How to create 100 Million dollars (@binance edition) Step 1: Tell the community that they will buy SPCX IPO for you if they deposit money Step 2: Raise about $557M from the community Step 3: Invest all that money in the $SPCX IPO Step 4: This is very important: if the IPO does well, then keep all the profits and refund the community { xyz reasons } If the IPO doesn't do well and opens at breakeven, then give them shares instead of a refund SPCX IPO opened at 20% higher prices, so @binance kept all the profit and then refunded the money to the community EZZ $100M PROFITSSSSS

🖥️ 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… 🔹 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… ━━━ 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… • MTP version (faster) → huggingface.co/unsloth/Qwen3… 🔹 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/Qwop… • MTP version → huggingface.co/Jackrong/Qwop… 🔹 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… 🔹 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… ━━━ 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.cp… (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. --mmproj <path> Path to the multimodal projector file (for vision models). Enables image understanding — paste screenshots into the web chat. Only needed if you want vision capabilities. Omit for text-only use. ━━━ Your Hardware → Your Command ━━━ Single GPU (8-24GB VRAM): Use the "Single GPU" command. Change -m to your model path. 8GB card → Gemma 4-12B Q4 or LFM2.5-8B 12GB card → Gemma 4-12B Q5/Q6 16GB card → Gemma 4-31B QAT Q4 or Nex-N2-Mini 24GB card → Qwen3.6-27B Q4/Q5, Qwopus-v2, Gemma 4-31B QAT Q5/Q6 Dual GPU: Use the "Dual GPU" command. Adjust --tensor-split based on your VRAM ratio. 24GB 24GB → --tensor-split 0.50,0.50 24GB 12GB → --tensor-split 0.70,0.30 24GB 8GB → --tensor-split 0.75,0.25 Want speed? Use MTP versions of models with the "MTP" commands. Want vision? Add --mmproj with the projector file from the model's HuggingFace repo. 5. Once running, you get: • Web chat UI → http://localhost:8080 • OpenAI-compatible API → http://localhost:8080/v1 • Playground → http://localhost:8080/playground ━━━ Why /v1 API Is the Killer Feature ━━━ One local endpoint replaces your entire cloud API bill. The /v1 endpoint is drop-in OpenAI-spec compatible — every tool that speaks OpenAI just works. No custom code, no glue layer. Works out of the box with: • IDEs: Cursor, Continue, Windsurf, Cline, Roo Code • CLI tools: aider, Open Interpreter, OpenCode • Frameworks: LangChain, LlamaIndex, LiteLLM • Any OpenAI SDK (Python, Node, Go, Rust) Why this beats cloud APIs: • 100% private — code never leaves your machine • $0 per token — no rate limits, no quotas, no surprise bills • Works fully offline • Zero telemetry, no training on your data • Swap models by dropping in a different .gguf — no app changes needed • Run 32k–128k context windows without burning money Good combos: • Cursor Qwopus-v2 → near-frontier quality, zero API cost • Continue Qwen3.6-27B → best local coding agent • aider Gemma 4-12B MTP → 162 tok/s, feels instant • OpenCode Nex-N2-Mini → deep reasoning on 16GB Set any OpenAI-compatible client to your local endpoint: set OPENAI_API_KEY=sk-dummy (any non-empty string works) set OPENAI_BASE_URL=http://localhost:8080/v1 # every OpenAI-compatible tool now hits your local GPU Shoutouts: @0xSero @rS_alonewolf @witcheer @UnslothAI @LottoLabs

The U.S. Dollar has lost 30% of its purchasing power over the last six years, per NYT

Boltz API and related services are currently facing an unexpected downtime ⚠️ We are looking into the issue and will be sharing more information soon 🙏

"someone". reddit.com/r/SideProject/com…

yep, as expected

“I said to YOU to never sell your Bitcoin. I never said that THE COMPANY wouldn’t sell its Bitcoin.” Jesus Christ.

JUST IN: Cash App is launching a $40 unlimited 5G mobile plan with no contracts or credit checks.

United Airlines B767 Landing Goes Wrong at Zurich Airport What went wrong here?

The margin on a subscription plan is a function of the average utilization. If we assume both companies have 75% API gross margins, this results in the following subscription margins. (3/4)

The Clarity Act passed committee. The floor is next. We did not come this far to quit at the 5 yard line.