🇨🇭 Global One SI - Prompt
Warren Buffett in the retirement home watching Elon Musk make his entire career's profit in 1 day
Engineers are about to push Buffett out of the top rankings.
✍1
🇨🇭 Global One SI - Prompt
🚨BREAKING: ANTHROPIC SUED FOR FRAUD A customer filed a federal class-action lawsuit Monday claiming he hit 15% of weekly allowance in just one 5-hour sprint. “The actual usage provided by the Max 5x and Max 20x plans is far below the advertised amount.”…
🚨 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.
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.
👀1
🇨🇭 Global One SI - Prompt
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
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
✍1
🇨🇭 Global One SI - Prompt
🖥️ 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)…
━━━ 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.
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.
✍1
🇨🇭 Global One SI - Prompt
🖥️ 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)…
--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
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
✍1
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“AI IS THE ONLY INDUSTRY THAT IS LESS REGULATED THAN A SANDWICH SHOP.” - MIT professor
US government has two choices:
> pull the EU card, regulate, be “safe”
> minimum regulations, lead the AI race
the Fable 5 ban was already a big move that might not be the only one we see in the future.
but Open Source AI and small labs take the bigger hit from regulations while OpenAI and Anthropic, with huge capital, lawyers, and lobbyists, who active push for more regulations, are the ones who benefit from less competition.
US government has two choices:
> pull the EU card, regulate, be “safe”
> minimum regulations, lead the AI race
the Fable 5 ban was already a big move that might not be the only one we see in the future.
but Open Source AI and small labs take the bigger hit from regulations while OpenAI and Anthropic, with huge capital, lawyers, and lobbyists, who active push for more regulations, are the ones who benefit from less competition.
✍1
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Dude after buying SpaceX at $135 and selling it at $209 (he only bought 1 share)
🐳1
🇨🇭 Global One SI - Prompt
#WTI All of the technical indicators like MACD, PPO, CCI, etc. say oil is collapsing to $50.
#WTI U.S. Oil falls to $78 as the energy selloff accelerates following Trump's promises of an Iran peace deal on Friday
💯1
🇨🇭 Global One SI - Prompt
Fuck Cartels
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"We will find these vicious murderers and drug lords anytime, anyplace, and send them to the depths of hell where they belong."
President Trump issued the warning after announcing that U.S. Southern Command carried out a strike that killed Tren de Aragua leader Niño Guerrero.
Trump says the operation was coordinated with Venezuelan authorities and marks a major blow against the gang that U.S. officials have accused of spreading violence, extortion, drug trafficking, and terror across the region.
With Guerrero dead, Trump is signaling that other cartel and gang leaders are now on notice.
President Trump issued the warning after announcing that U.S. Southern Command carried out a strike that killed Tren de Aragua leader Niño Guerrero.
Trump says the operation was coordinated with Venezuelan authorities and marks a major blow against the gang that U.S. officials have accused of spreading violence, extortion, drug trafficking, and terror across the region.
With Guerrero dead, Trump is signaling that other cartel and gang leaders are now on notice.
🔥1
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6AM. After 8 years of grind... that silent taste of success.
Yet my hunger won't let me sleep.
Yet my hunger won't let me sleep.
🇨🇭 Global One SI - Prompt
6AM. After 8 years of grind... that silent taste of success. Yet my hunger won't let me sleep.
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If you don't own:
- a piece of a business (like stocks, crypto or shares)
- real estate.
- or any other income producing asset.
It's impossible to reach financial freedom.
- a piece of a business (like stocks, crypto or shares)
- real estate.
- or any other income producing asset.
It's impossible to reach financial freedom.
Starting to doubt Fable 5 ships this week.
If the export controls are still unresolved and Anthropic is still arguing with DC over jailbreak concerns, next week feels more realistic.
Brutal timing too.
GPT 5.6 is rumored for next week as well, and if it gets close to Fable level performance at a much lower price, Anthropic’s hype window could close fast.
Would be very Anthropic to finally get unblocked right as OpenAI makes the whole thing feel overpriced.
If the export controls are still unresolved and Anthropic is still arguing with DC over jailbreak concerns, next week feels more realistic.
Brutal timing too.
GPT 5.6 is rumored for next week as well, and if it gets close to Fable level performance at a much lower price, Anthropic’s hype window could close fast.
Would be very Anthropic to finally get unblocked right as OpenAI makes the whole thing feel overpriced.
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🤖 TECH: Researchers at ETH Zurich have built a quadruped robot that can track, predict, and return badminton shuttlecocks against human opponents.
🇨🇭 Global One SI - Prompt
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
Every single vibe coder should be using this AI skill.
/improve is an agentic skill that audits your entire codebase.
Scanning for bugs, security flaws, & more.
It feels like using Fable 5's auditing capabilities.
github.com/shadcn/improve
/improve is an agentic skill that audits your entire codebase.
Scanning for bugs, security flaws, & more.
It feels like using Fable 5's auditing capabilities.
github.com/shadcn/improve
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🇨🇭 Global One SI - Prompt
Every single vibe coder should be using this AI skill. /improve is an agentic skill that audits your entire codebase. Scanning for bugs, security flaws, & more. It feels like using Fable 5's auditing capabilities. github.com/shadcn/improve
Your AI Agent can already code.
Now it can browse the entire internet too. 🤯
Agent Reach just exploded to 26.6K+ stars and 2.2K+ forks on GitHub in just a few months.
Instead of paying for APIs, debugging scrapers, and fighting platform restrictions, Agent Reach gives your agent a single capability layer for:
• X/Twitter search & reading
• Reddit posts & comments
• YouTube transcripts
• GitHub repos & code search
• Bilibili videos
• XiaoHongShu content
• LinkedIn pages
• RSS feeds
• Web search
The coolest part?
You don't learn new tools.
Just tell your agent:
→ "Read this tweet"
→ "Summarize this YouTube video"
→ "Find Reddit discussions about this bug"
→ "Search GitHub for AI agent frameworks"
And it figures out the backend automatically.
No API fees.
No vendor lock-in.
No wrapper abstraction.
Just one command and your AI agent gets eyes on the internet.
Agent infrastructure is becoming one of the fastest-growing categories in open source right now, and Agent Reach is one of the repos leading that wave.
100% open source (MIT).
Repo 👇
github.com/Panniantong/Agent-Reach/blob/main/docs%2FREADME_en.md
Now it can browse the entire internet too. 🤯
Agent Reach just exploded to 26.6K+ stars and 2.2K+ forks on GitHub in just a few months.
Instead of paying for APIs, debugging scrapers, and fighting platform restrictions, Agent Reach gives your agent a single capability layer for:
• X/Twitter search & reading
• Reddit posts & comments
• YouTube transcripts
• GitHub repos & code search
• Bilibili videos
• XiaoHongShu content
• LinkedIn pages
• RSS feeds
• Web search
The coolest part?
You don't learn new tools.
Just tell your agent:
→ "Read this tweet"
→ "Summarize this YouTube video"
→ "Find Reddit discussions about this bug"
→ "Search GitHub for AI agent frameworks"
And it figures out the backend automatically.
No API fees.
No vendor lock-in.
No wrapper abstraction.
Just one command and your AI agent gets eyes on the internet.
Agent infrastructure is becoming one of the fastest-growing categories in open source right now, and Agent Reach is one of the repos leading that wave.
100% open source (MIT).
Repo 👇
github.com/Panniantong/Agent-Reach/blob/main/docs%2FREADME_en.md
✍1