标签：#automation

Semrush has updated its keyword research workflow with AI features designed to turn work that could historically take days into minutes. The key distinction is not AI generation alone: Semrush is combining AI with its existing keyword data and domain-level context, aiming to give marketers faster recommendations without relying on unverified search-volume outputs. The official Semrush announcement describes changes across Keyword Overview, Keyword Magic Tool, and Keyword Strategy Builder. The updates include a domain-personalized Personal Keyword Difficulty (PKD) metric, Topical Authority analysis, and a redesigned planning tool previously called Keyword Manager. Semrush says the capabilities are included with paid subscriptions. For SEO teams, the practical development is a shift from treating keyword research as a collection of isolated volume and difficulty checks toward a workflow that evaluates whether a topic fits a specific website. That can reduce manual steps in discovery and planning, while retaining a connection to the tool's underlying data. What Semrush changed in its keyword research workflow The new workflow applies a combined data-and-AI approach to thematic relevance. Semrush says it analyzes the relationship between a target topic and a domain's core topics, then surfaces that context in the product interface. This matters because the same keyword can have different strategic value for different sites, depending on their established subject coverage. The updates cover three connected stages of research: Keyword evaluation: Personal Keyword Difficulty adds domain-specific context to keyword difficulty assessment. Topic relevance: Topical Authority is intended to show how closely a topic aligns with a domain's core areas. Planning: Keyword Strategy Builder has been redesigned to support automated keyword and content planning workflows. Workflow area Earlier approach Semrush AI-driven update Keyword difficulty Keyword-level evaluation Personal Keyword

The European Union's voluntary General-Purpose AI Code of Practice gives providers of general-purpose AI models a practical framework for supporting compliance with the EU AI Act. Finalised in July 2025, the code addresses transparency, copyright, and safety and security. Its public signatory list includes major AI and technology companies, but official EU material does not support claims that roughly 190 organisations have signed the GPAI code. The distinction matters for companies assessing AI suppliers. Signing the code can signal engagement with the EU's emerging governance expectations, but it is not a substitute for examining a provider's specific commitments, documentation, and product-level controls. The European Commission describes the code as a voluntary instrument, and its official GPAI Code of Practice page states that the signatory process and public information continue to be updated. What the GPAI Code of Practice covers The code is designed for providers of general-purpose AI models, a category that can include models used across multiple downstream applications. Rather than creating a separate legal regime, it is intended to help providers demonstrate how they can meet relevant AI Act obligations . Its three chapters cover different aspects of provider responsibility: Transparency: commitments related to information and documentation that can help downstream providers understand and use general-purpose AI models appropriately. Copyright: measures intended to address copyright-related obligations for providers of general-purpose AI models. Safety and security: commitments focused on managing risks associated with the most capable models, including systemic-risk considerations where applicable. Code chapter Primary focus Why it matters to AI buyers Transparency Provider information and documentation Helps buyers assess whether a model provider can supply information needed for downstream use. Copyright Copyright-related provider commitments Relevant

A Month With Bash — Part 3: Building Projects After all the expansions and syntax, I moved on to regex in bash. It wasn't too hard since I'd already worked with regex in Python, but alongside it I learned grep , sed , and awk — tools that turned out to be extremely useful for automation. I built a few mini projects and started automating some of my small day-to-day tasks. I won't go too deep into that here, but you can check out my learning-bash GitHub repo, which has all my learning scripts. From there I covered conditionals, loops, and repetitive tasks. Finally I learned about array variables in bash and shell options, went even further testing different ways of looping, and that's when I started actual project building(I am still building ) #!/usr/bin/env bash ## looping with range functions -- somehow # python style looping {start..end} for i in { 1..10 } ; do # this uses brace expansion so using vars wont work becase of execution sequencing echo $i done clear ## c - slyle looping for (( i = 0 ; i < 10 ; i++ )) ; do # variables works here well echo "hello $i " done ## using variables to loop clear start = 1 stop = 10 step = 2 for i in $( seq $start $stop ) ; do # this uses the seq command echo "hello world" done Conclusion Spending so much time on bash wasn't a waste. Not only did it force me to learn a huge number of commands, it changed how I think about my own machine — most of what I used to do manually, I can now automate. That shift alone made the month worth it. i am still learning and trying to get the best practices and things not to do THANK YOU FOR READING THIS FAR. That is a rough summary of me writing bash for a month there is really a lot left unsaid here but still building and learning. If you are just starting out with bash or if you haven't tried it hope this helps feel free to drop questions advice and corrections

