Cost-revenue ratio: Cost-revenue ratio (KUR) divides advertising cost by the revenue it generates, the inverse of ROAS. How marketers calculate it, and where the number misleads. https://ppc.land/cost-revenue-ratio/ #Marketing #CostRevenueRatio #ROAS #Advertising #DigitalMarketing
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RE: https://discuss.systems/@ricci/117483932656785886
great comprehensive thread about the infosec disaster this week and reflections thereupon
my only addition would be that the PHP vulnerabilities were apparently overblown by the AI company that publicized them, as a deceptive and very harmful marketing stunt
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https://www.europesays.com/pl/774572/ Ekrany w smartfonach od A do Z. Jak czytać specyfikację, rozumieć najważniejsze parametry i nie dać się marketingowi #AlwaysOnDisplay #amoled #CzęstotliwośćOdświeżania #ekran #hdr #ips #jasność #ltpo #Nauka #NaukaITechnika #NaukaTechnika #oled #PL #Poland #Polish #Polska #Polski #poradnik #ppi #PWM #rozdzielczość #Science #ScienceAndTechnology #ScienceTechnology #smartfon #Technika #Technology #Wyświetlacze
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Développez votre visibilité et notoriété sur internet, avec une publicité et/ou un article sponsorisé sur nos blogues exclusifs, sur l'habitat et l'immobilier, et le développement soutenable.
Plus de visibilité sur Internet, c'est plus de clients et donc plus de ventes !
Informations : https://bio.site/lameridionale
#Marketing #Publicité #publicitaire #mercatique #communication #SEO #GEO #SEA #Google #pub #France #Vaucluse #Gard #SearchEngine #Qwant -
Développez votre visibilité et notoriété sur internet, avec une publicité et/ou un article sponsorisé sur nos blogues exclusifs, sur l'habitat et l'immobilier, et le développement soutenable.
Plus de visibilité sur Internet, c'est plus de clients et donc plus de ventes !
Informations : https://bio.site/lameridionale
#Marketing #Publicité #publicitaire #mercatique #communication #SEO #GEO #SEA #Google #pub #France #Vaucluse #Gard #SearchEngine #Qwant -
When evaluating generative AI tools for writing emails, buyers routinely fixate on monthly subscription fees while ignoring the operational friction of migration. A low entry barrier—such as Rytr's $9-per-month tier or Writesonic's $12.67-per-month plan—masks the true cost of adoption: proprietary campaign builders, embedded workflows, and rigid export limits that make leaving a platform a costly endeavor. If your team pivots strategies or outgrows a tool within twenty-four months, the expense of untangling your email assets from a deeply integrated ecosystem often eclipses any initial savings.
ToolStarting priceKey limitBest forRytr$9/mo100k characters/moValue for moneyWritesonic$12.67/mo200K words (GPT-3.5) or 33K words (GPT-4.0)Writing cold emailsMailerLite$12/moFree for Power and Comfort plan usersSimple email marketing tools with AIHubSpot$20/moFull CRM platform integrationFull CRM platform with AI writingActiveCampaign$79/moAutomation and CRM integrationAutomation, CRM, and AI togetherThe Hidden Exit Trap Behind Low Monthly AI Pricing
The most deceptive aspect of AI email writers is how quickly simple text generation evolves into complex operational dependency. Standalone copy generators allow you to draft subject lines and body copy easily, but marketing platforms embed AI directly into your campaign infrastructure. For instance, MailerLite integrates its subject line generator, email writer, Smart sending, and landing page designer right into the campaign builder and drag-and-drop editor. While this streamlines day-to-day work, it creates a structural lock-in: your email templates, automation sequences, and AI-optimized content live entirely within their proprietary environment. Abandoning the platform means rebuilding your entire campaign machinery from scratch, turning a simple software cancellation into a major operational migration.Fast Onboarding and Free Tiers in Writesonic and Rytr
Getting started with standalone AI writers involves minimal friction, thanks to robust free tiers and straightforward introductory pricing. Writesonic provides a free version with 10,000 words, and its paid tiers start at $12.67 per month for 1 user and 200K words using GPT-3.5, or 33K words using GPT-4.0. For users seeking higher volume without caps, Writesonic's Unlimited plan starts at $16 a month with no word limit. Similarly, Rytr positions itself as a budget-friendly option starting at just $9 a month for 100k characters, complemented by a free plan and broad support across 30+ languages and 20+ tones. Setting up these tools requires virtually no infrastructure setup; you simply create an account, select your language, choose a tone or use case, and begin generating cold emails, sales copy, or marketing campaigns instantly.Evaluating Data Portability and Integration Limits
When assessing how easily you can abandon a tool, data portability and third-party integrations play a decisive role. Writesonic mitigates exit friction by offering a Zapier integration that connects it with over 5,000 apps, allowing you to route your generated email copy out of its V2 workspace and into external CRMs or email service providers with relative ease. On the other hand, all-in-one marketing suites and CRM platforms present stiffer portability barriers. Platforms like ActiveCampaign, which starts at $79 a month with a 14-day free trial, combine automation, CRM, and AI into a unified environment. While powerful for managing complex customer journeys, exporting deeply integrated behavioral data, automated tags, and AI-assisted workflows requires substantial manual intervention or custom data mapping if you decide to migrate.Billing Commitments and Seat Penalties Across Tiers
Financial lock-in is frequently enforced through subscription tiers, user limits, and billing structures. Standalone AI utilities typically offer monthly flexibility, such as Rytr at $9 per month or Writesonic's Unlimited plan at $16 per month, letting you scale down or cancel without penalty. Conversely, enterprise-adjacent marketing platforms often impose higher recurring commitments. ActiveCampaign's starting price sits at $79 per month, and specialized standalone options like Copy.ai scale up to $49 per month. If a growing team adopts a platform with strict seat minimums or advanced tier requirements—such as MailerLite offering its AI tools free specifically for Power and Comfort plan users—downgrading becomes painful because you risk losing essential campaign features or exceeding user caps mid-cycle.Balancing Two-Year Adoption Costs Against Migration Friction
For a business evaluating software with a two-year horizon, the cheapest tool to adopt is rarely the cheapest to abandon. A low-cost standalone generator like Rytr ($9/mo) or Writesonic ($12.67/mo) incurs virtually zero migration friction because you only store text snippets and copy variations. However, if you rely on a comprehensive marketing ecosystem like HubSpot ($20/mo) or GetResponse ($13.30/mo) for funnel building, CRM tracking, and AI email drafting, the cumulative two-year subscription cost is compounded by the hidden labor of data migration should you switch providers. Choosing a native marketing platform makes sense only if you plan to stay long enough for the workflow efficiency to outweigh the eventual cost of extracting your campaigns.Final Verdict: Choosing Your AI Writer Without Getting Trapped
If you need maximum portability and low financial risk, choose a flexible standalone tool like Writesonic or Rytr, where your data remains purely textual and easily transferable via integrations like Zapier. If your workflow demands an all-in-one marketing ecosystem, MailerLite provides a seamless internal AI email writer for Power and Comfort plan users, making its lock-in manageable if you are committed to its email designer. Avoid locking into expensive, deeply entrenched multi-feature automation platforms like ActiveCampaign unless your organization is fully prepared to absorb the high subscription cost ($79/mo) and the heavy migration friction required to ever leave them.#ai #writing #saas #tools #software #coding #development #engineering #inclusive #community
The True Cost of AI Email Writers: Why Exit Terms Matter More Than Sticker Price -
Planas will decide in November on the olive oil regulation.
The Minister of Agriculture, Fisheries and Food, Luis Planas, stated this Monday that “in the first days, the first week, of November” a decision will be made regarding the activation of the olive oil marketing regulation. He also specified that his int…
#dining #cooking #diet #food #MediterraneanOliveOil #OliveOil #Mediterranean #Olive
https://www.diningandcooking.com/2872187/planas-will-decide-in-november-on-the-olive-oil-regulation/ -
at work we're busy to sort out some regulation, how to comply, what to upgrade and such, ...
for over a year now
many people from several departments are working hard on this topic, many hours, ...
too long for management, which has stated it have to tell engineering what is their duty ...
so the man in charge, decided it is time to join forces, he generated a 21 min podcast by means of #salami (a.k.a. marketing stunt AI) and boldly claimed "I know now what the regulation is about and to do." -
RE: https://infosec.exchange/@raptor/117501541941100197
So, it does sound like PHP's runtime contains some bad structural decisions that are going to be hard to fix. That said... I'm having a hard time convincing myself that last week's fire drill wasn't AI marketing nonsense. Is the vuln actually exploitable in practice, or is this just more noise? Here's a great way to prove it.
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I just received a pitch email that DIDN’T start with a “you write about X, and I think you'll like X” AI-generated line. It was just a generic press release. And it was such a relief. Lesson learned for my own marketing, that AI generated shit does not make you stand out, it makes you… smarmy.
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n8n: The 208k-Star Fair-Code Workflow Engine That Replaces Zapier — And What Actually Happens When Your Workflow Runs
You've probably heard n8n called "open-source Zapier." That's true enough to be useful and wrong enough to be misleading. Zapier is a hosted product you rent. n8n is a Node.js application you run, point at your own database, and own end to end. No per-task metering. No vendor lock-in. A $5–10 VPS handles a surprising amount.But the marketing pitch isn't why engineers reach for it. The escape hatch is: the moment the visual builder runs out of road, you drop into a Code node and write real JavaScript (or Python). You never hit a wall you can't code your way through.
What most developers never learn is what actually happens between clicking "Execute Workflow" and seeing those green checkmarks light up on the canvas. The orchestrator that schedules nodes, synchronizes multi-input joins, keeps runs cancelable, and emits lifecycle hooks lives in roughly 1,250 lines of TypeScript. Let's open it up.
The Mental Model: A Stack, A Waiting Map, and One Loop
When you hit execute, n8n doesn't spin up a thread per node. It builds a single serializable run object —IRunExecutionData— that carries everything: the in-flight stack of nodes waiting to run, a waiting map for partially satisfied inputs, metadata, and final results. This same object travels from the editor to the server, into the engine, optionally across a Redis queue to a worker process, and back.// Simplified shape of the live execution object
interface IRunExecutionData {
startData: { destinationNode?, runNodeFilter? };
resultData: {
runData: Record<string, ITaskData[]>; // per-node run history
pinData: Record<string, INodeExecutionData[]>;
lastNodeExecuted: string;
error: ITaskData | null;
};
executionData: {
nodeExecutionStack: IExecuteData[]; // the work list
waitingExecution: Record<string, Record<number, INodeExecutionData[][]>>;
waitingExecutionSource: Record<string, Record<number, ISourceData[][]>>;
contextData: Record<string, any>;
runtimeData: Record<string, any>;
};
}The engine (
WorkflowExecuteinpackages/core/src/execution-engine/workflow-execute.ts) seedsnodeExecutionStackwith the start node, then enters a loop that shifts the front entry, runs the node, records the result, and enqueues downstream nodes. That's it. No hidden thread pool. No magic graph walker. Just a stack and a waiting map.Execution Order: v0 (Breadth-First) vs v1 (Depth-First)
The single most important setting in a workflow isexecutionOrder. It changes one line in the scheduler:const enqueueFn = workflow.settings.executionOrder === 'v1' ? 'unshift' : 'push';
- v0 (
push): New work goes to the tail. Older work runs first. The walk feels breadth-first — all siblings at depth 1 finish before depth 2 starts. - v1 (
unshift): New work goes to the front. The current branch keeps running. The walk feels depth-first — you chase one path to the bottom before backtracking.
This doesn't change the graph. It only changes scheduling. Sibling order in v1 still follows canvas position (top-left first), but only among ready siblings. The canvas is not the execution graph — it only shapes ordering where the scheduler has freedom.
