Engineering Process Improvement Projects

The fastest fix for the U.S. interconnection backlog is battery storage technology that's already cheap and quick to build, paired with a small tweak to how "capacity" gets defined. The core problem: connecting new generation to the grid now takes up to eight years in most U.S. markets, because interconnection studies model worst-case, full-output conditions years into the future and bill developers for every upgrade needed to survive them. More than 2,000 GW of proposed projects are stuck in queues nationally, enough to double the size of our grid. A generator or battery storage system sited right next to a data center, industrial load, or Wal-mart store can supply real capacity to that specific load even if it looks like a plain "energy-only" resource to the wider grid operator. The system operator's capacity-accreditation process wasn't built for this arrangement, so today's interconnection rules treat it as more red tape than it needs to be, even though the physical reliability benefit is real and local. Batteries are uniquely suited to unlock this fast. A BESS project can be permitted and built in 12–18 months, versus years to even procure a gas turbines or build new transmission. It's dispatchable to the minute, which is exactly the property that lets it opt into the "connect and manage" style of access Texas already uses. This means a lighter safety study and accept curtailment if the transmission grid is "full". For a battery, curtailment isn't even much of a compromise; it's how it operates today. In PJM alone, standalone battery storage totals about 67.5 GW of proposed capacity across 349 projects, the second-largest category behind natural gas. Yet PJM's own pilot cycle shows the bottleneck in action: only about 1.9 GW of that battery capacity actually reached a signed interconnection agreement, taking nearly two years even under the "fast" reformed process. The projects should all be greenlighted this year to respond to this crisis. Two federal proposals are converging on the fix. Sen. Martin Heinrich's new bill would create "BASED" service (Basic Access Service for Energy-Only Delivery) nationally — any new plant, including batteries, could connect fast on an energy-only basis and upgrade to full capacity status later. The Energy Cost Fairness and Reliability Act (S.4559) targets the colocated case directly: fast, non-firm energy access for a battery or generator serving a nearby large load, while requiring that any capacity pulled off the public grid be backed by real replacement generation, so other customers aren't left exposed. At the FERC/PJM conference next week, they need to unlock these batteries to provide the capacity required to get through the new few years while everyone waits for other technologies to come online after 2031.

A junior reached out to me last week. One of our APIs was collapsing under 150 requests per second. Yes — only 150. He had tried everything: * Added an in-memory cache * Scaled the K8s pods * Increased CPU and memory Nothing worked. The API still couldn’t scale beyond 150 RPS. Latency? Upwards of 1 minute. 🤯 Brain = Blown. So I rolled up my sleeves and started digging; studied the code, the query patterns, and the call graphs. Turns out, the problem wasn’t hardware. It was design. It was a bulk API processing 70 requests per call. For every request: 1. Making multiple synchronous downstream calls 2. Hitting the DB repeatedly for the same data for every request 3. Using local caches (different for each of 15 pods!) So instead of adding more pods, we redesigned the flow: 1. Reduced 350 DB calls → 5 DB calls 2. Built a common context object shared across all requests 3. Shifted reads to dedicated read replicas 4. Moved from in-memory to Redis cache (shared across pods) Results: 1. 20× higher throughput — 3K QPS 2. 60× lower latency (~60s → 0.8s) 3. 50% lower infra cost (fewer pods, better design) The insight? 1. Most scalability issues aren’t infrastructure limits; they’re architectural inefficiencies disguised as capacity problems. 2. Scaling isn’t about throwing hardware at the problem. It’s about tightening data paths, minimizing redundancy, and respecting latency budgets. Before you spin up the next node, ask yourself: Is my architecture optimized enough to earn that node?

🔒 Local optimisation is the silent killer of GRC careers. You can write flawless controls language and policies. You never got a qualified report in your career. You can crush your Jira queue and manage issues like a boss. And still be irrelevant to the actual risk posture of your company. Why? Because you optimised for your domain, not the system. Most GRC roles reward local wins: ✅ Implement a new policy ✅ Collect 100 pieces of evidence ✅ Close the audit findings in time But none of that matters if: - Those policies don't change behaviour - The evidence isn't used to drive assurance - The audit scope misses critical risk exposure This is local optimisation. You solve for your team, your sprint, your metrics. But real leverage lives in global optimisation: ⚙️ Did this control reduce attack surface? ⚙️ Did we unblock a revenue-critical customer? ⚙️ Did this exception inform our roadmap? GRC Engineering isn't about ticking faster. It's about aligning your part with the whole. Being "high-performing" in an irrelevant system is just... wasted throughput. Zoom out. Map your work to systemic outcomes. Because the future belongs to GRC professionals who understand that GRC engineering is about systems, not just about static deliverables. Any examples where you've caught yourself optimising locally instead of globally? #GRCEngineering #SystemsThinking