A Month With Bash — Part 2: Expansions Continuing from where I left off, the next thing I learned was special parameters in bash: "$*" $# $? $@ $N $- $0 Another important concept I picked up is how bash executes shell scripts. Bash is one of those languages that interprets each line as it goes — but it doesn't stop if a line fails. It continues on unless you explicitly set set -o pipefail (or -e , depending on what you want it to catch). Generally, the procedure looks like this: Tokenizing : splitting the line into tokens, usually split using the IFS value. Brace expansion : a mechanism by which arbitrary strings can be generated. echo file { 1,2,3 } .txt ## output: file1.txt file2.txt file3.txt Bash preserves the order from left to right. Tilde expansion : this is where expansion of special symbols takes place. ~ represents the HOME built-in variable ~+ represents PWD , the current working directory and others DIR = ~/Desktop # this is $HOME/Desktop echo " $DIR " Parameter expansion : introduced with the $ symbol. # ${} — the braces can be omitted for normal variables but not for array-type variables Command substitution : very important — it lets you assign the output of a command to a variable, and use commands inside if and for statements. Done with $(command to execute) . week_name = " $( date +%A ) " # gets the current day of the week echo " $week_name " Generally, $() spawns a new shell instance, so it's advisable to avoid it where possible, for latency reasons. Arithmetic expansion : just from the name, this allows evaluation of arithmetic expressions and substitution of the result. It starts with $(( expression )) . There are some rules — bash doesn't support floating point arithmetic natively, so you'd reach for bc if you need it. I won't go deep into that here since this isn't a full bash tutorial. Here's a simple BMI calculator I wrote while practicing this: #!/usr/bin/env bash # script calculates user's BMI and gives a recommendation set -euo pipefail #

Cómo solucionar el error “Enable JavaScript and cookies to continue” Este error aparece cuando Cloudflare (u otro proxy inverso de seguridad) detecta que el navegador del usuario no cumple con los requisitos mínimos para acceder al sitio: JavaScript está deshabilitado o las cookies no están permitidas . Pero en entornos reales, el problema suele ser más sutil: el navegador sí tiene JS y cookies habilitados, pero la configuración del entorno de ejecución (como un headless browser, test automation, o un scraper) no emula correctamente el comportamiento del cliente . 🔍 Causa raíz técnica Cloudflare emite un desafío (CAPTCHA o JS challenge) para verificar que el cliente es un navegador real. Si la respuesta no cumple con el desafío (por ejemplo, porque: El navegador no ejecuta el JS del desafío (headless sin soporte), Las cookies no se persisten entre solicitudes, El User-Agent o Accept-Language no coinciden con navegadores reales, Falta el Referer o Origin en headers, Se bloquean cookies de terceros (como las de Cloudflare), … entonces el servidor devuelve este mensaje estático en lugar de redirigir a la página solicitada. ⚠️ Nota crítica : Si estás usando herramientas como curl , requests de Python, o navegadores headless sin configuración especial, no pasarás el desafío de Cloudflare . Es intencional: Cloudflare bloquea tráfico no humano por diseño. ✅ Solución definitiva (por escenario) 🛠️ Caso 1: Navegador real (usuario final) Verifica que JavaScript esté habilitado : Chrome: Configuración → Privacidad y seguridad → Configuración de sitios → JavaScript → Permitido . Firefox: Preferencias → Privacidad y seguridad → Cookies y datos de sitios → Deshabilitar “Bloquear cookies y datos de sitios” . Limpia cookies y caché (especialmente para *.cloudflare.com ). Reinicia el navegador y vuelve a cargar la página. 🛠️ Caso 2: Automatización / Scraping (Python + Playwright/Selenium) No uses requests o urllib : no ejecutan JS. Usa un navegador real con soporte para Cloudflare. ✅

