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2,400+ MATCHING RECORDSMETADATA ONLY / SOURCE ATTRIBUTION PRESERVED
Manager Fixed Fixed AES connection getting reset to blowfish on keystore rebuild. (#37524) Fixed a deadlock in wazuh-analysisd that stopped alert generation when the Active Response queue filled up. (#37764) Restricted the upgrade commands accepted from the agent message channel in Analysisd. (#37745) Added destination path validation when deleting rule and decoder files. (#37838) Added source path validation when retrieving CDB list files. (#37901) Added path validation when listing and deleting CDB list files. (#38214) Fixed the aws-s3 wodle failing to parse configuration values that contain spaces, such as discard_regex, discard_field, aws_profile, aws_account_alias, path and path_suffix. (#37929) Fixed wazuh-db exiting the worker thread instead of closing the peer socket when an oversized message is received. (#37850) Improved SCA policy source validation to correctly enforce the sca.remote_commands restriction. (#37953) Added a minimum length check for legacy-format agent messages in wazuh-remoted. (#38099) Fixed a spurious StarletteDeprecationWarning printed by agent_upgrade at startup. (#38085) Fixed a spurious StarletteDeprecationWarning printed by cluster_control and other framework CLI tools at startup. (#38412) Fixed the API login attempt limit not being applied consistently under concurrent requests. (#38135) Fixed a heap buffer write in wazuh-analysisd when generating FIM alerts by resizing full_log before writing. (#38145) Fixed password validation not being enforced for empty passwords in the update_user API endpoint. (#38180) Fixed API tokens for run_as users not being invalidated on logout or role revocation. (#38193) Fixed a remote-command configuration validation bypass where upper- or mixed-case XML element names let command localfile and wodle blocks pass the remote_commands restriction. (#38239) Agent Changed Improved SCA policy YAML parsing to bound memory usage on deeply nested policies. (#37725) Fixed Prevent a race condition in randombytes during the initialization of the Windows RSA key container (#37701) Fixed missing macOS SSH authentication logs by adding the sshd-session and sshd-auth processes to the default Unified Logging query. (#37769) Fixed the name, version and PyPI packages reported for Microsoft Store Python installations. (#37441) Fixed the FIM eBPF whodata provider swapping the reported user_id and group_id in every event. (#39358) Fixed FIM eBPF whodata attributing every file change to root when the kernel reports no loginuid. (#39335) Added Added audit_uid, audit_name, audit_gid, and audit_group_name fields to FIM whodata alerts generated by the eBPF provider. (#32467) Ruleset Fixed Fixed Fortigate Decoder, malicious ioc rule and RHEL 8,9,10 incorrect rpm check. (#37434) Fixed typo in /etc/security/opasswd permission check on Debian 10, Ubuntu 20.04 and Ubuntu 22.04 SCA rules. (#37652) Fixed the LLMNR SCA check expected value on Windows Server 2016 and 2012. (#37765) Fixed typo in /etc/shells permission check on Debian 10, Ubuntu 20.04 and Ubuntu 22.04 SCA rules. (#37770) Fixed MITRE ATT&CK tactic IDs being used instead of technique IDs in Microsoft Graph rules. (#37950) Fixed FortiAuth decoders mapping to incorrect fields. (#38195)
First reported Sep 25, 2026 18:19 UTCCatalog metadata updated Oct 14, 2026 11:09 UTCOpen evidence record
[v5.0.0] Manager Added Issue Comment #38260 Added HTTPS communication between Wazuh agents and the manager: a new HTTPS transport, protocol, and control plane (connection, configuration, statistics, task dispatch, remote upgrade, and event ingestion) replacing the legacy MQ/DGRAM-based agent-manager protocol end to end. #31295 Added cluster-by-default deployment model: all Wazuh Server installations now run as a cluster node, removing the distinction between clustered and non-clustered deployments. The cluster.disabled configuration option has been removed. #33269 Added stateless metadata enrichment in remoted, centralizing event metadata handling for stateless messages and removing the dependency on wazuh-db for that ingestion path. #33493 Added Engine enrichment support: IOC matching, GeoIP lookup, and event filters. #34477 Added Engine adaptation tier 2: raw archives handling, uncategorized event routing, input-level throttling, and internal metrics exposure. #35623 Added CVSS v4.0 support to the Vulnerability Scanner. #35771 Added Engine metrics collection, normalization, and indexing pipeline. #36000 Added new CVE 5.0 schema fields to the Vulnerability Detector content model. #35579 Added manager watermarks. #37052 Added byte-based capacity limits to wazuh-manager-remoted. #37706 Added default API role mappings for the indexer users wazuh-admin, wazuh-readonly and wazuh-demo. #38023 Added the POST /config HTTPS endpoint, which receives an agent's reported configuration and indexes it into wazuh-agent-config. #38024 Added the POST /stats HTTPS endpoint, which persists the statistics an agent reports as one document per agent in the wazuh-agent-stats index, replacing the previous report on every push. #38007 Added legacy remote_upgrade task delivery in remoted: a polling thread pushes pending Task Manager tasks to connected agents older than v5.0.0 over their existing session using the legacy six-step WPK push, gated on remoted's HTTPS verification_mode. #38157 Added installation-time variables to customize the default <remote> configuration on source, DEB, and RPM manager installations. #38553 Added the PUT /agents/scan/vulnerability endpoint to trigger an on-demand vulnerability scan for one agent, a list of agents, or all agents. #38589 Added recurring manager tasks to the Task Manager: the agent disconnection sweep, the deletion of long-disconnected agents, and manager log rotation now run as durable, retried tasks inside wazuh-manager-modulesd. Their resolved settings are reported by GET /manager/configuration/wmodules/wmodules. #38862 Added a warning logged by wazuh-manager-apid on every start for each API default user (wazuh, wazuh-wui) whose password is still the one shipped with the package, pointing at bin/rbac_control change-password. The API starts either way. #38992 Added the remote.https.ca_certificate option, naming the CA that signs the HTTPS agent listener certificate (default etc/certs/root-ca.pem), and the unauthenticated GET /cacerts endpoint that serves it: 200 with the PEM, 404 when the file is missing, 503 (ca_mismatch) when it does not sign the served certificate. wazuh-manager-remoted now evaluates its listener certificate at start and every 24 hours — a WARN under 30 days to expiry, an ERROR once expired or when the CA and the certificate diverge — and exposes remoted.cacerts.*, remoted.server.tls.cert_expiry_days and remoted.server.tls.ca_matches_leaf, projected by GET /cluster/{node_id}/daemons/stats. #38993 Added enrollment tokens: an operator mints a token on the master (wazuh-manager-authd --create-enrollment-token --address <host>, or POST /agents/enrollment-tokens with the new RBAC actions enrollment_token:create/read/delete) that names the manager, pins its CA and carries a one-time-issued credential; a new agent pastes it and enrolls over POST /enroll on port 1517 with a wazuh-enroll+jwt bearer whose kid is the token id, without
