Bytebell MCP Cuts AI Code Copilot Costs by 90% for Enterprises

Day 23 of 90 | MozaicTeck Build Challenge What I Built, What Broke, and What I Fixed Today started with an engineering audit rather than new feature development. I discovered a critical gap in my version control structure. My GitHub repository was frozen at Day 10 while my HuggingFace deployment was fully updated at Day 22. Twelve days of backend work existed in only one place. The Problem: Two separate git repositories nested inside each other. One pointing to GitHub. One pointing to HuggingFace. No clear structure. No consistent push workflow. A single machine failure would have meant partial code loss. The Fix: •      Deleted the outdated my-rag-app repository from GitHub •      Created a clean mozaicteck-rag repository on GitHub •      Added GitHub as a second remote alongside the existing HuggingFace remote •      Established a two push workflow for every future backend update •      Removed __pycache__ from git tracking using git rm --cached •      Restored and verified the complete .gitignore Error Encountered: The __pycache__ folder was being tracked by git despite being listed in .gitignore. This is a common misconception. Adding a file to .gitignore does not remove it from tracking if git has already committed it. The correct fix is git rm --cached followed by a new commit. What I Built: After resolving the infrastructure issues I built the frontend Prompt Library page. A fully functional browsing interface connected to live MongoDB Atlas endpoints. Users can filter 120 AI prompts by category, search by keyword, and copy any prompt with a single click. The interface is dark themed, mobile responsive, and deployed to GitHub Pages. Key Engineering Decision: Identified an architectural gap between ChromaDB and MongoDB. The LLM currently feeds from ChromaDB which contains less structured data. MongoDB holds the same 120 prompts with proper category labels, difficulty levels, and tier classifications. Evaluating a migration of the RAG retrieval layer to MongoDB is now formally on the roadmap for Days 24 to 30. Tech Stack Today: •      React with Vite for the frontend •      FastAPI backend deployed on HuggingFace Spaces •      MongoDB Atlas for structured prompt storage •      Git with dual remotes for HuggingFace and GitHub Building in public from Abuja, Nigeria. Day 23 of 90. #buildinpublic #AIEngineer #RAG #MongoDB #React #FastAPI #GitHub #NigeriaToTheWorld #MozaicTeck #softwareengineering #90DayChallenge FastAPI MongoDB JavaScript Developer React GitHub Claude AI

Every developer is talking about Cursor and GitHub Copilot. Nobody is talking about the one that might beat them both. AWS Kiro — here's everything you need to know 👇 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗞𝗶𝗿𝗼? An AI-powered IDE built by AWS. Built on Code OSS — so you keep all your VS Code settings and plugins. Free to use during preview. Powered by Claude under the hood. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 𝗖𝘂𝗿𝘀𝗼𝗿? Cursor and Copilot take your prompt → generate code immediately. Kiro takes your prompt → generates a spec first → then generates code. This is called spec-driven development. And it changes everything. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 — 𝟯 𝘀𝘁𝗲𝗽𝘀: 𝗦𝘁𝗲𝗽 𝟭 — 𝗦𝗽𝗲𝗰𝘀 Type: "Add a review system for products" Kiro generates → user stories, acceptance criteria, edge cases. You review and approve before a single line of code is written. 𝗦𝘁𝗲𝗽 𝟮 — 𝗗𝗲𝘀𝗶𝗴𝗻 Kiro analyses your codebase → generates data flow diagrams, database schemas, API endpoints, TypeScript interfaces. You know exactly what will be built before it's built. 𝗦𝘁𝗲𝗽 𝟯 — 𝗧𝗮𝘀𝗸𝘀 Kiro generates tasks and sub-tasks, sequences them by dependencies, links each task to requirements. Unit tests, integration tests, loading states — all included automatically. 𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀: → Hooks — automations that trigger in the background. Auto git commits, auto documentation updates, auto code quality checks. → MCP Support — connects to databases, APIs, AWS docs, any external tool → Steering Rules — guide AI behaviour across your entire project → SageMaker Integration — connect directly to AWS infrastructure from your IDE 𝗖𝘂𝗿𝘀𝗼𝗿 𝘃𝘀 𝗞𝗶𝗿𝗼 𝗰𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻: → Cursor wins for fast iteration and quick fixes → Kiro wins for complex features that need upfront design → Kiro is the better choice if you build on AWS 𝗙𝘂𝗻 𝗳𝗮𝗰𝘁: During early access an engineer used Kiro to build an AWS integration. Kiro's agent code triggered a cascade that caused a real AWS service disruption. The internet called it "vibe too hard, brought down AWS." 