More than a year ago, which is practically ancient history in the AI years, I wrote a blog about using AI to build new self-service capabilities. It felt like the future. We built a self-service action that could create new self-service actions, helping us move faster, reduce bottlenecks, and scale a small Platform Engineering team supporting hundreds of developers. One of the most interesting parts was using Amazon Bedrock to generate Terraform code dynamically at runtime, allowing the system to determine how a new cloud resource should be provisioned using our existing Terraform modules. It worked. It was impressive. And… we removed it. Looking back, abandoning that approach turned out to be one of the best engineering decisions we made. At the time, it felt like an isolated technical decision. It wasn’t. Recently, we faced a much smaller problem. We wanted to automate the creation of DNS records in Cloudflare through our self-service platform. The first proposal was exactly what you’d expect today: “Let’s build a Claude Skill.” Immediately, I had a strong sense of deja vu. But my hesitation wasn’t about whether AI could do it — it was about whether it should. We were simply asking the wrong question. The Industry Shift A lot of engineers today feel like everything they learned over the last decade suddenly became less relevant. We are DevOps engineers. We are Platform Engineers. We used to spend time designing systems, defining standards, reviewing architectures, and planning before writing a single line of code. Every automation started with the same question: “How should we automate this?” Today, that question has quietly changed. Now we ask: “How can AI do this?” At first glance, that sounds like progress. And sometimes it is. Large Language Models have fundamentally changed the way we build software. Tasks that used to take hours now take minutes, and entire prototypes appear from a single prompt. The temptation is obvious. If AI can do it… why not let AI do

Picture the scenario: your invoicing pipeline generates a clean, branded PDF for a German B2B customer. It looks right. It would print fine, email fine, and satisfy anyone who opens it by hand. Then it bounces, because since January 1, 2025, that customer is legally required to receive invoices in a format their software can parse without a human retyping the totals. A pretty PDF isn't enough anymore, and honestly, for a machine, it never really was the point. The part that surprises people who haven't dealt with this yet: the mandate doesn't force you to give up the human-readable PDF. It just requires that PDF to carry a second, structured version of itself, riding along inside it. That format is called ZUGFeRD, with an internationally aligned sibling called Factur-X. If you've never had to build one, it's worth understanding the mechanics before the code, because it's a genuinely clever piece of engineering, not just a compliance checkbox. So how does a single file manage to be both a human-readable invoice and a machine-parseable one at once? What a ZUGFeRD invoice actually is Open a ZUGFeRD invoice in Adobe Acrobat or any PDF viewer and you see a normal invoice: logo, line items, totals, payment terms, nothing unusual. But embedded inside that same file, in its attachments, sits an XML document carrying the exact same invoice data in structured, typed form: invoice number, line items, tax rates, totals, every field an accounting system needs, tagged rather than buried in a paragraph a parser has to guess at. The container format making this possible is PDF/A-3 , the only PDF/A variant that permits arbitrary file attachments while still meeting the archival standard's long-term readability requirements. PDF/A-1 and PDF/A-2 explicitly forbid embedded attachments; PDF/A-3 was built for exactly this use case, which is why every ZUGFeRD file you'll open is, underneath, a PDF/A-3b document with an XML file riding inside it. The embedded XML follows EN 16931, the EU's

AI search is creating an attribution problem for marketers: a brand can help shape an answer in ChatGPT, Google AI Mode , or Perplexity without receiving a visit to its website. That makes rankings, impressions, and click-through rates incomplete indicators of visibility. New research from Wix Studio adds evidence that the content cited by AI systems follows recognizable patterns, while industry discussions increasingly point to measurement frameworks built around citations, answer presence, prompt coverage, and downstream influence. The key shift is not that website traffic has stopped mattering. It is that a click is no longer the only observable outcome of search visibility. When an AI interface summarizes options, recommends a product category, or cites a publisher, users may form an opinion or continue their journey elsewhere. Brands therefore need to separate direct referral traffic from their broader presence in AI-generated answers. What Wix Studio's research shows about AI citations Wix Studio's AI Search Lab research examines citations in answers generated by major AI search interfaces, including ChatGPT, Google AI Mode, and Perplexity. Published summaries describe a dataset of roughly 75,000 AI-generated answers and more than one million citations. Its central finding is that citations are not spread evenly across every kind of web page. Listicles, articles, and product pages account for a disproportionate share of the citations observed in the research. That is consistent with how answer engines retrieve and synthesize material: content that is clear, segmented, easy to scan, and closely matched to a question can be easier to extract into a response. A subsequent Search Engine Land summary of Wix Studio's work discussed a 25,000-URL dataset in which listicles represented a majority of AI citations. The precise mix should not be treated as a universal rule. Wix Studio's analysis covers a defined set of prompts and engines, and results can change with the