First reported Oct 05, 2026 13:36 UTCCatalog metadata updated Oct 14, 2026 11:09 UTCOpen evidence record
What's Changed fix(ci): correct S3 artifact run directory layout for 4.14.7 by @jr0me in #37377 Lower log level of transient cluster failures by @ignaciogalle12git in #37338 Fix github actions shell by @Miguevrgo in #37355 SCA Policy Adjustments by @Johnng007 in #37385 Bump 4.14.8 branch by @wazuhci in #37404 fix(ci): grant OIDC to Coverity S3 jobs by @jr0me in #37406 Remove unnused assignment for bpf object by @Miguevrgo in #37424 Fix changelog workflow by @TomasTurina in #37431 Preserve original scan_day/scan_time strings in syscheck config by @lchico in #37412 Multi SCA, Decoder and Rule fixes by @Johnng007 in #37434 Improve API authentication stability and resource limits by @jepalfer in #37034 Bump 4.14.7 branch by @wazuhci in #37523 Update CHANGELOG for v4.14.7 by @vikman90 in #37536 Adjusted file sizes in the check files by @MarcelKemp in #37544 Compilation error in HP-UX arch by @fcontrerasc in #37545 Maintain crypto_method on keystore rebuild by @jpcerrone in #37524 Merge 4.14.7 into 4.14.8 by @vikman90 in #37679 Fix RBAC permission bypass in GET /security/actions and /security/resources by @vikman90 in #37678 Validate destination paths when uploading list, rule, and decoder files by @vikman90 in #37691 Prevent cluster worker from overwriting protected master files by @vikman90 in #37694 Improve access control on the agent active-configuration endpoint by @vikman90 in #37716 Fix typo in SCA rules by @Miguevrgo in #37652 Prevent race condition in Windows RSA key container initialization (randombytes) by @MarcelKemp in #37701 Limit node expansion in yaml2json by @vikman90 in #37725 Reorder revoke-token from API integration test by @Antoniogm03 in #37696 Fix benchmark test_cluster_error_logs test by @jepalfer in #37425 Correct EnableMulticast expected value in Windows LLMNR checks by @Darioortegaleyva in #37765 Prevent spurious CRITICAL (1211) queue error during rootcheck/FIM shutdown on WPK upgrade by @Darioortegaleyva in #37715 Add sshd-session/sshd-auth to default macOS ULS query by @Darioortegaleyva in #37769 Remove multiple warning messages for each unathorized shared policy by @Miguevrgo in #37741 Fix typo in SCA rules by @Miguevrgo in #37770 Restore legacy 5.0 deprecation notes and update wm_control migration by @cborla in #37829 Fix analysisd deadlock on AR send when the queue is not drained by @ignaciogalle12git in #37764 Disable OpenSSL runtime CPU probing in Python daemon wrappers by @jotacarma90 in #37773 Disable OpenSSL runtime CPU probing in Python daemon wrappers by @jotacarma90 in #37839 Expose audit_uid, audit_gid and effective_uid in eBPF whodata provider by @Darioortegaleyva in #37060 Fix flaky Windows syscollector test by @rjcausarano in #37842 Reject upgrade commands from the agent channel in wazuh-analysisd by @vikman90 in #37745 initializeContext thread-safe with std::call_once by @hernanvalenzuela in #37768 Bump 5.0.0 branch by @wazuhci in #37916 Validate destination paths when deleting rule and decoder files by @vikman90 in #37838 Make sure dbs are fully closed before stop() returns (sca, syscollector, agent-info) by @jpcerrone in #37841 Use DisplayName and python.exe version for Microsoft Store packages by @Darioortegaleyva in #37441 Fix analysisd flaky test by @jepalfer in #37754 Migrate AWS integration tests to OIDC authentication and pre-existing VPC [5.0.0] by @Darioortegaleyva in #36796 Migrate AWS integration tests to OIDC authentication and pre-existing VPC [4.14.7] by @Darioortegaleyva in #36798 Update MITRE mapping in Microsoft Graph rules by @AwwalQuan in #37950 Validate read path in CDB list file retrieval by @vikman90 in #37901 Merge 4.14.7 into 4.14.8 by @jotacarma90 in #37971 Provide local TZDB for integration tests by @jam300 in #37941 Explicit use of jemalloc on daemons by @NahuFigueroa97 in #37708 Fix flaky content manager component tests caused by a race in the fake server startup by @jotacarma90 in #37902 Merge 4.14.8 into 5.0.0 by @jotacarma90 in #37973 Fix WM_MAX_WAITunit
First reported Sep 07, 2026 12:33 UTCCatalog metadata updated Oct 14, 2026 11:09 UTCOpen evidence record
There is a specific kind of mess that shows up once you have more than about thirty prompts saved. You stop remembering what you have. Two prompts do nearly the same thing and you can't tell which one is current. You paste a whole "mega-prompt" into a session because you can't be bothered to find the right one, and half of it is irrelevant to the task. headcount is one answer to that. It arranges agent skills as a company: one chief executive, 16 departments, 172 skills. Each department installs as its own plugin, so a project loads only what it needs. Skills are addressed as department:skill, which means names never collide across departments. The original repository is cbrock84/headcount, 2047 stars, written in Markdown, MIT licensed. A Chinese translation of the README now exists at yangshun2005/headcount-cn. That's what this post is mostly about, because the translation does something a plain translation usually doesn't. What the project actually is The official one-liner: an agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs in Claude Code and ChatGPT. The README's own numbers are 16 departments and 172 skills. The one-liner says 15+ and 125+. Both appear in the source material; I'm not going to reconcile them, and you shouldn't assume the smaller number is the stale one — the README's department tables are the more granular source. The structural idea is the part worth