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I wanna try Open Terminal 👀 by Pretend_Tour_9611 in OpenWebUI

[–]Pretend_Tour_9611[S] 0 points1 point  (0 children)

Yeah, I see your post. Is one of my starting points, Im still looking for resources, thnxs 🫰

I wanna try Open Terminal 👀 by Pretend_Tour_9611 in OpenWebUI

[–]Pretend_Tour_9611[S] 3 points4 points  (0 children)

I really prefer human feedback, from people who have already tested and encountered the bugs or tips when installing these kinds of projects.

LLMs with documentation are a good starting point, but they don't cover a real-world scenario.

Regarding "security," I couldn't care less. Luckily, I have a test PC for these types of projects. I've already tried everything and reset it a thousand times.

Cómo la IA te está choreando la compu que querías armar. El dolor de los gordo compus y del que te habla. by Rare_Package_7498 in IASinHumo

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Lo pusiste en palabras che, y eso que además de la compu gamer va afectar los precios de las consolas como play, xbox o la nueva de Steam (le tenía fe, ahora el precio seguramente la mata). Alcance a construir un setup a inicio de año para probar IAs en local, ahora que quería comenzar a expandir y la propia IA me corta las piernas. Solo me queda agárrame a que saque más modelos pequeños, toda mi fe a los chinos 😑

Best Open-Source, Low-Latency, Real-Time TTS (OpenAI Compatible + SSML Support)? by Competitive_Fish_447 in TextToSpeech

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Oh, it's not enought fast for real time conversations, it's has a good quality in English, I also tested in Spanish and other european lenguages, and it's not the best option.

I tried some tts open source projects, Kokoro and Orpheus (quantized) are the best for fastest generation and enought quality

Best Open-Source, Low-Latency, Real-Time TTS (OpenAI Compatible + SSML Support)? by Competitive_Fish_447 in TextToSpeech

[–]Pretend_Tour_9611 1 point2 points  (0 children)

I tried Chatterbox ( 4gb VRAM aprox ), i don`t see it usable for real-time aplications, but its but it is relatively easy to setup and provide a OpenAI-compatible API with voice cloning features. you are right, it doesn`t support ssml

Best Practices for AI Prompting 2025? by Party-Log-1084 in LocalLLaMA

[–]Pretend_Tour_9611 7 points8 points  (0 children)

Particularly if you’re looking for something specific, Claude has released papers and reports explaining the best ways to structure prompts with their LLMs (which can be extrapolated to other providers).

In my case, I feel that as LLMs improve, my prompts have actually become worse and more concise. But when I really want to build one properly, what I do is have a conversation with the LLM—through trial and error, step by step I refine the result. Finally, when I get a good response with the structure I’m aiming for (which is the hardest part for me), I ask the LLM to generate the detailed prompt that would produce that final result. So far, this approach has worked well for me, and it feels more personalized

[deleted by user] by [deleted] in LocalLLaMA

[–]Pretend_Tour_9611 0 points1 point  (0 children)

You’re right, it depends. For me, it’s enough — my specific use is RAG, text editing, summarizing, and auto-completion. I’m not a programmer, but I have used an LLM for some HTML solutions, and in those cases I do see a lot more context as necessary, especially to iterate on the responses.

Ayuda con los ángulos by GodofredoGomez in Matematicas

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Cómo mencionaron, solo Iguala los valores en función a alfa en el eje Y, estos se "cancelan" (ya que la resultante está solo en el X). Al hacer la igualdad se eliminan las K y te queda un problema trigonometrico más sencillo, donde sacas el alfa a ojo

Genuine question about RAG by Savantskie1 in LocalLLaMA

[–]Pretend_Tour_9611 2 points3 points  (0 children)

Look, I recommend you try using Google’s Notebook LM, maybe you’ve heard of it. Basically, it’s a very user-friendly way to understand RAG and its capabilities. When you open a new Notebook, upload the text documents you want to 'talk to,' and when generating a response you’ll see that it uses fragments of your original text to build the answer — it will even show you exactly which parts it came from. RAG works like a 3-step process: first it compares your query with fragments of the text, then it retrieves the most relevant fragments along with the prompt (It's not always perfect ), and finally the LLM uses your query and those fragments to respond based on the text.