CleanSlate is an open-source platform for running coding agents across your local machine and the cloud. Today, it works through an IDE, CLI, and SDK. Agents can understand a codebase, make changes, run commands, browse the web, and verify their work. We are now building longer-running autonomous cloud agents that can continue working without keeping your machine active. CleanSlate supports multiple model providers and is not tied to a single ecosystem. GitHub: https://github.com/TheWariend/CleanSlate Website: https://thewariend.com/cleanslate The project is still early, and I would appreciate honest feedback from developers who use coding agents.

Yelp has confirmed a licensing agreement with OpenAI that will extend Yelp content into AI platforms, including the OpenAI ecosystem powering ChatGPT. The deal positions Yelp’s reviews, ratings, photos and business information within a growing AI-driven local discovery experience, while opening a potential path for users to request quotes from local service providers through ChatGPT. The agreement is more consequential than a new search result format. Yelp is expanding its data-licensing strategy beyond conventional search surfaces, while ChatGPT gains access to a major source of local business content. For people asking an AI assistant where to eat, which contractor to contact or how a nearby business is rated, the quality, freshness and governance of the underlying data will matter as much as the answer itself. What the Yelp and OpenAI agreement covers In its February 2026 earnings and shareholder release , Yelp announced an agreement with OpenAI and described it as part of its AI transformation and strategy to license content for local discovery across AI ecosystems. That is the confirmed foundation of the development. Axios has reported the practical user-facing direction: ChatGPT will surface Yelp reviews, ratings, photos and other business details in responses to local queries. Yelp has also signaled that its Request a Quote capability could be integrated into ChatGPT in the near term, enabling users to initiate an inquiry with a service provider from the AI interface. Capability What the research supports Status Yelp content in ChatGPT Reviews, ratings, photos and other business details are expected to surface for local queries. Reported user-facing outcome of the confirmed licensing agreement Request a Quote in ChatGPT Users may be able to initiate quote requests with local service providers through the AI interface. Signaled for a future rollout Data timing and interface design Reporting describes real-time business data, but exact latency, update frequency

Google is expanding Gemini on two fronts at once: faster, lower-cost models for software and enterprise workflows, and a new robotics family designed for embodied, cross-robot control. The releases include Gemini 3.6 Flash , Gemini 3.5 Flash-Lite , Gemini 3.5 Flash Cyber, and Gemini Robotics 2 with related embodied-reasoning and on-device variants. The clearest immediate enterprise story is the widening choice of models for agentic work. In its official Gemini Flash announcement , Google positions 3.6 Flash as a general workhorse for coding, knowledge work, and multimodal tasks, while 3.5 Flash-Lite is aimed at workloads where response speed and cost efficiency are decisive. The robotics update extends the same broader push beyond software agents into systems that must reason about and act in physical environments. A broader Gemini stack for agentic workloads Gemini 3.6 Flash is generally available through Google's developer, enterprise, and consumer channels. Google says it improves on 3.5 Flash for coding, knowledge-work, and multimodal tasks, while producing around 17% fewer output tokens than 3.5 Flash. That token-efficiency claim matters because output tokens are a material part of both latency and inference spending in multi-step agent workflows. Google lists pricing for Gemini 3.6 Flash at $1.50 per 1 million input tokens and $7.50 per 1 million output tokens . The company describes the model as offering a lower cost per task, a metric that depends not only on token prices but also on how many tokens a task requires to complete. The supplied release information does not provide a price for 3.5 Flash-Lite, so it should not be inferred from 3.6 Flash pricing. Gemini 3.5 Flash-Lite occupies a different role. Google calls it its fastest and most cost-effective subfamily, with a stated output speed of 350 output tokens per second . It is intended for high-throughput agentic workflows where an organization may value quick model responses and high request volume ove