Multi-Input Joins: The Waiting Map
A node with two inputs doesn't run when the first branch arrives. The engine parks partial data inwaitingExecution[nodeName][runIndex]with per-input placeholders. As each upstream finishes, the engine fills its slot. Only when all required inputs are non-null does it enqueue the node.// Inside addNodeToBeExecuted (simplified)
if (requiredInputs.every(input => waitingExecution[nodeName][runIndex][input] != null)) {
// All inputs ready — enqueue the node
enqueueFn(executeData);
} else {
// Park partial data, wait for the rest
waitingExecution[nodeName][runIndex][inputIndex] = inputData;
waitingExecutionSource[nodeName][runIndex][inputIndex] = sourceData;
}After the main stack empties, a sweep pulls forward any waiting node that now has enough data — even if some optional inputs stayed empty. This means a join can complete after one branch dies early, while a node still missing required inputs stays parked.
Empty Output Kills the Branch (Unless You Say Otherwise)
If a node returns no data and doesn't enter waiting state, the branch ends. The scheduler adds no downstream work. SetalwaysOutputData: trueon the node and the engine fabricates a single empty item to keep the branch alive — useful when downstream nodes must run regardless.Legacy v0 keeps one compatibility path: it can still wake a downstream node connected via a second or later main input even when the upstream output is empty.
Cycles: Node State, Not Special Graph Logic
The scheduler doesn't treat cycles specially. It keeps pulling from the front of the same list. A node loops only if it schedules more work for itself.SplitInBatchesV3demonstrates the pattern:// Stores loop state in node context
const loopState = this.getContext('node') || { index: 0, items: inputData };
const batch = loopState.items.slice(loopState.index, loopState.index + batchSize);
loopState.index += batchSize;
this.setContext('node', loopState);if (loopState.index < loopState.items.length) {
return { loop: batch }; // schedules another visit to this node
}
return { done: loopState.items }; // exits the cycleThe loop guard in
processRunExecutionDatastops accidental repeats when the same node and run index return without progress.Queue Mode: Same Engine, Different Process
In standalone mode, the main process executes workflows directly. In queue mode (EXECUTIONS_MODE=queue), the main process enqueues a job into Redis (BullMQ) and returns immediately. A worker process dequeues, reconstructs the sameIRunExecutionDataobject, runs the exact same engine loop, and writes results back to PostgreSQL.┌─────────────┐ Redis Queue ┌─────────────┐
│ Main │ ──────────────────▶ │ Worker │
│ (UI/API) │ enqueue job │ (executes) │
└─────────────┘ └─────────────┘
│ │
│ PostgreSQL │
└──────────────────▶◀────────────────┘
shared stateThe main process serves the UI, REST API, and webhooks. Workers are stateless — scale them horizontally via HPA/KEDA based on queue depth. Dedicated
webhook-processorpods handle high-throughput webhooks without blocking the main UI. Python/JavaScript code nodes run in isolatedtaskRunnersidecars (V8 isolates viaisolated-vmfor JS, separate processes for Python) with RPC over WebSocket.The Data Model: Items, Runs, and
pairedItem
n8n's data model has three layers:Level 1 — Item:
INodeExecutionData—{ json, binary?, pairedItem?, metadata?, evaluationData? }. Nodes receive and return arrays of items on each input/output. This is the unit of data flowing through the graph.Level 2 — Run: Each node execution appends one
ITaskDatatoresultData.runData[nodeName]. The array index =runIndex.ITaskDatarecords input source, output data, timing, status, metadata, error state.sourcepoints back to the previous node, output, and run that supplied the input.Level 3 — Execution Object: The
IRunExecutionDatabundlesstartData,resultData, andexecutionData(the live scheduling state). The same object carries both result and live machine state — that's how n8n hands an execution to a queue worker, pauses it at aWaitnode, and resumes it later with history intact.pairedItemrecords lineage, not magic. An output item points back to one input item, or one input plus array position, or a list of links. The engine normalizes these as items move through the workflow. WhenWorkflowDataProxyresolves$node("HTTP Request").jsonin a downstream node, it walks this ancestry chain. Broken lookups surface as specific errors: missing upstream lineage, no route back to referenced node, multiple matches, pinned data short-circuiting the chain, invalid index, or intermediate node not yet run.AI Agents: Native, Not Bolted On
Since the 2.0 line, n8n ships a LangChain-based agent runtime. TheAgentnode maintains conversation history inChatMemory, calls language models for planning, executes tools based on model decisions, and accumulates results until goal completion. Tool calling uses the same execution engine — tools are just nodes withexecutemethods. Vector stores, MCP connectivity, and guardrails plug into the same architecture.Fair-Code, Not Open Source
n8n uses the Sustainable Use License. You can self-host and modify freely for internal business use, including paid consulting building workflows for clients. What you can't do is rebrand and resell it as your own hosted SaaS. Files with.ee.in the path are Enterprise-licensed. For an indie dev or team running their own automations, you're completely fine — but check the current license page rather than trusting any blog post on the fine print.What This Means for You
- No per-execution pricing: Run 10 or 10 million workflows on your $5 VPS.
- Full observability: Every execution persists to your database. Open any run, see exactly what each node received and emitted, replay it.
- Escape hatch: Code nodes run real JavaScript/Python with npm/pip access. HTTP Request node talks to anything with an endpoint.
- AI-native: Build agents that call tools, use memory, query vector stores — all self-hosted.
- Production architecture: Queue mode + PostgreSQL + Redis + S3 scales horizontally. The same engine runs in-process or distributed.
The Takeaway
n8n isn't "Zapier you host." It's a workflow engine with a visual authoring layer, a serializable execution model that survives process boundaries, a scheduler you can reason about, and an escape hatch to real code. The 208k stars aren't for the UI — they're for the fact that when the visual builder hits a limit, you don't file a feature request. You write the code.#opensource #automation #workflow #selfhosted #software #coding #development #engineering #inclusive #community
n8n: The 208k-Star Fair-Code Workflow Engine That Replaces Zapier — And What Actually Happens When Your Workflow Runs - v0 (
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Estávamos no carro, eu e meu companheiro que é psicólogo falando sobre a importância pra soberania, de termos essa rede nossa, sem interferência estrangeira. Eu já tinha percebido, pois eu trabalho com marketing digital há 14 anos. Não há mídia mais poderosa hoje do que o algoritmo do meta. Temos que virar o meta no segundo turno da internet. #vira #apresentacao
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What To Watch On YouTube Right Now – Part 160
Welcome back my readers, YouTube viewers and all others who followed this series of articles focused on YouTube videos worth watching.
Have you been searching for something fun or interesting to watch on YouTube? Do you feel bored right now and you crave for something to see on the world’s most popular online video destination?
I recommend you check out the following videos I found.
#1 Gone Girl reaction videos – When Gone Girl was released in local cinemas back in 2014, I never bothered to see it. Along the way, buzz about the movie spread like wildfire through North American entertainment websites and social media with assorted topics like toxic marriage, manipulation, dishonesty, defamation and betrayal discussed. I eventually saw Gone Girl via streaming just a few years ago and I can say it was a very intriguing movie to watch. That said, it is not surprising that a lot of movie reaction videos featuring it were posted on YouTube. Posted below are the more engaging reaction videos selected for you all.
https://youtu.be/_bwFe-Ixzfc?si=z_ZC43J9Lci7MAoW
https://youtu.be/bDOif5oGRdA?si=wRYdHMnIBKshNK-k
https://youtu.be/vFizXcVvGlo?si=-23mhwZvifEKHyRn
https://youtu.be/YP_Zsowf89g?si=F4WxSjtbjihi__2Y
https://youtu.be/kE9UkVG_VjM?si=jXPIR41Q_GJPG4p4
#2 Why Manga Keeps on Triggering the Internet – It is a very sad fact that there are a lot of fake fans and phony intellectuals out there who seriously believe they are experts on entertainment. This explains why there are so many video essays or so-called analysis focused on established entertainment franchises and entertainment media. That said, there are a lot of fake fans, pretenders, larpers and online tourists who posted their distorted views about manga (Japanese comics) and misinterpret what the original creators did. To learn exactly why, go watch Ranting for Vengeance’s video below. Pay attention to the details.
https://youtu.be/k0HEvIFsE10?si=Rq040XWs8N7cqhb8
#3 Maximum Carnage video game remembered – Back in the 1990s, I read the comic books of the Maximum Carnage crossover storyline but I never played the video game adaptation. Spider-Man and Venom: Maximum Carnage had a very ambitious marketing campaign that captivated many gamers, Spider-Man fans and comic book readers. There was this movie-like build-up that easily kept people thinking about the video game. When it was officially released, there were gamers who expressed disappointment after playing it. To examine what the video game’s quality and impact, go watch the video below.
https://youtu.be/yaa_CvZSkVY?si=aGO6s4IORB51HRKW
#4 The Pizzerias in the Mountains of Japan – There is absolutely nothing like exploring the countryside and other nature spots of Japan. Deep in the mountains of Tokyo and Saitama are pizzerias that offer visitors food with unique flavors, homemade ingredients and the immersive closeness to the natural environment. I personally want to visit these food joints the next time I visit Japan. Watch and learn from the Nippon TV video below.
https://youtu.be/d8hq4W9tgX8?si=sH3t16gWg2z8zsVN
#5 Totally Whatever reacts to Fast Times at Ridgemont High – It has been over forty years since Fast Times at Ridgemont High was first released in cinemas. To this day, a lot of film buffs considered it as one of the most defining movies Hollywood released in the 1980s, and also one of the most memorable American teen movies ever. Not only that, Fast Times at Ridgemont High was selected for preservation in the United States National Film Registry for being “culturally, historically, or aesthetically significant.” The old movie continues to impress first-time viewers and you should see how it impacts Totally Whatever in their reaction video below.
https://www.youtube.com/watch?v=Z5A2s5AhQHY
#6 Castlevania games revisited – This year marks the 40th anniversary of the original Castlevania video game released on the Famicom Disk System in Japan. The classic horror-themed, 2D side-scrolling adventure game produced by Konami made a huge impact on gamers who enjoyed 2D adventure games and the success it achieved led to the release of many sequels and spin-offs across different platforms. There is a lot of creativity and inspiration behind the production of the original game. To learn more about the original Castlevania and how it grew into an enduring game franchise, watch the video below.
https://youtu.be/XkaTiKc_edE?si=GSNFUN0kJHkD5y4C
#7 Castlevania games revisited – When it comes to the 1980s, Michael J. Fox is often remembered for playing the teenager Marty McFly in Back to the Future and Back to the Future Part II. Speaking of teenagers, Fox was the star in the 1985 coming-of-age fantasy comedy movie Teen Wolf where he played a high school student who could turn into a werewolf. As it was released after Back to the Future, Teen Wolf emerged as a surprise commercial success grossing more than $80 million worldwide. This old movie was featured in several reaction videos on YouTube and Ashleigh Burton’s reaction is fun to watch. Watch the video below.
https://youtu.be/WWowvVSFlQw?si=52sj-2lvFzFu8sBk
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Thank you for reading. If you find this article engaging, please click the like button below, share this article to others and also please consider making a donation to support my publishing. If you are looking for a copywriter to create content for your special project or business, check out my services and my portfolio. Feel free to contact me with a private message. Also please feel free to visit my Facebook page Author Carlo Carrasco and follow me on Twitter at @CarloCarrascoPH as well as on Tumblr at https://carlocarrasco.tumblr.com/ and on Instagram at https://www.instagram.com/authorcarlocarrasco
#1980s #1990s #2D #2DSprites #America #Asia #BackToTheFuture #BackToTheFuturePartII #CarloCarrasco #Carnage #Castlevania #CineBinge #comicBooks #comics #comicsBlog #EOMReacts #fakeFans #FastTimesAtRidgemontHigh #Fediverse #film #food #foodBlog #gay #geek #GoneGirl #Japan #kabataan #Konami #LGBTQ #LGBTQ #LGBTQIA #Manga #Mastodon #MaximumCarnage #MichaelJFox #movies #NationalFilmRegistry #Nippon #NipponTV #nostalgia #onlineVideos #pizza #RantingForVengeance #RetroGaming #Retrospective #sideScrolling #SpiderMan #SpiderManAndVenomMaximumCarnage #SpiderManMaximumCarnage #superhero #TBRSchmitt #TeenWolf #teenagers #The1980s #The1990s #TotallyWhatever #transgender #UnitedStates #UnitedStatesOfAmerica #UnitedStatesOfAmericaUSA #USA #Venom #video #videoBlog #videoGames #videos #VKunia #WhatToWatchOnYouTube #woke #WordPress #WordPressCom #YouMeAndTheMovies #youth #YouTube #YouTuber #YouTubers -
CapCut lanza hackathon gratuito de inteligencia artificial
CapCut realizará un hackathon gratuito el 28 de octubre en México Tech Week 2026. Habrá cuatro horas para crear videos, campañas visuales y propuestas de marca con herramientas de inteligencia artificial. El cupo será de 30 participantes.
