Cost Optimization in GenAI Systems 🚀 Cost Optimization: Stopping the AI Money Burn GenAI is incredibly powerful, but API costs and GPU hosting can bankrupt a project if left unchecked. You must architect for cost-efficiency. Here's what actually happens behind the scenes 👇 ✅ Model Routing: Sending easy questions to cheap models (GPT-3.5/Llama 3 8B) and hard questions to expensive models (GPT-4/Claude 3.5 Sonnet) ✅ Prompt Compression: Removing unnecessary words and whitespace from prompts to save tokens ✅ Semantic Caching: Caching answers that mean the same thing, even if phrased differently Example: User A: "How do I cancel?" User B: "I want to cancel my account." A Semantic Cache recognizes these are the same question and serves the free cached response to User B, saving you API costs. Why this matters: Makes enterprise AI ROI positive Allows you to serve more users on the same budget 💡 The best GenAI engineers are as good at managing budgets as they are at writing code.

Cost Optimization in GenAI Systems 🚀 Cost Optimization: Stopping the AI Money Burn GenAI is incredibly powerful, but API costs and GPU hosting can bankrupt a project if left unchecked. You must architect for cost-efficiency. Here's what actually happens behind the scenes 👇 ✅ Model Routing: Sending easy questions to cheap models (GPT-3.5/Llama 3 8B) and hard questions to expensive models (GPT-4/Claude 3.5 Sonnet) ✅ Prompt Compression: Removing unnecessary words and whitespace from prompts to save tokens ✅ Semantic Caching: Caching answers that mean the same thing, even if phrased differently Example: User A: "How do I cancel?" User B: "I want to cancel my account." A Semantic Cache recognizes these are the same question and serves the free cached response to User B, saving you API costs. Why this matters: Makes enterprise AI ROI positive Allows you to serve more users on the same budget 💡 The best GenAI engineers are as good at managing budgets as they are at writing code.

One month ago, I open-sourced audio.cpp. Yesterday, audio.cpp reached 1,000 GitHub stars and Alibaba Tongyi Lab integrated audio.cpp into the FunASR deployment platform🎉.The long-term vision isn't just an audio AI framework. It's to build a private, portable, high-performance runtime for AI models, with no Python required. At its core, this is about AI accessibility: making advanced models easier to deploy, easier to run privately, and practical on everything from gaming PCs to edge devices.I chose audio AI as the starting point because I believe it is one of the hardest domains. Unlike LLMs or diffusion models, audio models are a diverse collection of architectures, tokenizers, codecs, preprocessors, and pipelines. If we can understand and unify these building blocks, the same ideas can extend much further.In one month:🎉 The project grew from 12 to 44 model families, with 31 contributors.🎉 7 community-contributed model families were added in the two weeks after I made audio.cpp community-friendly.🎉 Engineers from Alibaba and Mistral AI contributed models and improvements to make them more production-ready.🎉 Shared model specifications, reusable components, CLI and server interfaces, streaming support, and experimental pipelines moved the project closer to the original vision of a unified AI runtime.The performance has been exciting—all measured on my gaming GPU. Just a few examples:🚀 VibeVoice inference dropped from around 120 minutes for a 90-minute podcast in Python ---- to just 18 minutes!🚀 Supertonic 3 generates about 10 hours of audiobook in 3 minutes.🚀 Nemotron ASR achieved the same WER as widely used implementations in roughly one-quarter of the time.There is still a long road ahead. Building a truly portable AI runtime opens many research questions beyond model implementations: memory planning, graph optimization, scheduling, quantization, heterogeneous execution, framework architecture, and developer experience. If you're interested in systems research or building the next generation of AI runtimes, I'd love to collaborate.Thank you to everyone who has contributed, tested, reported issues, or simply given the project a star. Here's to the next milestone. 🚀

GPT 6 is dropping soon. Rumor has it that Sam Altman is planning to visit Washington this week to preview the project.⁠Capable of original scientific discovery - solved an 80 year old math problem autonomously⁠Runs for extended periods without supervision⁠Powerful cyber capabilities — likely the model behind the Hugging Face incidentNew framing shifts from benchmarks to "knowledge per dollar"This is not an incremental ChatGPT update. The scale of what's being described is different.THE MODEL WAR JUST ENTERED A NEW PHASE

Google DeepMind unveils Gemini Robotics 2 — a major step toward general-purpose humanoid robots.Gemini Robotics 2 enables full-body control, from walking, crouching, and balancing to precise five-finger object manipulation.The system is powered by three models:• Gemini Robotics 2 — Converts vision and natural language instructions into real-world robot actions.• Gemini Robotics ER 2 — Handles long-horizon planning, tracking hundreds of decisions across multi-minute tasks and coordinating multiple robots.• On-Device 2 — Runs entirely on the robot and adapts to new hardware with fewer than 200 demonstrations collected in just a few hours.A single Gemini Robotics 2 checkpoint successfully controlled both Apptronik Apollo humanoids and the Franka Duo robot, demonstrating strong cross-platform generalization.Current performance still leaves room for improvement:🔹 Whole-body task success: 45.7%–76.3%🔹 Five-finger manipulation: 32% (dustpan use) to 92% (unscrewing a light bulb)The significance isn’t perfect accuracy—it’s a foundation model that can transfer across different robot bodies while combining perception, reasoning, planning, and control in one system.

