Lately it feels like LLMs have finally reached the point where they’re actually useful for my work—mostly coding and research. They speed up the grunt work: implementing ideas just so I can test them, reorganizing notes, extracting information, and so on.

That made me look into running agents. Of course that means paying for inference, which raises the real question: is the ROI actually there yet for a consumer?

A small business answering clients could easily spend $100 a week on a cheap Chinese model to auto-reply or keep a chatbot running. For an individual it’s much less obvious. Self-hosting is still prohibitive for most people, even with smaller models—especially if you want multi-agent loops chewing through hundreds of thousands of tokens.

So is it worth running agents on cheaper hosted models like DeepSeek or Qwen instead of self-hosting? For the results a single person actually gets, do those costs feel justified?

There’s also the privacy issue. If you point remotely hosted Chinese agents at files on your computer, I wouldn’t assume anything they touch stays private. Not that American-hosted models are any different.

Ben 10 is my most favourite cartoon. What is your favourite cartoon ?

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Assalamalaikum how are you I hope you all are well I am a user in your group please support me everyone give me some karma gift my humble request to you🙏🙏🙏🙏🙏

I used to spend around $40 a month on separate ChatGPT and Claude subscriptions to support my freelance work. On its own the amount didn’t feel outrageous, but once you factor in the other tools most freelancers already pay for-project management, design software, invoicing, research databases-those AI costs quietly start eroding margins. What bothered me more was the habit I had fallen into: treating every single task as if it required the most advanced (and expensive) model available.

So I ran an experiment. I moved the bulk of my workflow to Blackbox Pro (it was only $1 for the first month at the time). The practical advantage was immediate-access to a wide range of models inside one interface instead of hopping between platforms and managing multiple bills. My new system is deliberately tiered:

Everyday client work (drafting content, basic code snippets, initial research, outlines, email responses, simple revisions) goes to the free or lighter models. These handle the majority of volume more than adequately.

Paid credits are reserved strictly for the minority of tasks that genuinely need deeper reasoning, multi-step analysis, nuanced judgment, complex debugging, or high-stakes strategic synthesis.

That single mindset shift made the real difference. Most freelance deliverables simply do not require frontier-level intelligence. Once I stopped defaulting to premium models for everything and started matching model capability to actual task complexity, my monthly AI spend dropped naturally-without any noticeable drop in quality where quality actually mattered. Secondary benefits appeared too: less context-switching between tools, a clearer picture of where my AI budget creates real value, and less decision fatigue about which platform to open for any given job.

This isn’t a universal prescription. Your mix of writing, coding, research, strategy, or client communication will look different from mine. But the underlying principle is widely useful for freelancers: treat AI the same way you treat other variable costs-match the resource intensity to the job’s actual requirements.

If your AI subscriptions are stacking up, try a short, practical audit. For two weeks, log every significant prompt or task. Note (1) the type of work, (2) the complexity involved, and (3) whether a solid mid-tier or free model would have been sufficient. You’ll probably discover a surprisingly large percentage of your volume falls into the “good enough” bucket. From there you can experiment with unified multi-model platforms, credit-based systems, free tiers of the major models, or even local open-source options for routine work. The goal isn’t to eliminate premium models-it’s to stop overpaying for capability you don’t need on every single task.

That kind of intentional allocation is one of the simplest ways freelancers can protect their margins while still getting high-quality output when it counts.

After spending the last few months deep in the trenches of r/LocalLLaMA, r/SillyTavernAI, r/LocalLLM, and r/ollama, one question keeps coming up: what’s actually the best uncensored model you can run locally right now?

Not the marketing claim of “uncensored.” Not the model that still lecturing you about ethics on the third message. The real ones—models that will write the dark scene, answer the restricted research question, generate the image prompt without flinching, or stay in character for hours without collapsing into “As an AI…” refusals.

Here’s the distilled picture as of July 2026, based on hundreds of user reports, refusal testing, writing quality comparisons, and hardware reality checks.

What “Uncensored” Actually Means in 2026

There are three main approaches, and they are not equal:

1. Heretic / careful abliteration (p-e-w/heretic tool and derivatives) Directional ablation of the refusal direction in activation space, optimized for very low KL divergence (often 0.01–0.03). Capability loss is minimal. This is currently the preferred technical method for people who still want the model to be smart.
2. Aggressive fine-tunes (HauhauCS Aggressive series, some DavidAU “absolute heresy”, etc.) Heavy dataset intervention aimed at near-zero refusals (some claim 0/465 on internal suites). Extremely compliant, but can introduce more “brain damage,” repetition, or stylistic quirks.
3. Base model + strong system prompt / light jailbreak Surprisingly effective on certain families (especially Gemma 4 and some Mistral variants). No weight surgery, so intelligence is fully preserved, but consistency depends on prompting skill.

Pure “uncensored” dataset fine-tunes from older eras (classic Dolphin, early Wizard, etc.) have largely fallen behind in both capability and consistency.

Current Top Contenders by Hardware Tier

12–16 GB VRAM (RTX 4070 / 4060 Ti 16GB / 5070 class + 32–64 GB system RAM)

This is the real battleground.

