Implementing Kaizen In Workplace

The bottleneck in most security organizations is not where most boards think it is. Detection has improved dramatically over the last five years. Tools are better. Signals are more accurate. SOC teams are sharper than they have ever been. The bottleneck is in the space between detection and action. Who has to approve the containment call. How many people sit on the escalation chain. How long it takes to reach someone with actual authority at two in the morning. That decision chain was built for a threat environment where deliberation was affordable. Frontier AI has made it catastrophic. Palo Alto Networks Unit 42 research published this week found that agentic AI now enables adversaries to autonomously rotate infrastructure in hours, making traditional detection feeds obsolete at the moment of delivery. Our April research on autonomous AI cloud attacks documented that agent-based operations chain actions across services in seconds. The attacker is no longer waiting for human decisions to slow them down. The defender still is. This is not a security team failure. It is a leadership decision that has not yet been made. Three decisions that only senior leadership can make: 🔸 Pre-authorize containment actions. The CISO should be able to isolate a system, sever a network segment, or pull a vendor connection without convening a committee mid-incident. Every minute spent seeking approval is a minute the attacker is still operating. 🔸 Compress the approval chain. Identify the minimum viable approval chain for each class of response. Then test it against a scenario where the primary authority is on a flight at two in the morning. The chains that fail under that condition are the ones that matter most. 🔸 Decide which classes of response execute autonomously. Mean time to detect and respond measured in single-digit minutes is not achievable with a human in every step of the response chain. That decision has to be made deliberately, in advance, by someone with the authority to make it. This week’s Frontier AI for Boards goes deeper on the five questions worth bringing to your next board conversation on this, and what it actually costs the business per quarter to leave the bottleneck in place.

Operational bottlenecks are often mistaken for minor distractions. In textiles, challenges such as machine downtime, dye-house delays, working capital spikes, or capacity mismatches between spinning and weaving are not just inconveniences. They are critical leverage points for value creation and significant professional impact. Many leaders focus on optimising every area. However, sustainable throughput comes from identifying and rigorously managing the single constraint that governs the entire system. We apply the Theory of Constraints (TOC) at RSWM to convert operational friction into performance gains. TOC shows that local efficiency can be misleading. Keeping every department busy often creates excess work-in-progress, disrupting flow, increasing costs, and delaying deliveries. Instead, we follow a disciplined process: -First, identify what sets the pace of the value chain. This may include machinery misaligned with current market needs or process challenges like low Right First Time (RFT) rates in the dye house that reduce effective capacity. -Second, exploit the constraint by precise scheduling, strengthening discipline, and improving efficiency to extract more output without immediate capital deployment. -Third, align the rest of the organisation to the bottleneck’s pace to ensure smooth material flow across departments. Fourth, elevate the constraint through capital investment or process redesign, addressing capacity mismatches or refining product lines. -Finally, repeat the cycle, since the constraint shifts as performance improves. This approach has delivered tangible results at RSWM. Addressing dye-house bottlenecks increased throughput, reduced working capital requirements, and improved EBITDA. However, constraints change over time. Market shifts, such as China’s shift from a major yarn importer to an exporter, or recent U.S. tariffs affecting demand, can pose new challenges. In response, we adapt by exploring alternative markets, leveraging domestic opportunities, or innovating products to sustain growth. Our goal is to eliminate internal friction so operational excellence drives expansion. When the market is the only constraint, the organisation is positioned to thrive. #TheoryOfConstraints #OperationalExcellence #Textiles #Leadership #RSWM