🚀How to choose the right KPIs for your tech scale-up I've noticed a consistent challenge Many businesses collect extensive data but struggle to identify which metrics actually matter. Here's my top tips on choosing KPIs that will genuinely drive your business forward. 1️⃣ Start with strategy, not metrics: Your KPIs should reflect your strategy through numbers. Before opening any spreadsheets, ask yourself: 🎯where are you aiming to get to? 🥅what specific goals have you set for your team? 🥸how do you differentiate from competitors? Your answers should guide your choice of metrics, not the other way around. 2️⃣Balance leading and lagging indicators: Here's a practical example. If your goal is to increase premium tier adoption from 15% to 25%, that percentage is your lagging indicator. But you need leading indicators to drive progress. For your sales team, this might mean tracking: ✅number of upgrade conversations with existing customers ✅weekly demos of premium features ✅customer feature usage patterns These leading indicators help predict whether you'll hit your target and allow for adjustments while there's still time to impact the outcome. 3️⃣The essential metrics: Some metrics need consistent monitoring regardless of your strategy. In my experience, these include: ☑️MRR ☑️EBITDA ☑️Cash runway ☑️Customer LTV ☑️Customer churn Consider these your fundamental business health indicators. 4️⃣Make data collection seamless: Even the best-designed KPI framework fails if data collection is manual and inconsistent. Two key principles: 🖥️automate wherever possible 🐣capture data at its earliest possible point For example, don't wait for finance to categorize sales by department at month-end. Build it into your invoicing process. 5️⃣Consider the human element: Numbers need context to drive action. For KPIs to create change: 🗣️share them with the people who can impact them 🤔explain the reasoning behind each metric 🔎make them visible and accessible 🫧create clear accountability I've consistently seen that teams who understand why they're tracking certain metrics perform better than those who are simply told what to track. What separates effective KPI frameworks from ineffective ones? Keep your regular reporting focused on metrics that are: 🔗directly linked to strategy 😕simple to understand ✔️actionable by your team ❤️🩹critical to business health But maintain other data points in your systems. They become valuable when investigating problems or identifying opportunities. If you're working on refining your KPI framework, what's the one metric that's transformed how you view your business performance? Want to dive deeper into building effective reporting structures for your scale-up? DM me for a copy of our KPI framework template. #techscaleup #startupmetrics #businessgrowth #datadrivendecisions

As 2025 comes to a close, I am starting a short personal series. Each day, I will share one technical paper that, in my view, had a meaningful impact on how AI systems are being built this year. Day 1 starts with a paper that helped clarify a shift many teams were already experiencing in practice. The paper is “Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions.” While it is presented as a survey, its real contribution is giving structure to an area of work that had become fragmented. The central argument of the paper is straightforward. The main challenge in AI today is no longer improving individual models. It is optimizing systems composed of many interacting components such as language models, retrieval modules, tools, code, and agents. These systems are dynamic and context dependent, which makes traditional optimization approaches harder to apply. The authors offer a simple way to think about these AI systems. Instead of looking at each model or tool in isolation, they look at the whole system as a set of steps that can change based on the situation. They then show how the system can improve over time by learning from outcomes, either through clear success and failure signals or through written feedback generated by AI itself. This helps connect ideas like agents, retrieval-based systems, and tool-driven workflows into one coherent way of thinking about improvement. One part I found especially useful is the 2×2 taxonomy based on structural flexibility and learning signals. It makes it easier to understand why different agent frameworks behave the way they do and what trade-offs they make around cost, reliability, and control. This is directly relevant when designing systems meant to run in production. The paper also stays close to real engineering constraints. It discusses compute cost, token usage, lack of benchmarks for system-level behavior, safety risks that emerge from combining multiple components, and the absence of mature tooling. These are the same issues many teams are dealing with today. If earlier phases of AI focused on what models can do, this paper reflects a shift toward how complete systems should be designed, optimized, and evaluated. I will share the next paper in this series tomorrow. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal

