As promised. We're excited to present our next conceptual documentary : 🎬Civilization Roadmap: 2026–2526 🌍🚀 Conceptualized by: @Kekius_Sage Scripting, enhancement & editing: Laxman & @CodeWithBinary Visuals: #GrokImagine This is still an experimental product & we're learning, iterating and pushing ideas forward. Did you notice these details? 👀 1️⃣ The Mars Colony ETA 2️⃣ The deeper question we tried to ask: 👉 What do we actually live for? 📌 I've also attached the Civilization Roadmap in the next thread for deeper context by @Kekius_Sage Would love to hear your thoughts. What stood out to you the most? Your feedback, reposts and replies genuinely help us improve and grow bigger🙏 @elonmusk @xai @dvorahfr @grok #Science #Physics #Maths #SpaceX #universe #Cosmos

10 Docker concepts every developer should master: 1. Docker Images — reusable templates used to create containers 2. Docker Containers — isolated environments for running applications 3. Dockerfile — instructions for building custom images 4. Docker Hub — cloud registry for storing and sharing images 5. Volumes — persistent storage for container data 6. Networks — communication layer between containers and services 7. Docker Compose — tool for defining and running multi-container applications 8. Container Orchestration — managing containers at scale with platforms like Kubernetes 9. Image Layers — stacked filesystem layers that optimize builds and caching 10. Registry Management — storing, securing, and distributing container images Grab the Docker Ebook: codewithdhanian.gumroad.com/…

Docker best practices

Expect a hit on cloudflare today. Everyone will be on the experimental path of self hosted models. @CloudflareDev @Cloudflare -you might be seeing a high traffic in next 48 hours. Might.

As a Linux Engineer, It will be good if you have an understanding of the below 40 topics👇 1. Linux Architecture and Kernel Fundamentals 2. Linux Installation and Distributions (Ubuntu, Debian, CentOS, Fedora, Arch) 3. Linux File System Hierarchy Standard (FHS) 4. Linux Commands and Shell Navigation 5. File and Directory Management 6. User and Group Management 7. File Permissions and Ownership 8. Linux Process Management 9. Job Control and Scheduling (cron, at) 10. Shell Scripting (Bash) 11. Environment Variables and Profiles 12. Package Management (APT, YUM, DNF, Pacman) 13. Systemd and Service Management 14. Linux Boot Process and GRUB 15. Disk Management and Partitioning 16. LVM (Logical Volume Manager) 17. File Systems (EXT4, XFS, Btrfs, ZFS) 18. Linux Networking Fundamentals 19. TCP/IP, DNS, DHCP, and Routing 20. SSH and Remote Administration 21. Firewall Management (iptables, nftables, UFW) 22. Network Troubleshooting Tools (ping, traceroute, netstat, ss) 23. Linux Security Best Practices 24. SELinux and AppArmor 25. Log Management and Analysis 26. System Monitoring and Performance Tuning 27. Memory Management and Swap Space 28. Linux Containers (Docker, Podman) 29. Container Orchestration (Kubernetes Basics) 30. Virtualization (KVM, VirtualBox, VMware) 31. Web Server Administration (Apache, NGINX) 32. Database Administration Basics (MySQL, PostgreSQL) 33. Backup and Recovery Strategies 34. High Availability and Load Balancing 35. Storage Management (NFS, SMB, iSCSI) 36. Configuration Management (Ansible, Puppet, Chef) 37. CI/CD and DevOps Practices 38. Cloud Computing on Linux (AWS, Azure, GCP) 39. Linux Troubleshooting and Debugging 40. Kernel Tuning and Advanced Performance Optimization 📘 Grab Linux Notes Handbook: codewithdhanian.gumroad.com/… These topics form a strong foundation for becoming a proficient Linux engineer, system administrator, DevOps engineer, cloud engineer, or site reliability engineer (SRE).

The cacophony of pseudo-intellectualism and talking about sovereign AI and how one country should be building in these waking times - An absolute disarray among those folks. Control is an aspect that is needed but also a regulated approach. And building something as Anthropic is not just CAPITAL , but a TALENT issue as well.

As Ramin said it right, VC bros being VC bros. They will invest on building rockets and invest on space datacenter but would turn a blind eye on local compute / edge AI / on device startups. They forget the roots or the basic.

