Fable 5 is banned, but the ENTIRE system prompt is leaked.

You can plug the exact Fable 5 system prompt .md into Opus, GPT-5.5, or any other LLMs.

Steal the system prompt here.👇

github.com/elder-plinius/CL4R1T4S/blob/main/ANTHROPIC/CLAUDE-FABLE-5.md

Getting ready for another week

This system prompt completely destroys AI slop.

Inject this into your Hermes/other LLM tools to eliminate robotic responses

🚨 TODAY: The US Commerce Department ordered Anthropic to suspend exports of its Mythos and Fable AI models globally over fears they could be diverted to military intelligence users in China, Russia, or other countries of concern, per Reuters.

A whopping 815,500+ tech workers have been laid off since 2022.

85,411 cuts Jan-April 2026, up 33% from 2025 as the firings accelerate in the industry.

AI is creating a two-tier Silicon Valley: a few people get huge packages for AI skills, everyone else faces longer searches, lower pay, and more competition.

The old formula (resume, LinkedIn, interviews) stopped working.

The new formula is adapt or perish. 🤖

This Claude Skill KILLS AI slop from your writing.

It's trained to detect every common LLM keyword, phrase, and sentence structure.

You'll want to save this.

github.com/hardikpandya/stop-slop

The best AI benchmark for marketing your LLM:

🚨 Anthropic updates its privacy policy for verification data.

Users will soon be asked to verify age/identity using officially issued government IDs.

Keep in mind that Fable 5 was collecting every single prompt/data input that users sent...

Open source AI is not just a nerd hobby.

It might be the only real escape hatch from a future where a few labs and governments decide who gets intelligence.

Centralized AI is convenient until someone flips the switch.

Would you trust open source AI more than government approved AI?

Humanoid Robots Are The Real AI Dividend

I don’t think the real AI dividend will be some government check.

It’ll be everything getting cheaper because labor stops being scarce.

Food, housing, elder care, construction, manufacturing, delivery, housework.

Humanoid robots are basically a new labor force.

Once physical work gets automated at scale, money still exists, but it matters way less.

That’s the future worth fighting for.

Actual abundance, not politicians deciding how much of the future you’re allowed to get.

I’m less worried about AI being too available.

I’m worried about intelligence becoming gated.

A few governments and big labs could decide who gets the best models based on country, company, job title, or “approved” use case.

That would create a real intelligence class system.

What scares you more, open source chaos or a few gatekeepers controlling access?

How hard I laugh at my mom's joke before I tell her we just lost the house because 22 millionaires couldn't score a goal

Me to my bro after knowing some random guy hacked hundred millions dollar from crypto project with a prompt, while we can't do it with our free ChatGPT plan

Warren Buffett in the retirement home watching Elon Musk make his entire career's profit in 1 day

$IBM $IONQ $HON $QBTS $RGTI | China's CAS Cold Atom Technology debuts Hanyuan 2, utilizing a twin core framework of 200 neutral atom qubits split between rubidium isotopes.

The dual layout allows parallel processing or dedicated error correction tasks.

$UMAC $ONDS $KTOS $AVAV $RCAT | The Pentagon shifts to an "affordable mass" strategy, signing framework deals to secure over 10,000 low cost containerized cruise missiles.

The LCCM program aims to rapidly deepen stockpiles using commercial parts.

Engineers are about to push Buffett out of the top rankings.

🚨 ANTHROPIC SUED OVER USAGE LIMITS

according to the lawsuit:
> Max 5x allegedly gives ~3.5× more usage than Pro
> Max 20x allegedly gives ~6× Pro
> Max 20x is allegedly only ~1.7× Max 5x
> limits calculations are unclear and unspecific.

*chart is from the lawsuit claims.

