标签：#tools

Every AI code reviewer claims it respects your team's standards. Very few can tell you what those standards actually are. The test is mechanical. Ask the tool, or the vendor, one question: where do my rules live? If the honest answer is "we loaded a generic style guide plus whatever your PR description happened to say", then your standards aren't in the model at all. The reviewer is running on vibes and hoping your repo looks conventional enough to pass. The tools that genuinely track your rules share a shape: the standards are an input, not a hope. They read your rule files, your linter config, your past review comments, sometimes your docs. The review is judged against that artifact, which means when a comment fires you can ask "which rule?" and get a config line back, not a paragraph of model reasoning. That last part is the thing worth testing for. If a reviewer can't point to the specific rule it applied, it is not following your standards. It is approximating what it assumes standards look like. Those two feel identical for the first six months, then diverge exactly when you've stopped proofreading its output. A short checklist when you trial one: Does it ingest a rules file, or only the PR context? Can it point to the exact rule that triggered a comment? Does it adapt to your historical review style, or reset every run? Is a "rule" something you can open, read, and edit in the UI? If the answer to all four is no, you bought a very chatty spellchecker that happens to be trained on GitHub. The eval that actually decides it: can your own reviewer reproduce one of your team's real past review decisions, given only your real rules file? Run that before you hand it a production PR.

💻 One thing I dislike about coding-agent setups is how quickly they become part of one specific machine. Provider config goes in one place, session state somewhere else, local models live in another directory, and suddenly moving to a second machine means rebuilding the environment. OpenClaude-Portable takes a much cleaner approach. It packages the coding agent, runtime and persistent data into a self-contained folder. It supports cloud and local models in the same setup The project currently supports 9 provider options: Anthropic Claude OpenAI Google Gemini DeepSeek OpenRouter NVIDIA NIM Ollama LM Studio custom OpenAI-compatible APIs I like this because the portable part is not tied to one model vendor. I can use a cloud model when I want the strongest hosted option, then switch to Ollama or LM Studio when I want a local workflow. The important caveat is simple: cloud providers still need internet. Ollama can run offline after the initial setup. The "zero footprint" idea is more useful than it sounds The project redirects its persistent data into a local data folder. That includes provider settings, API keys, logs, session history, agent memory and local Ollama files. According to the repository, it does not write configuration into the host system. For me, this is the real feature. I do not care that the agent happens to be on a USB drive. I care that I can move the folder and keep my environment with it. 💾 There are two very different ways to run the agent The launcher offers a normal mode that asks before file writes or shell commands. There is also an optional Limitless mode that can run without approval prompts. I like that these are explicit choices rather than one hidden permission switch. For normal development I would keep approval mode on. For a disposable test project or a controlled autonomous task, the second mode could be useful. Sessions can survive the move Another practical detail is session resume. The project stores session history inside the por

Every guide I write ends in the same handful of questions. How much hardware do I actually need? What happens when one box dies? Are my backups real or just a feeling? A guide can walk you through a setup, but it can't do arithmetic about your lab — so I built eleven small tools that can, at peira.dev/tools . They're free, none ask who you are, and seven of the eleven keep working once the page has loaded — network unplugged, laptop in a cupboard, whatever. They share one lab profile This is the part that makes them a set rather than eleven unrelated pages. Describe your lab once — tick your services in the sizing calculator, press Save to profile — and the others pick it up. The failure simulator opens with your nodes already modelled; the backup planner knows what data you have; the power-loss playbook knows what's plugged in. Lab doc hands the whole thing back as a Markdown file. Nothing about that profile leaves your browser. No account, no sync, no server that could leak it — which is also why it doesn't follow you between devices. The Markdown export is how you carry it elsewhere. Plan the build Sizing calculator — asks what you want to run and recommends nodes, RAM, and storage. It cares most about RAM, because that's the constraint that actually bites; vCPUs overcommit happily, memory doesn't. Tick "survive one node failure" and it insists on three nodes (a two-node cluster loses quorum the moment one dies). Node failure simulator — kill a node and see which workloads fit on the survivors. It places the critical ones first and names the stranded ones. 3-2-1 backup planner — three copies, two devices, one offsite (the rule CISA recommends ). It's blunt: a snapshot on the same disk as the original is versioning, not a backup. Fix what's broken The overlay network diagnostic is a decision tree born from a miserable afternoon: a container couldn't reach a machine across a Tailscale subnet router, and three layers had to be right — the route in the guest, the ACL

