Will AI Agents Make Bias Worse?
What happens when biased models get memory, tools, and decision power Continue reading on Towards AI »
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Mura: The Source of Uneven Flow
In part 1, we explored the eight wastes ( Muda ) as the visible symptoms of inefficiency in software delivery. We saw how waste shows up in unfinished work, handoffs, long waits, rework, and lost talent. Those are the effects we can observe and feel. Those wastes are almost always the result of Mura (斑), a Japanese term from the Toyota Production System meaning "unevenness" or "inconsistency" in how work flows. It is the "hurry up and wait" cycle: periods of low activity followed by periods of frantic catch-up, that make delivery unpredictable and unsustainable. This post examines in depth how to identify uneven flow, and how modern software delivery practices work together to reduce inconsistency and create predictability. The Detection Kit The principles of Lean have been empirically val
Muri: The Root Cause of Overburden
Part 1 of this series was about recognising waste ( Muda ) and Part 2 was about how uneven flow ( Mura ) creates that waste. This final part is about the force that gives rise to both. The Japanese term Muri (無理) roughly translates to "overburden" or "unreasonable load". In the original Toyota Production System, Muri was physical: asking a worker to lift a box that was too heavy. In modern software delivery, it is the invisible pressure we put on the two load-bearing parts of any technology organisation: the people who change the system and the system they are forced to change. It's not dramatic, it's not loud and it doesn't announce itself with outages. Muri accumulates slowly and becomes the norm. And because of that, it's the most dangerous of the three. There's a well-known paper calle
Creating a 50 GB Swap File on Jetson AGX Orin (Root on NVMe)
Abstract This document describes the process of creating, tuning, and managing a large swap file on an NVIDIA Jetson AGX Orin 64 GB running Ubuntu 22.04.5 LTS aarch64. The configuration is specifically optimized for running large language models (LLMs) alongside CUDA, cuMB, and TensorRT by leveraging a fast NVMe SSD as the primary swap backing store. The implementation was validated using a 50 GB swap file configuration alongside existing zram layers. The procedure successfully extended the usable memory capacity, allowing for the deployment of larger models without triggering immediate Out-Of-Memory (OOM) errors, provided the storage-to-RAM paging latency is acceptable. This tutorial serves as a technical reference for advanced Jetson and Linux users. It provides a reproducible method for
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🚀 The Developer Who Survives 2026 Is NOT the One You Think
⚠️ The Hard Truth In 2026, being a “good developer” is no longer enough. You can: Write clean code ✅ Know Docker, Kubernetes ✅ Grind LeetCode daily ✅ …and still get replaced. Not by another developer. But by someone who knows how to use AI better than you. 🤖 The New Battlefield: AI-Augmented Developers Let’s be clear: AI is NOT replacing developers. But developers using AI are replacing those who don’t. The game has changed from: “How well can you code?” to: “How well can you THINK, DESIGN, and ORCHESTRATE?” 🧠 The 3 Skills That Actually Matter Now 1. 🧩 AI Orchestration (The Hidden Superpower) Most devs use one tool. Top devs use systems of tools : GPT → for architecture Claude → for reasoning large codebases Copilot/Cursor → for execution Local LLM → for privacy 👉 The magic is not in t
Mura: The Source of Uneven Flow
In part 1, we explored the eight wastes ( Muda ) as the visible symptoms of inefficiency in software delivery. We saw how waste shows up in unfinished work, handoffs, long waits, rework, and lost talent. Those are the effects we can observe and feel. Those wastes are almost always the result of Mura (斑), a Japanese term from the Toyota Production System meaning "unevenness" or "inconsistency" in how work flows. It is the "hurry up and wait" cycle: periods of low activity followed by periods of frantic catch-up, that make delivery unpredictable and unsustainable. This post examines in depth how to identify uneven flow, and how modern software delivery practices work together to reduce inconsistency and create predictability. The Detection Kit The principles of Lean have been empirically val
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server : fix logging of build + system info ( #21460 ) This PR changes the logging that occurs at startup of llama-server. Currently, it is redundant (including CPU information twice) and it is missing the build + commit info. macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
Stop Guessing What Caused Your Flaky Tests Fail or Pass
Flaky tests don’t fail when you expect them to. They fail when you least have time. One moment everything is green, the next your CI pipeline is red — and then, magically, it passes on rerun. ❌ ❌ ✅ → Passed So… what just happened? Was it a network issue? Timing? State leakage? The classic DOM detached? May be, the fixture didnt return the value? The Problem: We Only See the Final Outcome Most test reports show you only the final result , or you install a bunch of plugins that would scrap all the xmls for you to show you multiple tests of same title and you click each one of them to see which might have ran first? If a test fails twice and passes on the third attempt, all you see is: TestCheckoutFlow → Rerun TestCheckoutFlow → Rerun TestCheckoutFlow → Rerun TestCheckoutFlow → PASSED That “p
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