Por Gabriela Díaz | Reportera
Creadores de contenido, diseñadores y emprendedores podrán desarrollar proyectos con inteligencia artificial durante el CapCut Design Studio: Speed-to-Scale Hackathon, que se realizará el 28 de octubre como parte de México Tech Week 2026, en la ciudad de México. La participación será gratuita, con registro previo y un cupo limitado a 30 personas.
La actividad propone convertir ideas en videos, campañas visuales, contenidos para redes sociales, propuestas de marca o plantillas mediante CapCut PC y CapCut Design Studio. Los participantes dispondrán de cuatro horas efectivas de trabajo y deberán integrar al menos dos herramientas de inteligencia artificial de CapCut en sus proyectos.
La convocatoria está dirigida tanto a personas con experiencia en producción audiovisual y diseño como a quienes buscan experimentar con nuevas tecnologías creativas. No será necesario llegar con un proyecto terminado ni con un equipo previamente formado, pues los grupos y las ideas se organizarán durante el encuentro.
¿Cómo funcionará el hackathon de CapCut?
La jornada comenzará con una demostración de producto para presentar las funciones de las herramientas de CapCut. Después, los asistentes formarán equipos y trabajarán en sus propuestas durante un sprint creativo de cuatro horas.
Los proyectos podrán enfocarse en distintas áreas, entre ellas:
- Producción audiovisual: videos y piezas de contenido digital.
- Marketing y redes sociales: campañas visuales y materiales para plataformas digitales.
- Diseño y branding: propuestas de identidad de marca, recursos gráficos y plantillas.
- Comunicación de proyectos: presentaciones audiovisuales para emprendimientos e ideas de negocio.
Al finalizar, un jurado evaluará las propuestas que participen en una presentación en vivo o showcase. La dinámica contempla retroalimentación, premios y oportunidades de establecer contactos profesionales con integrantes de la comunidad tecnológica y creativa.
CapCut Design Studio llegará a México Tech Week 2026
El hackathon formará parte de la participación de CapCut en México Tech Week 2026, encuentro que reunirá actividades relacionadas con tecnología, emprendimiento e innovación del 26 de octubre al 1 de noviembre.
La empresa también presentará CapCut Design Studio, una herramienta impulsada por inteligencia artificial que busca facilitar los procesos de creación de contenido visual. Además, contempla un panel sobre tendencias de contenido y estrategias para desarrollar una idea desde su concepto hasta una pieza audiovisual terminada.
Los horarios del panel y los detalles de las demás actividades deberán consultarse en la programación oficial del encuentro.
Requisitos, sede y registro
El hackathon está dirigido a creadores, diseñadores, emprendedores y personas interesadas en aprender a utilizar herramientas de inteligencia artificial para desarrollar contenidos.
La participación será gratuita, pero requerirá registro previo y aprobación de la solicitud. El cupo anunciado es de 30 lugares, por lo que la organización seleccionará a los asistentes para integrar un grupo con intereses y habilidades complementarios.
La actividad está programada para el miércoles 28 de octubre, de 13:00 a 19:00 horas, en C. Lucerna 34, en la ciudad de México. Es indispensable llevar una computadora portátil propia para trabajar durante el encuentro.
4. MENSAJE PARA X
5. MENSAJE PARA FACEBOOK / INSTAGRAM / WHATSAPP / THREADS
6. COPETE SEO (40 PALABRAS)
7. ETIQUETAS SEO
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I used to treat ChatGPT like a vending machine. Need a React hook? Drop a coin. Need a PR summary? Drop another. Every ask started from zero — stack, conventions, what “done” meant — and every reply drifted a little from how we actually ship.
The waste wasn’t the tokens. It was re-explaining our frontend rules every single time, then cleaning up the same mistakes.
So I stopped pasting one-offs and started treating prompts like code: named, versioned, and checked into the repo.
One-off asks don’t compound
A throwaway chat is fine for a throwaway question. Frontend work rarely is throwaway:- “Write a Modal” without our focus-trap / Escape / return-focus rules
- “Add loading states” without our skeleton vs spinner convention
- “Review this PR” without what AI should skip vs what a human owns
- “Refactor this form” without our form library and error-message pattern
You get something that compiles. You don’t get something that matches the last three PRs. The next person (or the next you) pays the tax again.
What belongs in a prompt library
I keep a small folder —prompts/frontend/— not a novel. Each file is one job, one outcome shape.Mine look like this:
prompts/frontend/
pr-review-ai.md # what the model checks vs leaves for humans
new-component.md # scaffold + a11y buckets + token rules
design-token-change.md # changelog / RFC checklist before merge
test-plan.md # real coverage vs fake green
agent-ui-diff.md # component boundaries, no random token edits
accessible-react.md # roles / keyboard / contrast report requiredIf a prompt isn’t reused at least twice a month, it doesn’t earn a file. Sticky notes in chat stay sticky notes.
The shape every prompt gets
Same skeleton so humans and agents know where to look:# <job name>
## When to use
One sentence. If it doesn’t match, don’t force it.## Context to paste
- Framework / router / form lib (or “read from repo”)
- Design tokens source of truth
- Related golden files (paths)## Hard rules
Bullet list the model must not violate.
Prefer native HTML, keep tokens, no drive-by refactors, etc.## Output format
Numbered sections the reply must include.
Missing a section = reject the turn.## Done looks like
Concrete acceptance — not “looks good.”The “output format” line is the whole game. Models fill schemas. They invent vibes when you don’t give one.
From ChatGPT paste to agent / editor default
Once the files exist, wire them where work actually happens:- Cursor / Copilot / Claude project instructions — point at
prompts/frontend/or inline the two you use daily. - PR bot / agent — load
pr-review-ai.mdas the system brief; humans still own product judgment. - Slash or snippet —
@new-componentexpands the scaffold prompt so nobody retypes it. - CI-ish habit — if the agent reply skips a required section, treat it like a failed check and re-run with the same file.
You don’t need a fancy prompt CMS. Markdown in git is enough. Review prompt changes in PRs the same way you’d review eslint config.
A tiny migration story
Week 1 I had twelve ChatGPT threads titled “modal again.” Week 3 I had six files and deleted half the threads.What changed in practice:
- New Button / Modal / Combobox asks started from the same a11y report shape
- Token edits stopped sneaking hex literals into components
- AI PR comments stopped arguing about naming when the prompt said “leave naming to humans”
Same models. Less re-prompting. The library held the team memory the chat UI kept forgetting.
What not to put in the library
- Product pitches and marketing copy (keep prompts about shipping UI)
- One-shot debug sessions that will never recur
- Entire style guides pasted twice — link the real doc instead
- Secrets, private URLs, or customer data as “examples”
A prompt library is DX infrastructure. If it reads like a blog draft, move it to the blog.
Start with three, not thirty
Tonight, pick the three asks you keep repeating. Write each as one markdown file with When / Rules / Output / Done. Drop them in the repo. Point your editor agent at that folder.Next time you open a blank ChatGPT box for a frontend task, open the matching file instead. Paste once. Reuse forever. That’s the whole upgrade — from one-off asks to a library your future self (and your agents) can actually find.#frontend #ai #dx #productivity #software #coding #development #engineering #inclusive #community
From One-Off ChatGPT Asks to a Reusable Frontend Prompt Library -
You open the repo on your first morning, the README says "run the app, ask it something," and forty seconds later an answer appears on the phone. Nobody on the team can tell you whether that text came from the bundled on-device model, from a remote endpoint someone configured last quarter, or from a fixture left in a debug build. That uncertainty is not a small onboarding annoyance, because every later measurement you record inherits it.
This post is a first-hour exercise for a junior engineer joining a mobile AI repo. The goal is not to ship a feature, and it is not to improve latency or accuracy; the goal is to make provenance visible, so that when you do open your first PR you already know which execution path you exercised. Everything below is a proposed harness that you run on your own device, not a report of numbers I measured on yours.
Why provenance belongs in hour one
In a normal backend repo you can read the config and know which database answered. In a mobile AI repo the same request can be served by three different things depending on build type, network state, and whether an asset survived the packaging step. If you skip this check, you will eventually write a careful benchmark, show it to your reviewer, and discover the phone was calling a cached response the whole time.The exercise below gives you three things you can rely on for the rest of onboarding: a build that fails loudly when the model asset is missing, a debug banner that names the source of the last response, and a repeatable way to exercise the remote path on purpose.
The three sources to distinguish
- Bundled asset. A model file inside the app package, loaded from local storage with no network involved.
- Remote endpoint. An HTTP call to a server the app was pointed at, which may be a shared development host or something you started yourself.
- Fixture or cache. A stubbed response in debug builds, or a stored answer replayed from a previous run.
Your banner only needs to name which of these produced the last result. It does not need to be pretty, and it should not ship in release builds.
Step 1: make a missing model asset a build failure
Before you touch any inference code, prove that the file you think is packaged actually is. Add a verification task to the app module and wire it into the check lifecycle. Substitute your own asset name and the checksum your team stores alongside the artifact.// app/build.gradle.kts (proposed harness)
val modelDir = layout.projectDirectory.dir("src/main/assets/models")// Placeholder on purpose: copy the real digest from your artifact record.
val expectedDigests = mapOf(
"classifier-small.tflite" to "<sha256-from-your-artifact-record>"
)tasks.register("verifyModelAssets") {
val dir = modelDir
inputs.dir(dir)
doLast {
expectedDigests.forEach { (name, want) ->
val file = dir.file(name).asFile
require(file.exists()) { "Missing model asset: $name" }
val got = java.security.MessageDigest.getInstance("SHA-256")
.digest(file.readBytes())
.joinToString("") { "%02x".format(it) }
require(got == want) { "Checksum mismatch for $name\nexpected=$want\n got=$got" }
}
println("verifyModelAssets: OK")
}
}tasks.named("preBuild") { dependsOn("verifyModelAssets") }
Expected observation on a healthy checkout is a single
verifyModelAssets: OKline before compilation. Then deliberately rename the asset once, run./gradlew :app:assembleDebug, and confirm the build stops with the message you wrote. If it compiles anyway, your app has a runtime download path you have not found yet, and that is the most important thing you will learn all day.Step 2: print the source of the last response
A tiny recorder next to your inference entry point costs almost nothing and removes the guessing. Keep it debug-only, and never log prompt text or user content in it.// Proposed harness. Field names follow your app's existing types.
sealed interface ModelSource {
data class Bundled(val asset: String, val digestPrefix: String) : ModelSource
data class Remote(val host: String, val path: String) : ModelSource
data class Fixture(val name: String) : ModelSource
}object Provenance {
private val seen = ArrayDeque<ModelSource>()fun record(source: ModelSource) {
seen.addLast(source)
if (seen.size > 20) seen.removeFirst()
if (BuildConfig.DEBUG) Log.d("Provenance", describe(source))
}fun last(): ModelSource? = seen.lastOrNull()
private fun describe(s: ModelSource): String = when (s) {
is ModelSource.Bundled -> "bundled ${s.asset} sha256:${s.digestPrefix}"
is ModelSource.Remote -> "remote ${s.host}${s.path}"
is ModelSource.Fixture -> "fixture ${s.name}"
}
}Call
Provenance.record(...)exactly where the response is produced, not where the request is built, because a retry can change the answer's origin. If you use Compose, a two-line debug overlay readingProvenance.last()is enough; on iOS the same idea maps to a#if DEBUGview or anos_logline.What the banner buys you
- You can tell a reviewer which path your PR screenshot came from.