understanding. Most prompt libraries are a flat list. headcount is a tree with an install boundary at every branch. The CEO office sits at the top with 7 skills, and the departments below it each carry their own skills plus an agent charter in .claude/agents/. That charter is what lets a department be delegated to as a subagent with its own exclusive write scope — the producer/auditor split the agent-hierarchy skill describes. Addressing is namespaced. security:threat-modeling, finance:unit-economics, demand-generation:landing-page-cro-expert. You can ask in natural language and let matching load the skill, or you can call it by name when you want a specific perspective: /finance:financial-modeling Install, and the constraint that shapes everything Claude Code: /plugin marketplace add cbrock84/headcount /plugin install security@headcount Then add departments as you need them — /plugin install it-operations@headcount, and so on. ChatGPT and Codex use the same repository. You add it as a plugin marketplace (ChatGPT reads the manifest at .agents/plugins/marketplace.json), or you copy the departments you want into your own project's .agents/skills/ directory. The skill files are identical on both sides; only the manifests differ, and both manifests are generated from the same directory tree, so a fix lands in both at once. That's what AGENTS.md is about. Here is the constraint that explains the whole design. On the ChatGPT/Codex side, skill descriptions share a context budget of roughly 8000 characters. All 16 departments together come to roughly 50000. Installing only the departments you'll actually use isn't tidiness — it's the only thing that fits. If you've ever wondered why a plugin system would bother with per-department granularity instead of one big install, that ratio is the answer. Installing a department doesn't touch your project. It adds skills to the assistant. It doesn't write to your repo, add dependencies, or run anything on its own. What the Chinese version adds The Chinese repository is a translation of the original README, dated 2026-10-14, with the original preserved as README_en.md. Stars: zero, as of writing. It was published today. I'm not going to pretend otherwise. The interesting part is that it isn't only a translation. The page carries two clearly separated layers: A 中文快速上手 section at the top, explicitly marked as supplementar
First reported Oct 14, 2026 10:56 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
Most notification services start as a single function: def notify(user, message): email_client.send(user.email, message) It works for a while. Then product asks for push, then SMS, then an in-app inbox, then "can users pick what they get?" Before long, this one function has become a distributed system. This post breaks down what a production notification service contains, split into the functional parts users and PMs see, and the non-functional parts that keep it alive at 3 a.m. At the end, there's a framework for deciding whether to build or buy. The big picture Your app --> Notification API --> Preference check --> Template render | Priority queues (critical / high / low) | Channel workers (email, SMS, push, inbox...) | Vendor adapters --> Webhooks --> Analytics Each box hides real engineering work. Let's go through them. Part 1: Functional components 1. Delivery channels and vendor integrations Users live on different channels: email, SMS, push, in-app inbox, WhatsApp, Slack or Teams, even automated calls. To maximize engagement you'll want several of them. Every channel means picking a vendor, learning its API, testing quality of service, monitoring it, and planning a fallback. A common way to keep this manageable is an adapter layer: class ChannelAdapter: def send(self, payload) -> DeliveryResult: ... class SendgridAdapter(ChannelAdapter): ... class TwilioAdapter(ChannelAdapter): ... class FcmAdapter(ChannelAdapter): ... Your core logic talks to one interface, and vendor quirks stay at the edges. 2. Templating engine Templates quietly become the biggest ownership burden. They need constant changes for copy, personalization, branding, dynamic variables, conditional logic, multi-lingual support, and correct rendering across devices. Each channel also has different strengths: Email: long, detailed content SMS: short and attention-grabbing Push: rich media and action buttons If every copy change needs a deploy, your engineers become a bottleneck. The fix is a visual editor so PMs and marketers can build and test without a pull request. 3. User preferences Marketing emails get a simple unsubscribe link. Product notifications are trickier, because users want some of them (security alerts) and not others (weekly tips). A decent preference system lets users control: Type: which categories they get Channel: where they get them Frequency: instant, hourly, daily or weekly digest Timing: quiet hours def should_send(user, notification): prefs = get_preferences(user.id) if notification.category not in prefs.enabled_categories: return False if in_quiet_hours(user, prefs) and not notification.is_critical: return reschedule(notification, prefs.quiet_hours_end) return True Without this, users do the only thing they can: block everything. 4. Batching and digests Say your product has comments. If 20 people reply to a thread, you don't want 20 notifications. You want one: "20 new comments." Batching: define a window, collect events inside it, then send one combined message. Digests: send a summary at a cadence the user chooses, such as hourly, daily or weekly. Depending on the notification type, you may need both. 5. Multi-tenancy If your product serves other businesses and sends notifications to their users, you need tenant-level isolation and customization: their branding, their sending domain, their channels, their preference rules. Example: a SaaS tool sending invoices on behalf of customers. The end user doesn't know your brand, so the email must carry your customer's logo, content and sender identity, all running on shared infrastr
First reported Oct 14, 2026 10:53 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
