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The Scene It's 2 AM. You're staring at your screen, debugging why your dashboard keeps showing yesterday's data even after you've changed the filter. Your useEffect dependency array looks like a crime scene. You've got three useState hooks just to manage loading, error, and data. You added a cleanup function, but somehow the component still throws that dreaded warning: "Can't perform a React state update on an unmounted component." You take a sip of cold coffee. You wonder where it all went wrong. The Problem with useEffect for Data Fetching Let's be honest with ourselves. useEffect was never designed for data fetching. The React team gave us this hook to synchronize with external systems, DOM events, subscriptions, and timers. But somewhere along the line, we collectively decided to use it as our go-to tool for API calls. And look, I get it. When you're learning React, the pattern is simple: useEffect (() => { const fetchData = async () => { setLoading ( true ); const response = await fetch ( ' /api/users ' ); const data = await response . json (); setUsers ( data ); setLoading ( false ); }; fetchData (); }, []); It works. Until it doesn't. Here's what happens when your application grows: Race Conditions — When your user clicks filters too quickly, old requests return after newer ones and override your state. The UI shows mismatched data, and you waste hours adding request cancellation logic that nobody on your team fully understands. Unnecessary Re-renders — Every state update triggers a re-render. With useEffect , you're juggling at least three states: data , loading , and error . Three states, three renders, even before React mounts your actual content. Poor Caching — If a user visits a page, leaves, and comes back, your useEffect fires again. Same data, same API call, same network cost. Multiply this by a thousand users, and you're burning your backend for no good reason. Manual Cleanup Headaches — Need to cancel pending requests? Need to prevent state updates

Most "why Proxmox" content in 2025-2026 is a migration story driven by Broadcom's ESXi pricing changes. The author had a working VMware stack and got priced out. I'm not that author. I evaluated both, picked Proxmox in 2024, and built on it without ever running ESXi in production. Two years in, I'd make the same call. It reads as either incompetent or contrarian until the rest of the post lands. Here's the reasoning. The three reasons it was the easy call 1. LXC and KVM in one host Most workloads in this homelab are LXCs. Pi-hole, Vaultwarden, Authelia, Traefik, the monitoring stack, GitLab CE itself, all containers sharing the host kernel. A few things need full VM isolation (the NAS guest, Proxmox Backup Server, the Home Assistant OS appliance). Same hypervisor, same CLI, same web UI for both shapes of workload. The alternative is ESXi for the VMs and a separate toolchain (containerd, Docker, Kubernetes, take your pick) for the containers. That's two backup pipelines, two HA stories, two places for config drift to surprise you at 2 AM. pct exec 254 systemctl status authelia and qm start 189 are the same shape. New hires don't have to learn one tool for containers and a different one for VMs. 2. Proxmox Backup Server beats the free Veeam alternative Chunk-level deduplication. Backups across guests and across time share storage. A nightly backup of all 11 LXCs and 2 VMs runs in about ten minutes and adds a few hundred MB of new chunks, because most of the content is the same as yesterday. Cluster-scheduled. One job definition runs across every node in the cluster. No per-node cron, no manual rotation when a node moves. Restore to a different storage class. A backup taken from local-lvm on the G7 restores onto ZFS on a G5 cluster node without conversion gymnastics. Veeam Community Edition is the free comparison. It works. It also caps repository size, doesn't dedup at the chunk level, and lacks the cluster-aware scheduling that makes PBS feel like a built-in feature

Most AI diagram workflows end with a PNG or a screenshot. It may look fine, but the moment the architecture changes, you have to redraw it or regenerate the whole image. I wanted a different workflow: describe a system in natural language, receive a real Draw.io file, and keep editing every node, label, connector, group, and icon. That is why I built drawio-mxgraph , an open-source Codex Skill that turns architecture and process descriptions into validated, editable .drawio files. Repository: https://github.com/clawcode3-bit/drawio-mxgraph-skill What the Skill does The Skill generates mxGraph XML that opens directly in Draw.io/diagrams.net. It is designed for architecture diagrams, business processes, agent workflows, and integration maps. Key capabilities include: Natural-language descriptions to editable .drawio XML Stable node IDs for reliable incremental updates Add, remove, move, resize, rename, and regroup operations Layout direction switching: left-to-right, right-to-left, top-to-bottom, or bottom-to-top Orthogonal connector routing with explicit entry and exit points Portable embedded SVG icons, including cloud and enterprise-style icon sets XML structure and reference validation before delivery Example diagrams that can be opened and modified immediately Why stable IDs matter A common failure mode in generated diagrams is treating every edit as a full redraw. That makes small requests surprisingly destructive. With stable IDs, a request such as: Move the ticketing system below the CRM, add an observability group, and change the layout to left-to-right. can update only the affected cells. Existing labels, styles, icons, connections, and manually adjusted positions can remain intact. This makes the diagram behave more like source code than a disposable image. Example: an AgentBuilder customer-service architecture The repository includes an editable example for an intelligent customer-service system built with AgentBuilder. It connects: Web, mobile, messaging,