Most engineers think model cost is about API tokens or inference time.  In reality, it’s about how your requests compete for GPU scheduling and how effectively your data stays hot in cache. Here’s the untold truth 👇 1. 𝐄𝐯𝐞𝐫𝐲 𝐦𝐢𝐥𝐥𝐢𝐬𝐞𝐜𝐨𝐧𝐝 𝐨𝐧 𝐚 𝐆𝐏𝐔 𝐢𝐬 𝐚 𝐰𝐚𝐫 𝐟𝐨𝐫 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐲. .   Your model doesn’t just “run.” It waits its turn.   Schedulers (like Kubernetes device plugins, Triton schedulers, or CUDA MPS) decide who gets compute time — and how often.   If your jobs are fragmented or unbatched, you’re paying for idle silicon.   That’s like renting a Ferrari to sit in traffic. 2. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝐥𝐚𝐲𝐞𝐫𝐬 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐝𝐞𝐜𝐢𝐝𝐞 𝐲𝐨𝐮𝐫 𝐛𝐮𝐫𝐧 𝐫𝐚𝐭𝐞.   Intermediate activations, embeddings, and KV caches live in high-bandwidth memory.   If your model keeps reloading them between requests — you’re paying full price every time.   That’s why serving infra (like vLLM, DeepSpeed, or FasterTransformer) focuses more on cache reuse than raw FLOPS. The real optimization isn’t in “faster models.”   It’s in smarter scheduling and cache locality.   Your cost per token can drop 50% with zero model changes — just better orchestration. 3. 𝐓𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐭𝐚𝐱: 𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐞𝐯𝐢𝐜𝐭𝐢𝐨𝐧. When too many models share the same GPU cluster, the scheduler starts slicing compute and evicting caches.   This leads to context thrashing — where memory swaps cost more than inference.   At scale, this kills both performance and margins. So if you’re wondering why your inference bill doubled while latency stayed the same —   don’t blame the model.   Blame the infrastructure design. The real bottleneck isn’t model size — it’s architectural awareness.   Understanding schedulers, memory hierarchies, and caching strategies is what separates AI engineers from AI architects. And that’s exactly what we go deep into inside the Advanced System Design Cohort —   a 3-month, high-intensity program for Senior, Staff, and Principal Engineers who want to master the systems that power modern AI infra. You’ll learn to think beyond API calls — about how compute, caching, and scheduling interact to define scale and cost. If you’re ready to learn the architectures behind real AI systems —   there’s a form in the comments.   Apply, and we’ll check if you’re a great fit.   We’re selective, because this is where future technical leaders are being built.

I just trimmed 25% off my Qwen3-14B QLoRA run. Same GPU. Same code. One `pip install -U`. The Unsloth AI team shipped a collab with NVIDIA that fixes three things most training stacks were quietly bleeding time on. No new model. No accuracy hit. No hyperparameter tuning. Here's what each fix is actually doing under the hood: 1️⃣ 𝐂𝐚𝐜𝐡𝐞𝐝 𝐩𝐚𝐜𝐤𝐞𝐝-𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞 𝐦𝐞𝐭𝐚𝐝𝐚𝐭𝐚 Every transformer layer was rebuilding the same boundary info (cu_seqlens, max_seqlen, mask structure) and forcing a GPU-CPU sync per layer. Now it's built once per batch, reused L times. +43.3% forward, +14.3% per batch on Qwen3-14B QLoRA SFT. 2️⃣ 𝐃𝐨𝐮𝐛𝐥𝐞-𝐛𝐮𝐟𝐟𝐞𝐫𝐞𝐝 𝐜𝐡𝐞𝐜𝐤𝐩𝐨𝐢𝐧𝐭 𝐫𝐞𝐥𝐨𝐚𝐝𝐬 Activation reloads from pinned CPU were serializing on a single buffer, copy, wait, compute, next copy. Two buffers run copy + compute in parallel. +8.4% on 8B, +6.7% on 14B, +4.6% on 32B. Memory overhead stays under 0.5 GB. 3️⃣ 𝐀𝐫𝐠𝐬𝐨𝐫𝐭 + 𝐛𝐢𝐧𝐜𝐨𝐮𝐧𝐭 𝐌𝐨𝐄 𝐫𝐨𝐮𝐭𝐢𝐧𝐠 The naive torch.where(router_indices == expert_idx) loop was triggering one CPU-GPU sync per expert. One stable sort, one bincount, reuse offsets everywhere. +23% forward on GPT-OSS routing path. The pattern across all three: the math kernels were already fast. The bottleneck was glue code, rebuilding metadata, serializing copies, querying the runtime once per expert. Group once. Cache once. Overlap the rest. Auto-enabled on RTX laptops, B200 data center GPUs, and DGX Spark. Apache 2.0. Zero accuracy loss. If you train models, this is one update away. Link in the comments 👇