Save your AI conversations so they persist after closing tab Discussion | Link

Run multiple local coding agents on your machine Discussion | Link

Notes docked to your screen edge, with an AI agent inside Discussion | Link

Full-screen meeting reminders with AI briefings Discussion | Link

Voice annotate your Mac, get screenshots + clipboard manager Discussion | Link

I've been quietly building Antigravity Tools — a collection of 59 free, browser-based developer utilities — and today I'm sharing everything I built and learned. Why vanilla JS? No React, no build step. The main constraint I set for myself: zero dependencies, zero server, zero telemetry . When you paste your JWT token into jwt.io, it goes to their server. When you use an online regex tester, your test strings are logged. I built Antigravity Tools so every operation runs inside your browser, using native APIs. No Node.js backend No npm packages No webpack/vite/parcel No Google Analytics No cookies Everything runs on Web Crypto API , Canvas API , Web Audio API , and IndexedDB — all native to modern browsers. The 8 tool categories 🔐 Security & Auth Tools JWT Inspector — decode JWT header, payload, and check expiry locally RSA & ECC Key Generator — generate 2048-bit key pairs via SubtleCrypto Hash & Password Generator — SHA-256/SHA-512 via Web Crypto PII Masker — strip emails, credit cards, SSNs, IPs from text Universal Encoder/Decoder — Base64, URL, Hex, HTML entities, Unicode 🤖 AI & Prompting Tools AI Token Counter — estimate cost across GPT-4o, Claude 3.5, Gemini 2.0, DeepSeek R1 System Prompt Builder — structure agent instructions with XML tags and tool definitions AI Text Humanizer — rephrase robotic AI output into natural writing Prompt Cost Trimmer — compress prompts by 30–50% to reduce API costs ⚡ Dev & Code Tools JSON Workbench — beautify, validate, convert to TypeScript, Python, Go types cURL Converter — cURL → JS fetch, Python requests, Go, PHP Regex Tester — real-time match highlighting with capture group display Cron Builder — visual cron expression editor with plain-English output Git Command Helper — build undo/squash/cherry-pick commands visually Try it 👉 https://antigravitytools.app

OpenAI has officially introduced GPT-6 Astra , a new model it describes as its most capable and aligned to date. The launch centers on advanced computer use, software engineering, browsing, cybersecurity tasks and professional knowledge work. Astra is initially rolling out to a limited group of organizations, followed by availability for paid ChatGPT users and developers across the OpenAI API, Microsoft Azure and AWS Bedrock . The formal release supersedes earlier speculation around a potentially "special" model rollout. OpenAI’s official GPT-6 Astra announcement establishes the substantive news: a staged, multi-platform deployment with defined API pricing, large context capacity and capabilities aimed at completing more complex digital tasks. For businesses, the important question is less whether Astra is unusual and more whether its computer-use functions can reliably reduce manual work in existing processes. OpenAI positions the model for tasks such as filling forms, updating CRM records, managing calendars, researching the web, installing and troubleshooting software, and producing documents, spreadsheets and presentations that follow a user’s templates and style. What GPT-6 Astra adds Astra is designed to work across tasks that ordinarily require moving between software interfaces, web pages and business documents. That is a significant expansion from using a language model solely to draft text or answer questions. In the right workflow, a model that can navigate authorized tools and complete multi-step tasks could help teams reduce repetitive administrative work. OpenAI also highlights Astra’s performance in code generation and professional knowledge work. Its stated ability to install, test and troubleshoot software points toward more autonomous technical workflows, while its document-generation capabilities could be relevant for recurring reports, proposals, analysis packs and operational templates. The model’s published limits and access paths are also nota