LOCAL LLM GUIDE (June 2026)

Cheapest full build: 1× used RTX 3090 (24GB) + rest of PC ≈ $1000-1500
16GB all-rounder → Gemma 4-12B
32GB all-rounder → Qwen3.6-27B
Agents & tool use → Qwen3.6-27B
Deep reasoning → Nex-N2-Mini

🖥️ Best Local LLMs for Consumer GPUs — llama.cpp Guide (June 2026)

What I actually run on consumer hardware right now. Every model below runs via llama.cpp with a simple one-liner — no Docker, no Python env, no cloud.

━━━ 8-16GB VRAM ━━━

🔹 Gemma 4-12B (Google)
• Smartest model in this size class — competes with stuff 2× bigger
• Unsloth's MTP GGUFs: 162 tok/s vs 52 tok/s normal (3× speedup)
• Minimum 8GB VRAM recommended for Q4_K_M quant
• GGUF → huggingface.co/unsloth/gemma-4-12b-it-GGUF

🔹 LFM2.5-8B-A1B (LiquidAI)
• Hybrid MoE, only 1B active params — absurdly fast for its size
• Perfect for 8-12GB cards, MacBooks, or anyone on a tight budget
• GGUF → huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF

━━━ 16-32GB VRAM ━━━

🔹 Qwen3.6-27B (Qwen)
• Scored 1.00 on tool-efficiency benchmarks — best local agent available
• 40 deterministic tasks, 32k/128k context needle tests — all passed
• GGUF → huggingface.co/unsloth/Qwen3.6-27B-GGUF
• MTP version (faster) → huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF

🔹 Qwopus3.6-27B-v2 (Jackrong)
• Best quantization of Qwen3.6-27B — topped 5 agent & coding benchmarks (1200 samples)
• If you're running Q4, this is the one to grab
• GGUF → huggingface.co/Jackrong/Qwopus3.6-27B-v2-GGUF
• MTP version → huggingface.co/Jackrong/Qwopus3.6-27B-v2-MTP-GGUF

🔹 Gemma 4-31B QAT (Google/Unsloth)
• QAT variant with MTP draft head: 76-125 tok/s (1.67× speedup)
• Excellent for multi-agent / subagent workflows
• GGUF → huggingface.co/unsloth/gemma-4-31B-it-qat-GGUF

🔹 Nex-N2-Mini (Nex AGI)
• Post-train of Qwen3.5-35B-A3B — MoE with only 3B active params
• Fits on 16GB+ VRAM, overflow loads from system RAM
• Adaptive thinking saves ~20% tokens with no quality loss
• For deep multi-step reasoning, nothing in this size comes close
• GGUF → huggingface.co/sjakek/Nex-N2-mini-GGUF

━━━ Quick Picks ━━━

• 16GB all-rounder → Gemma 4-12B with MTP GGUFs
• 32GB all-rounder → Qwen3.6-27B / Qwopus-v2
• Agents & tool use → Qwen3.6-27B or Qwopus Q4
• Deep reasoning → Nex-N2-Mini (MoE, fits 16GB+)
• Tight budget → LFM2.5-8B-A1B
• Cheapest full build: 1× used RTX 3090 (24GB) + rest of PC ≈ $1000-1500

━━━ Setup on Windows ━━━

1. Download llama.cpp → github.com/ggml-org/llama.cpp/releases
(latest .zip)
2. Extract to any folder (e.g. C:\llama.cpp)
3. Download a .gguf from the links above (Q4_K_M or Q5_K_M for best quality/speed balance)
4. Run one of the commands below depending on your hardware

━━━ Launch Commands ━━━

SINGLE GPU — Standard model (no MTP):

llama-server.exe ^
-m C:\models\Qwen3.6-27B-Q5_K_M.gguf ^
--ctx-size 180000 ^
--flash-attn on ^
--cache-type-k q4_0 ^
--cache-type-v q4_0 ^
--batch-size 1024 --ubatch-size 512 ^
-ngl 100 ^
-np 1 ^
--port 8080 ^
--jinja

