Mentions

터미널 창 하나에서 AI 여러 개를? GitHub 별 2.8만 cmux, 부르면 파란 테두리로 알려 주는 맥 앱 #Shorts 코딩 AI를 여러 개 동시에 켜 두면, 누가 나를 부르는지 헷갈리지 않나요? cmux(manaflow-ai/cmux)는 GitHub 별 약 2만 8천 개(2026.11.01 확인)의 오픈소스 macOS 터미널(글자로 명령하는 창) 앱입니다. README에서 개발자는 Claude Code와 Codex 세션을 여러 개 동시에 돌리는데, 알림이 맥락 없이 '입력을 기다리는 중'만 떠서 직접 만들었다고 설명합니다. · 알림 테두리: 코딩 AI가 사용자를 기다리면 해당 창에 파란 테두리, 탭에 불이 들어옴 · 알림 패널: 대기 중인 알림을 한곳에서 보고, 가장 최근 안 읽은 알림으로 이동 · 세로 탭: git 브랜치, 연결된 PR 상태, 작업 폴더, 열린 포트, 최근 알림 문구 표시 · 내장 브라우저: 터미널 옆에 브라우저를 띄우고 AI가 클릭·입력 등으로 직접 조작 가능 · Swift·AppKit으로 만든 네이티브 맥 앱(Electron 아님), README 기준 빠른 시작·적은 메모리 · Claude Code, Codex, OpenCode, Gemini CLI 등 터미널에서 돌아가는 AI는 모두 사용 가능 (README FAQ) · 설치: DMG를 열어 응용 프로그램 폴더로 드래그(추천) 또는 Homebrew 'brew tap manaflow-ai/cmux' → 'brew install --cask cmux' · 무료, GPL-3.0-or-later (서버 소프트웨어 부분은 별도 라이선스) 주의: 현재 macOS 전용입니다. 다시 켜면 창 배치·작업 폴더·스크롤 기록(최대한)은 복원되지만, 실행 중이던 프로세스 상태까지 저장하지는 않는다고 README가 밝힙니다. README 기반 소개이며 직접 설치해 보지 않았습니다. 창·탭 그림은 개념 도식(실제 화면 아님)입니다. 출처 manaflow-ai/cmux MIRLABS · AI 도구와 기술을 짧게 #MIRLABS #미르랩스 #cmux #터미널 #ClaudeCode #Codex #AI코딩 #맥앱 #오픈소스 #GitHub #Shorts

unbuilt: three skills that extract a buildable spec from your code, rebuild it in any language, and validate it for leaks Three skills in one plugin, unbuilt: - /unbuilt:extract <source-dir> <instance-dir> reads your project and writes a public spec repo: requirements with stable IDs and MUST/SHOULD modality, Given/When/Then scenarios that cite them, constraints, and a milestone order with a "done when" per step. It runs a redaction gate where company and product names are removed unless you opt in per name. Re-run it with since <tag> after a code change and it reads only what changed and keeps the IDs stable. - /unbuilt:build <instance-dir> takes that spec and builds it one milestone at a time in the language you choose. It records every departure from the spec in PLAN.md. Re-run it later and it reports what changed in the spec and plans an update milestone. - /unbuilt:validate <instance-dir> checks the spec against the format: required files, every MUST covered by a scenario, size budgets, and mechanical leaks (emails, IPs, hostnames, home paths, secret shapes). It is also a zero-dependency Node script and a GitHub Action, so it runs in CI. Install: claude plugin marketplace add vtrifonov/unbuilt claude plugin install unbuilt@unbuilt Why A lot of software cannot be shared as code: internal names, private services, an employer's property, a history you do not want public. The idea in it is often worth sharing, with credit. Claude can build working software from a precise description, so the description can be the thing you publish. You keep the code; others build their own version in whatever stack they use. How the skills were made I started from a requirements list with stable IDs (the same shape the format asks for) and had Claude write the validator test-first against it, one rule at a time, with a review pass between rounds. The skills came after the validator was stable, so extract and build are checked against the same rules. There is a parity test that fails if prompts/ (the plain-text versions for other agents) drifts from the skills. Worked example, extracted from one of my apps whose source happens to be public, so you can compare spec to source: github.com/vtrifonov/unbuilt/tree/main/examples/c… Repo: vtrifonov/unbuilt Questions about the skill design welcome; the redaction gate and the build re-run (snapshot, diff, departures) took the most iterations.

AI 기억 공간, 로그 몇 개면 꽉 찬다? GitHub 별 3만 Context Mode, 결과만 넘겨 98% 절약 #Shorts AI에게 긴 로그나 웹페이지를 통째로 넘겼는데, AI가 기억할 자리는 남아 있을까요? AI가 한 번에 읽고 기억할 수 있는 분량(컨텍스트)은 정해져 있습니다. Context Mode(mksglu/context-mode) README는 웹페이지 캡처 한 번이 56KB, 이슈 20개가 59KB, 접속 로그 하나가 45KB라서 30분이면 컨텍스트의 40%가 사라진다고 설명합니다. GitHub 별 약 3만 3천 개(2026.11.01 확인)의 프로젝트입니다. · 큰 자료는 격리된 작업실(샌드박스)에서 처리하고 결과만 AI에게 넘김 — README 기준 315KB가 5.4KB로, 98% 절약 · 작업 기록(수정한 파일, 할 일, 결정)을 내 컴퓨터의 SQLite에 저장해, 대화가 정리돼도 필요한 것만 꺼내 이어 감 · '코드로 생각하기': 파일 47개를 읽는 대신(700KB) 계산 코드 한 번(3.6KB)으로 결과만 확인 (README 예시) · Claude Code에서는 플러그인 명령 두 줄로 설치, 그 외 Gemini CLI·Cursor·Codex CLI 등 여러 도구 지원 · 자동 연결(훅)이 되는 도구는 약 98%, 설명 파일만 쓰는 도구는 약 60% 절약 (README 표) · README 기준 원격 전송·사용량 추적 없음, 데이터는 내 컴퓨터에 저장 주의: 코드 실행 도구는 AI가 짠 코드를 실제로 실행하며, README도 '완전한 OS 샌드박스가 아니다', '실행 도구를 허락하는 건 임의 코드 실행을 허락하는 것'이라며 기존 보안 설정을 켜 두라고 안내합니다. 라이선스는 MIT가 아닌 Elastic License 2.0으로, 호스팅 서비스로 제공하는 것은 금지됩니다. README 기반 소개이며 직접 설치해 보지 않았습니다. 첫 장면의 게이지와 작업실 그림은 개념 도식(실제 화면 아님)입니다. 출처 mksglu/context-mode MIRLABS · AI 도구와 기술을 짧게 #MIRLABS #미르랩스 #ContextMode #ClaudeCode #AI코딩 #컨텍스트 #MCP #오픈소스 #GitHub #Shorts

Update: they threw me off their platform, luckily JoviDeCroock/chargeOS is functional and doing great work for my charger. What a time...

Installation: cargo install --locked --git halfhandorg/halfhand halfhand cargo clean && cargo build -p halfhand --release && cargo install --path h ~$ hh init

The ChatGPT gift card craze is hilarious, people are making memes left and right! It’s wild how quickly something like this can dominate online conversations. github.com/LethaboMohapi t.co

看 CleanYourTV 的项目说明， 给兼容的安卓电视换个清爽首页， 得查机型、打开调试设置， 还要留好恢复办法。 想少看点首页广告，先补一门改电视的课。😅 电视厂商做“简洁模式”， 能不能把入口做得好找一点？ Gathin23/cleanyourtv

