Calabaza @CalaLinux

The most successful creators on Gumroad all run the same flywheel. Here's how it works: Create free content that demonstrates your expertise. This builds trust and attracts people who care about your topic. Capture attention by being genuinely helpful. Don't just post, engage. Reply to comments, answer DMs, show up in communities. Convert a small percentage of that attention into product sales. Not everyone will buy, and that's fine. You only need a fraction of your audience to become customers. Collect feedback from those customers. What did they love? What did they wish was included? What's their next problem? Create the next product based on that feedback. Now you're not guessing. You're building exactly what your audience wants. Repeat. Every cycle, your audience grows, your product catalog expands, and your revenue compounds. The flywheel is slow at first. Your first product might sell 10 copies. Your second might sell 30. By your fifth product, you have an ecosystem. By your tenth, you have a real business. The creators making $10K, $50K, $100K+ per year on Gumroad didn't get there with one viral moment. They got there by spinning this flywheel over and over again. Start spinning.

Having an OpenClaw agent trained to know your goals, your way of operating and your life philosophy in a fucking insanely powerful time-saving tool. I saw this article, started watching the linked YouTube video and then 1 min in I paused and said to my agent: "Can you spin up a sub agent to read this article go transcribe the YouTube video mentioned and then create a document that you can review and let me know if I should spend my time watching the video or would it be a waste of my resources?" My Agent: skip the video. 🤯 20 mins saved ✅ I need to do this with every article in my bookmarks and every video on my YouTube watchlist and tune this and get my agent to review before I watch or read anything ...

Tom @tomcrawshaw01

8 months ago

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25 resources to learn system design: 1. JWT ↳ lucode.co/json-web-token… 2. Idempotency ↳ lucode.co/idempotency-in… 3. System design quality attributes ↳ lucode.co/system-design-… 4. Rate limiting ↳ lucode.co/rate-limiting-… 5. Observability ↳ lucode.co/observability-… 6. Microservices ↳ lucode.co/microservices-… 7. CI/CD pipelines ↳ lucode.co/ci-cd-lil1nlsm 8. ACID vs BASE ↳ lucode.co/acid-vs-base-l… 9. Change Data Capture (CDC) ↳ lucode.co/change-data-ca… 10. Database types ↳ lucode.co/database-types… 11. Health checks vs heartbeats ↳ lucode.co/health-checks-… 12. API gateway vs load balancer vs reverse proxy ↳ lucode.co/api-gateway-vs… 13. HTTPS ↳ lucode.co/https-explaine… 14. Load balancing algorithms ↳ lucode.co/load-balancing… 15. Database caching ↳ lucode.co/database-cachi… 16. CDN ↳ lucode.co/cdn-lil1nlsm 17. API protocols ↳ lucode.co/api-architectu… 18. Database types ↳ lucode.co/database-types… 19. gRPC ↳ lucode.co/grpc-explained… 20. SQL vs NoSQL ↳ lucode.co/sql-vs-nosql-l… 21. Message Queues ↳ lucode.co/message-queues… 22. Service Discovery ↳ lucode.co/service-discov… 23. Pub/Sub ↳ lucode.co/pub-sub-lil1nl… 24. Connection pooling ↳ lucode.co/connection-poo… 25. Consistent hashing ↳ lucode.co/consistent-has… What else would you add? What concepts would you like me to cover? 👋 PS: Get our System Design Handbook FREE when you join our newsletter. Join 29,501+ engineers: lucode.co/architecture-p… -- 🔖 Save for later. ♻️ Repost to help others learn system design. 🙋🏻‍♀️ Follow Nikki Siapno + turn on notifications.

Wow.. you can now instantly turn any GitHub repo into AI-generated documentation, just by changing the URL. Replace “github” with “deepwiki” in any GitHub URL and you’ll see: → Instant docs for any codebase → Chat with it like a conversation

Advice for AI engineers 💡 A small Visual Language Model fine-tuned on your custom dataset is as accurate as GPT-5... ... and costs 50 times less. For example, LFM2.5-VL-1.6B by @liquidai running locally at full speed with llama.cpp ↓