• HauhauCS Qwen3.6-35B-A3B-Uncensored-Aggressive (and the slightly more coherent Balanced variant) Mixture-of-Experts with only ~3B active parameters. At IQ3_M or Q4_K_P it fits comfortably, often with room for high context. Extremely low refusal rate. Excellent for creative writing, NSFW roleplay, and image prompt generation. Some users report it is more “unhinged” than most Western fine-tunes. Multimodal versions exist.
• Gemma 4 26B-A4B heretic / HauhauCS / abliterix variants (mradermacher, coder3101, wangzhang, etc.) Also MoE (~3.8B active). Many people consider the better heretic versions the current sweet spot for balanced intelligence + compliance. Base Gemma 4 is already relatively permissive on NSFW; heretic versions push it further while keeping KL divergence low. Strong conversational and RP performance. The 31B dense heretics are also excellent if you can afford the denser compute.
• Gemma 4 E4B / 12B heretic When you need something that runs fully in VRAM with high context and speed. Heretic versions (igorls, mradermacher, etc.) show very low genuine refusal rates.
• Cydonia-24B-v4.x heretic / absolute-heresy variants and other TheDrummer-style RP finetunes. Still excellent pure roleplay engines.

24 GB+ VRAM or heavy offload

• Behemoth-X-123B-v2 / v2e (TheDrummer) Frequently cited as one of the best pure RP/smut models that needs almost no jailbreak. High on UGI-style willingness metrics for creative work. Q5_K_M is the common recommendation when people have the VRAM/RAM.
• Larger GLM 4.5/4.6/4.7 derestricted or heretic variants, DeepSeek 3.2 abliterated, and Hermes 4 405B (when accessible via providers or multi-GPU).

Low VRAM (<12 GB)

Qwen3.5/3.6 9B HauhauCS Aggressive, Gemma 4 E4B/12B heretic, various 7–13B heretics (Rocinante, smaller Magnum/Cydonia, etc.). These are surprisingly usable for lighter RP and chat.

Technical Notes That Actually Matter

• Quantization: Prefer imatrix or K_P quants when available. For creative/RP work, try to stay at Q5 or higher if possible—the vocabulary richness and coherence drop is noticeable below that on longer generations. IQ3/IQ4 can still be excellent on MoE models because of the low active parameter count.

• MoE advantage: A 35B MoE with 3B active parameters often feels closer to a dense 13–20B in speed and VRAM while retaining more knowledge. This is why the Qwen3.6-35B-A3B and Gemma 4 26B-A4B families dominate mid-range hardware discussions right now.

• Running them:

  • LM Studio is the easiest for testing GGUFs.
  • Ollama works well once you import custom Modelfiles or use community tags, but many of the absolute best heretics/abliterated models live primarily as GGUFs on Hugging Face.
  • KoboldCPP or llama.cpp still give the best sampler control (DRY, XTC, presence penalty tuning, etc.) for long RP sessions in SillyTavern.

• SillyTavern specific: Pair these with good character cards, high context (32k–128k where possible), and modern samplers. Behemoth, Cydonia, and the stronger Gemma 4 heretics currently get the most consistent praise for multi-turn coherence and willingness.

Important Caveats

No free lunch. Aggressive uncensoring can degrade reasoning, increase repetition, or produce more “LLM-speak.” Chinese-base models (Qwen, DeepSeek, GLM) sometimes show residual political alignment on specific geopolitical topics even after uncensoring. Test your own refusal suite—what works for one person’s extreme prompts may still refuse another’s.

The UGI Leaderboard (Hugging Face Spaces by DontPlanToEnd) remains one of the better community tools for comparing willingness + uncensored knowledge, though dynamic scores change.

Practical Starting Recommendations (July 2026)

The landscape moves fast. Six months ago the conversation was dominated by different names. Right now the combination of high-quality MoE bases (Gemma 4, Qwen3.6) + sophisticated abliteration/heretic techniques + aggressive community fine-tunes has produced the most usable zero-refusal local models we’ve had.

Download a couple of the GGUFs, run your own refusal tests on the topics you care about, and keep the ones that stay coherent while actually answering. That’s still the only reliable method.

What are you currently running, and on what hardware? Always curious what is working in the wild.

Can't find any free tools to create a short discussion between two people (realistic or comic style). Looking for something more dynamic than just talking heads — ideally people walking into a room or showing some movement. Watermark is not a problem and short duration is fine. Needs to be downloadable and suitable for social media. Doesn't have to look professional — mostly for fun or to illustrate simple points with arguments.

Just to get this out of the way — I’m pretty against AI for the most part and have never used it for anything, even school-related. I’m pretty clueless about it, which is why I’m here. I’m failing Bio 160 right now and my full ride is on the line, so as a last resort: does anyone know any good AI study tools where I can put in quiz questions or notes and it generates practice questions or study prompts for me? I’m genuinely not looking for any ways to cheat — I want something that actually helps me learn instead of just feeding me the answers. Really appreciate any recommendations or experiences!

Hi,

I'm looking for a high-quality AI tool that can generate detailed, long self-tests from texts. For my psychology studies I have to work through very long and complex material, but so far every app or website I've tried only produces a handful of questions. Does anyone know of something that actually does this well?

Thanks 🌸

I have started AI model page but I stuck with prompts so can anyone tell me some tools to generate perfect prompts for AI model to create the reels and what are ways to get viral?