Javier Hermosilla

[Javier Hermosilla @jadaheto]

@stuntech Link ?

[Guillermo Casaus @_guillecasaus]

🚨 Google acaba de publicar una guía completa para dominar Nano Banana 2.

Explica sus nuevas capacidades, cuándo usarlo, cómo aprovechar Visual Grounding con Google Search y cómo hacer prompts para sus nuevas funciones.

Aprende a usarlo aquí 👇

[Javier Hermosilla @jadaheto]

@SantiTorAI CLAW

[Javier Hermosilla @jadaheto]

@mjkayum Send

[MJ Kayum @mjkayum]

🚀 Veo + AI = 100+ videos/min

We’ve officially entered the Mass AI Marketing era… and it’s insane.

⚡ UGC ads in seconds
📈 10x reach overnight
⏳ No agencies. No delays. Just results.
💡 All on autopilot. 24/7.

🔁 LIKE+RT
📥 Comment “Send”
✅ Follow to get the free guide.

[Javier Hermosilla @jadaheto]

@n8n_io Por fin

[Steve (Builder.io) @Steve8708]

how do Adaline and Apple make these crazy 3d zooming effects as you scroll?

it's easier than you think - let me show you how

prompts and code in thread:

[Javier Hermosilla @jadaheto]

@recap_david ASSISTANT

[David Roberts @recap_david]

$24,000 per year from this simple AI Dentist Voice Agent

(and why I'm crazy for giving it away for free)

A dental practice was losing $6,000+ in revenue every month from missed after-hours calls.

That's 20-25 potential patients walking away because no one was available to book their appointments.

So I built an AI voice assistant that handles after-hours dental bookings 24/7 using n8n and ElevenLabs based on internal policies and scheduling availability.

Here's what this system does:
→ Answers calls with a natural-sounding AI receptionist
→ Collects patient information and insurance details
→ Checks calendar availability in real-time
→ Books appointments automatically
→ Logs all patient details to a Google Sheet

The result? This similar AI voice system was sold to a dental practice for $24k per year by another entrepreneur!!

This isn't just about dental practices. Any service business losing money from missed calls can implement a similar system.

Want the complete n8n workflow template?

1. Retweet & Like this post
2. Comment "ASSISTANT"

I'll send you the entire system for free, a full setup walk-through video, including the ElevenLabs automation components.

[Tyler @tyler_agg]

I just trained a custom GPT that turns any idea into a Nano Banana JSON prompt

just describe the picture you want and it will:

– plan the photo like a creative director
– offer pro-level suggestions
– write the entire JSON prompt for you

all you have to do is copy and paste the JSON and you'll get images that look like a creative director made it

comment "nano" and I'll DM it to you (must be following)

[Javier Hermosilla @jadaheto]

@mikefutia IG

[Widya Bayu W @RalconStudio]

I love my job, anyway..

Sadewa AI automation agency just landed on the @framer marketplace!

This time, we’re picking 1 winner to receive a free copy of the template and all the bonuses (worth $99).

Repost + Comment "Sadewa" to enter.

Winners will be announced in 24 hours.

Good luck!

[Javier Hermosilla @jadaheto]

@RalconStudio @framer Sadewa

[John @ytb_johny]

I just unlocked ElevenLabs Premium for FREE. 🔥

Unlimited voice generations.
Zero cost.

It’s actually insane – perfect for YouTube videos, ads, podcasts & more. 🎙️

Want it too?
Reply w/ "VOICE", Follow me, Like & Repost – I’ll DM you the link.

[Javier Hermosilla @jadaheto]

@ytb_johny VOICE

[Aurimas Griciūnas @Aurimas_Gr]

Building even a simple 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗯𝗮𝘀𝗲𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 is a challenging task. Read until the end to understand why 👇

Here are some of the moving parts in the RAG based systems that you will need to take care of and continuously tune in order to achieve desired results:

𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹:

𝘍 ) Chunking - how do you chunk the data that you will use for external context.

- Small, Large chunks.
- Sliding or tumbling window for chunking.
- Retrieve parent or linked chunks when searching or just use originally retrieved data.

𝘊 ) Choosing the embedding model to embed and query and external context to/from the latent space. Considering Contextual embeddings.

𝘋 ) Vector Database.

- Which Database to choose.
- Where to host.
- What metadata to store together with embeddings.
- Indexing strategy.

𝘌 ) Vector Search

- Choice of similarity measure.
- Choosing the query path - metadata first vs. ANN first.
- Hybrid search.

𝘎 ) Heuristics - business rules applied to your retrieval procedure.

- Time importance.
- Reranking.
- Duplicate context (diversity ranking).
- Source retrieval.
- Conditional document preprocessing.

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻:

𝘈 ) LLM - Choosing the right Large Language Model to power your application.

✅ It is becoming less of a headache the further we are into the LLM craze. The performance of available LLMs are converging, both open source and proprietary. The main choice nowadays is around using a proprietary model or self-hosting.

𝘉 ) Prompt Engineering - having context available for usage in your prompts does not free you from the hard work of engineering the prompts. You will still need to align the system to produce outputs that you desire and prevent jailbreak scenarios.

And let’s not forget the less popular part:

𝘏) Observing, Evaluating, Monitoring and Securing your application in production!

What other pieces of the system am I missing? Let me know in the comments 👇

#LLM #AI #MachineLearning

[Nero @neropursue]

New month, new Giveaway!🔥

Get a @figma file with these stunning hero designs for FREE.

1. Comment "Hero"
2. Follow me - So I can DM it to you
3. Retweet (optional) - so more people can get the gift

[Javier Hermosilla @jadaheto]

@nerooeth @figma Hero

[Sarthak @designedbysg]

Giveaway time! 🚀

Get the @figma file with these designs for FREE!

1. Say " Hii "
2. Follow me (so I can DM you)
3. Retweet so more people can get it ( optional )

Thought I will start the month will a giveaway!! Thanks for being here early 🔥

[Javier Hermosilla @jadaheto]

@_sameerrr0 @figma Heros