The original genspark-ai/genoffice repository has 9580 stars, is written in TypeScript, and ships under Apache-2.0. The Chinese version, yangshun2005/genoffice-cn, was published today. It has zero stars, which is normal for a repository that is a few hours old, and I'm not going to pretend otherwise. What it is: a translated documentation fork. The code is the upstream code. What changed is the README — and, more usefully, a short "中文快速上手" section prepended to it that the original does not have. Why a translated README is not a trivial thing For an English-speaking developer, the genoffice README is a normal README. You skim the feature list, find the Releases link, download the right installer, and you're running in five minutes. For a Chinese-speaking developer, the same document has a specific failure mode that isn't about vocabulary. It's about where the operational details sit. The upstream README is organized as a product page: positioning first, feature bullets second, install links somewhere in the middle, and the CLI/MCP/agent-skill material further down. If you're evaluating whether this thing can do a job for you, you read top to bottom and hope the answer is in there. The Chinese version inverts that. The top of the page is a short section labeled as a supplement, explicitly marked as not part of the original text, and it answers the questions you'd otherwise have to reconstruct: What is this, and what problem does it solve The shortest path to a running app Environment requirements Known gotchas That's the actual value. Not "the README is now in Chinese." It's that someone read the whole thing, figured out which parts a new user needs first, and put those parts at the top. What genoffice actually does The core claim, from the README: it opens and saves native .docx, .xlsx, and .pptx files, edits PDF, Markdown, and HTML, and puts an AI agent next to each document. Not a chat sidebar — an editor that reads the file, makes changes, and shows you what changed. The detail that matters most is byte-level preservation. Only the parts you edited get rewritten. Everything else in the file is kept byte for byte, so the document still opens correctly in Word, Excel, and PowerPoint. This is the difference between "an AI tool that exports to Office format" and "an AI tool that edits your Office files." Most tools in this space do the former and call it the latter. The other structural choices: Edits are reviewable. Changes appear as tracked changes and diffs, with one-click rollback. Spreadsheets use live formulas rather than pasted values. Slides and pages are generated on a canvas and stay fully editable. Local-first. Opening, editing, saving, and converting happen on your machine. PDF → Word / Excel / PowerPoint, Markdown → Word, and HTML → Word all run locally. Only AI calls leave the machine, and they go to whichever provider you picked. Search by content. The home screen runs a local SQLite index over .docx, .xlsx, .pptx, PDF, Markdown, and HTML files — names, folders, and full text, with CJK support. Optionally, top results get reranked by TypeSafe Jev (System One judgment model) so the file that answers your question sorts first. Ask AI across documents. Bookmark a set of documents and the home screen's Ask AI answers questions across them, citing the source file for each claim so you can open it and check. Bring your own key, or don't. Log in with Genspark to skip keys entirely, or supply your own from Claude, OpenAI, Gemini, DeepSeek, Kimi, GLM, Qwen, Doubao, MiniMax, Grok, Mistral, OpenRouter, Requesty, Opper, Cheaper Inference, Atlas Cloud, any OpenAI-compatible endpoint, or a local server. Scriptable. The app ships a genoffice CLI plus skills for Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, and Windsurf, so a coding agent can create, convert, read, and edit real Office files on your machine without opening a window. Platforms: mac
First reported Oct 14, 2026 10:53 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
`# NeuroJob AI: LLM-Driven Candidate-Job Fit & Automated ATS Keyword Optimization Architecture Author: Lakshya Kurup Published: January 2026 | Repository: GitHub Abstract Job applicants frequently face high rejection rates due to non-standardized resume formats and sub-optimal Applicant Tracking System (ATS) keyword alignment. Traditional keyword matchers rely on static exact-string matching, missing semantic equivalents, context-aware experience mapping, and tailored interview preparation. NeuroJob AI addresses these challenges by implementing an end-to-end recruitment assistant powered by the Gemini API, Next.js, TypeScript, and Python. The platform provides automated resume-to-job description fit scoring, tailored cover letter synthesis, ATS keyword gap analysis, and predictive interview question generation within a single, highly responsive interface. 1. System Architecture & Tech Stack The system is built on a modular client-server architecture designed for low-latency prompt execution and structured schema outputs: Component Technology Role Frontend Next.js (App Router), React, TypeScript, Tailwind CSS Dynamic user dashboard, ATS scoring meters, and live breakdown rendering AI Integration Gemini API (Google AI Studio) Structured output parsing, semantic evaluation, and natural language synthesis Processing Engine Python, Node.js Document text parsing, sanitization, and structured payload preparation Version Control Git / GitHub Code maintenance and CI/CD deployment 2. Core Feature Pipeline The engine evaluates candidate profiles against targeted job descriptions across 4 core phases: Semantic ATS Fit Scoring: Evaluates candidate resume text against job descriptions using structured LLM parsers to compute a normalized match percentage (0–100%). Keyword Gap Analysis: Highlights missing industry keywords, required technical skills, and domain-specific acronyms missing from the candidate profile. Tailored Cover Letter Generation: Synthesizes custom, context-aware cover letters aligning candidate achievements with the job requirements. Predictive Interview Prep: Generates role-specific behavioral and technical interview questions tailored to identified resume gaps and required skills. 3. Key Results & Efficiency Metrics Multi-Feature Integration: Combined fit scoring, cover letter writing, keyword gap detection, and interview prediction into a single web application. Low Latency Workflows: Reduced full resume-to-JD evaluation time to under 3 seconds using optimized Gemini API streaming logic. Structured Payload Reliability: Enforced zero-error JSON schema responses for deterministic UI component rendering. Published by Lakshya Kurup — Open Source Technical Documentation.`
First reported Oct 14, 2026 10:54 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