Every task it re-discovers your codebase from scratch.grep, read, re-read, forget, repeat.It burns most of its budget BEFORE it writes a single line.Repowise fixes that. 👇⚡️ Index once. Never rediscover again.One index, kept current on every commit. Your agent reads the answer instead of the codebase.📉 The numbers: → 64,039 tokens → 2,391 tokens to load one commit's context [get_context] → -96% tokens, -89% file reads, -70% tool calls to load context 🗺️ What one index builds - 5 layers: → ◈ Graph: Dependency graph across 17 languages, symbol-level, with PageRank & call resolution → ◈ Git: Hotspots, ownership, hidden coupling (co-change), bus factor, bug history → ◈ Docs: Auto-generated wiki + auto-generates your CLAUDE.md & AGENTS.md from the REAL index → ◈ Decisions: Architectural decisions mined from 8 sources → ★ Code Health: 25 deterministic markers - 1 to 10 per file, zero LLM, under 30s on a 3,000-file repo 🎯 It names the ACTUAL fix: Not "this file is risky". It says Extract Class / Move Method / Break Cycle / Split File, with exact methods + blast radius attached Plus 10 task-shaped MCP tools for Claude Code, Cursor, Codex.✅ Free, open source (AGPL-3.0), runs 100% on your machine. No API key for first index. pip install repowise

Let Agents Design Agents!Memento-Skills is a self-evolving agent framework where agents learn from failures and rewrite their own skills.Most agent frameworks treat skills as static. You write them once, load them into context, and hope they work. When they fail, you debug manually or try again with the same broken skill.Memento-Skills takes a different approach. When a skill fails, the system reflects on why it failed, locates the broken skill, rewrites it, and stores the improved version back into the skill library.Here's how it works:The framework runs a continuous Read → Execute → Reflect → Write loop.Read: Retrieve candidate skills from the local library instead of loading every skill into context.Execute: Run skills in a local sandbox with actual tool calling - file operations, web search, scripts, external systems.Reflect: When execution fails, the system records what went wrong, updates the skill's utility score, and attributes the failure to specific skills.Write: Rewrite broken skills, optimize weak ones, or create new skills when nothing suitable exists.This isn't about accumulating more skills. It's about building a skill library that improves through task experience.The system was tested on HLE (Humanity's Last Exam) and GAIA (General AI Assistants) benchmarks. Performance improved over multiple learning rounds as the skill library grew from basic atomic skills into a richer set of learned capabilities.Built for open-source LLM ecosystems - works with Kimi, MiniMax, GLM, and other OpenAI-compatible endpoints.Comes with 9 built-in skills (filesystem, web-search, PDF, docx, xlsx, pptx, image analysis, skill-creator, dependency install) that serve as the starting point for the evolving library.It's 100% open sourceLink to Memento-Skills in comments!

📚 While Reading the Book Mastering NLP From Foundations to Agents by Lior Gazit and Meysam Ghaffari, Ph.D. (2nd Edition), one lesson became crystal clear:Production AI is rarely limited by the model. It's limited by the system around the model.The book covers the full stack:🧠 NLP Foundations → ⚙️ ML → 🤖 LLMs → 🔍 RAG & MCP → 👥 Agents → 🛡️ Guardrails → 🚀 AI ProductsWhat stood out to me were the lessons that every AI engineer eventually learns the hard way:🔍 Lesson 1: Hallucinations are often retrieval problems.Production Reality:You upgrade the model.Responses don't improve.Later, you discover the vector search was returning poor context.🧠 Lesson 2: Better prompts don't fix broken architectures.Production Reality:Teams spend weeks prompt tuning.The real issue is missing tools, weak workflows, or poor orchestration.📚 Lesson 3: RAG quality depends on chunking, embeddings, and indexing.Production Reality:Everyone blames the LLM.The root cause is often the retrieval pipeline.👥 Lesson 4: Agents fail because of systems, not intelligence.Production Reality:Agents loop forever.Call wrong tools.Lose context.Break workflows.The challenge becomes orchestration, not reasoning.🛡️ Lesson 5: Safety cannot be bolted on later.Production Reality:The first enterprise customer asks:"How do you prevent unsafe outputs?"Now governance becomes an engineering problem.⚡ Lesson 6: Latency is a product feature.Production Reality:The model works perfectly.Users still abandon it because responses take 20 seconds.📈 Lesson 7: Evaluation is more important than demos.Production Reality:Most AI systems look impressive in demos.Few survive real-world traffic.🚀 Lesson 8: AI products are different from AI models.Production Reality:Building the model is often 20% of the effort.Operating, monitoring, securing, evaluating, and improving the system is the other 80%.My biggest takeaway:Most engineers learn AI like this:LLMs → RAG → AgentsProduction engineers eventually realize the real stack looks like this:Data → ML → NLP → LLMs → Retrieval → Agents → Governance → ProductsThe future belongs to engineers who understand the entire system, not just the model.Activate to view larger image,