How to Audit Hidden Reminders and Context Usage in Claude Code Logs | Agent Lab Journal Agent Lab Journal Guides Glossary Advanced field guide How to Audit Hidden Reminders and Context Usage in Claude Code Logs Advanced · 45 min read · Local analysis · Updated August 1, 2026 The visible transcript in Claude Code is not necessarily a complete representation of everything recorded around a request. Service messages, internal reminder markers, tool payloads, and usage metadata can exist in session logs without appearing as ordinary chat turns. If you want to know how often ip_reminder occurs—or how input, output, cache creation, and cache read tokens are distributed—you need to inspect the stored records directly and preserve enough structure to avoid misleading totals. In this guide What this audit can establish Concrete investigation case Locate and select one session Preserve an auditable copy Run a quick structural check Build the full local report Interpret reminder and token data Verify the report independently Failure cases and repairs Limitations What this audit can—and cannot—establish This workflow examines one local session stored as JSON Lines (JSONL): a text format in which each line is normally an independent JSON value. It creates a report with: the selected file’s path, size, modification time, and SHA-256 digest; the number of physical lines, parsed records, blank lines, and malformed lines; every record containing the exact, case-sensitive string ip_reminder; the JSON paths at which the marker was found; timestamps and record types when those fields are available; per-record and aggregate input, output, cache creation, and cache read token values; a chronological CSV suitable for a spreadsheet or notebook; a machine-readable JSON report for later comparison. The report shows what is present in the selected file. It does not prove why a reminder was inserted, whether it was transmitted to a model exactly as stored, or how the client’s undocumented inte

Google Gemini offers an AI trip planner that combines travel research, itinerary generation and Google service integrations in one conversational workflow. The capability can surface real-time flight and hotel options, build itineraries around a traveler’s interests and adjust plans as needs change. Although Google is continuing to promote the feature, its official materials position it as an established part of the Gemini ecosystem rather than a newly launched product. The practical appeal is straightforward: trip planning often requires moving among airfare searches, hotel listings, maps, saved locations and notes. Gemini is designed to bring several of those steps together. On Google’s official Gemini AI trip planner page , the company describes prompts such as planning a four-day Tokyo visit around particular interests, then using Gemini to organize a tailored schedule by neighborhood. What Gemini’s travel planner can do Gemini’s travel functionality is framed as a consumer assistant for the research and planning stages of a trip. Users can describe a destination, trip length, interests or preferred travel style in natural language. Gemini can then help turn that input into an itinerary while drawing on relevant Google travel and mapping services. The official descriptions identify several connected capabilities: Real-time flight options through Google Flights. Real-time hotel options through Google Hotels. Customized itineraries organized around a traveler’s requested interests and locations. Plan adjustments during the trip , rather than a fixed itinerary created only before departure. Maps integration for navigation and points of interest along a route. Google’s Gemini Apps support material also confirms that the apps can help plan trips and retrieve live flight information. Maps integration matters because it extends the experience beyond trip inspiration: a user can move from deciding what to do to navigating to places and discovering points of interest whi

This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is the final installment (Part 4) of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. So far, we have discussed creating a prototype that automatically saves Gemini chat logs, converting them to Markdown for Obsidian integration, and elevating it to a safe, fully automated system. In this final installment, we will cover the "countermeasures for downtime due to screen specification changes," an unavoidable issue when operating tools that handle web data, and the core of the "self-reliance" humans should possess in the AI era. 1. The Web Data Extraction Compromise: "You Can't Extract What Isn't on the Screen" During development, there was a time when I thought, "I also want to record the exact date and time (timestamp) when the chat was sent." However, no matter how much I analyzed Gemini's screen structure, the exact timestamp of each utterance did not exist in the HTML. The fundamental rule of web data extraction is: "You cannot extract data that does not exist on the browser screen." As long as you are extracting data from the screen (DOM) rather than via an API, forcing the extraction of something that isn't there will require complex guesswork processes and will instead become a cause of trouble. Understanding this "technical limit," gracefully giving up on what cannot be done, and judging to maintain simplicity is also an important element of tool building. 2. Specification Changes Are Not Defects, But "Fate" As long as you deal with tools that extract data from other people's websites, the time will inevitably come when the tool suddenly stops working one day due to design changes or updates on Google's side. "It was working fine until yesterday, but suddenly it stopped saving." This is not a defect in the tool, but an unavoidable "fate" as long as you depend on someone else's platform. The important thing is not t