- You can catch a silent fallback before it becomes a "works on my machine" thread.
- You can reproduce a bug by forcing the source rather than hoping.
Step 3: exercise the remote path deliberately
Many teams use a hosted endpoint for early integration work, and some projects offer free model access plus a free server option so a new contributor can get a working call without a billing conversation. The open-source project MonkeyCode is one of these, and its operator describes a free model-access tier and a free server option for trying the workflow; the same operator-supplied description puts the free allowance at 10 million tokens. Treat that figure as a starting point, not a contract, and re-read the project's current terms before you plan any budget around it.Disclosure: This article was prepared as part of MonkeyCode's product outreach.
The useful part for onboarding is narrower than the marketing: a shared free endpoint lets you prove that your client's request shape, timeout, and error handling work before you depend on a host only three people can reach. Time the first call rather than trusting it.
# Shape only. Use the base URL and path from the project's own setup docs.
BASE_URL="https://<your-endpoint-from-the-project-docs>"for i in 1 2 3; do
curl -sS -o /dev/null \
-w "attempt=%{http_code} ttfb=%{time_starttransfer}s total=%{time_total}s\n" \
-H "Content-Type: application/json" \
-d '{"prompt":"ping","max_tokens":8}' \
"$BASE_URL/v1/complete"
doneRecord three numbers per attempt and note whether the first one is slower than the rest, which would suggest a cold path rather than a steady one. Do not run a load test against shared free infrastructure; three sequential calls teach you what a first-hour contributor needs, and concurrency experiments belong on a host your team controls.
When the banner disagrees with your mental model
SymptomLikely broken pathFirst checkBuild passes with the asset deletedSilent download or remote fallbackGrep for the model URL in build configBanner saysremotein airplane modeCached response or fixtureClear app storage, rerunBanner saysbundledbut answers varyPrompt template changedDiff template file againstmainFirst remote call takes much longerCold endpoint or cold radioRepeat three times, note radio stateWorks after reinstall onlyAsset copied at first launchInspect first-run copy logic
Each row is a rollback condition, not a curiosity. If your banner reportsremotewhile the device has no network, you have found something worth raising before you write feature code.Evidence to record before your first PR
Fill this in on your own hardware and paste it into the PR description or your onboarding notes. Reviewers trust a filled table far more than a sentence claiming it works.- Device model and OS version, plus whether it is a physical device or a simulator.
- Framework and dependency versions, including the inference runtime and build tool versions.
- Build type used for the observation, and the exact command you ran.
- Network state during each step, including radio type and whether airplane mode was toggled.
- Permission state for any sensor the feature reads, and whether you revoked it mid-run.
- Power conditions, such as plugged in versus battery, and any low-power mode.
- Which provenance value the banner printed for each observation.
Limitations and who should skip this
This harness assumes your app can load a model from local storage. If your product is server-only inference, a bundled-asset check adds noise and you should stop after step 2 and document the endpoint instead. Teams with a managed model registry may already enforce digests in CI, in which case duplicating the check locally is waste. Checksumming a multi-hundred-megabyte asset on every build slows feedback loops noticeably, so scope the task to debug variants or run it on a schedule.Finally, the banner is diagnostic tooling. It must be stripped or gated in release builds, because hostnames and asset digests are information you gain nothing from publishing.
If you run this in your own repo, I would like to see the environment rather than the conclusion: device, OS, framework versions, the transition steps you used, and whether the banner recovered, restarted, or silently disappeared. If you want a place to start the remote path without provisioning a host first, the project's free tier and free server option are the shortest route to a first honest measurement.#ai #mobile #android #testing #software #coding #development #engineering #inclusive #community
Know Which Model Answered: A First-Hour Provenance Banner for Mobile AI Repos -
Raleigh Government Events Guide October 27, 2026 Edition
Raleigh’s government calendar for Tuesday, October 27, 2026, includes meetings focused on Dix Park, planning, housing, the environment, public art and police advisory work. Two City Council committee meetings are listed as cancelled.
Here’s your DoRaleigh guide to the day’s scheduled meetings and what to check before attending or watching.
Morning Meetings: Dix Park and Planning
Dix Park Commission Meeting
Time: 9 a.m.
Category: Boards and CommissionsThe Dix Park Commission is scheduled to meet Tuesday morning. Residents interested in Dix Park can review the meeting agenda for the specific topics scheduled for discussion.
Planning Commission
Time: 9 a.m.
Category: Boards and CommissionsThe Planning Commission also meets at 9 a.m. Residents following development and land-use issues should check the agenda for the cases and items before the commission.
Planning your morning: These meetings start at the same time. Confirm locations and viewing options before deciding which to follow.
Two Raleigh Council Committee Meetings Cancelled
The following meetings are listed as cancelled for October 27:
- Community Safety and Quality of Life Committee Meeting: Originally scheduled for 11:30 a.m.
- Economic Development and Innovation Committee Meeting: Originally scheduled for 1:30 p.m.
Check the City of Raleigh calendar for future meeting dates and any rescheduling announcements.
Afternoon Meeting: Housing and Environment
Housing and Environment Committee Meeting
Time: 4 p.m.
Category: Council and Council Committee MeetingsThe Housing and Environment Committee is scheduled to meet Tuesday afternoon. Residents interested in these policy areas can review the agenda to see which matters are scheduled for consideration.
The supplied calendar does not identify individual agenda items or anticipated actions.
Evening Meetings: Public Art and Police Advisory Work
Public Art and Design Board Meeting
Time: 5 p.m.
Category: Boards and CommissionsThe Public Art and Design Board is scheduled to meet at 5 p.m. Check the meeting agenda for the projects and topics scheduled for review.
The supplied listing includes a “Virtual Meeting Link” label but does not provide a URL. Visit the official event listing to locate the correct access information.
Police Advisory Board Subcommittee Meeting
Time: 5 p.m.
Category: Boards and CommissionsA Police Advisory Board subcommittee meeting is also scheduled for 5 p.m. Review the official event page for the subcommittee’s agenda, location and participation details.
Because both evening meetings begin at the same time, check viewing options and the availability of meeting materials if you want to follow both.
How to Follow Raleigh Government Meetings
Before attending or watching a meeting:
- Confirm its status. Check for cancellations, time changes or other updates.
- Review the agenda. Calendar titles alone do not identify specific proposals, cases or votes.
- Check the location or virtual access details. Meeting addresses and access URLs were not included in the supplied information.
- Confirm public participation rules. A scheduled meeting does not automatically mean public comment will be offered.
Visit the City of Raleigh website and search the meeting title to find its official listing and available materials.
Stay Connected With Raleigh Civic News
Tuesday’s schedule offers opportunities to follow Raleigh’s public spaces, planning, housing, environmental policy, public art and police advisory work. Remember that the Community Safety and Quality of Life Committee and Economic Development and Innovation Committee meetings are cancelled.
Follow DoRaleigh.com for daily updates on government meetings, local festivals, and community happenings — your one-stop guide to everything Raleigh!
Connect With Us: Instagram | Facebook | BSky | Linkedin
Share With Us: Post your community News, Events, on our Submissions Page.
Advertise With Us: Interested in Advertising click here.Published by Bryan Tomlinson
Design, Optimization, and Marketing Services Provided by BTDesigns.pro
This guide is based on the supplied City of Raleigh calendar information. All times are Eastern. Confirm meeting details with the city before making plans.
#CityOfRaleigh #DixParkCommission #DoRaleigh #events #HousingAndEnvironmentCommittee #News #October2026 #PoliceAdvisoryBoard #PublicArtAndDesignBoard #RaleighCivicEngagement #RaleighGovernment #RaleighGovernmentMeetings #RaleighMeetingCancellations #RaleighPlanningCommission -
We are currently in the middle of a massive agent gold rush, with everyone stringing together task-specific bots, writing complex prompt chains, and orchestrating multi-agent frameworks to automate everything from code generation to workflows. But if you look closely at where these systems bottleneck, it becomes obvious that agents are just a transitional technology. They are the scaffolding. The real endgame is digital twins.
To understand why, you have to look at the structural limits of how we build and deploy these systems.
An agent is fundamentally a transactional construct. It is essentially a function-call loop wrapped around a language model, where you provide a system prompt and a set of tools, and it executes a bounded task before stopping or waiting for the next trigger. Because of this, agents suffer from severe context fragmentation. When you build a multi-agent swarm—such as a researcher handing off to a writer, who then hands off to a coder—they do not share a continuous reality. They just pass serialized text back and forth across brittle boundaries, losing nuance, intent, and implicit state along the way. You end up spending a ridiculous amount of engineering effort writing glue code and re-steering prompts just to keep the swarm from drifting off course.
A digital twin approaches the problem from the exact opposite direction. Instead of starting with a transient task, it starts with persistent state, identity, and continuous memory. A digital twin does not ask what tool to run next to complete a prompt; it acts as a persistent state engine that continuously models a real-world entity—whether that is a human operator, a complex codebase, or an entire infrastructure stack—retaining full historical context and behavioral constraints in real time.
This also changes how we handle execution. Agents are reactive by design, waiting around for a webhook, a prompt, or a cron job to fire before scrambling to execute a workflow. Digital twins introduce a predictive, simulation-first paradigm. Because a true twin maintains a high-fidelity mirror of its environment, you stop relying on fire-and-forget automation. Instead of just telling an agent to push a change and hope for the best, a digital twin architecture lets you run simulations against historical data and edge cases, find the failure points in a sandbox, and execute the optimal path autonomously. You shift from brittle task execution to high-certainty state transition.
Right now, developers are trying to patch the agent memory problem with vector databases and RAG pipelines bolted onto stateless models. While those help, they treat memory as an external lookup table rather than a core property of the system. A digital twin treats memory, logic, and execution as an integrated whole, where the identity layer is an evolving state machine rather than a paragraph stuck in a system prompt.
Ultimately, agents are the micro-services of this era—valuable, modular building blocks for executing specific workloads. But they are too fragmented and short-lived to form a cohesive autonomous future. The architecture we should actually be building toward consists of persistent, stateful digital twins that can spawn, manage, and coordinate tactical agents as mere sub-routines of a much larger, unified system.
When you move past the chat-bot phase of AI and look at a true personal digital twin, the shift is from a tool you talk to into an operational proxy that acts as you.
Instead of waiting around for prompts, a personal digital twin acts as a continuous, stateful extension of your identity, running in the background across your local workstation, cloud services, and real-world infrastructure.
Here is what that actually looks like in practice.