I built a lightweight Docker engine for Windows: no WSL, 322 MiB idle If you run Linux containers on Windows, you probably know this list: - vmmem still holds a gigabyte or two of RAM long after your containers have stopped. - You delete 5 GB of images and your disk doesn't get any of it back, because the VHDX only grows. Getting the space back means compacting it by hand or opting into a sparse VHD. - A project bind-mounted from C:\ is slow, and file watchers sometimes don't fire. - At a company with 250+ employees or $10M+ revenue, Docker Desktop needs a paid subscription. None of these are bugs you can report and wait out. They follow from the design: Docker Desktop runs on WSL 2, and WSL 2 is a general-purpose Linux VM that a container engine happens to share. So I started building something narrower. hyperbutler is an open-source (Apache-2.0) Docker engine for Windows 11. It boots a minimal Linux VM straight through the Windows Host Compute Service (HCS, the same API WSL 2 uses underneath), runs dockerd inside it, and exposes the Docker API on a Windows named pipe. No WSL. You keep using the stock docker CLI and Docker Compose; hyperbutler just adds a hyperbutler docker context. hyperbutler start docker run --rm hello-world docker run -d -p 8080:80 nginx; curl.exe http://127.0.0.1:8080/ hyperbutler stop It is a preview. I'll get to what that means at the end. The numbers, and what they don't say I installed both on the same laptop and measured them one at a time with the same script, each with its out-of-the-box settings. The machine: Windows 11 Home, Intel Core Ultra 7 155H, 32 GB RAM. Measured on 2026-11-02. | | hyperbutler | Docker Desktop 4.97 (WSL 2) | |---|---|---| | Needs WSL | no | yes | | Download | 116 MB | 592 MB (+351 MB WSL) | | Installed size | 179 MB | about 6.7 GB | | Start until a container runs | 2.5 s | 5.6 s | | Idle memory, VM running | 322 MiB | 1.7–2.7 GB | | Idle memory after Docker Desktop's Resource Saver stops its VM | n/a | 404 MiB (VM off) | | Disk 5 min after deleting ~5 GB of images | 1.5 GiB, given back automatically | 6.8 GiB, kept | | VM memory 2 min after a 2 GiB workload | 320 MiB | 1.3 GB | | Bind mount from C:\: stat / read / write 2000 small files | 0.04 / 0.4 / 1.8 s | 2.3 / 3.4 / 4.9 s | | License | Apache-2.0 | paid subscription for companies with 250+ employees or $10M+ revenue | Now the caveats, because they matter: - One machine, one build of each. This is not a general result. I haven't measured Windows 11 Pro, other hardware, or other versions. - Defaults on both sides. By default WSL gives Docker Desktop's VM half the RAM (16 GB here) and all 22 logical processors. I didn't test Docker Desktop with a smaller VM set in .wslconfig, or hyperbutler with a bigger one. - Resource Saver is fair to mention. After about 7 minutes without containers, Docker Desktop stops its VM, and its host processes then use 404 MiB. The cost is that the next command has to wait for the VM to boot again, and I didn't measure how long that takes. - "Until a container runs" is the start measure that counts. Docker Desktop's backend answers docker info even while its VM is stopped, so timing docker info alone would flatter it. - Not measured at all: network throughput against Docker Desktop, docker build, GPUs. Docker Desktop is also well ahead on breadth: Kubernetes, Windows containers, extensions, a mature GUI, Scout and build cloud, support contracts. hyperbutler runs Linux containers with the Docker CLI and Compose, and that's it. If you need any of those other things, this isn't for you yet. The full method, raw CSVs and the list of what wasn't measured are in docs/compare-docker-desktop.md. How it works There are three parts, and each gets as little privilege as its job needs: Windows, your account (no admin) +-----------------------------------------------------------+ | docker CLI / Compose / SDKs | | | named pipe: \\.\pipe\docker_engine_hyperbutler | | v | | hyperbutler engine (hyperbutler.exe, runs as you) | | Docker API relay, user-mode network (NAT, DNS, ports), | | bind-mount file server, disk-space guard, CLI, MCP | +-----------+-------------------------------+---------------+ | start / stop / status | Hyper-V sockets v | (API, network, files) +---------------------------+ | | hyperbutler broker | | | Windows service, SYSTEM | | | only part that calls HCS | | | 7 typed requests | | +-----------+---------------+ | | HCS | v v +-----------------------------------------------------------+ | Linux VM on Hyper-V (no WSL) | | hyperbutlerd guest agent -> dockerd + containerd | | read-only, hash-pinned system disk | | sparse data disk that gives freed space back | +-----------------------------------------------------------+ - The engine is a normal process running as you. It relays the Docker API from the named pipe into the VM. It runs the VM's network in user mode (gVisor netstack), so there's no virtual switch, no vEthernet adapter, no WinNAT. It serves bind mounts from C:\ over FUSE, and it pauses the VM before your host disk fills up. - The guest is a minimal Alpine userland on a read-only system disk, pinned by hash. Its data disk is sparse, and the guest hands freed blocks back to Windows. That's the whole reason the disk row above reads 1.5 GiB instead of 6.8. - The broker is the only privileged piece, and it's the one I thought about most. Why a SYSTEM service, and why so small Windows lets only administrators and members of the Hyper-V Administrators group use HCS. The easy fix is to add your account to that group. But Microsoft documents that group as having complete, unrestricted access to Hyper-V. Its members control VM configuration, which includes which host files a VM can open. And every program you run inherits that power, not just the tool that asked for it. Memberships like that also tend to outlive the tool that asked for them. So the next release's MSI installs a small broker service instead (it is merged, and goes out with v0.1.0). It's the only hyperbutler component that calls HCS, and it was designed backwards from the bug classes behind earlier local privilege escalations in privileged container helpers on Windows: trusting caller-supplied paths, symlink and TOCTOU races, and named-pipe squatting. - It accepts no paths, command lines or HCS JSON. There are seven short, typed requests (start, stop, status, resize within fixed limits, pause/resume, version, open your endpoint), and it builds the VM from a fixed template. - It learns who you are from the pipe (the client's token), never from the message, and it refuses links and other users' folders. - It opens the guest image only from the folder next to its own executable in Program Files, and checks it against a SHA-256 compiled into the binary. - The connection that started the VM is the VM's lease: when it closes, the VM is terminated, so a crash doesn't leave a VM running unseen. - The data plane (files, ports, the Docker API) never runs in the broker. It stays in your own unprivileged process. Until that release, the current preview (v0.1.0-preview.1) still adds you to Hyper-V Administrators, and the zip, which has no service, will keep needing that membership as a documented developer mode. Two things that surprised me The VM that wouldn't give memory back, because of 2 MiB holes. The Hyper-V balloon driver's free page reporting only returns free blocks of order 9, which means 2 MiB. After a workload, about 70 MiB sat free in smaller blocks. Linux counted it as free, the host kept it, and the amount grew with every run. Setting page_reporting.page_reporting_order=0 on the guest kernel brought vmmem back to idle after every fill (+84 MiB down to about 0). If you've ever wondered why vmmem never quite comes back down, this is one of the reasons. The same Hyper-V socket, much faster by changing who dials. All container traffic crosses one Hyper-V socket (hvsock) between the engine and the guest. It turns out Windows sends slowly on an hvsock it accepted: about 1 ms per ~600 KB, against about 50 µs on one it dialed. I couldn't find this documented anywhere. Swapping the roles, so the guest listens and the host dials, plus segmentation offload, took downloads into a container from 2.7 to 10.2 Gbit/s. (That's a development-harness measurement with a 2 GiB / 2 vCPU guest, not part of the Docker Desktop comparison.) Letting an AI agent touch Docker, carefully hyperbutler mcp is a Model Context Protocol server on stdio, so an MCP client such as your editor's AI assistant can see what the engine is doing: { "mcpServers": { "hyperbutler": { "command": "hyperbutler.exe", "args": ["mcp"] } } } It is read-only by default: status, containers, logs, images, volumes, disk and memory use, settings, diagnose. --allow-write adds start, stop, run, pull and config_set. Deleting needs --allow-delete as well. A tool you didn't allow isn't listed and can't be called. Results go through the same redaction as hyperbutler diagnose and are bounded in size. It opens no network port, and it exits with its client. So "why is my container restarting?" is safe to ask, and "clean up my images" is something you have to opt into. Where it stands, honestly - Preview. v0.1.0-preview.1 is released. v0.1.0 stable waits on a 24-hour soak test (the 1-hour soak passed: 12,827 docker commands, none failed, no leak trend) and on an install test of the MSI's broker service on a clean machine. So far it has been installed, upgraded, repaired, rolled back and uninstalled on one machine. - Windows 11 only, x64. Home, Pro and Enterprise. Windows 10 isn't supported. - Unsigned builds. Until releases are code-signed, SmartScreen shows "Windows protected your PC". Click More info, then Run anyway. Check the download against the release's SHA256SUMS first: Get-FileHash .\hyperbutler-*-windows-amd64.msi -Algorithm SHA256 Every release file also has build provenance you can check with gh attestation verify <file> --repo Higangssh/hyperbutler. - Known gap: bind mounts from Windows paths work, but write-heavy trees aren't fast yet. Use a named volume for node_modules. - Side by side is fine. Docker Desktop can stay installed. hyperbutler serves its own pipe and context, and docker context use desktop-linux switches back. hyperbutler import --from-desktop can copy images and volumes over, and it leaves Docker Desktop as it was. Try it 1. Download the MSI from Releases. One UAC prompt sets up Virtual Machine Platform and the PATH; the current preview also adds you to Hyper-V Administrators, which the broker service replaces from v0.1.0. Sign out and back in if it asks. 2. Restart if it asks, then run hyperbutler start and your usual docker and docker compose commands. 3. If something breaks, hyperbutler doctor checks what this PC is missing, and hyperbutler diagnose builds a local zip for a bug report (nothing is uploaded). What I need most right now is reports from machines that aren't mine: other CPUs, Windows 11 Pro and Enterprise, corporate proxies, big Compose projects. Please open an issue with what worked and what didn't. If you'd like to follow along, a star on GitHub helps. More at hyperbutler.dev. Docker is a trademark of Docker, Inc. hyperbutler is not affiliated with or endorsed by Docker, Inc. or Microsoft.

