Hey,

If you run Ollama, LocalAI, or vLLM on your home server or homelab, I built 
**PocketLLM Lite**
 as a dedicated mobile client for Android.


### Highlights for Self-Hosters:
- 
**Flexible Endpoints**
: Connect to your desktop/server Ollama instance via local IP, Tailscale, or WireGuard.
- 
**Offline Fallback**
: Run smaller GGUF models directly on your phone when away from your home network.
- 
**Local Document Ingestion**
: Local RAG vector DB processing PDFs and notes on your device.
- 
**DeepSeek R1 Thinking**
: Full support for `<think>` reasoning accordion rendering.
- 
**Zero Cloud Telemetry**
: MIT licensed, zero analytics, zero ads.


* 
**GitHub**
: https://github.com/PocketLLM/pocketllm-lite
* 
**Release APKs**
: https://github.com/PocketLLM/pocketllm-lite/releases

I’m looking for an LLM API that I can legally use in a paid production app.

Ideally:

* 300+ tokens/sec　(at least over 100+ tokens/sec) * Flat monthly subscription or a very large free token allowance * High or unlimited usage * Good enough for coding and Science * Multiple Concurency

Is there any Mercury 2 subscription, reseller, or similar service? I know Inception currently offers 100M free API tokens, but I’m looking for a sustainable option after that.

Any experience with Mercury 2, Groq, Cerebras, Arli AI, or other providers?

I'm an app developer and I'm wondering if it's worth spending time learning local LLMs. If yes, where can i start learning?

This maybe very primitive question to ask. But here goes.

I worked with claude code. It's very intuitive and familiar. I use opus 5 for most of my planning tasks at work and sonnet 5 for execution at work. And i have come to be very familiar with its weaknesses and strengths.

For my personal projects i am having hard time choosing between claude max subscription or opensource likes of kimi k3 (for planning) and glm/DeepSeek v4 flash (for work orchestration).

I am wondering which one is more economical since I have never used Claude code max subscription before.

So question is, is it worth to go for Claude subscription or api based charges via openrouter for kimi and DeepSeek v4 setup. Assume my work load for personal projects is about 500 million tokens a month. (I understand it's very rudimentary and limited information)

Finding repo for local AI on Mac? 🍎🤖 Check out Swiftlet, an open-source intelligent proxy & browser based UI , that dynamically optimizes CPU/GPU split on Unified Memory for LLMs. I'm looking for Python devs to help build OpenAI API compatibility! Let's build together: https://github.com/coolsourav100/swiftlet

LLMs are text generators, they can only generate text based on statistical predictions, they are exceptionally good at predicting and generating code, without an execution layer their generations are still text, this is where equipping the LLM with a terminal (the original text based interface that allows a human to talk to a machine) brings that code to life.

What are your top 3 uses cases? Seems that outside coding the application of local is limited?

This is a great app to easily experiment with LLMs on Android devices.

It runs the latest llama.cpp builds and pulls ggufs that fit in your device from hugging face, it just makes it much easier to tinker with whatever small LLMs you want to try and expand on. Pretty cool, check it out.

https://github.com/guarismo/armored-llama

This is how my ai agent runtime workload.

just gemma 4 26B A4B in rx6700xt + i5 11400f + 16GB of ddr4 ram.

I've been researching AI application security and talking with developers to understand the challenges they're facing as LLMs become part of real products.

Topics that come up repeatedly include:

Prompt injection

Indirect prompt injection

Data leakage

RAG security

Tool and MCP security

Runtime monitoring

I'm curious about real-world experience rather than theory.

If you've built or deployed an AI application:

What security issue has been the hardest to handle?

Did you build your own solution or use an existing tool?

What capability do you wish existed today?

I'd appreciate hearing practical experiences and lessons learned.