Continuous Environmental and Contextual Awareness
Right now, every AI interaction starts from zero. You have to explain who you are, what project you are working on, and what your preferences are. A digital twin maintains a persistent state model of your day-to-day reality. It tracks the active codebases you are building, your communication patterns, your schedule, and your current priorities. When an event happens—a pull request opens, an email arrives, or a deadline shifts—the twin evaluates it instantly against your real-world context without needing you to manually feed it background info.Autonomous Proxy Operations and Comms
An agent can draft an email if you tell it to. A digital twin handles communication and coordination proactively because it understands your voice, your boundaries, and your ongoing projects. It can triage inbound communications across your channels, negotiate scheduling, and handle administrative back-and-forth based on how you actually operate. It doesn't just sound like you because of a few system prompt examples; it writes and responds based on an evolving model of your actual decisions and historical behavior.Sandbox Simulation Before Execution
One of the biggest limits of current automation is the fear of breaking things. A digital twin changes that by introducing sandbox simulation. If you want to deploy a new infrastructure configuration, refactor a complex system, or launch an automated marketing pipeline, your twin can simulate the entire execution path first against your historical data and constraints. It stress-tests the logic, flags potential failure modes, and models the outcomes in a secure environment before anything touches production.Cross-System Orchestration
Right now, we use a dozen disjointed apps that don't talk to each other well. A digital twin acts as the central nervous system connecting your local machine, your cloud environments, and external APIs. It can spawn, manage, and coordinate tactical sub-agents to handle specific workloads—one agent spinning up a research crawl, another handling local file management, another writing boilerplate—while the twin itself maintains the macro-level vision and ensures nothing drifts off course.Ultimately, a personal digital twin isn't something you prompt. It’s an autonomous mirror of your operational workflow that absorbs the friction, handles the execution, and scales your capabilities without requiring constant babysitting.#ai #webdev #devops #architecture #software #coding #development #engineering #inclusive #community
Why "Agents" Are Just the Bridge: Digital Twins Are the True End State of AI -
Tokens Over Hires
In the middle of September I ran ccusage against every Claude Code session on my laptop since May. It walks the local logs, counts the tokens, and prices them at what the API would have charged. I expected a big number. What came back was about twenty billion tokens and just under fourteen thousand dollars, across every project I’d touched in four months, with most of it going to Pepper, which is now open for signups, so go check it out. Nineteen billion of those tokens were cached reads.
I sat with that for a minute. Fourteen thousand dollars is real money. At my current pace it works out to roughly thirty thousand a year. I could triple it and spend a hundred thousand dollars a year on tokens, and that still isn’t one engineer. In exchange, Chris and I have shipped more code than I’ve shipped in any stretch of my career. I wrote in September that the two of us merged 657 pull requests in our first thirty days with a budget instead of a platform team. When I put the ccusage number in those terms on a panel, it came to about a dollar fifty a pull request.
That number is what my laptop would have cost at API prices, and it isn’t what we actually pay. Here’s the whole bill for both of us. Two Codex subscriptions, one each, are $200 a month. Anthropic is $550 a month: a Max plan for me and Chris, plus two Vycari team seats, one for Chris and one that runs our automation. I have an Ollama subscription at $200 a year. Greptile is about $1,500 a month. All in, that’s a little under $2,300 a month, or about $28,000 a year, for both founders and every pipeline we run. It still isn’t one engineer.
The obvious conclusion is the one a friend reached when he heard the figure: if code is that cheap, just get more tokens. I thought so too, for about a day. Then I looked at what was actually slowing us down, and the token budget wasn’t on the list.
The Constraint Is Attention
Every agent I run needs things from me that tokens can’t buy. It needs a plan approved before it starts. It needs its pull request read when it finishes. When it hits something it can’t decide, it escalates, and somebody has to be there to answer. None of those steps is expensive on its own. The problem is what happens when I run enough agents that the steps overlap.
When I run too many agents, the cost goes up a little and the risk goes up a lot. The biggest thing that’s slipped past us so far was how we managed database connections. It got out of hand, a little at a time, while my attention was on other pull requests, and eventually it caused an outage. Chris had to roll forward while I was out of town, and I spent the following week building a proper database abstraction layer. He put it more bluntly than I would have: the thing that kills us is splitting our attention. That’s when stuff slips through.
When people ask how we decide what to spend, I give them the order. Optimize for human time, because that’s the scarcest thing we have. Then money, because that’s the second scarcest. Then everything around agent time. “I can waste agent time all day. I don’t care.”
So the number that limits us is how many agents one person can supervise well at the same time. I don’t have a precise figure for that ceiling. I know I’ve been over it, because I can feel the difference between a day when every pull request got a real read and a day when I was skimming to keep up.
Another friend told me recently about engineers at his company running up bills in the thousands to build simple internal copies of SaaS tools. His reluctant guess was that they just aren’t as good at using agents yet, and that it’s a skill people have to develop. I don’t know what they’re doing differently. It doesn’t sound like token maxxing, which I’ve never really understood either. I’d love to sit with engineers who run up giant bills and just watch. My guess is they’re using the agents inefficiently, and that what’s missing is supervision, which a bigger budget won’t fix.
Building for More Agents per Person
Almost everything I’ve built since the summer has been an attempt to raise that ceiling, and almost none of it has been about spending less.
The pipeline I described in The Agent That Never Merges is the first piece. Agents open pull requests continuously and never merge them, which means the supervision cost per pull request is bounded at one careful read. The pipeline absorbs the rest: triage, planning, the first round of review comments, the retries. I only see the work once it’s in a state worth my attention.
The second piece is where the work lives. I wrote about pulling Pepper apart into small, bounded repositories to keep CI fast and give agents less to load. It mattered just as much for my attention. An agent working in a repository with one job produces a pull request I can evaluate in one sitting.
We’re up to fourteen repositories now. Last week I moved our iOS app out of the monorepo into its own. That was 150,000 lines of code, and nobody noticed except Chris, who had to wait a day to land his next batch of iOS changes. Agents make refactors that size easy. Having the CLI and the iOS client in their own repositories also forces us to keep good API definitions between the server and its clients.
The third piece is delegation inside the agent layer itself. I run two Claude Opus 5.5 orchestrators on medium effort. One is the UTL, for uber tech lead, and its job is to spawn subagents that build features. The other is what I call the focused worker, and its job is working with me. It catches the manual tasks that come out of the UTL’s pull requests and keeps them in a ledger. Then we work through them together: systems changes, bringing up new platforms, migrating data, anything that needs my level of access and oversight. Everything else goes to a subagent under the UTL, Opus for design docs and code reviews, Sonnet for implementing a feature from a design doc. Every one of these agents can consult a model a tier above it as an advisor, and they all share a wiki for notes.
Anthropic is now advertising much the same pattern with Claude Projects, and I’m happy to see others arriving at the same answer. Chris has been building a loop that babysits open pull requests through review until they score well enough to be worth a human’s time. Each layer like that turns a job I used to do by hand into something I check. The ceiling goes up one delegated task at a time.
The Token Budget Was the Easy Problem
There was a week in September when the meter really was the constraint. We ran through our free credits and paid about $120 in overage. Then AutoDev ate the whole Vycari quota, and I did server cleanup by hand because I couldn’t do anything else.
Most of that was self-inflicted. I’d started with Fable as the orchestrator, with no cap on context, and burned a week’s quota in three days. The setup above fixed it. The orchestrators run on medium effort, and the cheaper model does the implementing. Opus answers the reviews because Sonnet is too literal about them, and a literal reading turns two review cycles into twelve. Every agent auto-compacts at 200K tokens, because Anthropic charges more for context past that point. With that setup, and the work spread across parallel sessions, I find it hard to spend all of my quota. My goal now is to hit 99% of it every week just before it resets. I keep a list of research projects, and if I have quota left 24 hours before the reset, I spin one up. That isn’t the same as maxing out tokens. I’m not trying to use as many as possible. I’m trying to use all the ones I’ve already paid for.
There’s also a second account on the same machine. Claude Code reads a
CLAUDE_CONFIG_DIRenvironment variable, so two shells with two config directories give you two accounts side by side. The second one runs the AutoDev and code review pipelines, and those use up its quota every week, so it isn’t a fallback for my own sessions. When I do run short, I supplement with other labs’ plans. Every one of those fixes took an afternoon or less. None of the supervision problems above has had an afternoon fix.Where the Money Actually Goes
While I had the numbers in front of me, I looked at where development spend actually lands, because it isn’t where I expected either.
Development tokens currently run about ten times what the product itself consumes, even with real people using it. Building the thing costs more than running it. That won’t last. The feedback from our private beta has been really good, and we now open Pepper to a limited number of new signups every day, so product usage is rising quickly.
Most of what we actually pay goes to code review. Subscriptions are the cheap way to buy tokens if you’re willing to hit limits, so they’re the smaller line. Greptile reviews every pull request we open, and at about $1,500 a month it’s our biggest single expense. We both think it’s worth it, because it’s the layer that lets the never-merge pipeline run without me reading every first draft.
In October I tested that. I spent a few days building a cheaper reviewer to replace it. I came out the other side saying, “I think we should just pay for it because I can’t do it cheaper.” What I could change was how often we call it. The agents kept asking for a re-review before they’d fixed anything. Because every repository shares a common dev environment, the fix went in once: our pull-request skill now has agents run
/simplifyand then/code-reviewbefore they’re allowed to open a pull request. On many of our repositories, our own reviewer also does the first round and only then calls Greptile. My goal is a pipeline where Greptile’s reviews come back with nothing to say. We’re not there yet, but we get a little better every week.I’d rather pay for review than have a second person do it, and the reason isn’t salary. The person would need supervising too, because review is a job I’d have to check, not one I can hand off.
What a Hire Is For Now
Five years ago, if I’d been getting a product like this ready for market, I’d have been hiring twenty to fifty engineers. The work would have needed that many hands, and most of what they’d have done was write code. Today the code is the cheap part. If I bring someone in to write code, I’ve added a person who needs plans approved and pull requests read, which is the exact resource I’m short on. A hire whose job is output adds coordination faster than it adds capacity.
I said on a podcast that came out last week that the code I’ve written would have cost about fifteen thousand dollars at API prices, and that I couldn’t hire engineers for that money. That’s true. The engineers I’d still want are a different kind from the ones I hired at Google. We need a few more people who can herd agents, not fifty.
The engineering hire that adds capacity is a different shape. It’s someone who can own a bounded service whole. Here’s the interface, here’s everything it needs to do, let me know when it’s done. That person brings their own supervision. They run their own agents, read their own pull requests, and answer their own escalations, and the only thing that crosses the boundary between us is the contract. Supervision divides along the interface instead of piling up on one person. That’s a tech lead’s job, and it’s the only kind of engineering hire I can see that raises the ceiling instead of lengthening the queue.
That raises a fair question about how anyone grows into that job now, which I tried to answer in The Questions Are the Job. The other advice I give is to work on open source. I’ve met people who showed they could work really efficiently with agents through open source projects, Gemini CLI in particular, and some of them ended up being hired onto my teams. Open source has its own problems right now. But if you show up as a thoughtful engineer with features or bug fixes that are well reviewed, well tested, and bring value to the project, I think you’ll be welcome. That’s one of the reasons I decided to open source the Pepper CLI.
The first hire probably isn’t an engineer at all. I’ve said this a few times now, including on stage at a panel in September. Neither Chris nor I are go-to-market people, and I may hire one before I hire another engineer. I can create piles and piles of code, even relatively good code. What I can’t do is agentically acquire customers. (Did I mention that Pepper is open for signups now?) Someone in marketing came up after the panel and told me I’d validated her existence.
The Third Trend
I’ve watched this happen twice before. Open source developer tooling made it so a small team didn’t need to buy or build its own compilers, databases, and frameworks. Cloud infrastructure made it so that team didn’t need to own hardware or a data center. Each time, the number of people you needed to start something real dropped, and the skills that mattered shifted toward the people who understood systems well enough to compose what was suddenly free.