ChatGPT.com reached about 1.09 billion monthly US visits in July 2026, a 48.38% year-over-year increase, according to Semrush Traffic Analytics data. In the same comparison, Bing.com traffic fell about 50.43%. The contrast does not show AI replacing conventional search overnight. Google and YouTube still led the US dataset by a wide margin. It does show that the places where people first discover information are changing, while many website analytics setups remain poorly equipped to show the full effect. The July 2026 snapshot puts ChatGPT ninth among the leading US sites measured by Semrush. Businesses that still view organic discovery mainly through Google rankings and familiar referral reports risk missing a growing part of the customer journey: a user may ask an AI assistant for options, follow a recommendation, and arrive on a website without a cleanly identifiable source in Google Analytics 4. The underlying Semrush US Trending Websites data compares July 2026 traffic with July 2025. It is a view of US web traffic in Semrush's ranked-site dataset, not a count of every search or AI interaction. Still, the scale of the movement makes AI-assisted discovery a practical measurement issue, not simply a trend to monitor. What the traffic shift means for SEO measurement The key implication is not that businesses should abandon established search channels. Google recorded roughly 25.31 billion monthly US visits in the July snapshot, while YouTube recorded about 10.27 billion. Those figures underline how large the established platforms remain. What has changed is the need to distinguish where discovery happens from the source that ultimately appears in analytics. ChatGPT's growth can create new paths to content, products and services. But referral details may be unavailable when users move from an AI interface to a website, particularly when the originating referrer is stripped. In GA4, those visits can be grouped as Direct or remain otherwise difficult to classify. Web

Building business software in East Africa means dealing with two hard operational facts. First, the Kenya Revenue Authority requires every business invoice to carry a digital fiscal signature and a verifiable QR code via eTIMS. Second, over 80 percent of commercial transactions settle through Safaricom M-Pesa. If your ERP cannot sign invoices in real time or match incoming Paybill payments automatically, your accounting team spends their days doing manual data entry. If your retail POS goes offline when the fiber cuts, you cannot legally issue receipts. To solve these problems, we built and published three production-ready modules on the official Odoo App Store. They support Odoo 17.0, 18.0, and 19.0 across both Community and Enterprise editions. Here is the technical architecture behind how we built them, how we handle network failures, and what we learned along the way. The Three Integrations Module Purpose Edition & Versions JengaStack eTIMS Real-time KRA OSCU invoice signing and fiscal QR codes Community & Enterprise (17.0, 18.0, 19.0) JengaStack M-Pesa Daraja STK Push and C2B Paybill/Till ledger auto-reconciliation Community & Enterprise (17.0, 18.0, 19.0) JengaStack eTIMS VSCU Offline-first virtual control unit and batched compliance sync Community & Enterprise (17.0, 18.0, 19.0) 1. Real-Time Fiscal Signing Without ERP Worker Blocking The standard KRA eTIMS Online Sales Control Unit (OSCU) flow requires sending invoice line items, tax classification codes, and buyer PINs to KRA over HTTPS. KRA returns control unit internal data (CU Information), an invoice sequence number, and a verification URL encoded as a QR code. The immediate trap many developers fall into is making a synchronous HTTP call directly inside Odoo's invoice confirmation method: # The anti-pattern: Blocking the main thread class AccountMove ( models . Model ): _inherit = " account.move " def action_post ( self ): res = super (). action_post () for record in self : response = requests . post (