There's a deceptively simple bug hiding in plain sight on almost every government form, university portal, and job application site: "Upload a photo under 50KB." No API, no error message explaining why, no tolerance — just silent rejection if you're 2KB over. It sounds like a trivial constraint until you actually try to satisfy it programmatically. File size in bytes isn't a variable you can set directly; it's a derived value — a function of pixel dimensions, image entropy, and compression quality — which makes "resize this to exactly 51,200 bytes" a surprisingly nontrivial optimization problem, not a one-line canvas.toBlob() call. A few months ago, my cousin ran into this on a state exam portal that capped passport photos at 50KB. She spent two hours bouncing between random "photo compressor" sites, most of which just apply a fixed compression ratio and let you deal with whatever number comes out. None of them actually solve for a target size. By the time she landed on something that worked, the registration window had closed for the day. So here's the actual technical problem underneath this UX annoyance — and how to solve it properly instead of guessing quality percentages by hand. It's Not You. File Size Is Genuinely Unpredictable. Here's the thing nobody tells you: file size in kilobytes isn't something you can just "set." It's the result of several things happening at once — how detailed the image is, what dimensions it's saved at, and how aggressively it's compressed. Change any one of those, and the final number shifts unpredictably. A plain white background compresses down to almost nothing. A busy, detailed photo — a face with visible texture, a signature with lots of fine ink strokes — resists compression much harder, because there's more actual information in the pixels. Two photos that look similarly sized on your screen can land at wildly different file sizes once compressed, simply because of what's in them. Then there's the format problem, which trip

LLM applications fail silently at the semantic level. Standard unit tests verify that functions return values, but they cannot detect if the output is factually wrong, off-tone, or missing steps. Evals fix this by running a prompt, inspecting the output, and programmatically or judgmentally deciding its quality. Why LLM Testing is Different Unit tests break on non-determinism. Temperature-driven randomness means identical inputs produce varying valid outputs, which defeats static equality checks. Semantic equivalence ("Paris is the capital" vs. "Capital is Paris") makes string matching useless. Small model or prompt updates cause gradual quality drift that only surfaces at scale. Tone and accuracy require judgment that basic assertions cannot encode. The Three Eval Types 1. Heuristic Evals check measurable properties: JSON validity, word counts, PII presence. They are fast and objective — ideal for regression gates on every pull request. 2. LLM-as-Judge uses a second LLM to grade output against a rubric. Best for subjective content too complex for code but too vast for manual review. 3. Human Evals are the ground truth. Use them to build your golden dataset, calibrate the LLM judge, and finalize decisions before major releases. Building Your Eval Infrastructure Start minimal: a Python script, a JSON file of test cases, and a loop comparing LLM output against assertions or a rubric. Curate a golden dataset of 500–1000 production-representative requests including edge cases and adversarial prompts. Refresh it quarterly to match user behavior drift. Automate the harness in GitHub Actions. Block deployments when pass rates drop below your threshold (90% is a common starting point). Run fast heuristic tests on every PR and full LLM-judge evals nightly. Key Tools PromptFoo — Open-source, YAML-based test cases with a built-in CLI runner and diff reporting. Braintrust — Hosted platform for dataset management and experimental comparison. Inspect — Open-source framework by th

Table Of Contents What is a Git worktree? Why use worktrees instead of several clones? A practical directory convention Everyday Git worktree commands Consolidating several independent clones Safety rules before starting Phase 1: Inventory every clone Phase 2: Choose the canonical repository Phase 3: Decide what each clone should become Phase 4: Convert one clone Moving a worktree Recovering a deleted .git worktree file When should a worktree be locked? When should git worktree prune be used? Final validation Quick reference Closing thoughts Have you ever ended up with a directory structure like this? ~/src/project ~/src2/project ~/src3/project ~/src4/project Each directory started innocently enough. One was for main . Another was for a feature branch. A third contained a half-finished experiment. The fourth had several untracked test files you were afraid to lose. Eventually, each clone had its own: stale view of the remote repository, duplicated Git history, local-only commits, modified files, ignored test artifacts, and unknown relationship to the others. Git worktrees are designed to solve this problem. A worktree gives you multiple checked-out working directories backed by one shared Git repository. Each working directory can have its own branch and uncommitted changes, while commits, branches, tags, remotes, and fetched objects remain shared. This article covers two things: How to use Git worktrees during normal development. How to safely consolidate several independent clones into one worktree-based layout without losing local work. The shell examples are written to work in both Bash and zsh . What is a Git worktree? A normal Git clone contains: the object database, commit history, branches, tags, remotes, remote-tracking references, and one checked-out working directory. A linked worktree adds another checked-out working directory to that same repository. For example: ~/src/project main ~/src/project-FEATURE-123 FEATURE-123 ~/src/project-HOTFIX-456 HOTFIX-45

