@xqiia: جيريك مافيا😂😋💕! #isfad #الاسفاد #taşacakbudeniz #هذا_البحر_سوف_يفيض #fyppppppppppppppppppppppp

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Saturday 08 August 2026 17:29:20 GMT

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Adem Sejoud :

اسم المسلسل بليز 🥰🥰

2026-08-09 23:21:51

2

ياحسين :

حلوين مع بعض فدوه❣️

2026-09-24 18:28:18

2

زهراء🌻 :

اشتاقيتلهمممم 😩😩😩❣️

2026-08-08 18:09:14

15

SADAN :

الابداع

2026-08-09 03:13:42

5

بنــين 🌟 :

ذكرياتهممممننن 😭❣️❣️❣️❣️❣️❣️.

2026-08-09 16:14:32

3

𝙼𝚎𝚕𝚎𝚔💘. :

مبدعتيي

2026-08-09 00:02:22

2

الملكة اُسَيْمَة😋❣️❣️ :

و صدق لقناته درس ما ينساه طول عمره

2026-08-24 23:48:23

1

🧿 ᴴᴱ⇣ناناميْ千 :

2026-08-24 18:36:56

0

𝙼𝚎𝚕𝚎𝚔💘. :

2026-08-09 00:02:25

3

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Только не отправляй никому😭🚀

Только не отправляй никому😭🚀

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O FLUXO VOLTOU A DAR AS CARAS! E A RUSH VEIO PRA SURPREENDER! 👀 O primeiro dia de final chegou chegando! O Fluxo voltou a aparecer, a Rush deu aulas e fechou o dia na 2ª colocação, e o MT7 garantiu o MVP! 🏆 Amanhã tem mais! Quem vai dominar o segundo dia e levantar a taça? 🔥 #FreeFire #FFWSBR

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🚨 If you use Claude Code regularly, these 5 GitHub skills could completely change how you think about tokens, context and coding efficiency. 💻🧠 Most people try to improve Claude Code by switching models or increasing reasoning. But sometimes the bigger win is simply giving Claude less unnecessary work to do. Less code. Less prose. Less irrelevant context. Fewer wasted tokens. Better use of the model you already have. ⚡ That’s what this carousel is about. I’ve broken down 5 GitHub projects designed to make AI coding workflows leaner and more efficient, each attacking a different bottleneck. 👇 🪨 1. Caveman The idea is brilliantly simple: make Claude say less. Instead of long introductions, filler and oversized explanations, Caveman pushes the agent toward compact technical answers. The project reports roughly 65% lower output token usage in its benchmark testing. Same technical goal, much less noise. 📉 👩‍💻 2. Ponytail This one tackles something different: over-engineering. Before Claude writes more code, Ponytail encourages it to check what already exists, use standard libraries, prefer native functionality and reuse dependencies. Its project benchmarks report around 54% less code, 22% fewer tokens and ~20% lower cost. 🔁 🍯 3. Honey Think of Honey as combining two principles: write less code AND say less about it. It targets unnecessary implementation, verbose explanations and bloated agent-to-agent handoffs. The project’s own testing reports meaningful reductions in output tokens and cost while aiming to preserve useful results. 🍯⚙️ 🔍 4. Claude Token Optimizer Sometimes the problem isn’t Claude’s response. It’s your entire setup. A huge CLAUDE.md file, unnecessary terminal output, unused instructions, poor defaults and other context overhead can quietly consume tokens every session. Token Optimizer is designed to audit that waste and show where your token budget is disappearing. 📊💸 📂 5. Claude Context Optimizer This attacks one of the biggest problems in long AI coding sessions: feeding Claude information it doesn’t actually need. It helps identify used versus unused context so the model spends more of its window reading relevant files instead of noise. Smaller context can mean faster, cleaner and potentially cheaper workflows. 🎯 And here’s the important part… You don’t need to install everything just because it exists. The smarter approach is to identify your bottleneck first. Claude too verbose? → Caveman. Constant over-engineering? → Ponytail. Too much code AND explanation? → Honey. Unsure where your tokens are going? → Token Optimizer. Huge messy project context? → Context Optimizer. 🔧 You can even experiment with a stack: clean the context first, audit the remaining waste, encourage reuse, reduce unnecessary implementation and then keep the final response concise. But test combinations carefully because every repo, model and workflow behaves differently. 🧪 And that brings me to the biggest warning in this post: benchmark percentages are not guarantees. These numbers come from the individual projects’ own tests. Your savings will depend on your repository, model, task complexity, prompt design and existing Claude Code configuration. So benchmark it yourself. ✅ Take one real task you already perform. Run it normally. Record tokens, cost, lines changed, completion time and output quality. Then enable ONE skill and run the same type of workload again. That is how you find out whether a tool actually improves your workflow instead of just looking impressive on GitHub. 📈 The bigger lesson? Better AI coding doesn’t always require a bigger model. Sometimes it requires a smaller, cleaner context and a smarter workflow around the model. 🧠⚡ 💾 SAVE this carousel for your next Claude Code setup. 📤 SHARE it with a developer burning through tokens. 💬 COMMENT “SKILLS” if you want a follow-up showing how to install these step-by-step. #TechSerks #ClaudeCode #ClaudeAI #Anthropic #AITools