Six coding agents were running. I could not tell you what any of them had done. Not roughly. Not approximately. The output was there, the files had changed, and the honest answer to "which one did that" was a shrug. Three questions in particular had no answer: which run burned the tokens, whether they genuinely ran at the same time or merely started together, and whether two of them had quietly edited the same file. That last one is the expensive question. An agent working on the wrong file looks exactly like an agent working on the right one, right up until you read the diff. The thing that was already true Every one of those runners writes a transcript to disk while it works. Claude Code does. So do Cursor, Codex, Gemini CLI, Copilot CLI and Kiro. The record of what happened was sitting in my home directory the entire time, in six different formats, none of which I had ever looked at. So runlanes does not wrap anything. There is no SDK, no instrumentation step, no account, and nothing to start before the run starts. It reads what the runner already wrote. The consequence is the part I did not expect to matter as much as it does: it works on runs that already finished. Most tools in this space need you to have decided, in advance, that this particular run was worth watching. This one can answer a question you only thought to ask afterwards. npx runlanes That opens a console on 127.0.0.1:4180 for whatever project you are standing in. There is no configuration file to write first. What it actually shows Now is every live session, across every runner it found, with what the main conversation spent against what it handed to subagents. On the session that motivated the whole thing, that split was 8.3 million tokens of conversation against 2.1 million delegated, which was not the ratio I would have guessed. The parallelism figure is the one I keep coming back to. Peak concurrency was four agents. The share of elapsed time where anything genuinely overlapped was 9% . Four

Hi DEV! I got tired of free PC cleaners that bundle a registry cleaner nobody needs, count up a scary number of "issues", and then dangle a paid version at the end. So I wrote the boring version. CleanGeek finds and clears: Temp folders, user and system Browser caches across the installed browsers Windows Update leftovers and Delivery Optimisation cache Crash dumps and error reports Thumbnail cache and font cache Recycle Bin, if you tick it Every item shows what it is and how much space it is worth. Nothing is deleted until you press the button, and you can untick anything you want to keep. Why I built it There is no registry cleaner in it and there never will be. Cleaning the registry has not meaningfully sped up a Windows machine in about fifteen years, and the risk of breaking something is real. Same reason there is no "optimise your PC" button. The tool does one job and tells you exactly what it did. The other reason is the business model. Free cleaners generally are not free, they are a funnel. CleanGeek has no paid tier to funnel you into, no upsell screen, and no telemetry. Tech stack .NET 8, net8.0-windows Avalonia for the UI, with Avalonia.Desktop and Avalonia.Themes.Fluent No third party cleanup engine, the scanning is all in the app Avalonia was the right call. WinForms would have been quicker to get moving but the styling story is grim, and WPF ties you down harder than I wanted. Honest caveat The installer is not code signed yet, so SmartScreen may warn on first run. I am sorting that out. If that is a dealbreaker for you, fair enough. Links Site: https://techygeekshome.info/cleangeek/ Source: https://github.com/techygeekshome/CleanGeek Video: https://youtu.be/Z2s2p3nkIvY If it misses something obvious on your machine, tell me and I will add it.

OpenAI has introduced GPT-6 Astra and its Pro variant, GPT-6 Astra Pro , in a staged rollout that spans ChatGPT, the OpenAI API, and cloud partners Azure and AWS Bedrock. The most important detail for teams planning to use the new models is that access is expanding in phases. OpenAI says Astra is rolling out first to a limited set of organizations, before becoming available to ChatGPT Plus, Pro, Business, and Enterprise users in the coming days. GPT-6 Astra Pro is intended for ChatGPT users on the Pro, Business, and Enterprise plans. That makes the launch more than a single ChatGPT update. It creates a multi-channel availability path for organizations that use ChatGPT directly, build with the API, or work through major cloud platforms. The official GPT-6 Astra announcement is the primary reference for OpenAI's rollout plan. The announcement confirms the model launch and broader access direction, but it should not be read as an immediate universal switch-on for every eligible account. Availability may vary while the staged deployment continues, and OpenAI is managing safety and access controls through its Daybreak and enterprise access programs . What the GPT-6 Astra rollout changes The rollout introduces two closely related offerings. GPT-6 Astra is the newly announced model, while GPT-6 Astra Pro is the Pro variant available to ChatGPT Pro, Business, and Enterprise users. OpenAI also places Astra across several delivery channels, which matters because businesses do not all adopt AI through the same interface. Offering or channel Confirmed access or availability Rollout consideration GPT-6 Astra Rolling out through ChatGPT, the OpenAI API, Azure, and AWS Bedrock OpenAI describes the ChatGPT rollout as phased GPT-6 Astra Pro Available to ChatGPT Pro, Business, and Enterprise users as part of the rollout Access may appear progressively as deployment expands ChatGPT Plus Included in OpenAI's planned broader Astra availability OpenAI says availability is coming in the f