In today’s fast-paced business world, setting clear objectives is crucial to achieving success. 𝐊𝐞𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐈𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫𝐬 (𝐊𝐏𝐈𝐬) are one of the most effective tools for aligning your strategy with business goals. They help measure progress, spot trends, and ensure everyone in the organization is working towards the same vision. But simply having KPIs is not enough—they need to be defined, tracked, and analyzed in ways that make them actionable and meaningful. 𝐻𝑒𝑟𝑒’𝑠 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑐𝑎𝑛 𝑒𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑙𝑦 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝐾𝑃𝐼𝑠 𝑡𝑜 𝑑𝑟𝑖𝑣𝑒 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐 𝑎𝑙𝑖𝑔𝑛𝑚𝑒𝑛𝑡 𝑖𝑛 𝑦𝑜𝑢𝑟 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛: 1. Define Clear Goals: The first step is to ensure that your KPIs align with the company’s overall objectives. Ask yourself, “What is the organization trying to achieve this quarter, this year?” KPIs should serve as the roadmap to these goals, acting as a guiding light for teams to follow. 2. Measure What Matters: Not all data is created equal. Focus on the metrics that have the biggest impact on your business. This means prioritizing KPIs that directly affect performance, customer satisfaction, revenue, and growth. Identify what truly drives success and avoid getting caught up in vanity metrics. 3. Make KPIs Actionable: KPIs are only valuable if they drive decision-making. Ensure that they provide real-time insights that enable teams to take immediate action. If a metric shows a problem, your teams should be equipped to address it swiftly and strategically. 4. Consistency is Key: Tracking KPIs over time allows you to spot trends and patterns that could indicate underlying issues or opportunities. Regular reviews help keep everyone on track and allow for adjustments when necessary. Consistency also ensures that you're not blindsided by sudden changes. 5. Accountability: Every KPI should have a clear owner—someone responsible for tracking, analyzing, and reporting on that metric. Accountability ensures that the right actions are being taken and encourages continuous improvement. By consistently aligning KPIs with your strategic goals, you create a roadmap that drives measurable progress and keeps everyone in sync. KPIs not only help you measure success but also serve as a powerful tool for making data-driven decisions and achieving long-term objectives. What KPIs have you found most effective in driving strategic alignment within your business? Share your insights in the comments! #BusinessStrategy #KPIs #DataDrivenDecisionMakingg #KeyPerformanceIndicators #PerformanceTracking

The construction industry has a core problem: we treat every building like a one-off prototype. That means costly learning cycles. Teams disband after handover, knowledge evaporates, and the next project starts from scratch. No wonder ecological innovation struggles to scale. This is why our industry stays inefficient while the world demands better sustainability and resource optimization. In 2008, I launched a research project to rethink building from the ground up: • bio-based materials,  • timber-hybrid systems,  • lower environmental impact,  • and far less energy input. By 2011, we had built our first eight-story wooden building. But I realized even my company, with over a billion turnover and 4,000 people, doesn't make a difference building three or four innovative buildings. The impact stays minimal. The breakthrough came when we stopped trying to scale the company and started scaling the knowledge instead. We created an open-source sharing platform. Instead of keeping our methods internal, we give our complete system to reliable partners in any country. They adapt it to local regulations and styles, but use the same proven core technology. This was the idea behind CREE BUILDINGS Now we have partners across multiple countries building with our system. Every improvement from every project gets shared back to the collective. This is how we create real industry transformation. The results speak for themselves. We execute 43% faster than conventional construction, which means lower interest costs and faster revenue generation for investors. Our operational costs are significantly lower, and tenants pay higher rents for sustainable buildings because corporations need green spaces to meet their carbon-neutral goals. We've proven the business case. Sustainable construction isn't just better for the planet; it's more profitable. But here's what really matters: we have the tools to change this industry right now. We don't need to wait for perfect technology or ideal policies. We just need to stop protecting our knowledge and start sharing it. The construction industry will transform when we move from prototyping every solution to systematically scaling the ones that work. Delivering products instead of headaches is key.

The journey from 5 prototypes to 20,000 production units was nothing short of a masterclass in Electronics manufacturing! Here are some key takeaways that significantly boosted our efficiency and quality: Testing Process Optimization: We drastically improved our testing workflow by implementing automated test sequences and in-line diagnostics. This not only reduced testing time but also ensured higher accuracy and reliability. Customized Elecbits Protocol-Based Jigs: We designed and built custom jigs tailored to our EBC protocol to streamline code uploading. This eliminated manual errors and significantly sped up the programming process, ensuring smooth and consistent code deployment across all units. 3D-Printed Stencils for Conformal Coating: Recognizing the bottleneck in our conformal coating process, we developed 3D-printed stencils. These stencils allowed for precise and consistent coating application, dramatically reducing application time and minimizing waste. This resulted in a much faster and more reliable conformal coating process. Robust Counterfeit Component Detection: We implemented a rigorous process to identify and prevent the use of counterfeit components and ICs. This involved strong control and monitoring of our supply chain, ensuring the integrity and reliability of our final product. It's amazing to see how targeted engineering solutions can make such a significant impact on large-scale manufacturing. We at Elecbits are now better equipped than ever to go from an idea to mass manufacturing in the most simpler, faster, and scalable manner. #ElectronicsManufacturing #ProductionOptimization #TestingAutomation #EmbeddedSystems #Engineering