😂 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝘃𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗰𝗼𝗱𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗵𝗮𝘃𝗲𝗻'𝘁 𝘀𝗼𝗹𝘃𝗲𝗱 𝘆𝗲𝘁. 𝗞𝗶𝗿𝗼 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝗮𝘁𝘁𝗲𝗺𝗽𝘁. Have you tried Kiro yet? Cursor or Kiro — what's your pick? 👇 #AWS #Kiro #AITools #BackendEngineering #Java #Developer #LearningInPublic #SoftwareEngineering #CloudComputing Kiro Image Credits Electromech Cloudtech Pvt. Ltd.

Migrating a Running Production App to Docker I worked on something very practical — not a fresh project, but an already running production system on EC2. 👉 No documentation 👉 Multiple background jobs 👉 Different queues and scripts running together So first step was understanding reality before touching anything 🔍 What I did: * Checked running processes using ps -ef * Identified multiple Celery workers (different queues like events, seats, scraping) * Found Django app running on port 8042 * Verified Redis and Elasticsearch connections * Understood how jobs were triggered (manual + scheduled) ⚠️ Observation: Production was running many parallel workers, not just 1–2 👉 That means concurrency + queue separation was already designed ⚙️ Then moved to Dockerization * Built clean Dockerfile * Used Poetry for dependency management * Disabled virtualenv (important for containers) * Configured Gunicorn instead of Django runserver * Added support for worker + beat scripts 💥 Real issue I faced:- 💥 1. Hidden Background Workers At first, I thought only Django app was running. After checking ps -ef, I found: * Multiple Celery workers * Different queues (events, scraping, seats) * Independent Python scripts running continuously 💥 2. Too Many Workers Running There were many worker processes running in parallel. 👉 Solution: * Separate worker containers (not single container) * Use concurrency config instead of manual duplication 💥 3. Wrong Assumption: One Container = Everything Initially tried to run: * Django * Celery worker * Celery beat 👉 in single container ❌ 👉 Problem: * Hard to scale * Hard to debug * Not production standard 👉 Fix: * Web container (Gunicorn) * Worker container (Celery) * Beat container (Scheduler) 💥 4. .env File Crash Issue Error: invalid literal for int() with base 10: '1 ' 👉 Root cause: Extra space in env variable 👉 Lesson: Whitespace can break production ⸻ 💥 5. Container Started but App Crashed Gunicorn started but workers failed. 👉 Root cause: Environment variables not loaded properly 👉 Fix: * Used --env-file * Verified inside container using printenv ⸻ 💥 6. Local vs Production Gap Locally everything worked fine But in container: * Path issues * Missing dependencies * Different Python environment 💥 7. Large Docker Context Size Build was slow (~500MB+ context) 👉 Cause: Unnecessary files copied 👉 Fix: Added .dockerignore: * venv * logs * cache 💥 8. Understanding Existing Infra Before Docker: * Redis already running * Elasticsearch already running * App tightly coupled 👉 Needed to map all dependencies first 🚀 Final Result: * Docker image working * Django running via Gunicorn * Ready for ECS deployment * Clear understanding of existing production architecture DevOps is: ✅ Understand existing system ✅ Map processes and dependencies ✅ Then containerize safely #DevOps #Docker #AWS #ECS #Django #Celery #Production #Debugging #Cloud #Learning

