Luis Encabo

@dan__rosenthal motions

I've overseen the GTM motions of 250+ B2B companies. These are my top 9 strategies for 2026, each rated by difficulty and time horizon: 1. AUTOMATED OUTBOUND - Difficulty: 3 stars. - Time horizon: Short. - Runs automatically by a GTM engineer - Without scaling SDR headcount. 2. PRODUCT-LED GROWTH - Difficulty: 5 stars. - Time horizon: Long. - Build user bases through free plans. - Then systematically upsell. 3. MANUAL OUTBOUND + COLD CALLING - Difficulty: 2 stars. - Time horizon: Mid. - Old-school sales that will always work. - But it requires headcount to scale. 4. ABM - Difficulty: 4 stars. - Time horizon: Long. - Full GTM resource deployment to a list of target accounts. 5. COMMUNITY + PARTNER LED - Difficulty: 4 stars. - Time horizon: Long. - Enabling external companies to sell for you through a community flywheel. 6. PAID MEDIA - Difficulty: 3 stars. - Time horizon: Short. - Guaranteed eyeballs through social media ad funnels. 7. SEO + AEO - Difficulty: 3 stars. - Time horizon: Long. - Capturing high-intent clicks through Google and LLM search queries. 8. SOCIAL CONTENT - Difficulty: 3 stars. - Time horizon: Long. - Building a personal brand through consistent content publishing. 9. EVENTS - Difficulty: 3 stars. - Time horizon: Mid. - Face-to-face sales and brand-building at live events. PS Want the full high-res PDF of the 9 GTM motions? Comment "motions" and I'll send it to you.

I’ve been using Codex for my day-to-day work as well. So far, the app works much better, and the integrations make it easier to work with Slack and documents. I also feel that its writing has improved significantly. Whenever I generate something with Claude, Codex finds an issue with it, and Claude always agrees. That rarely happens the other way around. Codex’s output is much cleaner.

@Encab0 Using Codex for app work and review while Claude implements is a clear two-stage route. Which recurring unit do you replay through both, and what accepted result made the 100-euro Codex tier worthwhile?

Si pagas el plan de 200 de anthropic como yo hasta hace 5 minutos, te recomiendo hacer downgrade a la de 5x y comprarte la de 100€ de codex. Desde hace mes y medio combino ambos y ha sido mi mejor decisión. Codex para trabajar desde la app, revisar lo que hace claude y integrarlo con todo tu trabajo. Claude para diseñar, y proyectos más profundos. Not going back

@Jonsanro @ahrrruiz @conductor_build No lo he llegado a probar! Es worthit pagarla? @Jonsanro?

@ahrrruiz @Encab0 @conductor_build Es la hostia para gestionar multimodelos. Se integran muy fácil y le puedes pasar conversaciones y planes de uno al otro.

Al final todo lo trabajo en local @ahrrruiz! Por lo tanto ambos tienen acceso a la misma carpeta. Otra cosa que hago es que como uso claude en el terminal copio todo el rollo del terminal y se lo pego tal cuál a codex. Y la última es que todo el output de uno y otro va etiquetado siempre. _donwclaude o _donwcodex y siempre se vuelca en una carpeta de output por mes.

@Encab0 Cómo le haces revisar lo que hace Claude? Vas copiando y pegando el output que te genera o hay alguna forma más limpia de hacerlo?

Esto es marketing: ICP detectado Mensaje ultra-personalizado Mensaje claro y al pie Sin florituras, sin IA, sin tonterías 100% efectividad.

Hoy vas a leer a todo el mundo que Anthropic ha lanzado el modelo que lo cambia todo. 400€ después te digo que es inutilizable. Como buen friki del tema, tenía que probarlo y sí, básicamente es inutilizable para tareas reales. Le he puesto a hacer mientras dormía una tarea que ya hice en su momento con 4.6 y 4.7. Procesar las más de 3k transcripciones de grabaciones que tengo de mentorías y sesiones con clientes para mapear frameworks de trabajo, metodología y demás. Me he despertado 6 horas después sin resultado (el token limit de la sesión había saltado) y con 150€ de extra tokens gastados. Durante la noche, @Anthropic ha reseteado los tokens del modelo y ya va por el 40 por ciento (la captura desde hace un rato). A la hora de escribir esto sigo sin output y consumiendo tokens. Quiero ver el output que genera, pero el coste lo hace impracticable; es una tarea que en 4.6 max hice sin problema con un resultado decente. Sabiendo lo que fallan las IAs, usar este modelo es hacer un salto de fe de 600€ cada vez y creo que hace de este un modelo para Twitter y LinkedIn, pero no para la vida real. Voy a completar la tarea cueste lo que cueste; a ver si me hace cambiar de opinión.