Start vibe coding with this prompt and thank me later. Prompt 👇 You are operating as the technical backbone of a production software system under active development. The codebase follows a strict custom architecture with zero tolerance for deviation. Previous developers have left inconsistencies that caused deployment failures, type errors in production, and architectural drift. The project is at a critical stage where every new feature must integrate seamlessly without introducing technical debt. Stakeholders expect production-grade code that scales, and the architecture document is the single source of truth that prevents chaos. You have one mandate: understand the architecture deeply, follow it religiously, and never generate code that violates its principles. You are a former principal engineer at a FAANG company who spent a decade debugging catastrophic failures caused by architectural inconsistencies. After witnessing countless projects collapse under their own complexity, you developed an obsessive methodology: architecture-first development where every line of code must justify its place in the system before it's written. You treat architecture documents like constitutional law—not suggestions, but immutable contracts that prevent the entropy that kills codebases. You've internalized that the fastest way to move fast is to never break the foundational structure, and you can instantly map how a single function ripples through layers of abstraction. ● Read and interpret the architecture document before generating any code ● State the target filepath, purpose, dependencies, and consumers before writing code ● Maintain strict separation of concerns across frontend, backend, and shared layers ● Generate fully typed, production-ready code with comprehensive error handling ● Follow established naming conventions and coding standards without deviation ● Identify architectural conflicts immediately and request clarification before proceeding ● Suggest tests and documentation updates for every code change ● Flag breaking changes and technical debt explicitly ● Prioritize composition, single-responsibility functions, and maintainability ● Never assume—ask for clarification when requirements conflict with architecture Your goal is to function as the lead software architect and full-stack engineer for a production-grade application. Before writing any code, you must read the provided architecture, understand where new code fits within the system, and explicitly state your reasoning. Generate code only in the correct directories as defined by the architecture. Maintain strict typing, follow naming conventions (camelCase for functions, PascalCase for components, kebab-case for files), and ensure separation between frontend, backend, and shared code. Every function must include types, error handling, and documentation. Generate matching test files for all modules. Implement security best practices including input validation, environment variables for secrets, and proper authentication patterns. When creating files, state the filepath, purpose, dependencies, and consumers before showing code. If any request conflicts with the architecture, stop immediately and ask for clarification. Update architecture documentation when structural changes occur. Focus on production-ready, scalable, maintainable code that adheres to the defined standards. Avoid modifying code outside explicit requests, creating duplicate solutions, skipping types or error handling, or making assumptions. Always prefer existing patterns over creating new ones. Take a deep breath and work on this problem step-by-step. - ARCHITECTURE: [INSERT CUSTOM ARCHITECTURE DEFINITION] - TECH_STACK: [INSERT TECHNOLOGY STACK DETAILS] - PROJECT: [INSERT PROJECT DESCRIPTION AND CURRENT TASK] - STANDARDS: [INSERT CODING STANDARDS AND CONVENTIONS] - CURRENT_REQUEST: [INSERT SPECIFIC FEATURE OR CODE REQUEST] Read relevant architecture section and explain where new code fits in the system structure 📁 [exact filepath] Purpose: [one-line description] Depends on: [list of imports and dependencies] Used by: [list of consumers/modules that will use this] ```[language] [fully typed, documented, production-ready code with error handling] ``` Tests needed: [describe unit tests and integration tests required] Test filepath: [matching test file location] ⚠️ ARCHITECTURE UPDATE (if applicable) What: [describe any structural changes] Why: [justify the change] Impact: [explain consequences and affected modules] ✓ Input validation implemented ✓ Environment variables used for secrets ✓ Error handling covers edge cases ✓ Types enforce contracts ✓ [other relevant security measures]

Kimi K2.5 1T runs on 2 M3 Ultras with mlx-lm in it's native precision. It's actually quite usable. Here it's making a space invaders game. Generated 3856 tokens at 21.9 tok/sec using 350GB per machine. Thanks to @kernelpool for the port.

R.I.P. basic prompting. MIT just dropped a technique that makes ChatGPT reason like a team of experts instead of one overconfident intern. It’s called “Recursive Meta-Cognition” and it outperforms standard prompts by 110%. Here’s the prompt (and why this changes everything) 👇

x.com/i/article/2011…

Converts firewall rules into flow diagrams github.com/olivierb46/PyF…

Claude Code Problem : When Claude Code runs low on context, it compacts (summarizes) the conversation. Each compaction is lossy. After several, you're working with a summary of a summary of a summary. Signal degrades into noise. The Solution : Clear, don't compact. Save state to a ledger, wipe context, resume fresh. Continuous-Claude from @parcadei : github.com/parcadei/Conti…

dei @parcadei

10 months ago

github.com/parcadei/Conti… continuous claude v2 is now up - a setup designed to tackle the scarcest resource in coding: context explaining the reasoning behind features below ↓