**Stop shipping GitHub PATs to your CI/CD pipelines.** 🛑 We just removed 4 files of complexity from every microservice in our platform. Here's the problem we hit at Harmoniq: We built an internal Python library that auto-tracks LLM token usage across 6+ AI microservices. Our first approach? ``` pip install git+https://${GITHUB_PAT}@github.com/... ``` Looks simple. Until you need it working in: → Jenkins pipelines → Docker multi-stage builds → GitHub Actions CI → Every developer's local machine That "simple" line turned into: - BuildKit `--mount=type=secret` in Dockerfiles - `git` installed in production images (just for pip) - PAT rotation nightmares - Broken builds when tokens expired **The fix took 30 minutes.** We published the library to AWS CodeArtifact — a private PyPI registry that sits in front of public PyPI. Now our requirements.txt looks like this: ``` my-internal-lib==0.1.2 boto3>=1.34 fastapi>=0.115 ``` That's it. Private and public packages from the same index. No PATs. No git in Docker. No secrets. "Why not JFrog Artifactory or Sonatype Nexus?" Because our entire stack already runs on AWS (ECS, IAM, ECR). CodeArtifact gives us **IAM-native auth** — our Jenkins EC2 role, developer SSO sessions, and ECS task roles all authenticate automatically. Zero extra credentials. Zero extra SaaS bills. Zero extra vendors to manage. The before/after: ❌ Before: - GitHub PAT in every pipeline - git installed in production containers - BuildKit secrets for Docker builds - Broken builds when PAT expires ✅ After: - IAM role handles auth (zero secrets) - Standard `pip install` — nothing special - 50MB smaller Docker images (no git) - Developers just run `make test` — it works **The unsexy infrastructure work is often the highest-leverage work.** If you're using `git+https://` for internal Python packages across microservices — stop. Set up a private registry. Future you will thank present you. --- What's your approach for distributing internal libraries across services? Drop it below 👇 #Python #AWS #DevOps #CICD #Microservices #CloudArchitecture #AIEngineering

GitHub is Dying? AI is Killing GitHub There are very few tools in a developer’s life that feel irreplaceable. For many of us, GitHub is one of them. And yet—over the past few months, we’ve seen something unusual: - Pull requests disappearing from UI - Search breaking under load - Data inconsistency concerns Not full outages. Something more subtle—and more dangerous. Partial system failure at scale. So the real question isn’t: “Is GitHub dying?” It’s: “Is GitHub hitting a scaling wall because of AI?” --- What Actually Changed Since late 2025, software development has shifted dramatically. Agentic workflows, AI copilots, automation pipelines—all of them generate: - More repos - More PRs - More API traffic - More background jobs This isn’t linear growth. This is explosive, compounding load. GitHub itself hinted at needing 30x scale (after already planning for 10x). That’s not scaling. That’s re-architecture territory. --- Why Systems Break at 10x At low scale → optimize for simplicity At high scale → optimize for survival Example: Postgres for rate limiting works at 1k RPS At 10k+ RPS → you introduce Redis, caching layers, async pipelines Nothing was wrong before. It just doesn’t survive new scale. Now imagine this across GitHub: - PR touches multiple subsystems - Cache miss → DB hit → latency spike - Retry → traffic amplification - One slow dependency → cascading failure This is classic distributed systems behavior. --- The Real Constraint: Zero Downtime GitHub is globally active. There is: - No quiet hour - No maintenance window - No safe migration time Every fix must happen live under load. That’s an entirely different engineering problem. --- What the Recent Incident Reveals ElasticSearch got overloaded → UI lost PR visibility Important: - No data loss - Git operations were fine - APIs were fine But UX broke. And that tells you something critical: GitHub is no longer just Git. It’s a coordination layer across dozens of systems. --- Engineering Signals You Should Notice From their fixes, we can infer: - Hot paths moved away from MySQL - Auth/session redesigned to reduce DB hits - Service isolation to reduce blast radius - Migration from Ruby → Go for performance paths - Focus on caching, queues, and decoupling This is not patchwork. This is deep systems evolution. --- The Bigger Insight GitHub has become a global choke point. Every tool—Copilot, Claude, VS Code, Replit—eventually pushes code here. That centralization creates: - Massive convenience - Massive pressure --- What This Means for You If you want to become a top engineer, don’t ignore this. Study this phase. Because real engineering is not: “Build a feature” It is: - Handling 10x growth - Designing for failure - Reducing coupling - Managing distributed complexity --- Final Thought GitHub isn’t dying. It’s being stress-tested by the future of software development.