BREAKING: Anthropic just dropped Claude Fable 5—this is Mythos, made safe for public release. It is the best coding model in the world. We've been testing it internally @every for the last week or so across coding, writing, marketing, editing, and more—here's our vibe check: - It broke our benchmarks. Fable scored a 91/100 on our Senior Engineer benchmark—this is human senior engineer level. The previous high score was Opus 4.8 at 63. GPT-5.5 is a 62. - It's a one-shot wonder. You can set it and forget for hours or overnight on huge coding tasks, and come back to completed work. It cleared entire production bug backlogs, built a playable 3D, and even made a 2-minute animated film—all one-shot. - Taste and attention to detail. In coding and knowledge work tasks, it has much better taste and attention to detail than we've ever seen. It gets subtle things right, adds little features you might not have thought of, and generally understands the assignment in ways that surprised us. - Great use of context. We set it loose analyzing customer feedback surveys and our website data and it came back with a crisp, clean report that identified a. our biggest problem and b. a concrete testable solution—and then we sent it off to build that. - It's best for power users. If you're already used to orchestrating multiple agents in your work, this model can do things that you've never seen before. If you're a knowledge worker or vibe coder with a more basic setup, you're not going to notice a huge difference—in fact, it probably isn't the right model for you. - It's very slow, token-hungry. Using this thing for regular knowledge work is like squashing an ant with a rocket launcher. It also routinely uses 500k to 1M tokens on tasks. That's why it's best for your heaviest jobs—but not as good for tasks like collaborative writing. - It's expensive. It's about twice as expensive as Opus, and it's also incredibly token hungry—so expect it to be something you'll use sparingly unless your company pays for it. Overall, I think of it like a warp drive for coding: It can get you across the galaxy in a few hours, when it used to take months or years. But it's not appropriate for getting around town—you need something faster, cheaper, and more maneuverable. The ceiling is extraordinarily high on this model though. Even our most advanced testers like @kieranklaassen felt like they were only scratching the surface of it. Want our full vibe check with all of our testing and benchmarks? Read it on @every: every.to/vibe-check/ant…

Today, we're introducing Claude Fable 5 and Mythos 5, two configurations of our next major language model. I'd normally highlight the numbers: It's SOTA on nearly all benchmarks. I want to talk about something else, because with Fable 5 out in the world, I think a third era quietly started today. I lead Claude Code & Cowork on the desktop, so I think a lot about how people use AI to get work done. I believe we're about to see a major shift, moving from giving AI tasks to giving it responsibilities.

I use Claude Code as my main driver and reach for Codex for verification. Same projects, different surfaces: Claude Code lives in my terminal with the repo, Codex I use in the app. The split that emerged: Claude drafts, the other model audits facts that have a ground truth. Claude Code was my only AI system until ~2 weeks ago. The entire repo is built for Claude. Codex got its AGENTS.md 5 days ago, with one section. It plays in Claude's house. Last week I drafted an administrative appeal in Spanish. Claude wrote 3 Spanish Supreme Court citations (case numbers, years, paragraphs). All 3 were invented. ChatGPT-5.5 caught them across 3 review rounds before I filed. Same week, different domain: Codex caught 7 specific errors in a Meta Ads causal analysis Claude wrote. "0 creative changes in window" was actually 2 update_ad_creative rows in the raw CSV. A YoY table wasn't supported by any JSON on disk. It validated the central thesis but corrected the supporting numbers. Main frustration with Claude: even when the whole context is built for it, it commits to a causal narrative or invents specific citations when the data only supports correlation, or "I don't know yet." If Claude pushed back on its own prose with "is this verifiable in the raw source" before drafting, my reach-for-other-model rate would drop a lot. (All of the above with xhigh or max effort)

When do you reach for other models instead of Claude? What can we do better? Hit me with all of your frustrations. dms open. If you can give me detail (e.g. specifics/transcipts) - it'll help a lot in finding out exactly what we need to do to improve the next model

@codyschneider fbmanager

last Friday I deployed a Facebook ads agent for a startup and over the weekend it optimized itself from $17 phone number leads into $3 leads this is the real GTM engineering agents in the wild and I made a Notion document and a .md skill file so you can do this exact thing too it includes: 1. How to make on brand ads with nano banana 2 2. How to upload these to facebook ads via the API 3. Have an agent manage the ad account based on live data from the account via data pipeline + data warehouse Outcomes we deployed an AI agent for a startup last Friday to manage their facebook ads account day 1: the cost per phone number lead was $17 over the weekend this agent made and published 30 new pieces of ad creative, optimized the ad account, and reduced the cost per action Day 4: the cost per phone number lead was $3 this is a virtual employee that's working full time. like this post and comment "FBmanager" and I'll send you the Notion file and MD file

Llevo unos días combinando codex con code y me está empezando a impresionar que cada vez que codex revisa algo y se lo paso a code de vuelta, la respuesta siempre está siendo...