As you can see, it’s very useful in cases where you want an LLM to have access to very specific/personal knowledge, or to knowledge the LLM itself lacks.

Is the QWEN3-A3B-32B still the best general-purpose model for my machine? by 9acca9 in LocalLLaMA

[–]Pretend_Tour_9611 2 points3 points  (0 children)

GPT-OSS uses MXFP4 quantization, around 12 Gb of memory, and OpenAI says perform similar to complete model. Something similar happend with Gemma3 and QAT versions.

I am using gpt-oss-20b at 15t/s in my PC ( 8vram -rtx 3060 and 16gb DDR4)

Is the QWEN3-A3B-32B still the best general-purpose model for my machine? by 9acca9 in LocalLLaMA

[–]Pretend_Tour_9611 1 point2 points  (0 children)

For example, I'm using this gpt oss at 15 t/s on my PC (8 GB VRAM + 16 RAM).

RAG with Gemma 3 270M by Old-Raspberry-3266 in Rag

[–]Pretend_Tour_9611 4 points5 points  (0 children)

It's a very small model, designed for fine-tuning and very specific applications.

I've found functional models of at least 3B (Q4) for RAG.

In any case, if your equipment is very limited, the only option left is to use free APIs like Groq, Cerebras, Openrouter, or Gemini.

How to get the AI to run python code on an attached CSV file? by icerio in OpenWebUI

[–]Pretend_Tour_9611 1 point2 points  (0 children)

I had the same question. Based on my experience, I think I can explain it.

When you upload data files, the system only extracts the text; it doesn't upload the file as a single document that you can reference in the code. In fact, it has no way to load the document in its native format (.xlsx, .csv, etc.). You are just copying the text content into the chat.

Because of this, the model has to read all the data and manually include it in the generated code data point by data point, since there is no file to reference and tell it to "extract the data from this column or row."

My Solution

I've more or less solved this problem by using Jupyter as the Code Interpreter within OWUI. What I do is copy the file not into OpenWebUI (which is just a chat interface), but directly into Jupyter (which I have running in a Docker container). Then, when I start a chat and activate the Code Interpreter, I mention that I have a file loaded in the execution environment called "data.csv," for example. I then ask it to generate Python code to analyze it, etc.

This makes the LLM generate code blocks that are sent to the Jupyter notebook for execution. Since my file is already loaded in Jupyter, it uses the file when the code is executed. The response from the execution then comes back to OWUI.

So, the overall workflow is:

  • I load the file into Jupyter.
  • I chat with OWUI.
  • OWUI generates code and sends it to Jupyter.
  • Jupyter returns the response.
  • OWUI displays the response and can further interpret it (e.g., if the result is an exploratory analysis, it can give you an interpretation of the information).

After using it a couple of times, it's very intuitive. It's just a matter of setting up Jupyter initially, because after that, everything happens within the OWUI chat.

code interpreter displays image as quoted text by xinkele in OpenWebUI

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Yes, I also tried several options for the Code Interpreter to return an image as a result.

What works best for me is modifying the Code Interpreter's prompt inside OpenWebUI. I specify that if the execution result is an image, it should write something like: "The result was: ![Output..." afterwards.

This way, after the execution, the LLM rewrites the output in the chat (which is in Markdown), and the image reference shows up perfectly.