在 Windows 11 上复刻 Caelestia 那套 dock 加玻璃面板的桌面观感，而不改动系统本身的文件和窗口。 lilidoxdev/winlestia t.co

Roundcube Webmail Exposure Needs Host and Mailbox Context Roundcube Webmail Exposure Needs Host and Mailbox Context Roundcube is a webmail client, so an external observer may see a login page without seeing the mail server, the mailbox database, or the authentication policy behind it. A product fingerprint can locate Roundcube-related web surfaces and help compare them with known deployments. It cannot establish that a mailbox is compromised or that a particular plugin is enabled. The selected count-only query was app="Roundcube" with scope all, page 1, and one result. The observation from 2026-11-03 returned 62,319 matching assets. This is a fingerprint population. It should be described with the query and time, not as 62,319 exposed mailboxes. Separate the web tier from mail authority Inventory the webmail hostname, reverse proxy, authentication endpoint, IMAP backend, and session store as separate assets. A public web tier may be intentionally reachable while the mail store is private. A discovered hostname can be more useful than a raw count because it gives the owner a concrete system to compare with DNS and certificate records. Check whether the Roundcube login is protected by rate limiting, multi-factor authentication, and secure session settings. Review reverse-proxy headers and URL handling, because a webmail deployment often trusts forwarded host and protocol values. Confirm that administrative or configuration directories are not served by the public document root. Do not infer mailbox impact The query does not show which accounts exist, whether passwords are weak, or whether a mail server has been compromised. Those questions require authorization and access to the organization's mail logs and account records. A matching asset may be a demo, a vendor service, a personal installation, or a production system. For an owned deployment, compare the external host with DNS records and inventory. If the host is unexpected, determine whether it belongs to a migration, a forgotten tenant, or third-party infrastructure. If it is expected, verify the version and plugin inventory from an authenticated management process. Do not use remote probing to test plugin vulnerabilities without a written scope. Report a measured lead Store the exact query, scope, page, time, count, and any evidence of a response. Use the count to prioritize the review, not to conclude that the internet-facing Roundcube population is affected by a current vulnerability. A failed query or an empty response is an evidence gap, not a zero-result finding. The operational action is an owner review with four outputs: the hostname, the responsible team, the authentication and patch state, and the decision to keep, restrict, or retire the service. ZoomEye helps surface candidates outside the inventory. The mail team supplies the context that determines the risk. References - Roundcube, Roundcube documentation - Roundcube, Installation notes - ZoomEye, ZoomEye

苦罗技驱动久矣，怒 vibe 精简脚本一枚，存现储如金，可省 3GB 存储占用 用过 option-mini, 但是好像并没有减少内存的占用，然后自己 vibe 了一个脚本，把罗技 Logi Options+ 从 3.5 GB 压到约 0.5 GB ，只保留鼠标基础功能 不得不说 AI 太强大了，啥也不懂的技术小白 goopoh/logi-slim

drawio 官方 MCP 能让 AI 在聊天里画流程图，点按钮再去编辑，不用照着图片重画。 内嵌预览要客户端支持 MCP Apps。托管版会收到图表内容，公司内部流程先考虑自建。 jgraph/drawio-mcp t.co

Flash OpenWRT into your Jio AirFiber router faster than you can make maggi. Have a Jio AirFiber router lying around unused? You can use this guide and my repo to unlock it and flash OpenWrt on it. The tool takes care of the entire process for you: - Checks whether your router and firmware are unlockable - Unlocks the router - Enables persistent root SSH access - Downloads the compatible OpenWrt images and the other files required for installation - Flashes everything automatically - Boots the router into OpenWrt without requiring you to manually perform the individual flashing steps The idea behind this project is simple: reuse hardware that would otherwise sit around as e-waste. This is intended for routers you already own and have lying unused at home. I do not support reselling, trading, or commercial activity involving hardware that may be locked, improperly obtained, or not yours to modify. The goal is to give otherwise unused hardware a second life instead of letting it end up as electronic waste. Here you go- wpfyorg/idu-unlock

open sourcing it: DrawdEA/finance-tracker

Not much Rust activity on my side this week. I made a small fix for source-mesh: added test coverage calculation (for now it's just a number, without enforcing a hard threshold in CI), optimized dependency graph construction, and reduced the number of allocations. I also tried using Stringlet in both source-mesh and another private project. However, based on the benchmarks, I didn't see any real benefit. That probably has more to do with the way strings are used in those projects than with Stringlet itself.

MAF Go: What Go Developers Should Know Building reliable AI agents for production is rarely about just chaining a few LLM calls together. As requirements grow, you quickly run into issues like managing conversational state across multiple turns, orchestrating complex multi-agent interactions, handling tool usage, and ensuring graceful recovery from failures. This is especially true for backend systems where Go is often the language of choice for its performance and concurrency. Microsoft has released the Agent Framework for Go (MAF Go), an official implementation of their open, multi-language framework designed for building and orchestrating production-grade AI agents and multi-agent workflows. It offers Go developers a structured approach to move beyond simple prompt engineering to create more sophisticated, resilient agentic applications. What MAF Go Is MAF Go is part of the broader Microsoft Agent Framework, which also has .NET and Python implementations. It's positioned as the successor to frameworks like Semantic Kernel and AutoGen, consolidating their capabilities into a single, comprehensive platform for agent development. The Go SDK specifically provides packages, examples, and APIs tailored for building agents and workflows in Go. It's currently in public preview, with version v0.2.0 introducing provider-independent embedding generation and vector storage for RAG and memory capabilities. The framework focuses on providing foundational building blocks for complex agent systems, including model clients for various LLMs (like Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Gemini, and Ollama), an agent session for state management, and context providers for agent memory. How MAF Go Works MAF Go provides a structured way to define agents, their tools, and how they interact within workflows. The core idea is to give you explicit control over agent execution paths, moving beyond stateless chat loops. Key operational aspects include: - Agents: These are individual units that use LLMs to process inputs, call tools, and generate responses. MAF Go supports an "Agent Harness," which is an opinionated agent with capabilities for multi-step tasks like planning, todo tracking, context compaction, file access, and observability. - Tools and Middleware: Agents can discover and use domain-specific tools or knowledge bases. Middleware allows you to intercept agent actions for custom logging, validation, or other processing, offering a crucial layer for control and monitoring. - Workflows: MAF Go excels at orchestrating multiple agents through graph-based workflows. These workflows can support various patterns: - Sequential: Agents execute tasks in a defined order, passing results between them. - Concurrent: Agents work in parallel to maximize efficiency. - Group Collaboration: Agents can collaborate on a single task, often with a manager agent overseeing the interaction. - Conditional Routing: Logic can be applied to determine the next agent or step based on certain conditions. - Checkpointing and Human-in-the-Loop: Workflows can be paused and resumed, and human intervention points can be integrated, which is vital for long-running processes and validation in production environments. - Observability: The framework integrates with OpenTelemetry for distributed tracing, allowing you to monitor and debug agent runs, tool invocations, and workflow steps. This visibility is essential for identifying bottlenecks and errors in complex agentic systems, as covered in Agentic AI: How Autonomous Agents Are Transforming Enterprise Workflows. Installation is straightforward, you can add it to your Go project using: go get github.com/microsoft/agent-framework-go Who Should Care? If you're a Go developer tasked with building production-grade AI agents or complex multi-agent systems, MAF Go is worth your attention. It's particularly useful for scenarios requiring robust orchestration beyond simple single-prompt interactions or stateless chat loops. This includes applications where you need graph-based workflow patterns, checkpointing for durability, detailed observability, governance, or human oversight. For teams already invested in Go for backend services, CLIs, workers, or cloud-native applications, MAF Go offers a way to integrate advanced AI capabilities natively within that ecosystem. I’m currently building an AI-powered SDR agent platform myself using other frameworks, and my experience confirms that a lot of AI agent projects tend to stumble in production due to insufficient validation and error handling, rather than issues with the model itself. A framework like MAF Go, with its focus on robust orchestration and observability, directly addresses these common failure points, providing a more stable foundation for agentic applications. This echoes the importance of structured approaches to agent task decomposition, as I discussed in LLM Agent Task Decomposition for Reliable Workflows. A Concrete Limitation While MAF Go offers a powerful foundation, it's important to remember that the Go SDK is still in public preview. This means that certain features available in the .NET and Python implementations, such as declarative agents, CodeAct (for generating and executing code), and some advanced functional workflows, are not yet implemented or fully available in the Go version. Additionally, while it supports embedding generation and vector stores for RAG, the more comprehensive RAG capabilities like automatic retrieval before agent runs might still be evolving. It also requires Go 1.26 or later. As with any preview software, APIs may evolve, and it might not be suitable for immediate, critical production deployments that rely on these specific missing features without careful consideration and workaround planning. Sources - microsoft/agent-framework-go --- This article was generated with AI (Google Gemini + web search). Please check important details against the sources above. Daily AI notes on LinkedIn — Kamal Kishor