🔥 I vectorized 3.5 million GitHub repos into a RAG pipeline in 2 days. Claude Code wrote the code. I wrote the system. Here's what that actually looked like — and why "AI does the work" is the wrong mental model. --- **The problem:** Build an indexing pipeline that extracts, chunks, and embeds 3.5M GitHub Enterprise repos into an OpenSearch vector store (AOSS) for a RAG assistant. **The naive assumption:** Good algorithm + fast infra = done. **What actually happened:** ✅ Throttling wasn't optional — it was survival. 3.5M repos hitting GitHub's API without rate limiting = instant ban. I had to design a token-bucket throttle with per-org backpressure before a single byte moved. ✅ One worker was a lie. I broke the pipeline into isolated workers — extraction, chunking, embedding — each horizontally scalable independently. Bottleneck in embedding? Scale only that. Clean separation saved hours of guessing. ✅ PostgreSQL WAL was killing me. Temporarily staging 3.5M rows with Write-Ahead Logging enabled was eating disk and slowing commits. I switched the staging table to `UNLOGGED` — no WAL, no overhead, data lives only for the job. Throughput jumped immediately. ✅ Every infra change needed a cost + time recalculation. Each optimization I made — more workers, bigger batches, async Temporal workflows — I re-ran the math. "Will this finish in 2 days? What does it cost at 10x load?" Without that discipline, I'd have shipped something that worked once and broke in production. ✅ Logs were the real debugger. I didn't guess bottlenecks. I instrumented every stage and read the logs. Chunking was fast. Embedding was the wall. OpenSearch ingestion was the second wall. Fix in order, not in parallel. --- **Now, about Claude Code.** It was dumb and smart at the same time — and that's not a criticism, it's a lesson. **Smart:** Once I had the design locked, Claude Code implemented it in minutes. Temporal workflows, async workers, chunkers, AOSS indexers — hundreds of lines of correct, structured code, fast. 2 days of engineering became possible because I wasn't writing boilerplate. **Dumb:** When I asked it to *design* the system, its suggestions were generic. Standard patterns, no awareness of the scale constraints, no intuition about where 3.5M records would actually break. It didn't know to use unlogged tables. It didn't calculate throughput. It didn't see the WAL problem coming. And that's fine — because that's *my* job. --- **The mental model shift I'm walking away with:** > AI writes the *what*. Humans design the *how* and the *why*. Claude Code is a force multiplier for implementation. It is not — yet — a systems thinker. The bottleneck moved from "can I write this code" to "can I design the right system fast enough." In the future, tools like Claude Code will implement core concepts the moment you articulate them. The competitive advantage won't be coding speed. It'll be **algorithmic thinking, system intuition, and knowing where things break at scale** before they do. That's the skill worth sharpening. --- What's your experience building with AI coding tools at scale? I'm curious whether the "design gap" is shrinking or if it's actually widening as the implementation gap closes. #AI #MachineLearning #RAG #VectorSearch #SystemDesign #ClaudeCode #SoftwareEngineering #DataEngineering #LLM #Temporal