This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is Part 3 of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. Last time, I talked about creating a system to automatically output Markdown (.md) files to Google Drive simultaneously with appending to a spreadsheet. With list management in a spreadsheet and a comfortable viewing environment in Obsidian established, it was getting very close to completion as a tool. However, as I continued to use it practically, new challenges emerged on the operational front. This time, I will share the risks I faced while transitioning from a "manual button" to "full automation," and the process of evolving into safe code. 1. I Want to Eliminate the "Hassle of Pressing a Button" During the prototype stage, the system was designed so that logs were saved by pressing a button placed on the screen. However, as long as a human operates it manually, there are inevitably limitations. If you are concentrating on the conversation, you might forget to press the save button and close the screen. If the conversation gets long, you might miss past utterances that are no longer displayed on the screen. "If I have the screen open and am conversing, I want it to automatically save in the background without bothering human hands." Thinking this, I asked the AI to write the code for full automation. 2. The Code the AI Produced: "Patrolling the Screen Every 1.5 Seconds" When I consulted the AI, it immediately presented code for full automation. The mechanism was, "Start a timer every 1.5 seconds, check the entire screen in the background, and send any new utterances." When I actually tried it, the logs accumulated automatically as soon as I conversed without pressing the button, and at first glance, it looked like exceptionally well-done full automation. However, I felt something was slightly off regarding this "monitoring on a 1.5-second cycle." 3. The B

What I wanted I'm building a system where all my daily records live in Notion, so I can point an AI at it and get feedback. Goals, tasks, daily logs, finances — those are all manual entry, and that's fine. But one day it hit me that weight would be nice to sync automatically. The requirements were simple: Every morning, my weight and body fat percentage get appended to a Notion database as one row No manual typing That's it. My scale is a Withings Body Smart. The design I picked first This one: Scale → vendor app → Apple Health → iOS Shortcut → Notion API I chose Apple Health as the hub for these reasons: It doesn't depend on the scale model. As long as the data lands in Health, the same implementation works for any vendor. No server required. A time-based Shortcuts automation handles it end to end — no always-on machine, no cron. Free. No extra subscription. Extensible later. Anything that's already in Health — steps, sleep, heart rate — could be added the same way (if I ever wanted to). Generic, zero cost, extensible. The design looked sound to me. Implementation Here's what the Shortcut looks like: 1. Find Health Samples [Weight] latest, limit 1 2. Get Details of Health Sample [Value] → variable Kg 3. Get Details of Health Sample [Start Date] → variable SampleDate 4. Format Date yyyy-MM-dd → variable Ymd 5. If Ymd == today 6. Text ← build the JSON 7. Get Contents of URL ← POST to the Notion API Step 5 matters. Without it, on a day you don't step on the scale, yesterday's weight gets appended under today's date . Here's the JSON built in step 6: { "parent" : { "database_id" : "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" }, "properties" : { "Date" : { "title" : [ { "text" : { "content" : "@@YMD@@" } } ] }, "Measured" : { "date" : { "start" : "@@YMD@@" } }, "Weight kg" : { "number" : @@KG@@ }, "Body fat %" : { "number" : @@FAT@@ } } } (My real database uses Japanese property names. What matters is that they match your database exactly.) I write this as a plain string in a

When I joined Entire, I noticed my boss spending a chunk of time every week writing detailed release notes, called Dispatches at Entire. It looked like a painful process. Each Dispatch had to cover changes across several repositories, explain why those changes mattered, credit external contributors, and carefully avoid leaking anything that was not public yet. I offered to take it over. I had solved a similar problem before, so I figured it would be an easy win. I built something similar and simpler at Block While I was at Block, I built a release notes generator for goose . It ran in GitHub Actions after a release workflow completed, checked out the new tag, compared it against the previous one, and handed goose a recipe to inspect the commit diff. Goose organized those commits into features, bug fixes, improvements, and documentation. Each entry got a short description and a PR link. The workflow then updated the GitHub release and posted the announcement to Discord, opening a thread if the notes exceeded the message limit. It was clean and effective, but it solved a very clean problem: one repository, one new release tag, public commit history, and concise output. So when I looked at Entire’s Dispatches, I assumed I could reuse the same playbook. Gather changes, run goose, post the draft. That assumption did not survive contact with reality. But a Dispatch turned out to be more complex A Dispatch spans multiple projects: the Entire CLI, entire.io, EntireDB, external agent integrations, and open source libraries like go-git, go-nuts, git-sync, and ForgeMark. Every project also ships on a different cadence. Some push to main and deploy continuously. Others bundle work into scheduled releases. The CLI maintains separate stable and nightly channels, which means a feature can be available to testers without being part of the latest stable tag. Then there are feature flags. Finding changes was not the hard part because GitHub APIs handle that easily. The hard part was