I built a tool that draws architecture diagrams from a repository, where every edge cites the file, line and commit it came from. Then I ran it against thirteen repositories it had never seen, and every single one of them found something wrong with it. There were thirteen. These are the ones worth writing down. The list says nothing about those codebases. It says something about testing: a tool that reads other people's repositories has to be tested against other people's repositories, and there is no substitute. The rule the tool works by Nothing is drawn that cannot be cited. Every edge in the output carries the file, the line and the commit that justifies it — click an arrow, see the import statement. If a reference cannot be resolved to something in the repository, it is not quietly dropped and it is not guessed at. It is reported as a gap. That second half is what made these bugs findable. A tool that silently drops what it cannot resolve looks perfect and is useless. A tool that reports gaps by name and count tells you, loudly, every time it is confused. Java: a library sharing your package prefix is not you Guava declares com.google.common . Truth is a separate library, and it lives in com.google.common.truth . My resolver matched on package prefixes, so Truth looked like Guava's own code, and every reference to it became a gap against a package Guava does not contain. 834 false gaps — 28% of the repository. The fix is to require the next path segment to look like a type before peeling, because com.google.common.truth.Truth peels to a package and com.google.common.collect.ImmutableList peels to a class, and those are different shapes. Java: a file importing its own nested type Java requires the import for a nested enum constant even inside the same file. Treating that as a dependency has you drawing an arrow from a file to itself. It accounted for all 137 remaining gaps on Spring Boot and all 34 on Guava. Java: static imports point one segment too deep import

Google is changing how manual actions under its Site Reputation Policy affect search visibility by region. From August 30, 2026 , the ranking impact of these actions will not apply to search results shown to people in the European Economic Area (EEA), while results shown outside the EEA may still be affected. For site owners with international audiences, that makes region-specific SEO monitoring more important than a single global view of performance. The change does not remove Google's Site Reputation Policy or its effort to address site reputation abuse . Instead, it changes how the consequences of a manual action are experienced in EEA search results. Google announced the update in its official Site Reputation Policy update , linking the regional change to its ongoing discussions with the European Commission and considerations related to the Digital Markets Act. What Google changed Google introduced its Site Reputation Policy in 2024 to address situations in which third-party content takes advantage of a host site's established ranking signals. The policy targets content that is published primarily to exploit a site's reputation in Search rather than to provide value consistent with the host site's purpose and oversight. The policy remains in place globally. What changes is the effect of a manual action for people searching from the EEA. Google says that when it applies a manual action under this policy, the impact will not apply to results displayed to users inside the EEA. Outside the EEA, the manual action can continue to affect the relevant site's search results. Enforcement consideration Search results in the EEA Search results outside the EEA Impact of a Site Reputation Policy manual action Does not apply to results shown to users in the EEA May continue to apply Potential treatment of the affected site portion Google may separate it in its systems so it can rank independently over time Google's announcement does not describe an equivalent regional change S