Go (Golang) is no longer just an "optional" skill; in 2026, it has become the gold standard for building high-performance, cloud-native backends. As someone deep into the world of Python and Microservices, I’ve seen the industry shift. While Python excels in AI and rapid prototyping, Go is winning the battle for scalability, concurrency, and execution speed. If you are planning to become a Complete Go Backend Developer this year, here is the roadmap you should follow: 1. Mastery of the Fundamentals (The "Go" Way) 🐹 Go is unique. You need to move beyond basic syntax and understand: - Strong Typing & Structs: No more classes; embrace the simplicity of structs and interfaces. - Error Handling: Get comfortable with explicit error checking—it’s a feature, not a bug. - Pointers: Understand memory management without the headache of C++. 2. Concurrency: The Killer Feature ⚡ This is why companies choose Go. - Goroutines: Learn how to run thousands of tasks concurrently with minimal overhead. - Channels: Master the art of communication between goroutines. - Select & WaitGroups: Manage complex asynchronous workflows like a pro. 3. The Modern Web Ecosystem 🌐 Forget bloated frameworks. In Go, the standard library is powerful, but for production-grade SaaS, focus on: - Frameworks: Gin or Echo for high-performance REST APIs. - Fiber: If you are coming from a Node.js/Express background. - Standard Library (net/http): Knowing how to build a server without any external dependencies. 4. Data & Persistence 💾 - ORM vs. Raw SQL: Use GORM for speed, but master sqlx or pgx for performance-critical queries. - Migration Tools: Use golang-migrate to keep your PostgreSQL or MySQL schemas in sync. - Caching: Integrating Redis for lightning-fast data retrieval. 5. Microservices & Communication 🏗️ Go was born for the cloud. Focus on: - gRPC & Protocol Buffers: For ultra-fast, type-safe service-to-service communication. - Message Brokers: Integrating with Kafka or RabbitMQ for event-driven architectures. 6. Cloud-Native Deployment ☁️ A Go developer in 2026 must be a DevOps-lite engineer. - Docker: Writing multi-stage Dockerfiles for tiny, secure images. - Kubernetes: Understanding how Go binaries perfectly fit into K8s pods. - CI/CD: Automating tests and builds using GitHub Actions. Why Go in 2026? It’s simple: It’s fast, it’s compiled, and it’s built for the distributed systems we are designing today. Whether you are building an AI-orchestration layer or a high-traffic fintech API, Go provides the reliability that modern SaaS demands. Are you planning to add Go to your tech stack this year, or are you sticking with Python/Node? Let's talk about the transition in the comments! 👇 #Golang #BackendDevelopment #SoftwareEngineering #GoRoadmap2026 #Microservices #CloudNative #SystemDesign #Programming #PythonToGo #TechCareer

For years, the AWS Lambda Handler Cookbook was missing one thing I kept putting off: real, production-grade CRUD across multiple functions with a single, unified Swagger. v9.6.0 finally fixes that, thanks to the event handler alpha feature in Powertools for AWS Lambda's event handler. What's new in v9.6.0: 🔧 Create, get, and delete order APIs as micro Lambda functions over DynamoDB 📄 Unified OpenAPI schema generated across all endpoints 🔍 Automated API breaking changes detection in CI 📑 Swagger published to GitHub Pages and always in sync with the code What you get overall in the cookbook template: 🏗️ Production-ready serverless project in Python with CDK infrastructure 🧪 Five testing strategies: unit, integration, infrastructure, security, and E2E ⚙️ CI/CD with GitHub Actions across dev, staging, and production environments 📊 CloudWatch dashboards and alarms with SNS notifications out of the box 🔒 WAF protection, input validation with Pydantic, and idempotent API design 🏷️ Feature flags and dynamic configuration via AppConfig 📈 Business KPI metrics and distributed tracing with Powertools for AWS Lambda Thanks to Leandro Cavalcante Damascena for developing the Powertools OpenAPI feature that enabled the unified schema. I hope you merge it soon :) 🔗 https://lnkd.in/dZe74TCc #AWSLambda #Serverless #AWS #OpenAPI #PowertoolsForAWS #PlatformEngineering