When you ask an LLM a question, latency is shaped by three layers: Hardware, model size, Inference engines and strategies. Choosing the right strategy depends on your bottleneck. The inference process splits into two distinct phases: Prefill and decode Three important metrics to identify the bottleneck. → 𝐓𝐢𝐦𝐞 𝐭𝐨 𝐟𝐢𝐫𝐬𝐭 𝐭𝐨𝐤𝐞𝐧 (𝐓𝐓𝐅𝐓): how long it takes before the users start seeing output. High TTFT = prefill bottleneck. → 𝐓𝐢𝐦𝐞 𝐩𝐞𝐫 𝐨𝐮𝐭𝐩𝐮𝐭 𝐭𝐨𝐤𝐞𝐧 (𝐓𝐏𝐎𝐓): the gap between successive tokens. High TPOT = decode bottleneck. → 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭: requests processed per second. If it is low despite acceptable TTFT and TPOT, the GPU is sitting idle, and the bottleneck is scheduling, not compute or memory. Which metric matters most depends on your application. A simple chatbot cares more about TTFT, while a coding agent user may care more about TPOT. 𝐓𝐓𝐅𝐓 𝐭𝐨𝐨 𝐡𝐢𝐠𝐡 (𝐩𝐫𝐞𝐟𝐢𝐥𝐥 𝐛𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤): → 𝘗𝘳𝘰𝘮𝘱𝘵 𝘤𝘢𝘤𝘩𝘪𝘯𝘨: skip recomputing shared prefixes, big win for long system prompt → 𝘍𝘭𝘢𝘴𝘩𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯: restructures how attention is computed and optimizes the data movement memory. It breaks the attention matrix into smaller tiles that fit entirely inside SRAM, 2–4x faster attention computation → 𝘊𝘩𝘶𝘯𝘬𝘦𝘥 𝘱𝘳𝘦𝘧𝘪𝘭𝘭: prevents large prompts from blocking other requests from getting their first token. 𝐓𝐏𝐎𝐓 𝐭𝐨𝐨 𝐡𝐢𝐠𝐡 (𝐝𝐞𝐜𝐨𝐝𝐞 𝐛𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤): → 𝘒𝘝 𝘊𝘢𝘤𝘩𝘦 & 𝘗𝘢𝘨𝘦𝘥𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯: eliminate redundant computation, manage cache memory dynamically. → 𝘚𝘱𝘦𝘤𝘶𝘭𝘢𝘵𝘪𝘷𝘦 𝘥𝘦𝘤𝘰𝘥𝘪𝘯𝘨: small draft model predicts tokens, large model verifies in batch. 2–3x faster → 𝘞𝘦𝘪𝘨𝘩𝘵 𝘲𝘶𝘢𝘯𝘵𝘪𝘻𝘢𝘵𝘪𝘰𝘯: FP32 to INT4/INT8, less data to move per token from HBM. → 𝘒𝘝 𝘤𝘢𝘤𝘩𝘦 𝘲𝘶𝘢𝘯𝘵𝘪𝘻𝘢𝘵𝘪𝘰𝘯 (𝘛𝘶𝘳𝘣𝘰𝘘𝘶𝘢𝘯𝘵): compresses KV activations to ~3 bits. 6x less memory, 8x faster attention on H100. 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐞𝐬 𝐮𝐧𝐝𝐞𝐫 𝐥𝐨𝐚𝐝 (𝐬𝐜𝐡𝐞𝐝𝐮𝐥𝐢𝐧𝐠-𝐛𝐨𝐮𝐧𝐝): → 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘣𝘢𝘵𝘤𝘩𝘪𝘯𝘨: evicts finished requests instantly, slots in new ones. 10–20x throughput vs static batching. → 𝘗𝘢𝘨𝘦𝘥𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯: also appears here, as dynamic memory paging lets the same hardware serve far more concurrent users. → 𝘔𝘪𝘹𝘵𝘶𝘳𝘦 𝘰𝘧 𝘌𝘹𝘱𝘦𝘳𝘵𝘴: only a subset of expert layers is activated per token, reducing per-token compute at scale. Modern inference engines like vLLM offer most of these techniques out of the box, so we don’t have to implement them ourselves. But understanding these concepts gives us a much better decision, and the next time your model runs slow, you know exactly where to look. Have you tried to implement these? What else should I add?

I wrote a long-form article about iOS performance optimization, based on my experience working with large-scale applications over the years. I wanted to go beyond the usual advice like “use LazyVStack instead of VStack” or “move work off the main thread.” Those tips can be useful, but real performance work usually starts earlier: with understanding where the actual bottleneck is, how it affects the user, and whether optimizing it will change anything meaningful. The article is built as a complete mental model for thinking about iOS performance. I cover product-level perception, metrics and observability, architecture and data flow, SwiftUI/UIKit trade-offs, rendering, networking, caching, energy usage, concurrency, algorithms, memory layout, and CPU-level behavior. Optimization is about knowing what to optimize, where to measure, how to reduce unnecessary work, and when leaving the code as it is may be the better engineering decision. This article is for engineers who design app architecture, work on complex screens, care about production quality, or want to understand performance beyond isolated tricks. If you have ever looked at a slow screen and wondered whether the problem is in SwiftUI, the network, state management, rendering, scheduling, memory, or the backend, this article should give you a structured way to reason about it. Full article: https://lnkd.in/ezz5n-je