Agentic tools are the third one. Anyone with a good idea can now get from concept to production on a budget that would have been a rounding error in a Google planning cycle. But the constraint didn’t vanish. It moved, the same way it did the last two times, toward the people who understand systems design well enough to drive the agents and notice when something has slipped through. I wrote a year ago that management skills were going to carry over to working with agents. I didn’t expect to be proving it with a two-person org chart and fourteen thousand dollars of tokens.
This isn’t only a small-company problem. At Google it was hard to get more than a few changes in a day, because each one waited on code review. I’ve talked to engineers there who now do all their work in experimental directories that don’t require review, then port it back to their team’s repository at a pace the review process can keep up with. That’s what happens when you don’t manage for human attention. Productive people spend their cycles waiting on the process.
When other companies ask me what to change, my answer has become: drive until human attention is the scarce resource, then build for that. If you’re reading your own ccusage number and wondering whether the answer is more tokens, look at where your attention went first. For us the answer has been more interfaces. Those are the places where one person’s supervision ends and another’s begins, or where an agent takes over a job one of us used to do by hand.
#Agents #AI #Developer #Startups -
Triangle Event Guide Free Events and Halloween Fun for Monday, October 26, 2026
Monday brings plenty of ways to explore Raleigh, Durham, Cary and the surrounding Triangle. The Triangle event guide for October 26, 2026, features free Halloween activities, a Diwali celebration, comedy, open mic nights and family outings.
Whether the plan is morning storytime with the kids or an evening of games and entertainment, here’s what’s happening around the Triangle.
Family Activities and Halloween EventsTotal Tot Time in Durham
Time: 10 a.m.–noon
Location: W.D. Hill Recreation Center, Durham
Admission: FreeStart the week with Total Tot Time, a free play program for children ages 5 and younger.
Halloween Storytime on the Roof
Time: 10:30 a.m.
Location: The Durham Hotel, Durham
Admission: FreeEnjoy a seasonal morning outing with Halloween Storytime on the Roof at The Durham Hotel.
Trunk or Treat at Gladwell Orthodontics
Time: 5:30–7:30 p.m.
Location: Gladwell Orthodontics North Raleigh
Admission: FreeGet into the Halloween spirit at Gladwell Orthodontics’ North Raleigh trunk-or-treat event.
Fall Carnival at Carolina Pines
Time: 6:30–8 p.m.
Location: Carolina Pines Community Park, Raleigh
Admission: FreeCelebrate the season with a free fall carnival at Carolina Pines Community Park. This evening event offers another option for families looking for Halloween-week activities in Raleigh.
Diwali Celebration in Downtown Cary ParkTime: 6:30–8 p.m.
Location: Downtown Cary Park, Cary
Admission: FreeCelebrate the Festival of Lights at Downtown Cary Park. The free Diwali celebration is a highlight of Monday’s community events in Cary.
Creative Activities, Reading and Community Events
Coloring Vinyl Night
Time: 2–9 p.m.
Location: KōMANA Brewing Company, Cary
Admission: FreeAdd a creative stop to the afternoon or evening with Coloring Vinyl Night at KōMANA Brewing Company.
Silent Reading Club
Time: 6–8 p.m.
Location: The Glass Jug Downtown Durham Taproom
Admission: FreeBring a book and settle in for a relaxed evening at Silent Reading Club in downtown Durham.
Democracy Unfiltered: A Voter Education Series
Time: 6–8 p.m.
Location: Raleigh Brewing, Raleigh
Admission: FreeMake time for civic learning at Democracy Unfiltered, a voter education series hosted at Raleigh Brewing.
Board Game Night
Time: 6–9 p.m.
Location: Bottle Theory, Apex
Admission: FreeSpend Monday evening playing board games at Bottle Theory in Apex.
Comedy, Karaoke and Movie NightEarly Karaoke
Time: Starts at 6 p.m.
Location: The Northside District
Admission: FreeStart the evening with a song at Early Karaoke at The Northside District.
Monday Movie Night
Time: 6:30–9 p.m.
Location: Compass Rose Brewery, Raleigh
Admission: FreeEnjoy a Monday movie outing at Compass Rose Brewery. Check with the venue for the featured film.
Cosmic Chuckles: Weekly Comedy Show
Time: 7–9 p.m.
Location: Flying Saucer Raleigh
Admission: FreeAdd some laughter to the start of the week with the Cosmic Chuckles comedy show at Flying Saucer Raleigh.
Halloween Air Hockey Tournament
Time: Starts at 8 p.m.; air hockey is also listed from 8–10 p.m.
Location: Boxcar Bar and Arcade, Raleigh
Admission: Listed as freeHead to Boxcar for a Halloween air hockey tournament. The supplied schedule includes two overlapping air hockey listings at the same venue, combined here into one entry. Confirm the tournament format and end time with Boxcar.
Open Mic Nights Around the TriangleOpen Mic Night at Steel String Brewery
Time: 6–11 p.m.
Location: Steel String Brewery, Carrboro
Admission: FreeEnjoy an evening of local performances at Steel String Brewery in Carrboro.
Open Mic Night at KōMANA Brewing Company
Time: 7–10 p.m.
Location: KōMANA Brewing Company, Cary
Admission: FreeKōMANA Brewing Company’s open mic night offers another Monday entertainment option in Cary.
Carrboro Open Mic
Time: Starts at 7:30 p.m.
Location: Speakeasy, Carrboro
Admission: FreeCatch Carrboro Open Mic at Speakeasy starting at 7:30 p.m.
Anyone interested in performing should contact the venue for sign-up procedures and available slots.
Explore More Things to Do Around the TriangleMonday’s lineup offers activities throughout the day, from toddler playtime and Halloween storytime to Diwali celebrations and evening entertainment.
Families can explore trunk-or-treat or the Carolina Pines fall carnival, while readers, music fans and comedy lovers have several free outings to choose from.
Follow DoRaleigh.com for daily updates on government meetings, local festivals, and community happenings — your one-stop guide to everything Raleigh!
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Event information is based on the supplied listings. Confirm schedules, registration requirements, age policies and promotional terms with organizers before attending. Free admission may not include food, drinks or other purchases.
#ApexEvents #CarrboroEvents #CaryEvents #DiwaliCelebration #DoRaleigh #DurhamEvents #events #FamilyActivities #FreeThingsToDo #HalloweenEvents #MondayEvents #News #October262026 #RaleighEvents #TriangleEvents -
Estrés financiero | mujeres pierden más el sueño por preocupaciones económicas, según el Inegi
El estrés financiero afecta el descanso en México. Una encuesta del Inegi revela diferencias entre mujeres y hombres; Kueski presenta una campaña basada en experiencias de clientes ante imprevistos económicos.
Por Deyanira Vázquez | Reportera
El estrés financiero afecta el descanso de millones de personas en México y tiene un impacto mayor entre las mujeres: 31.1% de ellas reportó falta de sueño por esta causa, frente a 23.4% de los hombres, según la Encuesta Nacional sobre Salud Financiera (ENSAFI) 2023.
En este contexto, Kueski, empresa de servicios financieros digitales, lanzó la campaña “Historias para dormir tranquilo”, una iniciativa que presenta relatos inspirados en experiencias de clientes que recurrieron a sus soluciones financieras para afrontar gastos inesperados y necesidades familiares.
La propuesta utiliza historias personales y piezas audiovisuales con formato de cuento antes de dormir para acercar al público a situaciones cotidianas, como la descompostura de un electrodoméstico o una emergencia médica. Sin embargo, los testimonios forman parte de una campaña comercial y no constituyen, por sí mismos, una evaluación independiente de los beneficios o riesgos de contratar crédito.
Estrés financiero: mujeres reportan más problemas para dormir
Los datos de la ENSAFI 2023, elaborada por el Instituto Nacional de Estadística y Geografía (Inegi) en colaboración con la Comisión Nacional para la Protección y Defensa de los Usuarios de Servicios Financieros (Condusef), muestran que las preocupaciones económicas pueden tener consecuencias físicas y emocionales.
Además de los problemas de sueño, la encuesta identificó diferencias entre mujeres y hombres en otras afectaciones relacionadas con el estrés financiero. El dolor de cabeza fue reportado por 34.3% de las mujeres y 24% de los hombres, mientras que los problemas de gastritis o colitis afectaron a 21.4% y 11.4%, respectivamente.
El estudio también encontró que 36.9% de la población adulta tenía un nivel alto de estrés financiero en 2023. Las mujeres registraron una proporción mayor que los hombres en esta categoría: 42.2% frente a 30.7%.
Las cifras permiten dimensionar el problema de las preocupaciones económicas en los hogares mexicanos y el vínculo que las personas encuestadas reportaron entre su situación financiera y su bienestar cotidiano.
Kueski convierte experiencias de clientes en relatos audiovisuales
La campaña “Historias para dormir tranquilo” se construye a partir de experiencias positivas de usuarios de la plataforma. Su narrativa busca mostrar cómo distintas personas afrontaron situaciones inesperadas y utilizaron productos financieros de la compañía para atender necesidades concretas.
Uno de los casos presentados es el de Juan Pablo, quien relató que utilizó la aplicación de Kueski cuando su refrigerador dejó de funcionar. Según su testimonio, pudo conseguir el dinero para comprar otro aparato en menos de 10 minutos.
“Estoy muy a gusto con la aplicación de Kueski, ya que me ha ayudado mucho para mí y para mi familia”, expresó el cliente al describir su experiencia.
Su historia refleja una de las situaciones que la campaña busca representar: la necesidad de resolver un gasto doméstico que no estaba contemplado en el presupuesto familiar.
La compañía también presenta el caso de Daniela, quien enfrentó una emergencia médica de su madre ocho días antes de su boda. De acuerdo con el relato difundido por Kueski, utilizó el dinero que había reservado para su vestido para atender la hospitalización y posteriormente recurrió a la plataforma para continuar con los preparativos de la celebración.
Ambas historias ilustran el enfoque de la campaña, aunque las experiencias individuales no permiten establecer que el crédito sea la alternativa más conveniente para todas las personas que atraviesan dificultades económicas.
De “Héroes silenciosos” a “Historias para dormir tranquilo”
La nueva iniciativa da continuidad a “Héroes silenciosos”, una campaña anterior con la que Kueski presentó historias de personas que trabajan, emprenden y atienden las necesidades de sus familias.
En esta ocasión, el énfasis se desplaza hacia los imprevistos y las decisiones financieras que pueden surgir ante una emergencia. La intención comercial consiste en mostrar situaciones en las que los servicios de la compañía formaron parte de la solución elegida por sus clientes.
Claudia Infante, directora de Marketing Integrado de Kueski, explicó que la iniciativa busca compartir experiencias que habitualmente no se cuentan, pese a que forman parte de la vida cotidiana.
“Todos hemos tenido un imprevisto que nos quitó el sueño. Lo que casi nunca contamos es cómo lo resolvimos. Esta campaña es para eso: para contar esas historias”, señaló.
Infante añadió que las preocupaciones económicas pueden estar relacionadas con necesidades del hogar o con planes familiares que se ven afectados por un gasto inesperado.
Televisión, redes sociales y un libro de historias
La campaña contempla una estrategia de difusión en televisión y redes sociales, además de un libro que reunirá relatos de usuarios con un formato inspirado en los cuentos para dormir.
Con estos canales, Kueski busca ampliar el alcance de las experiencias de sus clientes y acercar su propuesta a personas que enfrentan situaciones financieras similares.