LLMs have demonstrated exceptional performance across a wide range of tasks. However, their significant computational and memory requirements present challenges for efficient deployment and lead to increased energy consumption. It is estimated that training GPT-3 required 1,287 MWh, equivalent to the average annual energy consumption of 420 people! Recent research has focused on enhancing LLM inference efficiency through various techniques. To make an LLM efficient, there are 3 approaches: 𝟭. 𝗗𝗮𝘁𝗮-𝗟𝗲𝘃𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 focus on optimizing input prompts and output content to reduce computational costs without modifying the model itself. Techniques like input compression and output organization can be used to achieve this. Input compression involves strategies such as prompt pruning and soft prompt-based compression, which shorten prompts and thus reduce memory and computational overhead. On the other hand, output organization methods, such as Skeleton-of-Thought (SoT) and Stochastic Gradient Descent (SGD), enable batch inference, improving hardware utilization and reducing overall generation latency. These approaches are cost-effective and relatively easy to implement. 𝟮. 𝗠𝗼𝗱𝗲𝗹-𝗟𝗲𝘃𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 involve designing efficient model structures or compressing pre-trained models to enhance inference efficiency. This can be achieved through techniques such as efficient Feed-Forward Network (FFN) design, where approaches like Mixture-of-Experts (MoE) reduce computational costs while maintaining performance. These optimizations can be impactful in high-demand environments where maximizing performance while minimizing resource usage is critical, though they may require more significant changes to the model architecture and training processes. 𝟯. 𝗦𝘆𝘀𝘁𝗲𝗺-𝗟𝗲𝘃𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 enhance efficiency by optimizing the inference engine or serving system without altering the model itself. Techniques like speculative decoding and offloading in the inference engine can improve latency and throughput by optimizing computational processes. Furthermore, serving system strategies such as advanced scheduling, batching, and memory management ensure efficient resource utilization, reducing latency and increasing throughput. These optimizations are particularly useful for large-scale deployments where the model serves many users simultaneously. They can be implemented at a relatively low cost compared to developing new models, making them a practical choice for improving the efficiency and scalability of existing AI systems. As these optimization techniques continue to evolve, they promise to further enhance the efficiency and scalability of LLMs, paving the way for even more advanced AI applications. What other innovative approaches can we expect to see in the quest for optimal AI performance?

Central Electricity Authority (Cea) 2026 Amendment Sets New Technical Standards for #BESS, #Solar, and #Wind Projects in India effective from 1st April 2027. For years, BESS were largely seen as energy buffers. This amendment changes that narrative completely. Now, every BESS is expected to behave like a grid participant, not just a storage unit. • Active & reactive power control • Voltage regulation at the point of interconnection • Frequency response support This effectively aligns BESS with the expectations of modern #gridcodes. From a system design perspective, this pushes developers toward: • Advanced EMS architectures • High-performance PCS selection (with dynamic Q control) • Robust testing & validation frameworks ➤ Black Start & Grid-Forming Mandate For projects ≥50 MW, the regulation introduces a powerful requirement: Black start capability + Grid-forming inverter technology This is not a small upgrade—it’s a paradigm shift. Grid-forming (GFM) systems: • Establish voltage & frequency from scratch • Enable system restoration after total blackout • Support weak grid conditions where traditional generation struggles This aligns closely with global trends where grids are moving from synchronous inertia → #inverter-based stability. ➤ Performance Accountability Over 15 Years • ≥90% output after 5 years • ≥80% after 10 years • ≥70% after 15 years This introduces real accountability across the value chain: • Cell selection strategy • Thermal management design • Degradation modelling • Warranty structuring ➤ Solar: Moving Toward Traceability & Durability • Mandatory bypass diodes (reducing hotspot risks) • RFID tagging for lifecycle traceability • 25-year operational design requirement For floating solar: • UV & salt-resistant materials • Wind tunnel validation • Buoyancy testing This signals a move toward bankability through engineering discipline, not just capacity bidding. ➤ Wind Energy ≥500m distance from residential zones (noise mitigation) Offshore-specific requirements: • Scour protection • Marine-grade foundations • J-tube / I-tube cable systems • Offshore substations with helipad access This ensures that India’s offshore ambitions are built on global engineering standards from day one. ➤ Digital Data, Control & Grid Visibility • Remote operability via load dispatch centers • 90-day high-resolution data storage • Fault recording and analytics readiness ➤ Safety & Compliance • Multi-layer protection systems • Fire safety integration • Compliance with National Building Code From where I see it, this amendment does three things: → Only serious, system-level players will survive → Pushes India toward grid-forming future → Shifts focus from CAPEX to lifecycle performance Now is the time to rethink—compliance isn’t just about standards; it’s about building systems that perform for 15+ years. #cea #bess #energystorage #renewables #gridstability #indiaenergy #solarpanel #powersector #blackstart #lfpbattery