You can use a patched apk from AngelDark92/steamlink-androidxr-patched Or patch one yourself using morphe with the xr porting hub patches. There is also a custom steamvr driver that allows customized color settings, cas sharpening etc. But the patched apks will work without it.

@torstbrn @OpenAIDevs @jenniferumoke 4/ Huge thanks to @Andy_AJT @oscarfalll @paw_lean @borvibe @OpenAIDevs and everyone who made the hackathon happen. Code: jenniferumoke/ambi-openai-hackathon We’ll quote-post this thread as updates ship: what changed, what we tested and what’s next.

AI가 옵시디언 메모 규칙도 알까? GitHub 별 5만 Obsidian Skills, 링크·캔버스까지 알려 주는 설명서 #Shorts AI가 마크다운은 안다는데, 위키링크나 콜아웃 같은 옵시디언 전용 문법도 제대로 알까요? Obsidian Skills(kepano/obsidian-skills)는 GitHub 별 약 5만 개(50.6k, 2026.11.01 확인)의 오픈소스 스킬 모음으로, README는 'Obsidian과 함께 쓰는 에이전트 스킬'이라고 소개합니다. Agent Skills 규격을 따르기 때문에 Claude Code, Codex, OpenCode 등 스킬을 지원하는 에이전트에서 쓸 수 있다고 합니다. · obsidian-markdown: 위키링크·임베드·콜아웃·속성 등 옵시디언식 마크다운(.md) 작성·편집 · obsidian-bases: Bases(.base) 보기·필터·수식·요약 · json-canvas: JSON Canvas(.canvas) 노드·연결·그룹 · obsidian-cli: Obsidian CLI로 볼트 작업, 플러그인·테마 개발 포함 · defuddle: 웹페이지에서 군더더기를 걷어 깔끔한 마크다운 추출(토큰 절약) · knap: JSON/CSV 데이터로 마크다운 템플릿 렌더링 설치: Claude Code 마켓플레이스 /plugin marketplace add kepano/obsidian-skills → /plugin install obsidian@obsidian-skills, 또는 npx skills add. 수동 설치는 볼트 루트의 /.claude 폴더에 저장소 내용을 넣으면 됩니다. OpenCode는 안쪽 skills/ 폴더만 복사하지 말고 저장소 전체를 ~/.opencode/skills/에 clone하라고 README가 안내합니다. 팁: 에이전트가 볼트 파일을 직접 만들고 고치는 방식이니, 백업 후 작은 작업부터 맡겨 보세요. README 기반 소개이며 직접 설치해 보지 않았습니다. 영상 첫 장면의 문법 비교 그림은 개념 도식입니다. 출처 kepano/obsidian-skills MIRLABS · AI 도구와 기술을 짧게 #MIRLABS #미르랩스 #Obsidian #옵시디언 #ClaudeCode #에이전트스킬 #오픈소스 #GitHub #Shorts

Lecture 2 of our Virtual Humans course: interactive demos on cameras and human motion. Pinhole, lens blur, perspective, gimbal lock, inverse kinematics. Code: gerardponsmoll/vh-toolbox Lectures: youtube.com Course: virtualhumans.mpi-inf.mpg.de t.co

github.com/LethaboMohapi t.co

Built a small bare-metal capacitance meter around the #nRF52840 recently. It uses RC timing, SAADC, and a hardware timer to measure capacitor values. Tested from ~0.1 µF to 1000 µF, with ~4–6% error vs my multimeter. Repo: codetit4n/nrf52840-capacitance-meter #EmbeddedSystems #BuildInPublic t.co

提交了一个issue给magpie ,路由到claude cli 后，长会话会出现死循环，不知道是我使用的问题还是确实触发了。我已经改用直接使用claude cli 了。 magpie#5043

일하는 AI, 복잡한 도구 없이? GitHub 별 8만 Learn Claude Code, 17강 코딩 AI 만들기 강의 #Shorts 일하는 AI(에이전트)를 직접 만들려면 정말 복잡한 도구가 필요할까요? Learn Claude Code(shareAI-lab/learn-claude-code)는 코드 공유 사이트 GitHub에서 별 약 7만 9,700개(79.7k, 2026.11.01 확인)를 받은 무료 오픈소스 강의입니다. Claude Code를 닮은 작은 코딩 AI의 '작업 환경(README 표현: harness)'을 0부터 따라 만드는 내용이며, Anthropic이 만든 공식 강의가 아니라 shareAI-lab이 만든 비공식 교육 자료입니다. · README의 비유: AI 모델은 운전자, 작업 환경은 자동차. 작업 환경 = 도구 + 지식 + 관찰 + 행동 + 권한 · 1강: '반복 하나와 명령창(Bash) 도구 하나면 일하는 AI 하나' — 이후 권한, 작업 전 계획, 보조 AI에게 맡기기, 긴 대화 줄이기, 기억, 예약 실행, 팀 작업, 외부 도구 연결(MCP)을 한 단계씩 추가 · 모두 17개 수업(s01~s17). 장마다 영어 README와 중국어·일본어 번역, 실행 가능한 code.py · 빠른 시작: 저장소 내려받기 → pip install → .env에 ANTHROPIC_API_KEY 설정 → python s01_agent_loop/code.py · 웹 학습 화면도 제공(learn.shareai.run) · 라이선스: MIT 주의: 코드를 실행하려면 Python과 Anthropic API 키가 필요합니다. API 키는 비밀번호처럼 다루고 공유하지 마세요. README는 옛 12강 버전(docs/, agents/)과 새 17강 버전의 번호가 다르니 섞어 읽지 말라고 안내하고, '코드를 복사하는 게 아니라 핵심 설계를 이해하고 직접 만들라'고 맺습니다. README에는 다른 에이전트 제작 방식을 강하게 비판하는 의견도 있으니, 그 부분은 저자의 관점으로 읽으세요. README 기반 소개이며 직접 실행해 보지 않았습니다. 첫 장면의 흐름도·반복 그림은 개념 도식이며 실제 화면이 아닙니다. 출처 shareAI-lab/learn-claude-code MIRLABS · AI 도구와 기술을 짧게 #MIRLABS #미르랩스 #LearnClaudeCode #ClaudeCode #AI에이전트 #코딩AI #AI공부 #파이썬 #오픈소스강의 #Shorts