I’ve rarely seen managers slow teams down on purpose. But many do become the bottleneck. Not because they want control. Because every decision still routes through them. They become bottlenecks because their team can’t move without them. I’ve seen leaders spend over 20% of their day approving exceptions. Not strategic calls. Not complex judgment. Just routine decisions that kept flowing upward because no one built guardrails. Every approval request feels small. But stacked together, they consume the hours meant for strategic thinking. And the worst part? Most managers don’t notice it happening. They feel busy. They feel needed. They feel productive. But they’re not leading. They’re processing. The fix isn’t working harder or faster. It’s designing processes that don’t require you in the first place. If you’re the bottleneck, the fix isn’t trying to keep up. It’s redesigning what no longer needs your approval. Here’s how to stop being the bottleneck: 1️⃣ Audit your approvals for one week Track every decision that lands on your desk. Ask: “Did this actually require my judgment, or just my signature?” Most leaders are surprised by how few truly needed them. 2️⃣ Define the guardrails, not the answers Instead of approving every exception, define the boundaries. “If it’s under $X, proceed. If it affects Y, escalate.” Clear criteria let teams move without waiting. 3️⃣ Push decision rights down with the context Empowerment without information creates chaos. Share the reasoning behind your decisions so others can apply the same logic. 4️⃣ Make escalation uncomfortable, not automatic If every exception flows up without friction, that’s by design. Require a brief explanation of why this couldn’t be handled at their level. Over time, teams stop escalating what they can solve. 5️⃣ Protect strategic time like it’s a client meeting Block time for thinking, not just doing. If your calendar is full of approvals, you’ve outsourced your leadership to your inbox. 6️⃣ Create a decision log for patterns Track the exceptions that keep repeating. If the same type of request shows up three times, it’s not an exception anymore. It’s a missing policy. Write the rule and eliminate the ask. 7️⃣ Assign a backup decision-maker For every approval you own, name someone who can act in your absence. If no one else can approve it, you’ve created a single point of failure. Redundancy isn’t about trust. It’s about continuity. The goal isn’t to be less available. It’s to build a system that doesn’t need you to function. 💾 Save this if your days feel productive but your strategy feels stalled. ➕ Follow Rene Madden, ACC for leadership systems that reduce noise instead of managing it.

Slow APIs rarely fail because of code. They fail because of architecture choices. In 2026, API performance is not just an engineering concern. It directly impacts revenue, reliability, and user experience. Most latency problems come from six predictable bottlenecks. → 𝐀𝐬𝐲𝐧𝐜 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 Never block request flow for disk writes. Logs should be buffered and flushed asynchronously. → 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠 Opening a new DB connection per request kills throughput. Pools turn spikes into controlled load. → 𝐏𝐚𝐠𝐢𝐧𝐚𝐭𝐢𝐨𝐧 Returning massive datasets is self-inflicted latency. Serve data in predictable chunks. → 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 Hot data should not repeatedly hit the database. A well-designed cache absorbs most read traffic. → 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 Traffic distribution prevents single-node bottlenecks. It is resilience and performance combined. → 𝐏𝐚𝐲𝐥𝐨𝐚𝐝 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 Network bandwidth still matters. Smaller payloads reduce response time globally. 𝐓𝐡𝐞 𝐦𝐢𝐬𝐭𝐚𝐤𝐞 𝐦𝐚𝐧𝐲 𝐭𝐞𝐚𝐦𝐬 𝐦𝐚𝐤𝐞: They optimize individual endpoints. But performance problems are usually systemic. Database access patterns Network overhead Concurrency limits Infrastructure topology All interact. 𝐒𝐞𝐜𝐨𝐧𝐝-𝐨𝐫𝐝𝐞𝐫 𝐞𝐟𝐟𝐞𝐜𝐭: As AI agents begin consuming APIs autonomously, request volumes will increase dramatically. Inefficient APIs that work for humans break quickly under machine-scale traffic. API performance is no longer a backend optimization. It is infrastructure strategy. P.S. In your system today, what is the primary latency driver: database queries, network payload size, or concurrency limits? Follow Ashish Joshi for more insights