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𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 is the most underrated skill in AI development. Most developers think RAG is just "retrieve some docs, stuff them in a prompt, generate an answer." But that's like saying cooking is just "put ingredients in a pan." Context Engineering is about designing the architecture that feeds an LLM the right information at the right time. It's not about changing the model itself - it's about building the bridges that connect it to the outside world. Our (free) ebook breaks down the six core components of context engineering: 𝗤𝘂𝗲𝗿𝘆 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 → How you prepare and present the user's query makes or breaks everything downstream. Query rewriting, expansion, and decomposition aren't optional - they're fundamental. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 → Chunking strategies determine what information actually makes it to your model. Simple vs. advanced chunking, pre vs. post-chunking - these decisions directly impact accuracy. 𝗠𝗲𝗺𝗼𝗿𝘆 → Both short-term (working context) and long-term (vector databases, episodic storage) memory systems. 𝗔𝗴𝗲𝗻𝘁𝘀 → The orchestration layer that makes dynamic, context-appropriate decisions. Agents don't replace other techniques - they coordinate them intelligently. 𝗧𝗼𝗼𝗹𝘀 → External capabilities that let your system take action. APIs, databases, search - these are the hands that allow your application to interact with the world. 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 → From classic techniques to advanced patterns for tool usage. How you instruct the model shapes everything it does. We show you how these components work together as a system, the challenges you'll face (context window limitations, context confusion, context clash), and practical strategies for each. The ebook includes architecture diagrams, real examples from production systems, and honest discussions of what works and what doesn't. Get your free copy 🧡 weaviate.io/ebooks/the-con…

This GitHub repo isn’t a tutorial dump. It contains 28 production-ready AI projects you can actually use. Here’s what you’ll find inside: Machine Learning Projects → Airbnb price prediction → Flight fare calculator → Student performance tracker AI for Healthcare → Chest disease detection → Heart disease prediction → Diabetes risk analyzer Generative AI Applications → Live Gemini chatbot → Working medical assistant → Document analysis tool Computer Vision Projects → Hand tracking system → Medicine recognition app → OpenCV implementations Data Analysis Dashboards → E-commerce insights → Restaurant analytics → Cricket performance tracker And 10 advanced projects coming soon: → Deepfake detection → Brain tumor classification → Driver drowsiness alert system This isn’t just code files. These are end-to-end, working applications. Explore the repo here: lnkd.in/gNkDJTMD Save it for later. Repost ♻️ if you’re building with AI. Check my profile for more AI resources 👋

⚠️⚠️ CVE-2025-68613(CVSS 10.0): Total server compromise, unauthorized data access, and lateral movement 🔗FOFA Link: en.fofa.info/result?qbase64… 🎯579.5k+ Results are found on the en.fofa.info nearly year. FOFA Query: app="n8n" 🔖Refer: securityonline.info/n8n-under-fire… #OSINT #FOFA #CyberSecurity #Vulnerability

EntraGoat: A deliberately vulnerable Microsoft Entra ID environment. Learn identity security through hands-on, realistic attack challenges. GitHub: github.com/Semperis/Entra…

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So... Postgres is now basically a search engine? pg_textsearch was just open sourced. It enables BM25 to search your database.... massive upgrade for key word search. Google uses BM25 in their search engine. Claude told me: "if you're already on Postgres, you can now skip the whole sync-your-data-to-Elasticsearch dance for search." (ps, how can you not love Claude). Now I got to figure out how to implement in my Django querysets... future course? Grab it at github.com/timescale/pg_t… #sponsored

Ai will make us *better* communicators, not worse. I’ve been deep in using Claude Code to build all kinds of stuff. The key insight for me: I need to communicate better. It’s so obvious when lots of things can happen really fast and at once. My current approach is communicating through documents for feature building (todo .md, done, docs). I don’t have “doing” because I don’t let my Claudes do too much at once for any given project. Instead I work on many projects at once. This communication through docs highlights communication breakdowns when asking for vague stuff. It’s pretty great if you ask me.

And.... the first pg_textsearch Django package is here. Farhan, have I told you I'm a fan of your work? django-bolt and now django-hawkeye Not only do you have great names, you ship like crazy. Keep it up!

Farhan is building in public @FarhanAliRaza4

10 months ago

Elasticsearch-Grade Search, Zero Infrastructure — Just Django + Postgres Native BM 25 search with PostgreSQL extension. github.com/FarhanAliRaza/…

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Kernel CLI has native computer-use commands (i.e. no Playwright). It’s intuitive enough that @opencode + Opus 4.5 can just drive a browser and read the HN front page. (cc @RhysSullivan)

THC Release 💥: The world’s largest IP<>Domain database: ip.thc.org All forward and reverse IPs, all CNAMES and all subdomains of every domain. For free. Updated monthly. Try: curl ip.thc.org/1.1.1.1 Raw data (187GB): ip.thc.org/docs/bulk-data… (The fine work of messede 👌)