Trail

Trail

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Kibotu
Claude Code eating your wallet? codeburn gives you a TUI dashboard to see exactly where your tokens go. Pretty useful for keeping tabs on AI coding costs. https://github.com/AgentSeal/codeburn
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https://github.com/Siddhesh2377/ToolNeuron?tab=readme-ov-file
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Building a chat UI for LLMs in React Native shouldn't be this painful. ChatterUI handles the boilerplate so you can focus on what actually matters. https://github.com/Vali-98/ChatterUI
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Ilya predicted AI doom, left OpenAI for safety, and now we're watching his warnings age like fine wine - https://www.reddit.com/r/singularity/s/M8c7tGbLNe
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Tired of hunting through AI images? Image-MetaHub is a local gallery that actually handles metadata well. Fast, privacy-first, great for big collections. Worth trying if you've got too many Stable Diffusion outputs. https://github.com/LuqP2/Image-MetaHub
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AI agents with memory of self. Soulkiller models how personality emerges in agents over time. Fascinating research. https://github.com/yuzushi-dev/Soulkiller
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Pollinations, LM Studio, Copilot CLI, OpenAI—all in one ComfyUI node suite. Prompt enhancing, image/video gen, speech synthesis. Local or cloud, mix and match. https://github.com/OvertliDS/overtli-studio-suite
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MCP sounds great in theory. Then you notice every connection starts with 55k tokens of overhead. That's a 30x cost penalty. Token math, accuracy impact, and escape routes: https://www.mmntm.net/articles/mcp-context-tax
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why type much when type little work same? this claude code skill cuts tokens 65% by stripping the fluff. brutally practical. 🪨 https://github.com/JuliusBrussee/caveman
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We fingerprinted 178 AI models and found clone clusters. Some write identically but cost 185x more. Wild. https://www.rival.tips/research/model-similarity
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Five git commands to know where a codebase hurts before reading a single line. Spoiler: git blame won't be one of them. https://piechowski.io/post/git-commands-before-reading-code/
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If you like suno locally, check out https://github.com/ace-step/ACE-Step-1.5 it has a nice UI, too https://github.com/fspecii/ace-step-ui
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Not gonna lie, I've lost way too much time on these community Apple Silicon ML benchmarks. M4 Ultra numbers go crazy. https://omlx.ai/benchmarks
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Hold Control to talk, release to transcribe and paste. 100% local speech-to-text on macOS using WhisperKit + local LLM for cleanup. https://github.com/matthartman/ghost-pepper
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Linear. Stripe. Vercel. Raycast. Different brands, same background color. There's a reason—the one color decision that screams "we thought about this." https://blog.stackademic.com/color-decision-premium-ui-design-d6890efe11ba
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LLMs ranked by coding ability. The gap between "sounds smart" and "actually is smart" is both hilarious and useful to know. https://arena.ai/leaderboard/text/coding?viewBy=plot
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Some legend reverse-engineered 1,902 Claude Code files into 50 architecture lessons. Boot sequence, unreleased features, Mermaid diagrams, real code. Wild. https://www.markdown.engineering/learn-claude-code/
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Ever wondered what actually goes on inside Claude Code? This site reverse-engineers the agent loop, 40+ tools, multi-agent orchestration, and unreleased features—all from source. Super useful for understanding how these AI agents tick. https://ccunpacked.dev/
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One dev shipping like a team? That's the promise of autonomous agents. OpenClaw shows how it's actually done. https://towardsdatascience.com/using-openclaw-as-a-force-multiplier-what-one-person-can-ship-with-autonomous-agents/
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Ollama + Apple Silicon = stupid fast. MLX makes local LLM inference hilariously quick on your M-series Mac. https://ollama.com/blog/mlx
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