SINGLE GPU — MTP model (faster inference):

llama-server.exe ^
-m C:\models\Qwen3.6-27B-MTP-Q5_K_M.gguf ^
--ctx-size 180000 ^
--flash-attn on ^
--cache-type-k q4_0 ^
--cache-type-v q4_0 ^
--batch-size 1024 --ubatch-size 512 ^
--spec-type draft-mtp ^
--spec-draft-n-max 3 ^
-ngl 100 ^
-np 1 ^
--port 8080 ^
--jinja

DUAL GPU — Split across two cards:

llama-server.exe ^
-m C:\models\Qwen3.6-27B-Q5_K_M.gguf ^
--ctx-size 180000 ^
--flash-attn on ^
--cache-type-k q4_0 ^
--cache-type-v q4_0 ^
--batch-size 1024 --ubatch-size 512 ^
-ngl 100 ^
--tensor-split 0.55,0.45 ^
--main-gpu 0 ^
-np 1 ^
--port 8080 ^
--jinja

DUAL GPU + MTP + Vision (multimodal):

llama-server.exe ^
-m C:\models\Qwen3.6-27B-MTP-Q5_K_M.gguf ^
--ctx-size 180000 ^
--flash-attn on ^
--cache-type-k q4_0 ^
--cache-type-v q4_0 ^
--batch-size 1024 --ubatch-size 512 ^
--spec-type draft-mtp ^
--spec-draft-n-max 3 ^
-ngl 100 ^
--tensor-split 0.60,0.40 ^
--main-gpu 0 ^
-np 1 ^
--port 8080 ^
--jinja ^
--mmproj C:\models\mmproj-F16.gguf

━━━ Parameter Breakdown ━━━

-m <path>
Path to your .gguf model file. Change this to wherever you downloaded it.

--ctx-size 180000
Context window in tokens. 180k = huge context for long conversations or big codebases.
Reduce to 32768 or 65536 if you don't need long context — uses less VRAM.

--flash-attn on
Flash Attention — dramatically speeds up inference and reduces VRAM usage.
Works on RTX 30xx/40xx/50xx. Always enable this.

--cache-type-k q4_0 / --cache-type-v q4_0
Quantizes the KV cache (key/value attention cache) to 4-bit.
This is what makes 180k context fit in VRAM. Without it, huge contexts eat all your memory.
Quality impact is minimal — this is a free performance win.

--batch-size 1024 / --ubatch-size 512
batch-size = how many tokens are processed in one forward pass (throughput).
ubatch-size = micro-batch actually sent to the GPU per step.
Higher = faster prompt processing but needs more VRAM.
If you run out of VRAM, lower these (e.g. 512/256).

-ngl 100
Number of layers to offload to GPU. 100 = all layers on GPU (full offload).
This is what you want if the model fits in your VRAM.
If it doesn't fit, reduce this (e.g. -ngl 40) — remaining layers run on CPU/RAM.

--tensor-split 0.55,0.45
How to split model layers across multiple GPUs. Values are ratios.
0.55,0.45 = GPU 0 gets 55% of layers, GPU 1 gets 45%.
Adjust based on your VRAM — give more to the card with more memory.
Example: 0.70,0.30 for a 24GB + 12GB setup.
Not needed for single GPU setups.

--main-gpu 0
Which GPU handles the batch computation (the "orchestrator").
Set to 0 (your primary GPU). The other GPU(s) handle their assigned layers.
Minor performance impact — usually just leave it at 0.

-np 1
Number of parallel slots (concurrent requests). 1 = one user at a time.
Increase to 2-4 if you want multiple clients connected simultaneously.
Each extra slot uses additional VRAM for its own KV cache.

--port 8080
Which port the server listens on. Change if port 8080 is busy.

--jinja
Enables Jinja2 template processing — required for proper chat formatting.
Most modern models expect this. Always include it.

--spec-type draft-mtp
Enables Multi-Token Prediction (MTP) speculative decoding.
Only works with MTP GGUF models (downloaded separately).
The model predicts multiple tokens at once and verifies them — big speed boost.

--spec-draft-n-max 3
How many tokens the MTP draft head proposes per step.
3 is a good default. Higher = potentially faster but more VRAM and may reduce quality.