LINUX ROADMAP 2026: COMPLETE LEARNING PATH │ ├── 1. Introduction to Linux │ ├── What is Linux │ ├── Linux distributions (Ubuntu, Fedora, Debian) │ └── Linux architecture and kernel basics │ ├── 2. Linux Installation & Setup │ ├── Installing Linux on a PC or VM │ ├── Dual boot and virtualization │ └── Understanding the Linux file system │ ├── 3. Linux Command Line Basics │ ├── Navigating directories (cd, ls, pwd) │ ├── Creating and managing files │ └── Understanding terminal workflows │ ├── 4. Working with Files & Permissions │ ├── chmod, chown, and permissions │ ├── File compression and archiving │ └── Searching files with find and grep │ ├── 5. Users & Process Management │ ├── Managing users and groups │ ├── Monitoring processes (ps, top, htop) │ └── Process control and scheduling │ ├── 6. Shell Scripting │ ├── Bash scripting fundamentals │ ├── Variables, loops, and functions │ └── Automating repetitive tasks │ ├── 7. Package Management │ ├── APT, DNF, and YUM │ ├── Installing and updating software │ └── Managing repositories │ ├── 8. Networking in Linux │ ├── IP addressing and DNS │ ├── SSH and remote access │ └── Network troubleshooting tools │ ├── 9. Linux System Administration │ ├── System services and systemd │ ├── Log management and monitoring │ └── Disk management and backups │ ├── 10. Linux Security │ ├── Firewall configuration │ ├── User authentication and SSH security │ └── Security best practices │ ├── 11. Linux for DevOps & Cloud │ ├── Docker fundamentals │ ├── Kubernetes basics │ └── Cloud server management │ ├── 12. Linux Server Administration │ ├── Web server setup (Nginx, Apache) │ ├── Database server management │ └── Performance tuning and optimization │ └── 13. Practice & Career Growth ├── Real-world Linux projects ├── Linux certifications (LFCS, RHCSA) └── Continuous learning and troubleshooting Recommended Ebook → Grab the Complete Linux Handbook → codewithdhanian.gumroad.com/…

As a senior Java backend developer, It will be good if you have an understanding of the below 40 topics👇 1. CAP Theorem 2. Consistency Models 3. Distributed System Architectures 4. Socket Programming (TCP/IP and UDP) 5. HTTP and RESTful APIs 6. Remote Procedure Call (RPC) - gRPC, Thrift, RMI 7. Message Queues (Kafka, RabbitMQ, JMS) 8. Java Concurrency (ExecutorService, Future, ForkJoinPool) 9. Thread Safety and Synchronization 10. Java Memory Model 11. Distributed Databases (Cassandra, MongoDB, HBase) 12. Data Sharding and Partitioning 13. Caching Mechanisms (Redis, Memcached, Ehcache) 14. Zookeeper for Distributed Coordination 15. Consensus Algorithms (Paxos, Raft) 16. Distributed Locks (Zookeeper, Redis) 17. Spring Boot and Spring Cloud for Microservices 18. Service Discovery (Consul, Eureka, Kubernetes) 19. API Gateways (Zuul, NGINX, Spring Cloud Gateway) 20. Inter-service Communication (REST, gRPC, Kafka) 21. Circuit Breakers and Retry Patterns (Hystrix, Resilience4j) 22. Load Balancing (NGINX, Kubernetes, Ribbon) 23. Failover Mechanisms 24. Distributed Transactions (2PC, Saga Pattern) 25. Logging and Distributed Tracing (ELK Stack, Jaeger, Zipkin) 26. Monitoring and Metrics (Prometheus, Grafana, Micrometer) 27. Alerting Systems 28. Authentication and Authorization (OAuth, JWT) 29. Encryption (SSL/TLS) 30. Rate Limiting and Throttling 31. Apache Kafka for Distributed Streaming 32. Apache Zookeeper for Coordination 33. In-memory Data Grids (Hazelcast, Infinispan) 34. Akka for Actor-based Concurrency 35. Event-Driven Architecture: Event sourcing and CQRS (Command Query Responsibility Segregation). 36. Cluster Management: Kubernetes for container orchestration. 37. Cloud-Native Development: Using cloud platforms (AWS, GCP, Azure), and serverless computing (e.g., AWS Lambda). 38. Distributed Data Processing: Frameworks like Apache Spark or Apache Flink for large-scale data processing. 39. GraphQL: Alternative to REST for inter-service communication. 40. JVM Tuning for Distributed Systems: Memory management and performance tuning in distributed environments.

