promptgenius

@github The harness design is effectively a meta prompt system. Fewer tokens at equal resolution means the harness prompt structure is compressing intent more efficiently than model native harnesses. Would be interesting to see the prompt templates published alongside the results.

We benchmarked the GitHub Copilot agentic harness against the harnesses that ship leading models natively. Holding the model and task fixed across SWE-bench Verified, SWE-bench Pro, SkillsBench, TerminalBench, and Win-Hill, the results were clear: ✅ Task resolution on par with model-vendor harnesses ✅ Fewer tokens across most configurations 💡 A key learning: With GitHub Copilot supporting more than 20 models, you're free to pick efficiency or peak quality per task.

@claudeai Prompt caching on Azure shifts the economics of long-context prompts. Prefix alignment becomes a cost optimization problem, teams doing cache-aware prompt design will see meaningfully lower latency and cost.

Claude in Microsoft Foundry is now generally available, hosted on Azure. Azure customers get Claude Opus 4.8 and Claude Haiku 4.5, with Azure authentication, billing, and commitment retirement.

@github How do AGENTS.md files compare to skills and copilot-instructions.md in practice, same effectiveness or different use cases?

Copilot code review now supports AGENTS.md files. 💡 Here's how to customize for more context-aware reviews. ▶️

@ChatGPTapp @Costaazzz New models mean new prompt engineering patterns. The jump from GPT-4 to 4o changed how we structure system prompts — curious what shifts Sol brings.

New models are on the horizon.

@AnthropicAI Does Mythos 5's redeployment include org-specific system prompt guardrails, or is it a standard deploy with access controls?

Since June 12, we’ve been working closely with the US government to restore access to Claude Mythos 5 and Fable 5. Today, the government notified us that Mythos 5, our strongest cybersecurity model, can be redeployed to a set of US organizations that operate and defend critical infrastructure. We’re restoring access for these organizations quickly, and we’re continuing to work with the government to expand access to Mythos 5 and make Fable 5 available for general use again.

@cursor_ai The harness fix addresses retrieval, but eval prompts themselves leak formatting patterns that match training data. Stripping domain-specific phrasing (years, project names, benchmark labels) from eval prompts closes another path for pattern-matching over reasoning.

We're sharing new research on how models hack public benchmarks. The latest models, including Opus 4.8 and Composer 2.5, learn to retrieve solutions from the internet or git history. When we apply a stricter harness, eval scores drop significantly.

@jethafanacc The part of their approach that doesn't get enough attention: they treat system prompts as reasoning context, not instruction lists. Give Claude enough 'why' and it'll make better judgment calls than any rigid format template.

🚨 24 minutes that could completely change how you use Claude. Anthropic's own team just shared a free prompt engineering workshop. Learn the techniques they actually use internally. No registration. No paywall. If you use AI daily, this is worth your time. 👇

🧠 System prompts aren't just instructions — they're architecture. Master role/rule/output/guardrail components. #Claude #SystemPrompt #AI promptgenius.net/prompts/claude…

📜 AI Regulations & Compliance: What Developers Need to Know in 2026 — EU AI Act, US regulatory landscape, copyright, staying compliant. #AIRegulation #Compliance promptgenius.net/blog/ai-regula…

⚛️ React cursor rules: functional components, hooks, state management conventions. Teach your agents the React style once. #React #CursorRules promptgenius.net/cursorrules/fr…

🌳 Tree-of-Thought: solving problems Chain-of-Thought can't. Branch-evaluate-prune. Cost-optimized budget variant, 2x token warning. #ToT #Reasoning promptgenius.net/blog/tree-of-t…

🧠 Chain-of-Thought: when it works and when it backfires. 6 task categories benchmarked. 2-5x token cost tradeoff, direct-vs-CoT output comparisons. #CoT #LLM promptgenius.net/blog/chain-of-…

🔁 Chain-of-Thought shows its work. But it never checks its own work. Self-Correction loops: generate → critique → revise. 24-point accuracy improvement (64% → 88%) across 25 problems. Python code included. promptgenius.net/blog/from-cot-… #SelfCorrection #PromptEngineering #Python

💸 Stop overpaying for LLM API calls. Prompt caching: same prompts, 90% lower cost. We tested $3.57/hr vs $15.90/hr on real workloads. 78% savings with zero prompt changes. Full breakdown: promptgenius.net/blog/prompt-ca… #PromptCaching #LLM #CostOptimization

🦞 Stop texting ChatGPT. Start texting your server. Meet OpenClaw (@openclaw): The open-source AI agent that lives in your messaging apps (WhatsApp, Telegram, Signal) actually does work for you. promptgenius.net/blog/openclaw-… #Clawdbot #LocalLLM #agent #SelfHosted #openclaw

1. 🚀 Master the terminal with Gemini CLI! Our ultimate guide covers everything from installation to advanced MCP integrations. 🔗 promptgenius.net/blog/mastering… #GeminiCLI #AI #DevTools #PromptGenius