Achieving 3x-25x Performance Gains for High-Quality, AI-Powered Data Analysis Asking complex data questions in plain English and getting precise answers feels like magic, but it’s technically challenging. One of my jobs is analyzing the health of numerous programs. To make that easier we are building an AI app with Sapient Slingshot that answers natural language queries by generating and executing code on project/program health data. The challenge is that this process needs to be both fast and reliable. We started with gemini-2.5-pro, but 50+ second response times and inconsistent results made it unsuitable for interactive use. Our goal: reduce latency without sacrificing accuracy. The New Bottleneck: Tuning "Think Time" Traditional optimization targets code execution, but in AI apps, the real bottleneck is LLM "think time", i.e. the delay in generating correct code on the fly. Here are some techniques we used to cut think time while maintaining output quality: ① Context-Rich Prompts Accuracy starts with context. We dynamically create prompts for each query: ➜ Pre-Processing Logic: We pre-generate any code that doesn't need "intelligence" so that LLM doesn't have to ➜ Dynamic Data-Awareness: Prompts include full schema, sample data, and value stats to give the model a full view. ➜ Domain Templates: We tailor prompts for specific ontology like "Client satisfaction" or "Cycle Time" or "Quality". This reduces errors and latency, improving codegen quality from the first try. ② Structured Code Generation Even with great context, LLMs can output messy code. We guide query structure explicitly: ➜ Simple queries: Direct the LLM to generate a single line chained pandas expression. ➜ Complex queries : Direct the LLM to generate two lines, one for processing, one for the final result Clear patterns ensure clean, reliable output. ③ Two-Tiered Caching for Speed Once accuracy was reliable, we tackled speed with intelligent caching: ➜ Tier 1: Helper Cache – 3x Faster ⊙ Find a semantically similar past query ⊙ Use a faster model (e.g. gemini-2.5-flash) ⊙ Include the past query and code as a one-shot prompt This cut response times from 50+s to <15s while maintaining accuracy. ➜ Tier 2: Lightning Cache – 25x Faster ⊙ Detect duplicates for exact or near matches ⊙ Reuse validated code ⊙ Execute instantly, skipping the LLM This brought response times to ~2 seconds for repeated queries. ④ Advanced Memory Architecture ➜ Graph Memory (Neo4j via Graphiti): Stores query history, code, and relationships for fast, structured retrieval. ➜ High-Quality Embeddings: We use BAAI/bge-large-en-v1.5 to match queries by true meaning. ➜ Conversational Context: Full session history is stored, so prompts reflect recent interactions, enabling seamless follow-ups. By combining rich context, structured code, caching, and smart memory, we can build AI systems that deliver natural language querying with the speed and reliability that we, as users, expect of it.

How to Spot Operational Bottlenecks Slowing Your Growth Growth is exciting—until it hits a wall. Sometimes, it’s not the market, product, or team. It’s those pesky operational bottlenecks silently sabotaging your progress. Here’s how to identify them before they slow you down any further: 1️⃣ The Symptoms of Bottlenecks • Think of bottlenecks like flu symptoms for your business: • Constant Firefighting: You’re always “fixing” the same issues. • Missed Deadlines: Projects drag on without an apparent reason. • Overloaded Team Members: Certain employees seem perpetually swamped. • Cost Overruns: Expenses keep creeping up with no clear ROI. 2️⃣ Common Culprits 👩💻 Manual Processes: Are your team members stuck doing things that software could handle faster? 📊 Poor Visibility: Without accurate data, you’re flying blind. 📞 Communication Silos: Teams or departments that don’t talk slow down decision-making. 👟 Outdated Tools: That free software you started with might not scale with your ambitions. 3️⃣ How to Diagnose Bottlenecks 1. Map Your Processes: Write down how work flows in your business. Where does it slow or stop? 2. Review Financials: Look for rising costs in specific areas—these often highlight inefficiencies. 3. Listen to Your Team: They know where things get stuck. Ask what tools or processes frustrate them. 4. Customer Feedback: Are clients waiting longer or getting frustrated? That’s a red flag. 4️⃣ Quick Wins for Clearing Bottlenecks • Automation: Stop manually entering data. Tools like QuickBooks or KarbonHQ can save hours. • Real-Time Metrics: Invest in real-time dashboards to monitor cash flow, project progress, or inventory. • Delegate Smarter: Make sure workload distribution isn’t piling on your rockstars while others wait for tasks. • Streamline Communication: Weekly meetings or tools like Slack can reduce the endless email threads. 5️⃣ Long-Term Solutions Fixing bottlenecks isn’t a one-and-done. Build a culture of continuous improvement: • Regularly audit your processes. • Stay open to new tools and technologies. • Encourage feedback loops from employees and customers alike. Every business has bottlenecks. Successful ones tackle them head-on, freeing up time and resources to focus on growth. Which bottleneck are you tackling right now? Let’s hear your story in the comments! 💬