La compañía informó que ha otorgado más de 45 millones de préstamos. Esta cifra corresponde al volumen acumulado de operaciones reportado por la empresa y no debe interpretarse como el número de clientes únicos ni como una medición de los resultados de la campaña. –sn–
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#NoticiasMX #PeriodismoParaTi #PeriodismoParaTiSociedadNoticias #Cdmx #ClaudiaInfante #Condusef #créditoEnMéxico #economíaFamiliar #educaciónFinanciera #ENSAFI2023 #estrésFinanciero #faltaDeSueño #finanzasPersonales #HistoriasParaDormirTranquilo #INEGI #Información #InformaciónMéxico #Kueski #México #Morena #noticia #noticias #NoticiasMéxico #NoticiasSociedad #préstamosDigitales #saludFinanciera #SN #Sociedad #SociedadNoticias #SociedadNoticiasCom #sociedadNoticias #SociedadNoticiasCom -
Pest control businesses manage more than inspections and treatments. Every service visit involves appointment scheduling, technician coordination, travel planning, customer communication, and recordkeeping.
As a company grows, these administrative responsibilities become increasingly difficult to manage through spreadsheets, phone calls, and disconnected software.
Artificial intelligence (AI) and workflow automation offer new ways to organize these processes. However, their value depends on how well they solve real operational problems.
This article examines five areas where AI and automation can support pest control businesses, along with the technical considerations companies should evaluate before adopting new systems.
- Smarter Appointment Scheduling
Traditional appointment scheduling often requires office staff to coordinate technician availability, customer preferences, service duration, and travel distances manually.
Scheduling software can automate several parts of this process.
For example, a scheduling system might:
Display available appointment windows based on technician calendars.
Assign jobs according to service area and technician availability.
Send automated appointment confirmations.
Notify customers when appointments change.
Identify scheduling conflicts before bookings are finalized.
AI-assisted scheduling can go further by analyzing historical job durations and recommending suitable appointment windows.
However, these recommendations are only as reliable as the underlying data.
A business with inaccurate service durations or incomplete technician availability records may still experience scheduling problems, even after introducing automation.
Technical consideration: Look for scheduling systems that support configurable service durations, technician constraints, calendar integrations, and manual overrides.
- Route Optimization for Field Technicians
Travel time is a significant operational consideration for businesses that send technicians to multiple customer locations each day.
Basic route planning can arrange appointments geographically. More advanced optimization systems consider additional constraints, including:
Appointment time windows
Technician starting locations
Estimated travel times
Service duration
Technician skills and availability
Vehicle capacity or equipment requirements
Some systems use optimization algorithms to recommend more efficient visit sequences.
AI-based prediction may also help estimate travel times or anticipate schedule disruptions.
Consider a technician assigned six appointments across different neighborhoods.
A poorly organized schedule might require unnecessary backtracking. A routing system can evaluate possible visit sequences and recommend an order that better satisfies the day's constraints.
Importantly, route optimization is not automatically AI. Many useful routing systems rely on established mathematical optimization methods rather than machine learning.
For pest control companies, the practical question is whether the system produces workable routes while respecting real service requirements.
- CRM Automation and Customer Communication
Customer relationship management (CRM) systems help businesses organize customer information, service histories, recurring treatments, and communication records.
Automation can reduce repetitive administrative work.
For example, a completed service appointment might trigger the following workflow:
The technician marks the job as completed.
The system updates the customer's service history.
A service summary becomes available.
The customer receives a completion notification.
The system schedules a future reminder when appropriate.
This workflow does not necessarily require generative AI. Many CRM platforms support rule-based automation using triggers, conditions, and actions.
AI may provide additional capabilities, such as summarizing customer interactions or categorizing incoming service requests.
Nevertheless, businesses should review AI-generated information before using it in sensitive customer communications or treatment documentation.
Technical consideration: Evaluate how the CRM handles customer consent, communication preferences, recurring service records, and data access permissions.
- Connecting Software Through APIs and Integrations
One of the most useful opportunities for pest control technology is connecting systems that would otherwise operate independently.
A business might use separate applications for:
Website inquiries
Customer management
Appointment scheduling
Technician dispatch
Invoicing and payments
Reporting and analytics
Without integrations, employees may need to enter the same information into multiple systems.
Application programming interfaces (APIs) allow software applications to exchange information through defined interfaces.
For example, an integration could connect an online booking form with a field service management platform.
When a customer submits a booking request, the integration might create a customer record, generate a service request, and notify office staff.
A typical architecture could look like this:
Website Booking Form
|
v
API / Integration Layer
|
v
Field Service Management System
|
+--> Customer Record
|
+--> Service Request
|
+--> Scheduling Queue
|
+--> Notification WorkflowFor developers implementing these integrations, several considerations matter:
Authentication: API access should use secure authentication mechanisms and appropriately scoped credentials.
Validation: Incoming booking data should be checked before records are created.
Duplicate prevention: Retry requests should not accidentally create duplicate customers or jobs.
Error handling: Failed integrations should be logged and retried safely.
Privacy: Customer addresses, contact details, and service records should be handled according to applicable privacy requirements.
Before purchasing software, businesses should investigate whether the vendor offers documented APIs, webhooks, native integrations, or supported third-party automation tools.
- AI-Assisted Reporting and Business Analysis
Pest control businesses generate operational data through appointments, service records, customer interactions, and invoices.
Reporting tools can organize this information into useful business metrics.
Examples include:
Completed appointments per technician
Average time spent on service visits
Appointment cancellations
Recurring service activity
Travel time between appointments
Outstanding invoices
AI-assisted analysis may help identify patterns or generate plain-language summaries of reporting data.
For example, a system might highlight an increase in rescheduled appointments or identify service areas with unusually long travel times.
However, AI-generated summaries should be checked against the original records.
A useful reporting system should allow managers to trace a recommendation or observation back to the underlying data.
- What Should Businesses Evaluate Before Adopting AI?
Adding AI features does not automatically make a software platform better.
Before investing, pest control businesses should ask several questions.
Does the software solve a specific operational problem?
Start with a measurable issue, such as missed appointments, repetitive data entry, or inefficient dispatching.
Can employees understand and override automated decisions?
Technicians and dispatchers need practical ways to correct schedules and handle unexpected circumstances.
Does the system integrate with existing tools?
A platform that creates additional manual data entry may undermine the intended benefits.
How is customer information protected?
Review data access controls, retention policies, vendor agreements, and security practices.
How will performance be measured?
Establish a baseline before implementation. Compare outcomes such as scheduling errors, administrative time, or travel efficiency after deployment.
Businesses should also distinguish between genuinely useful AI capabilities and conventional automation presented with AI-focused marketing language.
- A Practical Implementation Approach
For smaller pest control businesses, introducing several new systems simultaneously can create unnecessary complexity.
A more manageable approach is to implement technology in stages.
Stage 1: Document existing workflows. Identify how appointments, service records, and customer communications are currently handled.
Stage 2: Select one problem to address. For example, automate appointment confirmations before attempting a complete dispatch-system replacement.
Stage 3: Test with a limited group. Introduce the workflow to a small team and document errors, exceptions, and employee feedback.
Stage 4: Measure results. Compare the new process against the previous workflow using consistent metrics.
Stage 5: Expand carefully. Introduce additional integrations or AI-assisted features only when the earlier implementation is functioning reliably.
This approach makes it easier to identify which technologies provide practical benefits.
Conclusion
AI and automation are expanding the options available to pest control businesses, particularly in scheduling, route planning, CRM workflows, software integrations, and reporting.
The most effective systems are not necessarily those advertising the largest number of AI features.
They are the systems that support reliable operations, provide useful information, integrate with existing workflows, and allow employees to remain in control of important decisions.
For independent research into pest control software, scheduling tools, CRM platforms, and technology comparisons, explore Pest Tech Research.
Better Technology. Stronger Pest Control.#automation #ai #software #api #coding #development #engineering #inclusive #community
How AI and Automation Are Changing Pest Control Business Operations -
Noteworthy News From Raleigh October 25, 2026 Edition
Raleigh and the Triangle have plenty of noteworthy news to follow, from major passenger rail funding and affordable housing progress to new businesses, fall festivals and early voting deadlines. This DoRaleigh roundup brings together updates from Raleigh, Durham, Cary and Chapel Hill, along with North Carolina business and community headlines.
Raleigh Development, Transportation and Community News
S-Line passenger rail receives $550 million in federal fundingThe Raleigh-to-Richmond passenger rail project has reached another funding milestone. Federal transportation officials announced $550 million for the S-Line, supporting work toward a route expected to reduce travel time between the two cities by up to 90 minutes while increasing service and reliability. The funding advances the project; it does not mark the start of passenger service.
Source: U.S. Department of Transportation.
Affordable rental homes take shape in Southeast RaleighSoutheast Raleigh Promise is making progress on Promise 25, a development bringing 25 affordable rental homes to nine city-owned parcels. The city approved $3.3 million in gap financing for the project, which includes a mix of housing types. Initial homes have been completed, with the full project expected to finish by summer 2027.
Sources: City of Raleigh and WRAL.
Downtown South apartment plans move forwardKane Realty has filed plans for 350 apartments as the first phase of Downtown South, according to Raleigh Magazine. Construction is planned to begin by the end of 2026, making this a development to watch as Raleigh adds housing.
Durham park improvements face delaysPlanned amenities at Durham’s East End and Long Meadow parks will not begin construction this fall as originally expected. INDY Week reports that rising costs and design challenges have pushed the work back by approximately two years while officials pursue $15 million in federal grant funding.
Source: INDY Week.
Wake County receives farmland preservation fundingThe Wake County Soil and Water Conservation District received approximately $10 million in state funding tied to farmland protection and agricultural growth, according to CBS 17. The awards include $7.9 million for 12 projects and a separate $2 million award, with more than 600 acres slated for permanent protection.
Raleigh tracks progress toward 24,000 treesRaleigh’s goal is to reach 24,000 trees planted by 2032. The city’s Every Tree Counts initiative includes trees planted by residents, businesses and community partners, giving private-property plantings a role in the communitywide effort.
Source: City of Raleigh.
Midtown real estate forum set for October 27The Midtown Raleigh Alliance’s Real Estate Forum takes place Tuesday, October 27. Local real estate leaders will discuss housing trends, neighborhood development and projects shaping North Hills and Midtown, according to Raleigh Magazine.
Raleigh Restaurants, Nightlife and New Businesses
Low Bar Lounge opens beneath Whiskey KitchenLow Bar Lounge has opened below Whiskey Kitchen in downtown Raleigh. The underground venue offers cocktails, beer, wine and THC seltzers, with nightly service until 2 a.m. The space draws inspiration from a limousine company that once occupied the property and was previously home to digital art venue IMURJ.
Food industry professionals can also look for Sunday Family Meal, a rotating buffet offered Sundays at 5:30 p.m. for $20. Check with the lounge for current availability and participation details.
Sources: WRAL and Raleigh Magazine.
Sippin’ Santa returns to Wye HillWye Hill Kitchen & Brewing will host Sippin’ Santa from November 13 through December 31. The seasonal pop-up brings tropical holiday decorations and themed cocktails to the restaurant at 201 S. Boylan Avenue. Reservations are available through Wye Hill’s event page.
Details: Sippin’ Santa at Wye Hill.
South Block plans North Hills locationAcai bowl, smoothie and juice brand South Block is coming to 4121 Main at North Hills Street. Founded in Arlington, Virginia, the company plans to open beside bartaco in North Hills’ Main District during the first quarter of 2027.
Source: North Hills.
Raleigh construction executive backs luxury handbag brandCooper Tacia General Contractors CEO John Cooper has invested more than $1 million in Dadov, the luxury handbag company he co-founded with designer Islam Dadov, according to Triangle Business Journal. The brand debuted at Paris Fashion Week this fall and is exploring boutiques in Paris and major U.S. cities. A standalone Raleigh store is not currently planned, though Triangle retail placement remains a possibility.