AI coding agents can modify an unfamiliar file in seconds. The slower question is often more important: Why does this code look this way? The answer may be scattered across old local sessions: one turn investigated the bug, another rejected an approach, and a later turn made the edit. Git preserves the code change, but not necessarily the surrounding agent conversation. I added a local query layer to ThoughtDAG so a developer—or a coding agent—can deliberately retrieve that history before editing: npx thoughtdag why src/lib/api.ts It searches supported local agent transcripts for turns that changed, read, or discussed the file and returns links to the matching source turns. Observation is not explanation The difficult part was not text search. It was avoiding a false claim of causality. If a session record shows a file edit, ThoughtDAG can report that as an observed change: Δ storedProviders → storedProviders, storedVision… If the agent later says why it made the change, that is useful—but it is still the agent's account, not a verified causal fact. ThoughtDAG marks that separately: ≈ candidate explanation from the agent response This distinction matters when old session history becomes input to another agent. A fluent explanation should not silently harden into ground truth just because it was retrieved. Retrieval stays deliberate For regular use, the same index can be exposed through read-only MCP tools: npm install -g thoughtdag thoughtdag setup mcp The agent can then call why_check , why_file , find , and recall_turn before changing code. Retrieval is explicit; matching history is not automatically injected into every prompt. The index stays on the local machine, and source session files are never modified. The current CLI covers local Claude Code, Codex, and ThoughtDAG canvas conversations. What this does not prove This is a developer preview, not a complete audit trail. An observed edit proves that the recorded session changed a file, not that every reason for

In the fast-paced world of software development, every second counts. Seamless tool interaction is not just a convenience; it's the bedrock of high developer productivity . Even seemingly minor hitches, like a non-functional 'copy link' button, can subtly erode efficiency, leading to frustration and lost time. A recent GitHub Community discussion highlighted just such an issue, where a user reported that the 'Copy link' button in Pull Requests (PRs) was consistently failing, specifically when using the Arc browser on macOS. This isn't merely about a broken button; it's a window into the complex interplay between browsers, web APIs, and the essential tools we rely on daily. The Reported Problem: A Month-Long Frustration The original post by vovapyc detailed a persistent problem: the 'Copy link' button in GitHub PRs had been broken for at least a month. The user specified their setup: Arc browser, MacBook Pro M1 Pro, and macOS 26.2. For dev teams, product managers, and delivery leads, a recurring point of friction like this, preventing a quick share of a PR link, represents a tangible drag on workflow. Imagine the cumulative time lost across a team if every developer had to manually copy URLs from the address bar multiple times a day. GitHub's automated response, while a standard and necessary part of their feedback loop, acknowledged the feedback and assured the user that their input would be reviewed. However, it didn't immediately offer a solution or explanation for the bug, leaving the user, and potentially others experiencing similar issues, in limbo. Diagram illustrating the three gates: Secure Context, Document Focus, and User Permission, that must be passed for the Clipboard API to function.## The Expert Insight: It's Likely the Browser, Not GitHub The true insight, and the crux of this discussion, arrived from hoangperry . Their comprehensive breakdown suggested that the issue was almost certainly browser-specific rather than a core GitHub bug. This distincti

¿Tu equipo comparte credenciales por Slack? ¿Discuten arquitectura en herramientas que almacenan todo en servidores de terceros? Existe una alternativa que no depende de ninguna nube: NETO . ¿Qué es NETO? NETO es un chat peer-to-peer diseñado para equipos de desarrollo que trabajan en la misma red local. No hay servidores centrales, no hay cuentas, no hay datos saliendo de tu oficina. Abres el navegador, y ya estás comunicándote con tu equipo. ¿Cómo funciona bajo el capó? La arquitectura de NETO combina tres tecnologías clave: mDNS (Multicast DNS): Permite el descubrimiento automático de peers en la red local sin necesidad de configurar servidores DNS ni registrar direcciones manualmente. Tu equipo aparece de forma instantánea. WebRTC: Establece conexiones directas entre navegadores. Los mensajes viajan de punto