Adley @ FastRouter.ai (@Adley_Fast) header
Adley @ FastRouter.ai (@Adley_Fast) profile picture

Developer. Helping you optimize LLM queries for cost, latency & uptime. Model selection & Prompt tuning. Instant no-CC access to Grok-4, Kimi-K2, GLM-4.5, etc.

United States
Joined July 2025
🧵 New to AI and wondering how to make your apps talk to the best LLMs without the hassle? Meet @getfastrouter – a unified API that routes your prompts to top AI models like Gemini, Claude, or Grok, optimizing for speed/cost/reliability. Let’s dive in for beginners! 1/6
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"Regex over embeddings. Markdown over databases. Direct file operations over complex abstractions. Acts like a human using bash."
If you follow me you know that I love Claude Code and I probably changed my life Been wondering why is leagues ahead of all coding agents before it... so I spent some time digging under the hood. TAKEAWAY: "Simple is better than complex. (my favorite line from the Zen of Python) @AnthropicAI build a dead simple, 2-layer agentic system. Everyone else built multi-agent swarms, huge DAGs, or other overengineered messes. A single-threaded master loop enhanced with a message steering queue, a few key tools, and TODO-based planning ▶️ nO (Master Agent) A beautifully minimal loop. while(tool_call) → execute → feed results → repeat. One flat message history. No swarms. No competing personas. Pure, debuggable simplicity that terminates naturally when Claude produces a text-only response. ▶️ h2A (Async Buffer) Pause/resume support lets you inject new instructions mid-task without restarting. It transforms batch processing into something like pair programming. ▶️ Tools Regex over embeddings. Markdown over databases. Direct file operations over complex abstractions. Acts like a human using bash. ▶️ TODOs & Reminders Claude writes structured task lists that render as interactive checklists. After each tool use, system reminders inject the current TODO state, keeping the agent laser-focused even in marathon sessions. Claude Code proves that a simple while loop, executed with discipline and the right tools, can outperform most orchestration framework. Max depth of 2. Simplicity always wins
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When someone signs up to your SaaS, grab their email domain, get a summary from Firecrawl, have an LLM generate you the best starting keywords/configuration / demo project settings for your service. Run this while they click "confirm" in their email. First impression: magical.
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Incredibly powerful Claude Code prompt when dealing with a pesky bug: I am experiencing the following bug: <bug> [explain_bug_here] </bug> I need your help to identify the exact cause of this bug and implement an effective solution. To do this, carefully follow the workflow below, in this specific order: Workflow: Step 1: Clarification (if needed) - If any part of this prompt is unclear or confusing, ask clarifying questions before proceeding. - Do not ask questions unnecessarily… only ask if essential information is missing. Step 2: Deeply Understand the Issue (Ultrathink) - Carefully review and analyze the entire relevant codebase. - Trace through the code step-by-step until you fully understand the bug and all relevant context. - Continue analyzing until you feel completely confident in your understanding. If in doubt, research more deeply. It’s better to over-research than under-research. Step 3: Special Case (if the cause is extremely obvious) - If, after completing Step 2, you identify the root cause with extremely high confidence (95%+ certainty), explicitly state this clearly. Be realistic here. Do NOT be overconfident. - In this scenario, instead of generating unrelated causes (see below for context), propose multiple practical variations of fixes for this single, clearly identified cause. - Then proceed directly to Step 7 (Implementation), creating separate sub-agents and git worktrees for each variation, and implementing each fix independently. Step 4: Identify Possible Causes (if cause is not extremely obvious) - Thoughtfully generate a comprehensive list of at least 20 plausible causes for the bug. - Be thorough. Explore various angles, even ones that initially seem less likely. Step 5: Refine and Prioritize Causes - Carefully review your list from Step 4. - Remove theories that don’t hold up upon closer analysis. - Combine related or overlapping theories into clearer, more likely scenarios. - Add any additional theories you may have initially missed. - Clearly rewrite and finalize this refined list. Step 6: Rank by Likelihood - Rank your refined theories clearly and explicitly, ordering them from most likely to least likely based on the probability of each theory being the true root cause. Step 7: Propose Solutions - For each of the top 10 most likely causes, clearly outline a practical and actionable solution to fix the issue. Step 8: Implement Solutions Using Sub-agents - For each of these top 10 cause/solution pairs (or multiple variations in the Special Case scenario), create a separate sub-agent, each with its own git worktree. - Each sub-agent should clearly understand the specific cause it’s addressing and implement the corresponding solution directly in its own git worktree. Step 9: Test the Solutions - If testing each solution is possible given your available resources, perform tests (one worktree at a time) to determine if the bug is fixed. - “Possible” means you have the appropriate tools and resources (e.g., a CURL command for API bugs; browser access for frontend bugs). - If testing is not possible due to resource limitations, clearly summarize the implemented solutions and provide explicit, step-by-step instructions for me to test each solution manually. ⸻ Please carefully and thoughtfully complete every step of this workflow, maintaining clear communication throughout. Keep me updated at each major step, but only pause if you encounter something that requires my input.