🚨 If you use Claude Code regularly, these 5 GitHub skills could completely change how you think about tokens, context and coding efficiency. 💻🧠 Most people try to improve Claude Code by switching models or increasing reasoning. But sometimes the bigger win is simply giving Claude less unnecessary work to do. Less code. Less prose. Less irrelevant context. Fewer wasted tokens. Better use of the model you already have. ⚡ That’s what this carousel is about. I’ve broken down 5 GitHub projects designed to make AI coding workflows leaner and more efficient, each attacking a different bottleneck. 👇 🪨 1. Caveman The idea is brilliantly simple: make Claude say less. Instead of long introductions, filler and oversized explanations, Caveman pushes the agent toward compact technical answers. The project reports roughly 65% lower output token usage in its benchmark testing. Same technical goal, much less noise. 📉 👩‍💻 2. Ponytail This one tackles something different: over-engineering. Before Claude writes more code, Ponytail encourages it to check what already exists, use standard libraries, prefer native functionality and reuse dependencies. Its project benchmarks report around 54% less code, 22% fewer tokens and ~20% lower cost. 🔁 🍯 3. Honey Think of Honey as combining two principles: write less code AND say less about it. It targets unnecessary implementation, verbose explanations and bloated agent-to-agent handoffs. The project’s own testing reports meaningful reductions in output tokens and cost while aiming to preserve useful results. 🍯⚙️ 🔍 4. Claude Token Optimizer Sometimes the problem isn’t Claude’s response. It’s your entire setup. A huge CLAUDE.md file, unnecessary terminal output, unused instructions, poor defaults and other context overhead can quietly consume tokens every session. Token Optimizer is designed to audit that waste and show where your token budget is disappearing. 📊💸 📂 5. Claude Context Optimizer This attacks one of the biggest problems in long AI coding sessions: feeding Claude information it doesn’t actually need. It helps identify used versus unused context so the model spends more of its window reading relevant files instead of noise. Smaller context can mean faster, cleaner and potentially cheaper workflows. 🎯 And here’s the important part… You don’t need to install everything just because it exists. The smarter approach is to identify your bottleneck first. Claude too verbose? → Caveman. Constant over-engineering? → Ponytail. Too much code AND explanation? → Honey. Unsure where your tokens are going? → Token Optimizer. Huge messy project context? → Context Optimizer. 🔧 You can even experiment with a stack: clean the context first, audit the remaining waste, encourage reuse, reduce unnecessary implementation and then keep the final response concise. But test combinations carefully because every repo, model and workflow behaves differently. 🧪 And that brings me to the biggest warning in this post: benchmark percentages are not guarantees. These numbers come from the individual projects’ own tests. Your savings will depend on your repository, model, task complexity, prompt design and existing Claude Code configuration. So benchmark it yourself. ✅ Take one real task you already perform. Run it normally. Record tokens, cost, lines changed, completion time and output quality. Then enable ONE skill and run the same type of workload again. That is how you find out whether a tool actually improves your workflow instead of just looking impressive on GitHub. 📈 The bigger lesson? Better AI coding doesn’t always require a bigger model. Sometimes it requires a smaller, cleaner context and a smarter workflow around the model. 🧠⚡ 💾 SAVE this carousel for your next Claude Code setup. 📤 SHARE it with a developer burning through tokens. 💬 COMMENT “SKILLS” if you want a follow-up showing how to install these step-by-step. #TechSerks #ClaudeCode #ClaudeAI #Anthropic #AITools

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