teams, elite clubs, sports medicine clinics, and longevity programs - and why it remains a core tool in our performance stack. #SportsVision #NeurocognitiveTraining #HighPerformanceSport #SportScience #AthleteDevelopment

Developing a Competency-Based System for the Company A Competency-Based System (CBS) ensures employees develop the necessary skills, knowledge, and behaviors to perform their jobs effectively. It aligns training, assessment, and career progression with business goals. Here’s how to build a CBS step by step. Step 1: Create a Competency Profile Compare job responsibilities (from Job Descriptions or Job Task Analysis) with competency standards to define the required skills. Step 2: Develop a Competency Library The Competency Library consists of Competency Units (Standards) relevant to each role. We can adopt, adapt, and tailor standards from the Australian Competency-Based System, available at www.training.gov.au, including: ✔ RII (Resources & Infrastructure) ✔ MEM (Manufacturing & Engineering) ✔ BSB (Business Services) ✔ TAE (Training & Education) Step 3: Develop a Competency Matrix A Competency Matrix maps job positions to their required competency units, ensuring structured workforce development. Step 4: Develop a Competency Scorecard A Competency Scorecard defines the competency requirements at each level within a job, supporting career progression. Step 5: Use Power BI for Competency Tracking A Power BI dashboard integrates: 📊 Competency Profiles 📊 Competency Library 📊 Competency Matrix 📊 Competency Scorecard This allows real-time monitoring, gap analysis, and workforce planning. Step 6: Develop Assessment Materials Assessments should align with competency standards and include: ✅ Theoretical Tests – Evaluating knowledge. ✅ Practical Assessments – Measuring technical and behavioral skills. Step 7: Develop Training Materials Training should be competency-based, covering all performance criteria and knowledge elements from the standards. Step 8: Create Individual Training Plans Assessment results should guide personalized training plans, ensuring employees receive targeted learning to close skill gaps. Conclusion A Competency-Based System builds a skilled and efficient workforce. By using Australian Competency Standards from www.training.gov.au and Power BI dashboards, companies can streamline workforce development and ensure training aligns with business needs. Are you looking to implement a Competency-Based System in your company? Let’s connect! #CompetencyBasedTraining #WorkforceDevelopment #MiningIndustry #CompetencyMatrix #Training #HRDevelopment #PowerBI #BusinessSuccess

If you’re a data analyst, learning cloud skills is one of the highest-ROI moves you can make right now. Not because it’s trendy. But because it changes how valuable you are. Here’s what cloud skills actually do for you as a data analyst 👇 1. You move from “reporting” to “impact” With cloud skills, you’re no longer just pulling data and building dashboards. You can: 👉 work with massive datasets without performance issues 👉 support real-time or near real-time analytics 👉 design data workflows that scale with the business. That’s the difference between showing insights and powering decisions. 2. You become harder to replace Many analysts know SQL, Excel, and Python. Fewer analysts understand: 👉cloud data warehouses 👉automated data pipelines 👉access control, security, and cost optimization Once you touch the infrastructure layer, you stop being interchangeable. And that’s leverage. 3. You unlock better roles and faster growth Cloud skills naturally position you for: 👉senior analyst roles 👉analytics engineering 👉data engineering transitions Even if you stay an analyst, cloud knowledge puts you closer to engineering teams and leadership, where growth happens faster. 📍Free resources to start learning cloud (no excuses) You don’t need paid courses to begin. These are genuinely solid and free: 🔹 Microsoft Azure 👉 Microsoft Learn: https://lnkd.in/dqngCZYp 👉 Azure Free Account: https://lnkd.in/dhWngNJ7 👉 Azure YouTube Channel: https://lnkd.in/dKCwwswG Best if you work with Power BI, SQL Server, or Microsoft-heavy stacks. 🔹 Amazon Web Services (AWS) 👉 AWS Training & Certification: https://lnkd.in/dHGAhtw8 👉 AWS Free Tier: https://lnkd.in/dgeAD86Y 👉 AWS Tech Talks (YouTube): https://lnkd.in/dG6tbs7F Great for data pipelines, warehousing, and production-scale analytics. 🔹 Google Cloud Platform (GCP) 👉 Google Cloud Skills Boost: https://lnkd.in/dwq-Cyn7 👉 GCP Free Tier: https://lnkd.in/dYDcHUNE 👉 Google Cloud Tech (YouTube): https://lnkd.in/d-kmsFNA Excellent for BigQuery, analytics at scale, and modern data stacks. 📍You don’t need to become a cloud engineer. But as a data analyst, understanding how data lives, moves, and scales in the cloud will quietly separate you from the crowd. Start small. Stay consistent. Your future self will thank you. ♻️Repost to educate your network