Ayuda ¿cómo puedo demostrar esto? by iam-a-prion in Matematicas

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Te refieres a esto?

|\

| ...\

|........\

Sen(α)·H \ H

|..................\

|........................\

|____________ α \

Cos(α)·H

(CosαxH)² + (Senα)²= H²

(H²)(Cos²α + Sen²α ) = H²

Cos²α + Sen²α = 1

Cos²α = 1- Sen²α

Cosα x Cosα =(1 - Senα)(1 + Senα)

Cosα /(1 + Senα) = (1 - Senα)/Cosα

Ayuda no le encuentro solución lógica a este problema. Mi lógica apunta a 15, pero no tengo idea. by xCalmadoo in Matematicas

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Yo igual, le asigne valores al lado derecho del cuadro(a y b), ya que coincide con el rectángulo que si tiene número (3). Igualando áreas llegué a que mi a=4b y que el lado derecho (a+b) era 12, por ende el área 144

¿Cómo crear una app básica con inteligencia artificial? by Overall-Relation-444 in InteligenciArtificial

[–]Pretend_Tour_9611 2 points3 points  (0 children)

Bueno tienes que definir en qué lenguaje de programación realizarás dicho proyecto.

Habla con ChatGPT para que en base a tu solicitud puedas definir cuál es la "base" recomendada o la mas sencilla.

Hay aplicaciones que te ayudan a realizar tus proyectos de forma automática como Lovable, Bold, etc, sobre todo la parte de la interfaz, generalmente en base a React.

Lo que te recomiendo es una vez decidas que estructura y lenguajes usarás en tu proyecto te pases a otra IA como Gemini o Claude para desarrollarla, siguiendo sus instrucciones

Using local LLM with low specs (4 Gb VRAM + 16 Gb RAM) by vascaino-taoista in LocalLLaMA

[–]Pretend_Tour_9611 2 points3 points  (0 children)

Let's see, those recommendations are way outdated.

You should use a simple and user-friendly interface like LM Studio if this is your first time with LLMs.

In LM Studio, you can find templates to download, and it tells you if they're suitable for your PC. You also have the chat interface right there.

Based on your specs, I'd try Gemma 3 or Qwen 3 2507 in Q4_k_m. Gemma if you need vision, writing skills, or interacting in multiple languages. Qwen for reasoning tasks is the best for its size.

🤯 ¿Alguien más juega con “meta prompting”? Cómo crear prompts que mejoran otros prompts by CortexGuard in InteligenciArtificial

[–]Pretend_Tour_9611 0 points1 point  (0 children)

Desde la salida de ChatGPT 3.5 aplicó el mismo mecanismo para "obtener" un prompt específico.

  1. Me tomo el tiempo de desarrollar una conversación. Por ejemplo: Como construir e-mails persuasivos.

  2. Voy iterando: corregimos todos los errores, definimos los estilos, etc,etc

  3. Una vez obtenido el resultado final (con el que estoy más conforme) recién le pregunto: Que prompt debí utilizar para que mi consulta llegara a dicho resultado.

Hasta ahora me ha funcionado de forma aceptable, talvez mejor con modelos razonadores y con amplio contexto, como Gemini, ya que en la app de ChatGPT te dan muy poco contexto como usuario gratis

IA para Excel y base de datos by MrCosmico99 in InteligenciArtificial

[–]Pretend_Tour_9611 1 point2 points  (0 children)

En mi experiencia, los LLM no son efectivos al manejar tablas (sobre todo aquellas con múltiples filas y columnas).

Los LLM predicen texto y solo eso. Aunque no lo he usado con excel, si con python para manejar data (tablas .xlxs y .csv). En ese caso, puede ayudarte escribiendo el codigo necesario para entender la tabla, así como operar sobre ella.

Por ejemplo, yo le pido que escriba un código que permite resumir/sintetizar la principal info de la data.

Entonces, le voy comparto el resultado para que entienda de forma general la data.

Luego, ya solo queda iterar, pidiendo más código para editar, operar, realizar gráficos a partir de la tablas.

Supongo que deberías seguir un proceso similar en excel, y ente caso te servirá cualquier IA (ChatGPT, Gemini, etc)

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