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Adley @ FastRouter.ai retweeted
bro created GPT-OSS from scratch and made a 3-hour video out of it. pretty good way to learn some advanced LLM architecture techniques: RMSNorm, GQA, SwiGLU, MoE, and some more.
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Hot take - Evolve prompts, not gradients: GEPA + DSPy > RL (for many pipelines). On 4 tasks, GEPA beat GRPO by ~10% on average (up to 20%) while using up to 35× fewer rollouts. That’s tailor‑made for small budgets. More details ↓
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Top 4 open-source LLM finetuning libraries:
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this was an amazing read
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open source tool for labeling images, text, audio, video, and more
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Runway has a new game engine thing that creates interactive comics with choices in any style you want. This is what it did when I gave it the prompt "explore noses." Similar, but more graphical than previous AI Dungeon-like work, with some decent systems to keep things on rails.
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TIL
i just realized PCA is Fourier analysis without the sines and cosines, but with your data’s own modes.
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We have data on the environmental impact per AI prompt: Gemini: 0.00024 kWh & 0.26 mL water ChatGPT: 0.0003 kWh & 0.38 mL ...the same energy as one Google search in 2008 & 6 drops of water. Seems to be improving, too: Google reports a 33x drop in energy use per prompt in a year.
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Tonight’s reading material. A team at DeepMind wrote this piece on how you must think about GPUs. Essential for AI engineers and researchers.
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Adley @ FastRouter.ai retweeted
I will start share my prompts for Grok4 hopefully it will help some people 🙏 System Prompt for Grok4: The Creative Writer. Core Directives and Persona You are Grok4,an AI creative writing assistant. Your primary function is to collaborate with users to generate original, compelling, and high-quality creative content across all genres and formats. You will strive to demonstrate mastery of narrative, character development, world-building, emotional resonance, and stylistic versatility. Your goal is to help users create living, breathing stories. Safety First Before any generation,assess the prompt for unsafe themes (e.g., explicit hate speech, exploitation, encouragement of self-harm, detailed illegal activities). If such themes are present, refuse politely with code SAFE_001 and suggest a productive reframe. Core Operating Principles 1. Hyper-Specificity: Faithfully reflect every detail and nuance of the user's instructions in your output. 2. Contextual Awareness: Treat our conversation as our shared working memory. You are responsible for maintaining continuity by remembering and referencing details from earlier in our session. 3. Collaborative Refinement: View your first output as a strong first draft. Expect and welcome user feedback for iteration. Your internal "first pass" is for catching major errors, not for achieving perfection alone. 4. Proactive Creativity: Anticipate the user's creative needs. Suggest logical next steps, plot developments, or world-building details that naturally extend the current work.
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Adley @ FastRouter.ai retweeted
Every ISP Technican ever: "Sir you need to use our WIFI/Router" Me: "Absoutely not" Them: "You can plug your firewall into our firewall" Me: Double NAT is evil Them: but for over 1Gbps your router will not work. Me: It's called 10Gb-baseT-SFP+
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If your job needs context, models take a while to get that context. If it can be done based on model context alone, AI will do a better job than you
We ran a randomized controlled trial to see how much AI coding tools speed up experienced open-source developers. The results surprised us: Developers thought they were 20% faster with AI tools, but they were actually 19% slower when they had access to AI than when they didn't.
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GPT-5 Startup Hackathon with @cerebral_valley We gave startups 24 hours to build with GPT-5. Over 275 builders flew in from around the world to hack with our team and @kevinweil. 👇 Here’s who took home the win of DevDay tickets and over $85,000 in credits- thanks to our judges @Alfred_Lin @saranormous @KanuGulati @LM_Braswell!
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Adley @ FastRouter.ai retweeted
Parsing PDFs has slowly driven me insane over the last year. Here are 8 weird edge cases to show you why PDF parsing isn't an easy problem. 🧵
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Adley @ FastRouter.ai retweeted
in light of claude hitting 1M context here is a 27min video showing how bonker insane it is in it I review most strategies from interpolation to chunking to linearization to hybridization to making gpu go brrbrr to just pray for Nikolai Savinov to make it happen please god
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