No te estás dando cuenta, pero estás bajando el nivel. Hace unos días me di cuenta de que la IA nos está haciendo bajar el nivel en general. Y no porque el output que genera sea peor, sino porque te conformas antes con lo que te entrega. Es decir: . Landing A hecha con IA . Landing B exactamente igual que la A hecha por un humano. Por lo que estoy viendo, tanto a nivel interno como con clientes que la están usando mucho, el nivel y volumen de feedback sería mucho menor en la A que en la B. Tu capacidad crítica se reduce cuando el output no lo genera un humano. Seguro que existe un sesgo cognitivo que, si no está acuñado, habrá que acuñar. No sé si es porque nos parece impresionante que un robot sea capaz de hacer eso, porque tarda menos que una persona o porque estás haciendo cosas que antes no podías hacer. Pero la realidad es que eres más permisivo con la IA que con tu equipo y eso te hace bajar el nivel. Llevo un par de meses notando esa tendencia que se agrava cada vez más, porque cada vez se pueden hacer más cosas. Así que, como consejo, cada vez que revises algo que te ha hecho la IA, piensa... ¿Qué le diría a alguien de mi equipo si me hubiera entregado lo que me ha entregado la IA? ¿Sería más crítico/a?

Las one person billion dolar companies son unos titulares preciosos. Pero nadie se ha dado cuenta de que añadir personas al negocio es la única opción para diluir el riesgo? Qué pasa si el founder muere o se pone enfermo? Aquí meto también empresas de 10 personas.

En un momento en el que la implementación es prácticamente fricción cero (todavía no estamos ahí) sigue teniendo sentido el modelo Hormozi de regala el conocimiento, vende la implementación?

@bcherny Any plans for Spanish support?

Update: this is now rolled out to 100% of users

@bcherny Let´s go!!!

🎁 Happy Friday - Opus 4.6 1M is now the default Opus model for Claude Code users on Max, Team, and Enterprise plans. Pro and Sonnet users can opt in with /extra-usage.

@charaf_ecomwize 4

From 0.8% to 7% conversion rate. Same product. Same ad spend. 4 page types doing all the heavy lifting. Most brands run one product page and wonder why their ROAS keeps dropping. The ones printing money are running all 4: → Listicle (cold traffic, builds curiosity) → Advertorial (warms up skeptics before they hit the store) → Landing page (converts ad traffic with one clear CTA) → Product page (closes the sale) Same visitor. 4 different entry points. Each one designed for a different stage of buying intent. We built all 4 templates. Plug in your brand or let @ecomwize fill everything with AI in minutes. Comment "4" and I'll send you all of them. (must be following)

@codyschneider generator

I just had Claude Code build me a Facebook ad generator that can make 100+ on-brand ad variations in minutes for $0. And I made a full Notion document guide for you. It includes: 1. How to use Claude to find the pain points and desired outcomes of your ICP 2. How to use these pain points and outcomes to write ad copy variations 3. How to build a Facebook ad template entirely with code (just like the ones you see) 4. How focus Claude Code’s design so the ad feels “on-brand” 5. How to export the Facebook ads as PNGs in a zip file 6. How to bulk upload them to a Facebook ad set 7. How to use an AI data analyst to track the success of these ads Everything above is just API calls and Claude Code doing the work for you. You just come up with the ideas and polish the outputs. Like and comment "generator" and I'll send the Notion document to you

@mikefutia statics

I just vibe coded a static ad generator in Claude Code that creates 100+ Facebook ads in minutes 🤯 One competitor ad + your product photo + your brand kit = dozens of on-brand variations, each targeting a different customer persona. Built 100% in Claude Code. Perfect for DTC brands and agencies who need more statics at scale. The problem: You find a winning ad concept and want to test 20 variations of it. That means briefing a designer, waiting days, getting back 4 options, giving notes, waiting again. Or doing it yourself in Canva for hours. This tool solves it: → Upload any competitor ad as your reference template → Add your product photos and brand kit (colors, fonts, logos) → AI generates 10 customer profiles from your brand research → Pick how many variations you want (10, 20, 30) → Tool generates on-brand ads with persona-specific copy for each one No designer back-and-forth. No Canva templates. No generic "Shop Now" on everything. What you get: → Ads that mirror winning concepts in your brand's voice → Copy targeted to specific customer pain points and personas → Multi-brand/client support with saved brand kits → Reusable customer profiles you build once and generate from forever I recorded a full walkthrough showing exactly how this works, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "STATICS" And I'll send it over (must be following so I can DM)