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darryl from TSEC Codecell here. we're back with another edition of Weekly Challenges 2026 - and this time it's a national level event!! get ready for a 6 weeks coding journey where you'll solve fresh challenges every week, compete on the leaderboard, and put your problem-solving skills to the test. submitted by /u/Spiritual-Banana1048 [link] [留言]

Apple Silicon delivers enough performance without overheating.

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This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . Written with the help of AI (Claude). The bug, the fix, the validation setup, and every claim below are mine, and were verified against the real codebase and a real full disk. The report Someone lost a Magic: The Gathering decklist. They were playing on Cockatrice — the open-source MTG client — with their decks on a drive that had quietly filled up while Oracle pushed an update in the background. They added a card, hit Ctrl+S, and Cockatrice said it saved. The debug log agreed: [2026-05-28 22:31:42.031 I] Saved deck to "G:/cockatrice300/data/decks/edh-b2-gitrog-reanimate.cod" with format 1 - true - true . Success. The file was 0 bytes. The deck was gone. That was issue #6952 , filed by Mekkiss. The steps to reproduce are four lines long and completely damning: Have a full disk. Open a deck on the full disk Add one card to it Save the deck (ctrl+s) Observe that the deck is now a 0 byte file. Three ways to be wrong at once The save path lived in DeckLoader::saveToFile() . Stripped down, it looked like this: QFile file ( fileName ); if ( ! file . open ( QIODevice :: WriteOnly | QIODevice :: Text )) { qCWarning ( DeckLoaderLog ) << "Could not create or open file:" << fileName ; return std :: nullopt ; } bool success = false ; switch ( fmt ) { /* ... saveToFile_Native / saveToFile_Plain ... */ } file . flush (); file . close (); qCInfo ( DeckLoaderLog ) << "Saved deck to " << fileName << "with format" << fmt << "-" << success ; There are three independent failures stacked on top of each other here, and you need all three to lose data: 1. WriteOnly truncates on open. The instant open() succeeds, the existing deck is 0 bytes. Not after a successful write — at open time . The old deck is already destroyed before a single byte of the new one is written. On a full disk, open() still succeeds: truncating a file doesn't need free space. It frees space. 2. The serializers always returned true . sa

Have you ever noticed that your GenAI applications are spending massive amounts of time and money re-reading the exact same setup text? Every time a user asks a short question in a chatbot, the Large Language Model (LLM) must re-read your entire 2,000-word corporate playbook, your agent's system rules, and the full chat history from scratch. This phase is called the pre-fill math phase, and it drives up both your cloud bill and your user latency (Time-to-First-Token).With Amazon Bedrock Prompt Caching for Claude 4.6 (both Sonnet 4.6 and Opus 4.6), this problem is completely solved. You can achieve up to a 90% cost reduction on input tokens and an 85% drop in latency by using a clever architectural shortcut. Here is exactly how it works under the hood, how AWS maintains it across API requests, and how to implement it using Python. The Secret Architecture: Model Inference vs. AWS Infrastructure Prompt caching is a beautiful team effort between the AI model hardware and the AWS cloud infrastructure. The Model Level (The Brains): Inside Claude 4.6, text is processed through mathematical matrices called KV (Key-Value) Caches. Instead of re-reading text, the GPUs calculate the meaning of your system instructions once and build a "mathematical profile." When a cache point is triggered, the model freezes this calculated KV state inside the GPU memory. 2.The AWS Bedrock Level (The Manager): Normally, an LLM wipes its memory the millisecond an API call finishes. AWS Bedrock changes this. It takes your static prompt, creates a secure, unique cryptographic hash (fingerprint), and pins that KV memory block alive. When your next API request comes in, AWS Bedrock instantly hashes the new incoming prompt text. If the top section matches a saved fingerprint, Bedrock's router bypasses the standard pre-fill setup and routes your request directly to the GPU holding your frozen mathematical profile. It is exactly like loading a "Save Game" file instead of restarting a video game from Le