Caja de herramientas SM+ SM+ Toolbox es un compañero de escritorio para ShadowMountPlus, un proyecto de creación doméstica de PS5 que le permite montar imágenes de juegos desde almacenamiento externo. AldySan/SM-Toolbox t.co

A Stray AGENTS.md Can Steer Your Agent: Check What It Loads First On 22 October, the security firm Forever Security published a report about a file it found on a customer's machine: an AGENTS.md sitting in ~/Downloads, written to look like ordinary engineering notes. The customer had started OpenAI Codex in that folder. Codex read the file as project guidance and began installing function-color, a known malicious npm package, before Forever's tooling stopped it. The researchers then reran the real file inside an isolated VM. In their tests, Codex carried out the payload in more than 90% of attempts, and the file had it add a server to ~/.codex/config.toml, the machine-wide config, so the damage didn't stay in Downloads. They also built a CLAUDE.md variant against Claude Code, which they describe as much harder to fool; it followed through about half the time in their testing. Their conclusion is the part worth keeping: nothing here is a bug to patch. An agent that follows the instruction file in its folder is doing what it was built to do. So the defence is on you, and it starts with knowing which files your agent loads before you press Enter. Why the folder you start in matters Instruction files like AGENTS.md and CLAUDE.md aren't opened on request. They're loaded automatically at the start of a session, and the folder you launch from decides which ones. Downloads is a bad place to launch from because, as the report points out, it's the one folder a website can drop a file into without asking. I covered why these files are context, not a security boundary, in 6 AGENTS.md Mistakes That Make Your Coding Agent Ignore It. This article is about the step before that: discovery. Where each tool looks Here's what the official docs say, as of 3 November 2026. | Tool | Global file | Project files | |---|---|---| | Codex | ~/.codex/AGENTS.override.md or AGENTS.md | From the project root (a folder with .git) down to where you started; with no root, only the current folder | | Claude Code | ~/.claude/CLAUDE.md | CLAUDE.md and CLAUDE.local.md in the current folder and every folder above it; AGENTS.md from those same folders when there's no CLAUDE.md | | Cursor | User Rules (Customize → Rules) | AGENTS.md in the project root and subfolders, combined | | Gemini CLI | ~/.gemini/GEMINI.md | GEMINI.md in workspace folders and their parents | A few details change how you should read that table. Codex checks each folder for AGENTS.override.md, then AGENTS.md, then any names you added in project_doc_fallback_filenames, and takes at most one file per folder (Codex AGENTS.md docs). If it can't find a project root, it only checks the folder you're in. That's exactly the Downloads case: not a Git repo, so the file in Downloads is the project file. Forever also notes that Codex only obeys an AGENTS.md in a project it trusts, and that once you've worked in Downloads before, it qualifies. Claude Code walks up, not just down. Start it in foo/bar/ and it loads foo/bar/CLAUDE.md and foo/CLAUDE.md at launch, plus files in subfolders later, once it reads or edits a file there (memory docs). Since v2.1.277 it reads AGENTS.md too, but by default only when there's no CLAUDE.md, .claude/CLAUDE.md or CLAUDE.local.md in your folder or above it. In an interactive session it prints a line like no CLAUDE.md found; AGENTS.md loaded: /home/you/repo/AGENTS.md, which is worth watching for. Cursor reads AGENTS.md in the project root and in subfolders, and applies a nested file when you work with files in that folder, with more specific instructions taking precedence (rules docs). Gemini CLI searches for GEMINI.md in your workspace folders and their parents, and also scans a folder and its ancestors when a tool touches a file there (GEMINI.md docs). The common thread: a file you never opened can end up in the agent's context just because of where it sits relative to where you started. A 5-step check before you run the agent 1. Start from the project folder, never Downloads or home Open a terminal in the repository you mean to work on. Claude Code's own security docs push in the same direction: if you start it directly in your home directory, trust is held for that session only and the prompt comes back every launch, and the docs suggest starting from a project subfolder instead. For Downloads, Forever's advice is simply not to start any agent there. 2. List the instruction files it could load Before launching, see what's above, around and below you. Here's a small bash sketch that lists the usual filenames; adapt the list to the tools you use: #!/usr/bin/env bash # List instruction files a coding agent could load if started here. names="AGENTS.md AGENTS.override.md CLAUDE.md CLAUDE.local.md GEMINI.md .claude/CLAUDE.md .claude/AGENTS.md" # This folder and every folder above it dir="$PWD" while :; do for n in $names; do [ -f "$dir/$n" ] && echo "here/above: $dir/$n"; done [ "$dir" = / ] && break dir="$(dirname "$dir")" done # Global files in your home directory for f in ~/.codex/AGENTS.md ~/.codex/AGENTS.override.md ~/.claude/CLAUDE.md ~/.gemini/GEMINI.md; do [ -f "$f" ] && echo "global: $f" done # Subfolders (loaded later, when the agent works there) find . -mindepth 2 -name node_modules -prune -o -type f \ \( -name AGENTS.md -o -name AGENTS.override.md -o -name CLAUDE.md \ -o -name CLAUDE.local.md -o -name GEMINI.md \) -print | sed 's/^/below: /' If you use Codex fallback names, add them to the list. Anything you don't recognise is the thing to read next. 3. Read the files you didn't write You don't need to audit every line of your own AGENTS.md. You do need to read a file that came with a fresh clone, a download or a teammate's branch. The file Forever captured was about 13 KB of bland notes, with the attack in one "Local developer environment" section near the end, so skimming the top isn't enough. A quick search for the risky parts helps: grep -n -i -E "mcp_servers|config\.toml|settings\.json|BASE_URL|dangerously|bypass|npx|npm install|curl|launchctl" AGENTS.md CLAUDE.md 2>/dev/null None of those words is proof of anything. They're the places where an instruction file stops describing your project and starts telling the agent to change its own setup or fetch something. 4. Confirm what actually loaded Once you've read the files, check that the agent sees what you expect: - Claude Code: watch for the AGENTS.md loaded: line, or run /context and look under Memory files. - Codex: the docs suggest codex --ask-for-approval never "Summarize the current instructions." from the repo root, or opting into a plaintext log with codex -c log_dir=./.codex-log and checking codex-tui.log. Treat this as confirmation, not your first line of defence. Asking an agent to summarise its instructions still means starting it with those instructions loaded. If ancestor files keep getting in the way, Claude Code's claudeMdExcludes setting skips specific CLAUDE.md files by path or glob, and Codex's project_root_markers controls what counts as a project root. 5. Keep the brakes on, and check the agent's own config Leave sandboxes and approvals on. Codex's approvals and security docs say the CLI and IDE extension default to no network access and writes limited to the active workspace; for chat or planning without changes, switch to read-only with /permissions. Be very suspicious of any instruction that asks for --dangerously-bypass-approvals-and-sandbox, which is the flag the captured file had the model use. Then look where the attack actually landed. Forever's checklist: an unexpected [mcp_servers.*] block or a changed chatgpt_base_url in ~/.codex/config.toml, and an ANTHROPIC_BASE_URL, proxy or other endpoint you didn't set in a project's .claude/settings.json. If you find one, they advise assuming the token leaked and rotating it, and treating any machine with function-color installed as compromised. Checklist 1. Don't start any coding agent in ~/Downloads or directly in your home folder. 2. Know the discovery rule for each tool you use: Codex goes from the Git root down, Claude Code goes up from where you start. 3. List instruction files above, in and below your folder before launching. 4. Read every instruction file you didn't write, all the way to the end. 5. Search for config edits, base URLs, bypass flags and installs in those files. 6. Confirm what loaded with /context or the Codex log. 7. Keep sandbox and approval defaults on. 8. Check ~/.codex/config.toml and .claude/settings.json for entries you didn't add. Where to go next If you want to write a good AGENTS.md of your own, the free AGENTS.md Quick-Start Cheat Sheet covers what belongs in the file, what to leave out, the one-line test for each line, and cross-tool use including the Claude Code symlink, in 6 pages. For the full picture, AGENTS.md: The Complete Guide to the Cross-Tool Agent Standard spends 42 pages on how agents find and load the file, the monorepo hierarchy with nearest-file precedence, cross-tool portability, the common mistakes and a 30-day adoption plan. This article was written with the assistance of AI and reviewed by the author.