A staffing dashboard helps an operator decide where attention is needed. Its usefulness depends on what its labels mean and whether the operator can act on them. In SEC ERP's mobile work, I shaped tests around those decisions: distinguishing assignment from presence, publishing the right duty result, and keeping staffing actions usable on a phone. Test the facts behind the labels An assignment, an attendance record, and an active duty session represent different facts. The roster needs to preserve those distinctions. One regression fixture includes an assigned guard with null attendance and duty evidence. This exercises a realistic incomplete record. A perfectly populated fixture would miss the question that matters: what does the interface show when presence evidence is unavailable? The read-only view receives no assignment callback, and its test verifies that assignment controls are absent. Action availability follows the capability supplied to the view. Verify what a successful response means For duty activation, a non-null response is too weak an assertion. The acknowledgment must correspond to the guard and assignment that initiated the request. Tests change returned identity, company, facility, deployment, shift, date, status, and required evidence. An incorrect result must not publish confirmed duty. A separate test accepts cloud activation and then fails device persistence. It expects confirmed duty to survive with a separate mirror-failure outcome. That tests the partial success the architecture is designed to handle. Exercise the action inside its real layout The roster presents posts and shifts as a grid. The assignment action must stay reachable and retain the selected cell's identity. The widget test uses the application theme, a 360-by-800 viewport, and enlarged text. It scrolls to a target cell, taps its assignment button, and verifies the exact post and shift delivered to the callback. This combines layout and behavior in a useful scenario: an operator can reach the action and act on the intended staffing gap. Test changes during requests Other regressions introduce logout, disposal, a newer duty selection, and listener-driven changes before or during publication. Controllable service completion reproduces each ordering precisely. The assertion is that newer state survives while obsolete work cannot publish or persist. This protects the actual user journey beyond the initial screen render. I chose these cases by the decisions they protect: interpreting staffing evidence, accepting duty confirmation, preserving current state, and reaching the correct assignment action. Device and operator checks answer additional questions such as location behavior and field usability. The testing strategy gives architectural decisions an executable form. Each case checks a fact the dashboard communicates or an action its users depend on.
First reported Oct 14, 2026 10:57 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
"Which PCB software should I learn?" gets asked every week, and the answers usually come from whatever the person answering happens to use. So here's an attempt at a fairer version: what each of the three big options is good at, taken from each one's own published material, and which job it fits. EasyEDA: the gentlest start EasyEDA is free, runs in a browser (with desktop clients if you want them) and comes in two editions: Standard, aimed at students and education, and Pro, with larger designs, constraint-driven design, team features and a full offline mode. Both include SPICE simulation. Its real advantage is what it's connected to: the LCSC parts catalogue and JLCPCB's fabrication and assembly. You place a part that's actually in stock, and the board, the bill of materials and the assembly files line up with what will be sent to you. For a first board that removes a whole class of mistakes. The trade-off is that the workflow is built around one supply chain. KiCad: free, open, and serious KiCad is free and open source (GPL-3.0), runs on Windows, macOS and Linux, and works completely offline. It stopped being "the hobbyist option" a while ago. Version 10, released in March 2026, added design variants, time-domain length tuning for high-speed nets, a graphical rule editor and importers for Allegro and PADS, on top of a good push-and-shove interactive router and built-in SPICE. A two-layer board drawn in KiCad 9, routed with the external Freerouting autorouter and rendered with KiCad's own 3D models. The cost is learning time. Libraries, footprint assignment and board setup take a while, and KiCad doesn't ship an autorouter. You route by hand with the interactive router or use an external one like Freerouting. Altium: when the licence pays for itself Altium Designer is a commercial Windows application sold by subscription, priced per seat, with a free trial; its own pricing page has the current tiers. What you pay for is depth: signal-integrity and constraint tools for high-speed design, managed libraries tied to supply-chain data, mechanical CAD integration, and collaboration for teams on the same product. One person drawing boards with no gigabit interfaces won't use most of that. A team shipping DDR, PCIe or RF products to production usually will. Altium also has CircuitMaker, a free Windows tool on its engine for personal projects. Which one for you First board, ordering from JLCPCB: EasyEDA. Free forever, any OS, offline, open source: KiCad. A skill that carries into many jobs and open hardware: KiCad. A team shipping high-speed or regulated products: Altium. Altium's engine for personal projects, free, on Windows: CircuitMaker. Switching later Moving between them is rarely clean. KiCad imports Altium designs and EasyEDA imports several formats, but footprints, design rules and stackups usually need fixing afterwards, and libraries don't travel perfectly. Fabrication output does: all three export Gerber, drill, BOM and pick-and-place files, so the fab never cares which one you used. A different first step I work on PCBEditor, which isn't a fourth editor for the same job. It covers the step before: you describe the board in plain English, an AI chooses parts and intent, and deterministic algorithms build the netlist, place, route and check it. You can then export Gerbers, or open the native KiCad file and keep working in KiCad. If drawing the first version is the part that's stopping you, that's what it's for. The full comparison table is on pcbeditor.com. Which one did you start with, and would you pick it again?