security #api #domain #subdomaintakeover #defcon #whois #rapidapi #threatintel DEF CON 32 made one thing clear: open-source security chips and hardware keys are having a moment. But while badges get the spotlight, most real-world attacks still start with something far less glamorous — a forgotten DNS record, a dangling CNAME, or a missing DMARC policy. Subdomain takeover remains one of the most reliable paths from "benign misconfiguration" to "account compromise." If your organization owns dozens or hundreds of domains, manual checks do not scale. This is where an API-first domain intelligence tool becomes essential. In this post, we'll use the Domain WHOIS API to automate: WHOIS/RDAP lookups and domain-age checks DNS record enumeration and SSL certificate inspection Subdomain discovery and takeover-risk scoring Email-security validation (SPF, DMARC, DKIM, DNSSEC, MTA-STS) Historical snapshots via /history Why subdomain takeover still matters A subdomain takeover happens when a DNS record points to a third-party service — GitHub Pages, Heroku, AWS S3, Vercel, etc. — that is no longer registered under your account. An attacker can claim the dangling endpoint and suddenly serve content under your brand's domain. Bug bounty programs consistently rank subdomain takeovers as high-severity findings because they enable phishing, session hijacking, and reputation abuse. The root cause is usually an orphaned CNAME that nobody is monitoring. The fix is continuous monitoring. Instead of running dig , whois , and openssl by hand, we can consolidate everything into a single API call. What the Domain WHOIS API returns The API combines several data sources into one response: Capability Use case WHOIS via RDAP Ownership, registrar, creation/expiration dates DNS records A, AAAA, CNAME, MX, NS, TXT records SSL certificate Issuer, expiry, SANs, validity Subdomain discovery Asset inventory and shadow-IT detection Takeover risk Dangling CNAME/A-record scoring Email security SPF, DMARC,

Disclosure: BrowserAct sponsored this piece. The BrowserAct links below are affiliate-tracked — I get...

Every idea gets run through a one-sentence test before it is allowed to count as a real idea at all Most ideas die for one of three specific reasons, not vague lack of enthusiasm An idea only earns a build slot once it has survived contact with a real, repeated problem A maybe-later list holds the rest on purpose, and I check it far less often than people assume The One-Sentence Test I Run Before Anything Becomes an Idea I get more ideas than I could ever build. That is not a boast, it is a liability if I do not manage it, because every one of those ideas feels exciting for about twenty minutes, and excitement is a terrible filter for what is actually worth my evenings. So before an idea is allowed to sit on any kind of list, it has to pass one test: can I describe the smallest useful version of it in a single sentence, with no "and" in the middle. That sounds small, but it kills more ideas than any other step in the process. "A tool that tracks my Claude usage and also shows analytics and also has a community feature" does not pass. "A tool that warns me before I hit my usage limit" passes. The first sentence is a pitch for a platform. The second sentence is a pitch for a Tuesday evening. I want the second kind, because the second kind is the one I actually finish. I did not always work this way. Early on, an idea earned space on my list the moment it sounded interesting, and my list grew into a graveyard of half-described plans that all needed a paragraph to explain. A paragraph is a warning sign now, not a feature. If I need more than one sentence to say what the smallest version does, the idea has not actually taken shape yet, it has just acquired enthusiasm, and those are different things. The test also forces honesty about scope early, before I have sunk any real time into something. An idea that needs "and" is usually two or three ideas wearing a trenchcoat, and pulling them apart at the sentence stage is far cheaper than pulling them apart three weeks into a