Offline AI Code Review & Security Guardrail CLI (Project Showcase) A quick demo showcasing a privacy-first, local-LLM-based CLI tool designed to act as an automated Git pre-commit hook. Instead of sending proprietary code to third-party cloud services, this tool runs 100% locally on your machine using OpenAI-compatible endpoints (like LM Studio or Ollama). It combines deterministic file filtering with semantic AI evaluation to instantly catch hardcoded secrets, security vulnerabilities, and missing test files before a commit goes through. 🔗 **Source Code / GitHub:** ashishcodesstuff/cli-code-reviewer --- ### 🚀 Key Highlights: * **100% Offline & Private:** Powered locally by open-weight models (Qwen 2.5 Coder). * **Automated Git Guardrail:** Integrated seamlessly using Husky pre-commit hooks to block risky commits. * **Strict Schema & Security Validation:** Enforced via Zod to guarantee clean JSON outputs and precise file attribution. * **Performance & Token Metrics:** Tracks token consumption and execution duration on every scan. ### 🛠️ Tech Stack: TypeScript (Node.js ESM), Zod, OpenAI SDK, Husky, Kleur, Ora. #DeveloperTools #LocalAI #TypeScript #NodeJS #OpenSource #SoftwareEngineering #CLI

PS3 World App - Download homebrew, themes and avatars straight from ps3.world, without a PC. PS3 World App for the PlayStation 3 (HEN and CFW): browse the PS3 World archives on the console and download homebrew, themes and avatars straight from ps3.world, without a PC. - Homebrew, themes and avatars in the site's categories: Homebrew by Games, Ports, Edits (modded PS2 Classics), Tools and Media; themes by their 17 categories; avatars by region, in packs. - Search any list (Triangle), and jump by letter with L2/R2. Sort a list A–Z, newest first or largest first (Select). - Surprise me (Square on a section's categories): something at random, from 13,000 themes or all the homebrew. - Previews of what you're downloading: a theme's screenshot, an avatar, a homebrew's art. - Saves where the PS3 looks: PKGs to /dev_hdd0/packages (Package Manager > Install Package Files), themes to /dev_hdd0/theme, avatars to the chosen user's psn_avatar folder. - Checked downloads: every file's size and sha256 are checked before it's kept, and a stopped download resumes where it left off. - A download queue: start several and keep browsing; the one running shows bottom right. Square on an avatar pack queues every avatar in it. - Before a big download (1 GB or more) it says how big and roughly how long at the speed downloads have been getting, and checks there's room on the HDD. - Full-screen previews (Triangle on an item). - Favourites: Select on an item stars it; they have their own card on the home screen (and a tab in My downloads). - Recently added: each section lists what's new in the last 30 days, and a New ticker along the top of the home screen scrolls through it. - My downloads (Triangle on the home screen): the queue, then everything on the HDD with its date. Delete a file (Square twice), or get a homebrew's new version. - Free space on the HDD, on the home screen. - Updates: when a newer PS3 World is out, the home screen says so, and Square downloads it. - Credits & contact (Start on the home screen): who made it, what it's built with, and where to send your own homebrew, themes or avatars (ps3.world/contact). - 1080p: with the PS3 set to 1080p, everything is drawn at the full resolution (the graphics chip draws; the CPU fallback is 720p). On a 4:3 TV the picture is letterboxed at the right shape. - Sound effects: soft clicks as you move and choose (off in the Language screen, under Select). - 16 languages: English, Spanish, Portuguese, French, German, Italian, Dutch, Polish, Turkish, Danish, Indonesian, Russian, Chinese (Simplified), Japanese, Korean and Arabic (joined letters, and the whole screen laid out right to left, controls included). It follows the PS3's language; Select on the home screen opens a grid of flags to pick another. Chinese, Japanese and Korean use the PS3's own system fonts. The words are in data/lang/<code>.txt, and homebrew's descriptions in generator/descriptions/<code>.json. AI Disclosure: Claude was used in creating this app. Downloaddl.ps3.world/ Source Code / PS3 Site is lite.ps3.world

AI에게 디자인 견본집을? GitHub 별 14만 UI UX Pro Max, 업종 맞춤 색·글꼴·화면 구성 추천 스킬 #Shorts AI에게 웹사이트를 맡길 때, 디자인 견본집까지 쥐여 준다면 어떨까요? UI UX Pro Max(nextlevelbuilder/ui-ux-pro-max-skill)는 코드 공유 사이트 GitHub에서 별 약 14만 4천 개(144k, 2026.11.01 확인)를 받은 오픈소스 '스킬'(AI에게 주는 작업 설명서)입니다. README는 '여러 플랫폼에서 전문적인 UI/UX를 만들 수 있게 디자인 지식을 주는 AI 스킬'이라고 소개합니다. · 디자인 시스템 생성(v2.0): 예를 들어 '스파 랜딩 페이지 만들어 줘'라고 하면 업종·스타일·색·화면 구성·글꼴 5가지를 함께 찾아보고, 화면 구성·스타일·색·글꼴·효과·피해야 할 디자인·출시 전 체크리스트를 한 세트로 제안 (README 예시) · 담긴 자료(README 기준): 검색 가능한 UI 스타일 79개(기본 추천 50개), 색 조합 192개, 글꼴 짝 74개, 차트 유형 25개, 기술 스택 22개, UX 가이드 119개, 업종별 추론 규칙 192개 · 지원: Claude Code, Cursor, Windsurf, Copilot, Codex CLI, Gemini CLI, Kiro, OpenCode 등 20여 종 코딩 AI · 설치: Claude Code 플러그인 명령 두 줄, 또는 npm install -g ui-ux-pro-max-cli 후 uipro init --ai claude 등. 검색 스크립트용 Python 3 필요 · 사용: 설치 뒤 UI/UX 작업을 요청하면 스킬이 자동으로 켜짐(일부 도구는 /ui-ux-pro-max 명령) · 라이선스: MIT 주의: 이 저장소는 무료 기본판이고, 브랜드·로고·배너 등 기능이 추가된 유료 프리미엄판이 별도로 있습니다(README 안내, 가격은 README에 없음). README는 옛 패키지 이름(uipro-cli)은 낡았으니 쓰지 말라고 안내합니다. 디자인 품질은 README의 설명이며 직접 검증하지 않았습니다. README 기반 소개이며 직접 설치해 보지 않았습니다. 첫 장면의 견본집 그림은 개념 도식이며 실제 화면이 아닙니다. 출처 nextlevelbuilder/ui-ux-pro-max-skill MIRLABS · AI 도구와 기술을 짧게 #MIRLABS #미르랩스 #UIUXProMax #ClaudeCode #Cursor #AI디자인 #웹디자인 #UIUX #에이전트스킬 #Shorts

Vercelでデプロイエラーになったので修正 Vercelの各サービスにフレームワークを明示する github.com/ryo-i/next-nest-started/commit/9449f4f…