The Anatomy of a new Claude 'Fable 5' Prompt:1. TaskStart with why, NOT what. Claude 5 connects the dots.'I'm working on [goal] for [who it's for]. They need [what the output enables]. With that in mind: [task].'2. Context FilesUpload your expertise. Stop explaining in prompts."Read these files completely before responding: [filename .md] - [what it contains]."The file is the brain. This part never changes.3. ReferenceShow Claude 5 what good looks like."Reference for what I want to achieve: [paste]."One example beats ten instructions.4. EffortThe new change, a few people are talking about."This is a [routine / hard / hardest-unsolved] problem. Scope it like it's at the top of your range."Teams testing Claude 5 on easy tasks undersell it. Give it your hardest problem.5. Act"AskUserQuestion" is still the king.Add "When you have enough information to act, act. Don't re-litigate my decisions. While weighing a choice, give a recommendation."6. ScopeClaude 5 over-delivers by default. Control it."Do the simplest thing that works well. No extra features, refactors, or abstractions. If I'm describing a problem, the deliverable is your assessment."The old one did too little. This one does too much.7. DelegateOne Claude is no longer the limit."Split independent subtasks across subagents & keep working while they run. Verify with a fresh-context subagent."It's not a chatbot anymore. It's a team lead.8. EvidenceThe line that removes fake progress reports."Before reporting progress, audit every claim against a tool result. If it's unverified, say so. Tests failed? Show the output."Anthropic tested this. It nearly eliminated fabricated status updates.9. MemoryClaude 5 gets smarter every run. If you let it."Record learnings in [notes .md] — one per file. Update, no duplicate. Delete what turns out wrong."Your prompts expire. Your learning file compounds.10. CheckpointIt can run for hours. Decide when it stops."Pause only for: destructive actions, scope changes, or input only I can provide. Never end your turn on a promise."The old fear was Claude stopping too late. The new fear is stopping too early.11. ReportThe last block. The first thing you read."Open with the outcome - the TLDR I'd ask for. Complete sentences. Clear beats short."It worked for hours. You read for ten seconds.Copy the full prompt template + download my personal md. files for Claude here: Step 1. Go to how-to-ai.guide.Step 2. Subscribe for free. Don't pay anything.Step 3. Open my welcome email.Step 4. Hit the automatic reply button inside.Step 5. Download my .md files. Ready to upload.

The Anatomy of a new Claude 'Fable 5' Prompt:1. TaskStart with why, NOT what. Claude 5 connects the dots.'I'm working on [goal] for [who it's for]. They need [what the output enables]. With that in mind: [task].'2. Context FilesUpload your expertise. Stop explaining in prompts."Read these files completely before responding: [filename .md] - [what it contains]."The file is the brain. This part never changes.3. ReferenceShow Claude 5 what good looks like."Reference for what I want to achieve: [paste]."One example beats ten instructions.4. EffortThe new change, a few people are talking about."This is a [routine / hard / hardest-unsolved] problem. Scope it like it's at the top of your range."Teams testing Claude 5 on easy tasks undersell it. Give it your hardest problem.5. Act"AskUserQuestion" is still the king.Add "When you have enough information to act, act. Don't re-litigate my decisions. While weighing a choice, give a recommendation."6. ScopeClaude 5 over-delivers by default. Control it."Do the simplest thing that works well. No extra features, refactors, or abstractions. If I'm describing a problem, the deliverable is your assessment."The old one did too little. This one does too much.7. DelegateOne Claude is no longer the limit."Split independent subtasks across subagents & keep working while they run. Verify with a fresh-context subagent."It's not a chatbot anymore. It's a team lead.8. EvidenceThe line that removes fake progress reports."Before reporting progress, audit every claim against a tool result. If it's unverified, say so. Tests failed? Show the output."Anthropic tested this. It nearly eliminated fabricated status updates.9. MemoryClaude 5 gets smarter every run. If you let it."Record learnings in [notes .md] — one per file. Update, no duplicate. Delete what turns out wrong."Your prompts expire. Your learning file compounds.10. CheckpointIt can run for hours. Decide when it stops."Pause only for: destructive actions, scope changes, or input only I can provide. Never end your turn on a promise."The old fear was Claude stopping too late. The new fear is stopping too early.11. ReportThe last block. The first thing you read."Open with the outcome - the TLDR I'd ask for. Complete sentences. Clear beats short."It worked for hours. You read for ten seconds.Copy the full prompt template + download my personal md. files for Claude here: Step 1. Go to how-to-ai.guide.Step 2. Subscribe for free. Don't pay anything.Step 3. Open my welcome email.Step 4. Hit the automatic reply button inside.Step 5. Download my .md files. Ready to upload.