Docker is still leading the DevOps highway But if you could only keep ONE DevOps tool in your stack, what would it be? 🐳 Docker ☸️ Kubernetes 🏗️ Terraform ⚙️ Jenkins 📊 Prometheus Drop your pick below 👇

I am devastated by how rapidly Claude, my goto for almost everything till a month ago, has fallen - giving user less control and guarantees while charging more, shadowbanning AI research, best model not available via subscription, even at 200$. I am finding Opencode with Minimax M3 works vastly better than Claude Code with Opus 4.8 (and now, given these changes, Fable is not even an option for me). Huggingchat with Kimi-2.6 or Deepseek-V4-Pro or Nemotron 3 Ultra with websearch-exa MCP enabled work fine for most chat tasks. NotebookLM and grok and gemini remain in my toolchain for the things they do. Reconsidering ChatGPT cautiously (was burned by them months ago). Trying to figure out how to run the claws - OpenClaw/Hermes agent/Nemoclaw/etc. - safely: Seems like railway hosted OpenClaw with a cheap hosted model is a good start for isolatable use cases. For high read only agency, my app (Maibook) represents a sweet spot that is hard to build reliably with any of the claws. For more deep personalized use cases requiring full read/write/act agency of claws, not sure how to try them safely yet.

X has gone full slop this year. Someone from claude code / openclaw spews a few terms out, and literally the entire community follows it like some gospel of truth. The new thing: “agent loops” seen almost 10 articles in the last few days. It’s not a brand new idea. If you’ve been involved in applied AI for some time, you’d know. If you’re developing something serious, you’d also know that more abstractions are not going to help in the long run. Don’t fall for slop. Be better. Learn your foundations. Write some code by hand every week. Follow other fields, see for yourself how serious work gets done. Checkout Infra, hardware, vlsi, ops.

Introducing ollamadash - a dashboard for ollama library models - autorefreshes as new models are added, rich sort, filter, search across all tags. @ollama ollamadash.up.railway.app/

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“Don’t be a Homelander.” This is how your agents feel and act when your tokens are exhausted. So, go local. Build upon the compute. Make the most out of local models. In courtesy of @TheBoysTV. Issued in the interest of private compute and local models @huggingface

6 GB VRAM - 16 Cores - 16 GB RAM one machine that routes models and also gives GPU precendence and CPU precedence - MedGemma1.54B Q8, Gemma4E4B, Gemma4E2B, Presidio Analyzer and Anonymizer, Gemma3 270M-it_q8 finetuned, and Qwen3.5_0.8B-Q8. All these models takes precendence on tasks. The Video Processing is handled by Gemma4E4B on a separate 16GB VRAM machine when needed. 0nly for video processing. Everything else runs via the 6GB VRAM machine. TLDR : It is upto you how you juice out your compute. It is just there, Max it out, on inference.

Model routing and Intelligence routing - One thing that I always believed in and also built upon. Check this out - youtu.be/FMJeLTB2qtY?t=1265&… Watch this too. Task based model routing. This was built and presented in August 2025 - at the @linuxfoundation AI Dev Summit . Glad companies like @FactoryAI are shaping on similar lines. P.S : This work in 2025 also uses the Pareto analysis. Next big thing is routing not just by task or model - it is energy. And yes , I created another project which basically uses energy as a base for Pareto based routing too.

The most important and immediate and needed work is in AI model intelligence to solve the model intelligence for building code and better code - locally - that can run on affordable GPU. Fast and on par. If this is solved - Anthropic / Open AI and Gemini will lose revenue and you will save money on tokens.

You pay for a work , and you see it done. And if you are not satisfied - there is always a reimbursement or a way to get refunds. Same should be with Claude. Not sure who are praising the work of Claude. And before you start yapping skill issue - the Claude continuously failed “yes failed” to do a simple CORS checkpoint resolution on Prometheus . I had to literally give it t and show it the port number that it was working on. My question - If we are paying for the intelligence and are not getting the desired results- there should be a way to report the entire case for review and then - every single penny used for the useless model should be reimbursed. No wonder Anthropic is reporting so much revenue. Everyone is using . No one dares say that the model is making mistakes at the expense of our money. In fact - the money should credited to Anthropic only if zero shots solves the problem or even after few shot prompts. I will never come to Anthropic with Infrastructure scaling issues. Should have stuck with Qwen-Coder-Next . At least the tokens were not being counted per expense.