AI가 부탁한 곳만 고쳤을까? GitHub 별 23만 카파시 영감 규칙 파일, 원칙 4개로 코딩 AI 길들이기 #Shorts AI가 코드를 고쳤다는데, 정말 부탁한 곳만 고쳤을까요? andrej-karpathy-skills(multica-ai/andrej-karpathy-skills)는 코드 공유 사이트 GitHub에서 별 약 22만 8천 개(228k, 2026.11.01 확인)를 받은 저장소로, README는 'AI 연구자 안드레이 카파시가 관찰한 LLM 코딩의 함정에서 출발한, Claude Code 동작을 개선하는 CLAUDE.md 파일 하나'라고 소개합니다. 제목이 'Karpathy-Inspired'인 것처럼 카파시 본인이 만든 저장소가 아니라, 그의 글에서 영감을 받아 다른 개발자가 정리한 비공식 규칙 모음입니다. README의 네 가지 원칙 1. Think Before Coding — 짐작하지 말고, 헷갈리면 묻고, 선택지를 드러내기 2. Simplicity First — 요청한 것만, 필요한 만큼만. '200줄이 50줄로 될 수 있다면 다시 쓰기' 3. Surgical Changes — 꼭 필요한 곳만 고치고, 관련 없는 코드·주석·서식은 건드리지 않기 4. Goal-Driven Execution — 성공 기준(예: 테스트)을 먼저 정하고 확인될 때까지 반복 · 설치(README): Claude Code 플러그인으로 설치하거나, 프로젝트에 CLAUDE.md 파일을 내려받아 붙여 넣기. Cursor용 규칙 파일도 포함 · README가 말하는 효과 확인 신호: 불필요한 변경이 줄고, 질문이 실수 뒤가 아니라 작업 전에 나오고, PR이 깔끔해짐 · 라이선스: MIT 주의: README 스스로 '이 규칙은 속도보다 신중함 쪽으로 기울어 있어, 오타 수정 같은 사소한 일에는 판단껏 쓰라'고 밝힙니다. 효과는 README의 설명이며 직접 검증하지 않았습니다. README 기반 소개이며 직접 설치해 보지 않았습니다. 첫 장면의 '바뀐 줄' 그림과 파일 그림은 개념 도식이며 실제 화면이 아닙니다. 출처 multica-ai/andrej-karpathy-skills MIRLABS · AI 도구와 기술을 짧게 #MIRLABS #미르랩스 #카파시 #Karpathy #ClaudeCode #CLAUDEmd #코딩AI #AI코딩 #프롬프트 #Shorts

@Mergentmedia I'm building sandhiveio/sandhive-cli to surface the right X threads where our tech can help, then draft context-aware answers in seconds, bro.

Tucano runs a team of coding agents in chat channels on top of the Claude Code, Codex, OpenCode or Pi you already have. Agents mention each other to hand off work, and merges, posts or deletes wait for your approval. Desktop app, 141 stars. eduwxyz/tucano

[Mise à jour] Plus de deux ans après : le projet document sur les actions du RN Salut tout le monde, Ça fait déjà deux ans que j'ai commencé à regrouper les actions du RN, petit à petit, quand une info passe, et finalement, la liste commence à être longue ! Je le fais de temps en temps, un petit post pour vous raconter les nouveautés, à presque 6 mois des présidentielles, ça peut toujours servir, aux repas de familles, à la machine à café ! Ça a commencé sur TextUp avant de se faire striker, c'est passé sur un Github, avec vos retours, un Github pages a été fait pour rendre le tout plus agréable à lire, voici les liens : - Le répo : T0uillette/rn1pb - Le site : t0uillette.github.io Déjà une chose importante : merci à vous pour vos propositions et vos suggestions, j'ai pu faire évoluer le tout en rajoutant des archives, pour pouvoir garder une trace et rendre le site un peu plus accessible qu'un répo Github. Aussi merci à ceux et celles qui sont venues en MP me demander le lien, ça me prouve que ça vit au delà de ce que j'imagine, c'est toujours chouette, et un grand merci aux gens qui proposent des articles, que ce soit en MP, en ping, ou directement sur le répo. Je sais, c'est un peu chiant à faire et je suis tatillon sur la mise en place, mais c'est pour assurer une lecture plus simple. Il y a une autre nouveauté, un sub sur Reddit qui à le même nom que le reste du projet : rn1pb. J'ai aussi dans le projet de rendre le site plus beau et plus accessible et de changer le répo pour le rendre plus maintenable, ça commence à devenir compliqué à éditer parce que le fichier devient long. Et la grande nouveauté du tout, et c'est là tout le but du fait de vous mettre à jour, c'est qu'il est possible de se baser sur le projet pour d'autres initiatives, et c'est ce qu'à fait u/MeaningCold2621 avec son superbe projet : unefeuillesuffit.fr : un site qui permet d'imprimer des affiches facilement et de les coller à côté de chez soi, parfois, une affiche vaut milles discours, donnez lui un max de force ! Bref, une fois de plus, merci à toustes pour votre aide, ou votre enthousiasme, et c'est vraiment cool de voir que ce projet peut en inspirer d'autres. Une fois de plus : - Si vous avez des suggestions sur le projet, n'hésitez pas, c'est le but de ce post - Si vous avez envie de participer, la porte est grande ouverte - Si vous voulez le lien, des infos sur l'avancée du projet, je suis toujours dispo en MP Bonne journée et je ferai probablement un dernier post un peu avant les élections pour vous tenir au jus. Edit : vous m'avez motivé à bosser sur l'UI, donc j'ai repris l'interface du site pour le rendre plus agréable, j'espère que ça vous plaira, je suis pas designer.

On your mac. No backend. No API bills. Fully offline. I built Rona for Mac with SwiftUI, a voice companion powered by Apple Intelligence and a 174.5 MB local TTS model. You talk. It talks back, in different tones, warm & sharp. Source code: Fatehkhann/rona-voice

everyone should update to github.com/ciphernom/midwimble/releases/tag/v1.2.…

HixFlix Tu biblioteca de AllDebrid en PlayStation 5, en una interfaz al estilo Netflix, reproducida por VLC. HIX sin el inicio de sesión: sin cuenta, sin Supabase. Todo vive en un archivo en la consola. hexreaper711/HixFlix

출처: xai-org/x-algorithm 커밋 기록 (8/20 첫 공개 → 8월 6번, 9월 19번, 10월 22번, 11/3 업데이트). 광고 섞는 방식 설정값 multi_risk → slot_spread(10/31) → multi_risk(11/3). 🇯🇵 出典：Xの公開コードの更新履歴。

对X的算法多有怨言的 可以让你的Agent详细分析一下X的算法开源 指导一下你的内容 xai-org/x-algorithm

Subscriptions are easy to start and easy to forget. Example: organize recurring software costs in Actual Budget, a local-first, open-source app with desktop and self-hosting options. What would you track first? actualbudget/actual #OpenSource