Triangle Business, Technology and Education
Raleigh Founded enters a new chapterFormer executive directors Lauren Romer and Jess Porta are taking the lead through their new venture, Lotos Ventures, as Raleigh Founded’s original ownership group winds down operations at the end of October. The transition encompasses the North Street, Downtown Cary and Centennial Campus locations, continuing the coworking community’s mission of connecting entrepreneurs and businesses.
Source: Raleigh Founded’s announcement.
Loren Gold named next Visit Raleigh president and CEOLoren Gold will become president and CEO of the Greater Raleigh Convention and Visitors Bureau, also known as Visit Raleigh, on January 1, 2027. He succeeds Dennis Edwards, who is retiring at the end of 2026. Gold has served as executive vice president since 2007 and brings nearly four decades of hospitality experience.
Source: Visit Raleigh.
NC TECH recognizes industry leadersNC TECH announced eight recipients of its 2026 Individual Awards, including SAS CEO Jim Goodnight, Vantaca CEO Ben Currin and Charlotte CIO Markell Storay. The honorees will be celebrated November 16 at the Sheraton Imperial Hotel in the Research Triangle Park area.
Details: NC TECH Awards.
Pendo introduces Floor 404 puzzle gameRaleigh-based Pendo has launched Floor 404, a free daily puzzle game focused on improving AI agents. According to GrepBeat, players solve challenges by supplying virtual agents with better context, illustrating concepts behind the company’s Pendo for Agents platform.
Cary drugmaker announces FDA approvalAsahi Kasei Therapeutics announced FDA approval of Hovilpri, or pritelivir, for mucocutaneous herpes simplex virus lesions that are refractory to standard antiviral treatment in immunocompromised adults. The approval covers lesions with or without resistance to standard treatment. The company expects U.S. availability before the end of 2026.
Source: Asahi Kasei.
Duke and UNC place in WalletHub’s top 25Duke University ranked No. 14 and UNC-Chapel Hill ranked No. 24 in WalletHub’s 2027 Best Colleges and Universities rankings. The study compares institutions across factors including costs, student selectivity, campus experience and educational outcomes.
Source: WalletHub.
North Carolina ranks fourth for doing businessNorth Carolina placed No. 4 in Area Development’s 2026 rankings of the best states for doing business. The report highlighted strengths including innovation and research, manufacturing workforce skills, industrial site availability, government responsiveness and business costs.
New Bern entrepreneur wins startup fundingEntrepreneur Dan Spangler won $30,000 in the NC Small Business Center Network’s inaugural startup competition for Rinse-A-Poo, according to Business North Carolina. The ergonomic pet-waste cleanup product will use the funding to move from prototype toward production.
People, Science and Local Learning
Christina Koch takes NASA leadership roleNASA astronaut Christina Koch will become acting deputy chief engineer on November 1, helping oversee engineering across agency programs. Her experience includes the Artemis II mission and a 328-day stay aboard the International Space Station.
Source: NASA.
Wake libraries introduce free STEAM Lab programsWake County Public Libraries has launched STEAM Lab for students in third through fifth grades. Free, hands-on activities explore science, technology, engineering, art and math, with topics such as bridge design and geology. Families should check their branch’s calendar for sessions and participation details.
Millbrook staff help welcome a babyStaff at Millbrook Magnet High School helped a contracted security officer deliver her baby on campus, according to CBS 17. School officials reported that both mother and child were doing well.
Cyclist finishes 22,757-mile journey in RaleighJeff Molby completed a 22,757-mile bicycle journey in downtown Raleigh in October. His ride raised awareness of the American Cancer Society’s Road to Recovery program and the need for transportation assistance for people receiving cancer treatment.
Source: WUNC.
Upcoming Triangle Events and Important Deadlines
Wake County early voting ends October 31Early voting for the November 3 general election continues through 3 p.m. Saturday, October 31. Wake County voters can use any of the county’s 18 early voting sites. Check official locations, hours and available wait-time information before heading out.
Details: Wake County election information.
Pumpkin Palooza returns to Pullen ParkPullen Park’s Pumpkin Palooza runs Friday, October 30, through Sunday, November 1, from 10:30 a.m. to 4:30 p.m. The all-ages event features seasonal crafts, games and a digital scavenger hunt compass, with a train ride included. Tickets cost $14, and advance purchase is required.
Tickets and details: City of Raleigh.
Taste of China Festival comes to Cary November 7The annual Taste of China Festival returns to downtown Cary on Saturday, November 7. The free-to-attend celebration features Chinese food, cultural performances and community activities. Food and other purchases are separate from admission.
Details: Taste of China Festival.
Festifall Arts Market returns to Chapel HillThe 53rd Festifall Arts Market takes place Saturday, November 7, from noon to 5 p.m. at 100 E. Rosemary Street in downtown Chapel Hill. The free event features handmade goods, local artists and live performances, with pottery, jewelry, soaps, candles and more to browse.
Details: Chapel Hill Community Arts & Culture.
Banana Ball returns to Durham in June 2027The Loco Beach Coconuts and Firefighters are scheduled to play at Durham Bulls Athletic Park June 18–19, 2027. Fans can join the ticket lottery through October 31, 2026, for a chance to purchase tickets. Joining the lottery does not guarantee access.
Details: Banana Ball schedule and ticket lottery information.
Follow DoRaleigh.com for daily updates on government meetings, local festivals, and community happenings — your one-stop guide to everything Raleigh!
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Share With Us: Post your community News, Events, on our Submissions Page.
Advertise With Us: Interested in Advertising click here.Published by Bryan Tomlinson
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#AffordableHousing #BananaBall #CaryNews #ChapelHillEvents #DoRaleighNoteworthyNews #DurhamNews #events #Festifall #News #northCarolinaBusiness #PullenPark #RaleighDevelopment #RaleighNews #RaleighRestaurants #SLineRail #TasteOfChinaFestival #TriangleNews #WakeCountyEarlyVoting
Editor’s note: Opening dates, event details and development plans may change. Check with individual businesses, venues and organizations for the latest information. -
Most businesses know how to track their Google rankings.
But what happens when a potential customer stops searching on Google and starts asking ChatGPT, Perplexity, or another AI assistant which product they should choose?
Your website might rank well. Your content might get traffic. Your competitors might even have fewer backlinks than you.
Yet when a customer asks an AI assistant for recommendations, your competitors appear while your brand is nowhere to be found.
That creates a new visibility problem: which questions are you losing in AI search?
I'm building Aurvikon to help answer that question.
The problem with measuring AI visibility
Traditional SEO gives us familiar metrics:- Keyword rankings
- Impressions and clicks
- Organic traffic
- Backlinks
- Search performance
These metrics remain valuable. But they don't fully explain how your brand appears inside AI-generated answers.
Imagine someone asking an AI assistant:
- What are the best tools for improving AI search visibility?
- Which platforms help SaaS companies monitor their brand in AI answers?
- Compare three alternatives for tracking brand recommendations in AI search.
- Which tool should a startup use to understand its visibility in ChatGPT?
Your brand might appear in one answer and disappear from the next.
A competitor might be recommended repeatedly. Another company might be described as the category leader. Your own product might be missing entirely.
Looking at a single AI-generated answer won't tell you the whole story.
You need to understand visibility across the questions your potential customers actually ask.
The shift: from keyword rankings to question-level visibility
I'm interested in a more useful way to think about AI search visibility.Instead of asking only:
"Does my brand appear in AI search?"
Ask:
"For which relevant buyer questions does my brand appear, where do competitors appear instead, and what can I do about it?"
That second question leads to much more actionable work.
Consider three possible findings from an AI visibility audit:
1. You're missing from important questions.
People ask questions directly related to your product category, but the AI response doesn't mention your company.
2. Your competitors are being recommended instead.
Your product may be relevant, but competing brands are consistently appearing in the answers you're trying to win.
3. Your brand appears, but not in the way you want.
An AI response might mention your company without accurately communicating its core use case, differentiator, or value.
These are different problems. They shouldn't all receive the same recommendation to "publish more content."
What we're building with Aurvikon
Aurvikon is an AI visibility tool focused on helping businesses understand how their brands appear across AI answer platforms.The core idea is straightforward:
Identify the questions where your brand is losing visibility, understand what appears instead, and turn those findings into an action plan.
A practical workflow looks like this:
Step 1: Identify the questions buyers ask
Start with questions your target customers might ask before choosing a product.Don't limit the research to your brand name. Include category searches, comparisons, alternatives, use cases, and purchasing decisions.
Step 2: Test those questions across AI platforms
Run relevant questions across the platforms you want to monitor, such as ChatGPT, Perplexity, and Gemini.Treat the responses as observations to analyze, not as permanent rankings.
Step 3: Identify where you're losing
Look for patterns:- Which questions mention your brand?
- Which questions mention competitors but not you?
- Where are you missing from relevant comparisons?
- Which product capabilities or use cases aren't being communicated?
- Which questions deserve attention first?
This is where an audit becomes more useful than a collection of screenshots.
Step 4: Turn findings into actions
Every visibility gap should lead to a hypothesis.Maybe a product page doesn't explain a use case clearly. Maybe your comparison content is weak. Maybe your product lacks supporting content for a particular buyer question.
Investigate the evidence, make the relevant improvement, and test again.
The goal isn't to manufacture mentions. It's to make your brand easier to understand, evaluate, and recommend when it genuinely fits the user's needs.
The difficult part isn't generating an AI visibility report
AI answers change. Results can vary by wording, location, model version, and time.That means a report saying "your brand was mentioned" is only a starting point.
A useful system should help you move from observation to diagnosis:
Buyer question → AI response → competitor comparison → visibility gap → action → retest.
That repeatable workflow is the problem space we're focused on at Aurvikon.
We're especially interested in making the results useful to founders and marketing teams who need to decide what to fix next, rather than simply collecting another dashboard of numbers.
What this means for SaaS founders
If you're building a SaaS company, think about the questions someone would ask immediately before buying your product.Not just:
"What is [your brand]?"
But:
- What is the best alternative to [competitor]?
- Which tools solve [specific customer problem]?
- Compare products that offer [important capability].
- What should a small business use for [desired outcome]?
- Which platform is best for [specific use case]?
These questions reveal how AI assistants represent your category and which products they surface for potential buyers.
They can also reveal gaps in your positioning and content strategy.
AI visibility isn't a replacement for SEO. It's an additional area to measure as customers discover products through AI-generated answers.
I'm building Aurvikon around one practical question
I believe the most useful starting point for AI visibility isn't another generic score.It's knowing which valuable questions your company is losing, which competitors show up instead, and what you should investigate next.
That's the problem we're working on with Aurvikon.
I'd love to hear from other founders, SEO professionals, and SaaS marketers:
If you could see every important buyer question where AI assistants recommend your competitors instead of you, what would you want the tool to tell you first?
Would you prioritize the competing brands, the missing content, the sources behind the answers, or the specific actions most likely to improve visibility?
I'm interested in how people are approaching this problem in practice.#ai #seo #startup #showdev #software #coding #development #engineering #inclusive #community
Your Brand Can Rank on Google and Still Be Invisible in AI Search -
MarketInk: After 50 years in the Padres’ press box, Steve Dolan favors the Brewers in World Series
Between working as a sportswriter and a statistician, Alpine resident Steve Dolan estimates he has watched in person…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #MLB #2026WorldSeries #elections2026 #SanDiegoPadres #SanDiegoPressClub #Sports
https://www.newsbeep.com/us/895032/ -
MarketInk: After 50 years in the Padres’ press box, Steve Dolan favors the Brewers in World Series https://www.rawchili.com/mlb/837841/ #2026WorldSeries #Baseball #Elections2026 #MLB #Padres #SanDiego #SanDiegoPadres #SanDiegoPressClub #SanDiego #SanDiegoPadres