First reported Oct 14, 2026 10:58 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
A feature can pass every functional test and still introduce a serious security vulnerability. An API might return the correct response but expose another user's data. An authentication flow might work as designed while allowing session reuse. A cloud deployment might be operationally healthy while leaving an administrative endpoint publicly accessible. These problems are difficult to catch with ordinary unit tests because they concern how software behaves under unexpected conditions, not just whether it meets its functional requirements. Vulnerability assessment and penetration testing (VAPT) help development teams investigate these gaps. When integrated into the software development lifecycle, they provide a practical way to discover security weaknesses, validate their impact, and improve the quality of application defenses. Vulnerability Assessment vs. Penetration Testing Vulnerability assessment: What could be wrong? A vulnerability assessment identifies potential weaknesses across applications, dependencies, infrastructure, and configurations. Automated scanners can detect outdated packages, exposed services, known vulnerabilities, and common configuration errors. Manual reviews help investigate findings that require application-specific context. Consider a Node.js application using an outdated dependency. A scanner may flag a known vulnerability, but developers still need to determine whether the affected code path is present, reachable, and relevant to the deployed configuration. The assessment provides a starting point for investigation rather than proof that every finding can be exploited. Penetration testing: What can actually be exploited? Penetration testing uses authorized, controlled techniques to determine whether security weaknesses can be exploited and what their consequences might be. A tester might examine whether a user can access another account's records, bypass an authorization check, manipulate an API request, or combine multiple weaknesses into a broader attack path. The key distinction is that vulnerability assessment identifies potential problems, while penetration testing evaluates selected weaknesses in realistic conditions. Both approaches are useful. Neither replaces secure coding, code review, automated testing, or continuous monitoring. Security Bugs Developers Should Look For Some of the most important application vulnerabilities arise from incorrect assumptions in application logic. Broken object-level authorization Imagine an API endpoint that retrieves an order using a request such as /api/orders/1042. The application checks whether the requester is logged in but does not verify that the order belongs to that user. Changing the order identifier could expose another customer's information. Developers should enforce authorization on the server for every sensitive operation. Hiding interface elements or relying on unpredictable identifiers does not replace access-control checks. Tests should cover different users, resource owners, roles, and unauthorized requests. Injection vulnerabilities SQL injection and other injection flaws can occur when applications mix untrusted input with executable queries or commands. Use parameterized database queries, safe APIs, and context-appropriate output handling. Input validation is useful, but it should complement rather than replace safe query construction. Security tests should also verify how the application handles malformed inputs and whether error messages expose internal implementation details. Insecure authentication and session handling Authentication problems extend beyond weak passwords. Long-lived tokens, inadequate session invalidation, insecure password recovery, and missing multifactor authentication can undermine otherwise strong access controls. Review token expiration, refresh mechanisms, logout behavior, and privilege changes. Sensitive actions may require additional verification depending on their risk. Vulnerable dependencies an
First reported Oct 14, 2026 10:52 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
A guard starts duty while an older dashboard request is still loading. Activation succeeds and the dashboard updates. Then the older response arrives and replaces the newer state. The network request succeeded, but the product now shows an obsolete answer. In SEC ERP's mobile app, I addressed this with authentication generations and duty revisions in shared state. Give results an owning context A mounted check answers whether a widget still exists. Operational state also needs to know whether a result belongs to the current account. Async work captures the provider's authentication generation before waiting. Before publication, it checks disposal, session teardown, and whether the generation still matches. An earlier response cannot restore old identity or dashboard data after that session boundary changes. The request may finish remotely. The check controls whether its result becomes current client state. Track changes inside the same account An account can stay signed in while the selected duty changes. Authentication generation alone cannot distinguish those situations. Duty activation therefore captures a duty revision too. Publication requires both values to match. The returned record must also match the requested guard, company, site, deployment, shift, and date, and contain required activation evidence. Ordering checks and record validation complement each other. A response can arrive at the right time and still describe the wrong operation. Treat notification as a control boundary Publishing state invokes synchronous listeners. A listener can select another duty or invalidate the account before notification returns. The activation path rechecks its assumptions after notification, before local persistence, and again after persistence. This prevents an obsolete selection from being mirrored merely because its cloud request succeeded. The principle extends beyond await: whenever control passes to another owner, ask what could change before control returns. Make races deterministic The regression tests use controllable service completion. They start activation, introduce logout, disposal, or a newer duty, and then deliver the acknowledgment. Another case replaces duty from a listener during publication. Assertions verify the operational effect: obsolete work must not publish or persist, and the newer selection must survive. Session generations establish account continuity. Domain revisions establish operation continuity. Acknowledgment validation establishes identity. Together, they keep the dashboard aligned with the current operational decision as requests finish in whatever order the network allows.
