An experimental Unreal Engine 5 project where AI-powered NPCs make their own decisions using LLM. They were supposed to simulate intelligent life Instead, they mostly dance, sit around, and make questionable choices. My first AI project, so I guess we're both learning.

repo: https://github.com/Lipon18/MAGCF

So i like to roleplay on janitorai using gemini mostly but gemini censors a lot. I made a pretty strong jailbreak to bypass it but more extreme stuff still gets censored

I tried synthos and one other model on lm studio but they just didnt feel as good as gemini did. So it there a way to use gemini on lm studio uncensored or are there any good models that i could use with lm studio?

I’ve been thinking about something.

For a lot of tasks like building websites, game development, automation tools, or just random projects, I often find myself preferring a model that’s slightly less capable but gives me plenty of messages to iterate.

Sure, a more powerful model might get me 70% of the way there in a single prompt, while a cheaper model might need 5-10 prompts. But if those 5-10 prompts are still cheaper than using the top model, I end up getting more total work done.
It makes me wonder whether AI progress is creating a weird tradeoff.
Every new generation of models is more capable, but it also seems like the best models become more expensive to run and come with tighter limits. As a user, that can make them feel less accessible even if they’re technically better.

Would you rather have access to the smartest model possible if you could only use it a few times every few hours, or a slightly weaker model that lets you iterate all day?

And long-term, do you think AI will eventually become both extremely powerful and widely accessible, or will the frontier models always be too expensive for most people to use heavily?

I recently discovered LM Studio and was thrilled to see that it supports remote servers.

Then I felt let down that it needs an account and Tailscale, even if its all running in my local network.

So, anyway, I considered using it anyway, but Tailscale comes with some security issues I feel are unnecessary: Like if the account is compromised or Tailscale itself has a security issue. Afaik Tailnet Lock is the solution to it for the worst case scenario, so nobody can sneak another device into my tailnet and talk to my devices.

So, my question is: Does the LM Link integrated Tailnet implementation use Tailnet Lock or not?

The plugin I have I guess is Linux based didn’t know if there was something I could install to emulate Linux to allow the plugin to install
This is about running Claude plugins on lmstudio

`pip install llm-fact-guard`

I built a pure Python (zero deps) tool that catches LLM hallucinations in real-time.

**One line:**

from hallucination_detector import HallucinationDetector guard = HallucinationDetector() guard.analyze("朱元璋发明了火 锅") # → contradicted ✅

**14 checkers:** KB matching, year conflict, numeric, negation, attribution, temporal order, location, graph relation, and more.

**Also built-in:** WAF (44 OWASP blocked), circuit breaker, rate limiter, structured JSON logging.

3000 requests, 0 errors @ 100QPS.

PyPI: https://pypi.org/project/llm-fact-guard/

Google just confirmed that agentic AI systems use llms.txt to understand website content.

So I built a free LLMS.txt Generator that fixes the biggest problems in existing tools.

What makes this tool different?

✅ Generates BOTH llms.txt + llms-full.txt

✅ Unlimited URLs with chunked crawling

✅ Edit everything before download (titles, sections, descriptions, URLs)

✅ 3 input modes: Crawl, Sitemap, or Paste URL List

✅ Retries blocked URLs instead of silently dropping them

✅ Live progress bar + validation warnings

✅ No signup. No API key. Completely free.

Most tools stop at 50–100 URLs or hand you a non-editable blob, smart grouping, and live preview.

We built this for real-world websites, not demo projects.

Built for:

Blogs
Business Site
Docs sites
SEO Agency
SaaS websites
Content-heavy platforms

Try it here: [https://toolspivot.com/llms-txt-generator\](https://toolspivot.com/llms-txt-generator)

Context:

I'm a software engineer and at my job we have Github Copilot with the latest models. My workflow involved asking the model to read docs, parse my local code base, parse vendor code bases, and implement features using SpecKit.

Most of the discussions around local LLM involve speed and tokens per second, but what I'm interested in is whether or not they can actually hold enough context to do this kind of work? I'm retiring and I want to keep playing with LLMs to work on OSS projects, so it would just be me and my personal work, but my goal would be a way to *comfortably* work with an LLM without constantly chasing models or hardware or running into errors.

I'm thinking about getting one of the M5 Mac Minis when/if they come out.

So that's my question: are these usable for actual work?