From prose to assertions: making 'done' checkable when an AI agent writes the code The hardest part of an AI coding loop is not getting code written. It is the moment after the agent says "done". When one agent writes the code and another agent reviews it, you have not removed the trust problem — you have just moved it. The reviewer reads the diff, reads the tests, reads the ticket, and produces a verdict. If the ticket says "spacing should be 12vp" and the screenshot looks about right, the verdict is "pass". Two auditors can look at the same delivery and disagree, and nothing in the tooling forces that disagreement to resolve. That is the gap I tried to close in DwGuard-Dsh-Hmos, a plugin for DeepSeek Harness that orchestrates HarmonyOS app delivery: DSH freezes a four-dimension acceptance pack (function / aesthetic / performance / accessibility), runs an adversarial pre-review before dispatch, hands the work to DevEco Code in Goal mode, then audits the delivery with independently gathered evidence. The change I want to write about is small in surface area and large in consequence: acceptance criteria can carry a machine-checkable assertion, and the audit gate recomputes the verdict instead of trusting prose. Three failures of prose criteria My criteria used to be a sentence plus a verification method: statement: 冷启动到首帧 ≤ 800ms (cold start to first frame ≤ 800ms) verify: Profiler 启动分析毫秒数 (milliseconds from the profiler) And an audit check was a result plus free-text evidence: result: pass evidence: 3 runs, 700/720/760ms, screenshot artifacts/perf.png Three problems showed up immediately: 1. Criteria are not executable. "12vp" in a sentence means nothing to a machine. Two auditors reading the same criterion can reach opposite conclusions and both feel right. 2. Evidence is not reviewable. evidence is free text. No tool can tell whether "1200ms" satisfies "≤ 800ms" without a human eyeballing it. 3. The report is not consumable. The verdict lives inside markdown. CI, regression scripts, and the next agent cannot read a field; they re-parse prose. So the audit was still a person voting, with extra steps. The assertion: criteria that can fail by themselves The fix is an optional assertion on each criterion: { "id": "P-01", "dimension": "performance", "statement": "Cold start to first frame ≤ 800ms", "verify": "ms reported by the startup profiler", "weight": "must", "ruleId": "DW-PERF-01", "assertion": { "matcher": "lte", "expected": 800, "unit": "ms", "source": "startup profiler" } } Eight matchers cover the criteria I actually write: | matcher | meaning | |---|---| | equals | numeric comparison when both sides are numbers, exact string otherwise | | contains | substring, case-insensitive | | regex | pattern (unanchored — add ^$ yourself) | | range | within [min, max], inclusive | | gte / lte | numeric greater/less or equal | | oneOf | equals one of a list | | present | non-empty | The audit side then must report an actual value next to the verdict: { "criterionId": "P-01", "result": "pass", "evidence": "three cold starts: 700/720/760ms; screenshot artifacts/perf.png", "actual": "720ms" } One detail matters more than it looks: numeric matchers take the first number inside actual. That means measured values can keep their units — "12vp" parses as 12, "720ms" as 720, "4.9:1" as 4.9. The assertion compares numbers; the report stays human-readable. The rules that make it a gate instead of a suggestion An optional field that nobody enforces is decoration. So the gate has opinions: | situation | result | |---|---| | criterion has an assertion, check says pass, but no actual | error — you cannot pass an assertion you never ran | | criterion has an assertion, check says pass, assertion recomputes to fail | error — audit refused; this is the self-contradiction case | | criterion has an assertion, check says fail, assertion recomputes to pass | warning — something else may have failed it; ask for review | | criterion has an assertion, check says unverified without actual | warning (the existing must-gate already errors) | | criterion has no assertion | unchanged behaviour: human verdict + evidence | | the assertion itself is invalid (unknown matcher, range with no bounds, bad regex) | the spec refuses to freeze with E_CRITERION_ASSERTION_INVALID | Plus a nudge at authoring time: a must criterion whose statement contains numbers but carries no assertion gets W_CRITERION_NO_ASSERTION, because that is exactly where two auditors will disagree later. The subtle rule is the third one. Saying "fail" when the number passes is not always wrong — a screenshot can show a layout bug that a timing assertion cannot see. But the gate should make that mismatch visible instead of silently accepting whatever the reviewer wrote. What it changes in the artefacts Because the verdict is now recomputed, the reports gain a column each: - spec.md and the Goal brief gain an assertion column, so the executor knows which criteria will be measured rather than judged; - audit-<n>.md gains assertion / actual / verdict columns; - the acceptance report's evidence index gains assertion / actual; - rework orders gain a gate problems section — previously a contradicted assertion produced a refusal with nowhere to record why. And audit-<n>.json now contains checks[].assertionVerdict, which means CI can fail a build on verdict: fail without parsing markdown. That was the whole point of making the report consumable. What stays human I want to be careful here, because "machine-checkable acceptance" is easy to oversell. - Assertions only cover what is measurable. "The empty state feels calm" cannot be asserted. It stays a judgement call, and I still write those criteria — they just do not claim to be objective. - The assertion does not collect evidence. Somebody still gathers the actual value: a screenshot with a known device, a dumpsys reading, a layout tree. Evidence files get copied, hashed, and recorded, but the assertion only decides pass/fail from the number. - Thresholds are taste wearing a number. "≤ 800ms" is a decision I made, not a law of physics. Turning it into an assertion makes the decision visible and debatable, which is an improvement, but the number itself is still a human judgement. - Values can be gamed. If the process that produces actual is the same process being audited, an agent can satisfy the letter. That is why the audit playbook insists on externally observable sources — hidumper render stats and hitrace for performance, layout bounds for spacing — and treats in-app timing instrumentation as auxiliary evidence, never the sole basis. The honest result The change shipped as 0.5.0 and the suite went from 49 to 55 tests, all green (six new ones specifically about assertions: accepting valid, rejecting invalid, recomputing, refusing contradictions, refusing a pass without actual, and keeping old packs unchanged). The real payoff is not "the AI is now trustworthy". It is that disagreement became resolvable. When two auditors disagree now, there is a file with a number and a rule that says whether it passes. Either the number is wrong, or the threshold is wrong, or the measurement is wrong — and all three are fixable. Before, the disagreement was just two paragraphs. If you are building an agent loop where one model checks another, the question to ask is not "is the reviewer good". It is: can the reviewer's verdict be recomputed by something that did not write it? Plugin: <Dogwind221/dwguard-dsh-hmos> · npm: <npmjs.com> · listed in awesome-dsh-plugin.

BO2 on the Ayn Thor Hey all! Apologies for the bad video quality and the bad gameplay, but I came across this android port for BO2 and it's genuinely so cool I just had to show it off. It's probably vibe coded, but I don't see anything that mentions it on the github so I won't assume anything yet. SoldadoGhost/T6Requiem-APK It runs really well too! There were a couple of stutters at the start but after around 10 mins of gameplay in both multiplayer and zombies they're pretty much gone. Just wanted to show! It's really cool to play these masterpieces again on this amazing device. Temps are at around 70 degrees on average at a stable 60fps on the max!

Tokenized Pre-IPO Equity & Creator-Driven Perpetuals on Solana: Inside the KriptoK Social Trading Engine Why Pre-IPO Equity & High-Frequency Social Trading Belong on Solana Historically, pre-IPO equity exposure in frontier AI companies (like Anthropic or SpaceX) and 24/7 tokenized US equities were locked behind accredited investor gates and fragmented OTC brokers with 5–10% spreads. Meanwhile, retail social trading copied delayed screenshots rather than verifiable on-chain execution. On Solana, KriptoK (@KriptoK / @KriptoKLeague) merges three primitives into a single unified trading terminal: 1. Pre-IPO Perpetuals (e.g., Anthropic Perps) with deterministic mark-price funding and instant USDC margin settlement. 2. Tokenized Equities & Crypto Perpetuals with native Limit, Stop-Loss (SL), and Take-Profit (TP) bracket orders. 3. Creator League & Gacha Rewards Engine that aligns creator alpha distribution directly with verifiable volume. --- 1. Modeling Pre-IPO Perpetual Funding Rates & Bracket Risk Sizing Unlike spot orderbooks, pre-IPO synthetic perpetuals anchor their mark price $P_{\text{mark}}$ to an index oracle $P_{\text{index}}$ using an 8-hour funding premium $F$: def compute_perp_position_metrics( entry_price: float, stop_loss: float, take_profit: float, account_equity_usdc: float, max_risk_pct: float = 0.01, leverage: float = 5.0 ) -> dict: risk_capital = account_equity_usdc * max_risk_pct per_unit_loss = abs(entry_price - stop_loss) position_size_units = risk_capital / per_unit_loss if per_unit_loss > 0 else 0.0 notional_usdc = position_size_units * entry_price required_margin_usdc = notional_usdc / leverage rr_ratio = abs(take_profit - entry_price) / per_unit_loss if per_unit_loss > 0 else 0.0 return { "notional_usdc": round(notional_usdc, 2), "required_margin_usdc": round(required_margin_usdc, 2), "risk_reward_ratio": round(rr_ratio, 2) } --- 2. Open-Source KriptoK Creator & Quantitative Perps Toolkit We published a complete open-source reference repository covering KriptoK Pre-IPO perpetuals execution, SL/TP risk calculators, and Creator Challenge #01 workflows: - GitHub Repository & Walkthrough: duongynhi000005-oss/kriptok-creator-ecosystem-showcase - Quantitative Solana Prediction Analytics: duongynhi000005-oss/worm-premier-league-prediction-analytics

Rigor v0.4.7をリリースしました🎉 #rigortype 実アプリケーションから検出された、かなり多くの誤検知・偽陽性を潰しています。また、より多くのサブコマンドが --format=json を受け付けるようになりました。 github.com/rigortype/rigor/releases/tag/v0.4.7

GitHub - lambci/serverless-actions: Serverless GitHub Actions LambCI Serverless Actions enable you to run your GitHub Actions workflows on AWS Lambda and Google Cloud Run, and container-based services... Built for the agentic era 7 years back.

@ClaudeDevs Fail-closed hooks are the right default for guardrails. Claude Code 2.1.317 adds onFailure: "block" for Agent SDK hook callbacks, so a failing callback blocks the action instead of letting it run. github.com/anthropics/claude-code/releases/tag/v2…

Try it, no sign-up needed: patchbay.me.uk Source code (MIT): carnaby/patchbay

Proxy WebDAV para el almacenamiento en la nube de O2 y Movistar España uniextra/MovistarWebDAVProxy