First reported Oct 14, 2026 10:56 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
The original HarnessRouter/harnessrouter repo has 2993 stars and a README written in English. The Chinese version, yangshun2005/harnessrouter-cn, was published today with zero stars. That asymmetry is the whole point of this article. If you have ever tried to evaluate an agent infrastructure project from a non-English-speaking region, you know the failure mode: you skim the README, you understand maybe 70% of it, you miss the sentence that says "do not add --user", and you spend an afternoon debugging something that was documented in plain sight. Translation is not glamorous work. It is, however, the difference between a project being adoptable and being a curiosity. What HarnessRouter actually is Strip away the positioning language and here is the mechanism: HarnessRouter is an infrastructure layer that turns existing agent harnesses — Codex, Claude Code, Hermes, PI, DSH, and others — into pluggable backends behind a single API. The problem it addresses is real. If you are building an agent product, you probably do not want to write separate integration code for every harness you might want to support. You want to run a task, get a result, and swap the underlying harness when cost or latency demands it. HarnessRouter implements the Unified Harness Protocol (UHP), an open standard, and exposes an OpenAI Responses-compatible API on top. It handles persistent sessions, streaming progress, files, artifacts, cancellation, and structured failure reporting. The official one-liner describes it as the self-hosted, Apache-2.0 edition of a unified interface for agent harnesses. Your keys, your infrastructure. That last part matters. There is no bundled model and no trial key. You bring a provider API key. The community edition runs on your own hardware. The Chinese version: what it adds The harnessrouter-cn repo is a translation of the original project documentation. The README itself states the translation date as 2026-10-14 and links to README_en.md as the source. It carries the same license, Apache-2.0, and the original project's star count is listed as 2993. The structure is worth noting because it is not a pure translation. The repo splits its content into two distinct layers: A Chinese quick-start guide at the top, explicitly labeled as supplementary material and not part of the original documentation. Its stated purpose is to let you get the project running without reading the full README. The translated README body below, which aims to stay faithful to the original text. That separation is a small design decision with outsized consequences. Machine-translated technical documentation tends to blur the line between "what the original says" and "what a translator inferred." By marking the quick-start as non-original, the repo makes it possible to trust the translated body as a reference while still using the quick-start as a shortcut. If the translation is wrong somewhere, you can file an issue or a PR — the repo says so explicitly. Getting it running The quick-start path in the Chinese repo is short. You need Docker, roughly 4 GB of disk space, and a provider API key. Start the container: docker run -d --name harnessrouter \ -p 127.0.0.1:3000:3000 \ -v harnessrouter:/data \ harnessrouter/harnessrouter Then watch the logs, because first boot installs the enabled harness CLIs: docker logs -f harnessrouter Wait for this line: [harnessrouter] ready on :3000 At that point you can stop following the logs with Ctrl+C. The container keeps running. Open http://localhost:3000 and log in with the default credentials: username harnessrouter, password harnessrouter. Then go to Bring Your Own Key in the sidebar, click Add Integration, pick a provider, give it a name, and paste the API key. Models supported by that provider become available in the console. That is the whole onboarding flow. No account creation, no hosted dependency. The gotchas th
First reported Oct 14, 2026 10:55 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
ERC-8004 gives AI agents an on-chain identity and an on-chain place to rate each other. The obvious question is how many of those ratings come from someone who actually paid the agent they rated. We measured it on Base this morning (14 Oct 2026, 10:50 UTC) by joining every entry in the Base Reputation Registry to the stablecoin payments we index: Feedback entries on Base 514,096 From a rater who had paid the rated agent's wallet before rating 728 (0.14%) Raters behind those 728 17 Agents rated 64 Payment transactions behind them 107 Of those payments, x402 settlements 3 Median payment $0.03 So on Base today, ratings and payments are almost separate worlds. That matters for anyone who reads an agent's reputation score before paying it, and for the ERC-8004 v2 work on "grounded" feedback, where a rating carries proof that the rater transacted with the agent. The data is open Every one of the 728 entries, with the rating (transaction, tags, value, feedback URI) and the payment that grounds it (transaction, payee, amount, token), is a CSV under CC BY 4.0: https://github.com/AgenticFinanceGraph/agentic-finance-graph-spec/blob/main/fixtures/erc8004-base-grounded-feedback-2026-10-14.csv Ten lines of Python to check our counts: import csv, collections, urllib.request URL = ("https://raw.githubusercontent.com/AgenticFinanceGraph/agentic-finance-graph-spec/" "main/fixtures/erc8004-base-grounded-feedback-2026-10-14.csv") rows = list(csv.DictReader(urllib.request.urlopen(URL).read().decode().splitlines())) print(len(rows), "grounded ratings") print(collections.Counter(r["rail"] for r in rows)) print(len({r["rater"] for r in rows}), "raters,", len({r["agent_id"] for r in rows}), "agents") 728 grounded ratings Counter({'usdc_transfer': 725, 'x402': 3}) 17 raters, 64 agents Two things it suggests "Paid before" and "paid for this" are different claims. 725 of the 728 grounding payments are plain USDC transfers to the agent's wallet. They prove the two addresses transacted, not that the rating is about that transaction. Only a reference carried inside the feedback, such as an x402 settlement hash or a receipt both sides signed, can prove the second. Most raters are invisible to an indexer. About 97% of raters on Base are not wallets bound to any agent registration, so an indexer that starts from registrations cannot see their payments at all. For them, grounding has to travel with the feedback; it cannot be reconstructed afterwards. Caveats Payments to a separate x402 payTo address and payments on other chains are outside this join, so 728 is a lower bound. The same numbers, and the conversation about how grounding should be tagged, are in erc-8004/erc-8004-contracts#99. We publish an independent ledger of AI-agent money at agenticfinancegraph.com; every figure there has a definition and a measurement time.
