submitted by /u/der_gopher [link] [留言]

Curious about the initial review distribution for Main Track theory papers this year. Our paper received 4/3/3 with confidence 3/3/3. From previous years, I've had the impression that theory papers often receive more conservative initial scores than some other areas, and I've also heard people saying that initial scores seem generally lower across many disciplines this cycle. If you have a theory submission, would you mind sharing your initial scores (and confidence, if you're comfortable)? It would be interesting to see whether there is any noticeable pattern or whether this is just anecdotal. Please only share if you're comfortable, and it'd be helpful to mention that it's a theory paper so we're comparing like with like. submitted by /u/Mammoth-Leg-3844 [link] [留言]

Nota: ✋ This post was originally published on my blog wiki-cloud.co Introduction Artificial intelligence is evolving at an unprecedented pace and is transforming how people and businesses interact with technology. Over the past few years, much of the focus has been on generative AI models, which can create text, images, code, audio, and other types of content from natural language instructions. These capabilities have marked a significant and transformative shift in how we perform many tasks, allowing AI to move from a specialized technology to an accessible tool for millions of users. However, we are entering a new stage. Artificial intelligence models are no longer limited to simply answering questions or generating content. They can now be autonomous, understand objectives, analyze context, decide what steps to take, use tools, consult different sources of information, connect with APIs, execute actions, and collaborate with other specialized agents to complete more complex tasks. This evolution is giving rise to what is known as agentic artificial intelligence, an approach in which AI systems can act with a greater level of autonomy and actively participate in business, technical, and operational processes. Instead of simply offering a recommendation, an agent can search for information, validate data, coordinate different activities, and execute a sequence of actions aimed at achieving a specific goal. Within this new scenario appears Google Agent Development Kit , also known as Google ADK , is an open-source framework developed and designed by Google to facilitate the creation, evaluation, and deployment of artificial intelligence agents. ADK provides developers with a structure for defining agent behavior, connecting them to language models and external tools, managing sessions and memory, coordinating multi-agent systems, and evaluating their performance before deploying them to production. Thanks to this code-based approach, Google ADK allows you to build e

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

Widget support was one of the earliest Codename One requests. We dismissed it for years because a widget must render while the application UI is not running. A normal Codename One Component needs the application renderer, event dispatch thread, and live object graph. A home-screen widget gets none of those. What is Codename One? Codename One is an open-source framework for building native iOS, Android, desktop, and web apps from a single Java or Kotlin codebase. Learn more at codenameone.com . The missing piece had been under our nose for a decade. Steve added background processes so an app could refresh data without showing its UI. That solves the update side. The rendering side becomes possible once the widget is data rather than a live component. PR #5365 turns that observation into com.codename1.surfaces , one API for home-screen widgets, Live Activities, Dynamic Island, Android ongoing notifications, and desktop floating widgets. The dead-process rule An external surface is a piece of application state that the operating system can render outside the app. The app publishes a serializable layout and a timeline of state maps. The platform persists that data, then renders it with its own surface technology. You cannot attach a Java listener to a widget. There may be no Java process to invoke. You assign a string action ID instead. A tap launches the app and delivers that action after startup. The simulator implements the same model. Open Widgets > Widgets Preview to inspect every registered kind, move through its timeline, change size and appearance, and click actions without creating a device build. Widget kinds exist at build time iOS and Android compile widget galleries into the native application. The kinds must therefore be known during the build. Add a surfaces.json resource: { "liveActivities" : true , "kinds" : [ { "id" : "delivery_status" , "name" : "Delivery" , "description" : "Track your order" , "iosFamilies" : [ "systemSmall" , "systemMedium" ] } ] }

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

Mike Flanagan's latest Stephen King adaptation, Carrie (this will be his fourth), is slated to make its debut on Amazon Prime on October 7th. The new trailer dropped at Comic-Con 2026 and doesn't contain many surprises. If you're familiar with Carrie at all, it hits all the expected notes. Though it's clearly been updated for […]

If you want to tweak your Android Auto settings, the software has a developer menu you can check out.

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

"Night gathers, and now my watch begins." The Night's Watch didn't exist to fight wars nobody saw coming — they existed because someone had to actually stand on the Wall and notice when something crossed it. That's the exact problem I kept running into with AI agents, and it's why I built Nights Watch for the "Agents of SigNoz" hackathon: a runtime resilience layer that catches an agent quietly drifting off its plan, explains why, and recovers — automatically. The problem nobody's watching for Most agent failures aren't dramatic. An agent doesn't crash, it doesn't throw an exception, it doesn't get flagged by a content filter. It just... does something slightly different from what it was asked. Told to "find and book a flight under $400," a subtly-drifted agent might reason its way into a $1,200 upgrade and report back "done" — technically true, catastrophically wrong. Nothing in a normal observability stack notices this, because nothing failed . The agent succeeded at the wrong thing. I wanted a system where SigNoz wasn't just a dashboard you check after something breaks — where it actively fed a decision-making loop while the agent was still running . Architecture, in one rule Everything else in the project falls out of one non-negotiable decision I made on day one: rollback state has to be local and durable, never dependent on an external service being reachable. If your resilience system's own safety net depends on a third-party API being up, you haven't built resilience, you've built a second point of failure. So the split looks like this: Local, critical path (SQLite): the Checkpoint Manager. Every agent step writes a durable checkpoint — plan, budget consumed, completed steps — to disk via Node's built-in node:sqlite . Rollback reads from here, always, no exceptions. SigNoz, decision-support only: the Policy Engine queries SigNoz's Query API for prior-run context before scoring severity, and the Explanation Layer calls SigNoz's MCP server to ground its natura

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

Hey everyone, My team and I have been working hard on this project: https://peppy.bot It's a direct replacement for ROS 2. We already have the OpenArm robot (https://openarm.dev) working on the platform, both v1.0 and v2.0, plus Isaac Sim and MuJoCo integration. If you're in a hurry, head over to https://docs.peppy.bot/quickstart/ and get started in 5min. Our long term vision is to allow anyone (even non-devs) to go from a prompt to real humanoid robot actions, first in simulation, then on the p