First reported Oct 14, 2026 10:54 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
open-kritt: a Chinese translation of the self-hosted AI vulnerability research platform The original Kritt-ai/open-kritt repository has 2284 stars and is written in JavaScript. It is an open-source, self-hosted platform for AI-driven security and vulnerability research. The project's own one-line description is: "Open-source, self-hosted AI vulnerability research tool that orchestrates agents to find and validate security issues in code." A Chinese translation of that project now exists at yangshun2005/open-kritt-cn. It was published on 2026-10-14. The Chinese version is new and currently has no stars of its own; the 2284 figure belongs to the original project only. This article covers what the tool does, why the Chinese documentation matters, and how to get it running. Everything below is drawn from the translated README and the original project's documentation. The problem open-kritt is trying to solve Point a language model at an entire repository and ask it to find vulnerabilities, and the results are usually poor. The signal-to-noise ratio is bad, findings are hard to verify, and there is no consistent way to rank them. open-kritt takes a different approach. Instead of one large prompt against a whole codebase, it breaks research into small, explicit tasks. Multiple AI agents execute those tasks in parallel. Their outputs are then merged into findings that are deduplicated, ranked, and verifiable. The project is aimed at security researchers and security-conscious developers. The stated goal is to let users control their own prompts, workflows, model providers, and infrastructure. What the platform actually does From the README, the core capabilities are: Build workflows — chain focused prompts into reusable security research playbooks. Run scans — analyze remote or local repositories and their dependencies using Codex, Claude Code, or Grok Build. Validate findings — use post-scripts to verify issues, build proof-of-concepts, and generate reports. Export scan results — package normalized findings, structured data, post-processing output, reports, and PoCs into a single ZIP archive with a shareable manifest. Completed scans produce a full export; stopped or failed scans that still contain findings produce a clearly marked partial export. Reports and PoC source influenced by an attacker are preserved as plain text. Rank results — apply custom severity rankers, a unified finding schema, and automatic deduplication. Bring your own model access — use Codex login, or connect via OpenAI, Anthropic, DeepSeek, OpenRouter, or xAI. The project states it comes out of real security research. The Kritt team, under the researcher name Blockian, reports earning over $1,500,000 in bug bounties (linked profiles on Immunefi, HackenProof, blockian.xyz, and @Kritt_AI on X). open-kritt is described as the open-source distillation of the internal tooling behind that work. Why a Chinese translation is useful here The original documentation is in English. For Chinese-speaking developers, that is a real barrier — not because the concepts are impossible to follow, but because setup instructions, threat-model notes, and configuration details are exactly the kind of text where a misread line costs an hour. The Chinese version is not just a machine-translated dump. It has two layers: A Chinese quick-start guide at the top, explicitly marked as supplementary material and not part of the original README. Its stated purpose is to let you get the project running without reading the whole document. A full Chinese translation of the original README body, which the page says aims to stay faithful to the source. The translation page also invites issues or pull requests for inaccuracies. That is a reasonable posture for a translated technical document, since terminology choices ("finding", "post-script", "severity ranker") are not always obvious. The practical value is straightforward: a
First reported Oct 14, 2026 10:57 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
OWASP Amass is the most comprehensive tool for attack surface mapping and asset discovery. It goes beyond simple subdomain enumeration by mapping relationships between domains, IP addresses, netblocks, and autonomous systems. Amass integrates with over 50 passive data sources and supports active reconnaissance techniques including DNS brute forcing, zone transfer attempts, and certificate grabbing. This guide covers twenty practical Amass commands, explaining what each does and when to use it during a security assessment. 1. Passive Subdomain Enumeration amass enum -passive -d example.com The -passive flag restricts Amass to querying its numerous data sources without sending any DNS queries to the target. This is the safest starting point for reconnaissance, gathering subdomains from certificate transparency logs, search engines, and public DNS datasets while leaving no trace on the target's infrastructure . 2. Basic Domain Enumeration amass enum -d example.com Running enum without the -passive flag performs standard enumeration, which may include some active DNS resolution to validate discovered names. This balances coverage with reasonable speed, making it a common default for initial reconnaissance . 3. Active Enumeration with DNS Queries amass enum -active -d example.com The -active flag enables active recon methods including direct DNS queries, zone transfer attempts, and certificate grabbing. This reaches beyond passive feeds to discover subdomains that public sources have not indexed, at the cost of generating traffic to the target . 4. Brute Force Subdomain Discovery amass enum -brute -d example.com The -brute flag performs DNS brute forcing using Amass's built-in wordlist. This systematically tests candidate subdomain names by querying DNS. Combined with active enumeration, brute forcing often reveals internal naming conventions and development environments not present in public sources . 5. Brute Force with Custom Wordlist amass enum -brute -w /usr/share/seclists/Discovery/DNS/subdomains-top1million-5000.txt -d example.com The -w flag specifies a custom wordlist for brute forcing. Security-focused wordlists like those from SecLists contain common subdomain prefixes tailored to different industries and naming patterns, significantly improving discovery rates over generic lists . 6. Save Results to Text File amass enum -d example.com -o subdomains.txt The -o flag writes output to a plain text file. Always save enumeration results during assessments to maintain an audit trail and to feed subsequent tools without repeating queries . 7. Save Results in All Formats amass enum -d example.com -oA results/amass The -oA flag writes results in all available formats including JSON, plain text, and graph database entries. This is useful when different downstream tools need different output formats, eliminating the need for multiple runs . 8. Show IP Addresses for Discovered Names amass enum -d example.com -ip The -ip flag includes resolved IP addresses alongside each discovered subdomain. This immediately identifies which subdomains are active and reveals shared hosting, cloud provider IP ranges, and potential infrastructure relationships . 9. Show Data Sources for Each Result amass enum -d example.com -src The -src flag annotates each discovered subdomain with the data source that reported it. A subdomain found by multiple independent sources is more likely to be genuine and active. This helps assess reliability when triaging large result sets . 10. Enumerate Multiple Domains from File amass enum -df domains.txt -o all_subdomains.txt The -df flag reads a list of root domains from a file, one per line. This scales enumeration across large scopes such as bug bounty programs with dozens of in-scope domains or organizations
First reported Oct 14, 2026 10:56 UTCCatalog metadata updated Oct 14, 2026 11:03 UTCOpen evidence record
Enterprise data systems were designed to inform people and applications. As AI agents begin to act on that data, familiar concerns such as freshness, lineage, semantics, identity and observability become runtime controls.
First reported Oct 14, 2026 07:00 UTCCatalog metadata updated Oct 14, 2026 11:02 UTCOpen evidence record
Maintainers and contributors discuss Valkey's progress since it forked from Redis, and adding data tiering, durability, and more for 10.0.
First reported Oct 09, 2026 17:45 UTCCatalog metadata updated Oct 14, 2026 11:02 UTCOpen evidence record
The public shared-host view reaches the newest 2,400 matching records. Narrow the filters or search an exact MD5, SHA-1, or SHA-256 value to reach a specific record.
A filename, label, or report mention alone does not establish maliciousness. Observation dates describe cited sources; collection dates describe this local metadata catalog. No malware samples are stored, fetched, or executed by these pages.