🐳 Docker didn't just change how I deploy code. It changed how I think about code. Before Docker I lived in "it works on my machine" hell. After Docker — my laptop, my colleague's machine, and AWS EC2 run the exact same container. Zero surprises. But here is what nobody tells you when you start with Docker: The basics are easy. The production lessons are brutal. Here are 5 things I learned the hard way after containerising real FastAPI apps at Visital: 1️⃣ Multi-stage builds are not optional Single-stage Dockerfile = 900MB image. Multi-stage Dockerfile = 120MB image. Same app. 87% smaller. Faster deploys. Less attack surface. Build tools belong in the build stage — not your production container. 2️⃣ Always add health checks Without a health check — Docker thinks your container is "running" even when your app has crashed inside it. A 3-line health check in your Compose file saves you from ghost containers that show green but serve nothing. 3️⃣ Never store secrets in your Dockerfile I see developers write ENV DB_PASSWORD=mysecretpassword directly in Dockerfiles. That password now lives in your image history forever. Use .env files. Use AWS Secrets Manager. Never hardcode credentials. 4️⃣ Use .dockerignore like you use .gitignore Copying your entire project folder including node_modules, .git, venv into your image is a disaster. A proper .dockerignore file keeps your image clean, small, and fast. 5️⃣ Name your containers — always docker ps showing romantic_einstein tells you nothing at 2 AM during an incident. Always use container_name in your Compose file. Future you will be grateful. Docker is not just a DevOps tool. It is the bridge between "I wrote it" and "it runs in production." Every Python and Java developer who has not containerised their first app yet — start today. The learning curve is 2 days. The payoff is your entire career. 💬 Which Docker lesson hit you the hardest? Drop it in the comments — I read every single one. 👇 ♻️ Repost if this helps even one developer on your network. w3schools.com #Docker #DevOps #Python #FastAPI #BackendDevelopment #CloudComputing #AWS #SoftwareDeveloper #Kubernetes #ContainerOrchestration #Programming #TechCareer

🚀 From Source Code to Production: My First Real Docker Deployment This week I completed the EpicBook Docker Capstone as part of the DevOps Micro-Internship, and it completely changed how I think about deploying applications. Taking a Node.js project from raw code to a production-ready deployment taught me something important: systems rarely fail when everything is perfect, they fail when dependencies, timing, or infrastructure behave unpredictably. 🏗️ What I Built A containerized 3-tier architecture using Docker Compose: • Nginx Reverse Proxy • Node.js Backend API • MySQL Database Flow: User → Nginx → Backend → Database ⚙️ Production-Focused Improvements To make the system more realistic and stable, I implemented: • Multi-stage Docker builds for smaller images • Network isolation so the database isn’t publicly exposed • Named volumes for persistent MySQL storage • Structured JSON logging for observability • CORS configuration for controlled API access 🧠 Reliability Insight One configuration made a huge difference: depends_on with service health checks. This ensured: • Backend starts only after MySQL is healthy • Nginx waits until the backend is ready Result: predictable startup with no race conditions. 📉 Optimization Win Multi-stage builds reduced the image size from ~700MB to ~113MB (≈84% smaller). Benefits included faster deployments, improved CI/CD efficiency, and a smaller attack surface. ⚠️ Real Issues I Solved During deployment I encountered several real-world problems: • Azure DevOps pipeline queue delays → solved using a WSL self-hosted agent • Docker Compose container errors during redeploy → resolved with clean container removal • Azure misinterpreting Docker logs as failures → fixed by redirecting stderr These challenges turned out to be the most valuable learning moments. 💡 Key Takeaway Reliable systems aren’t defined by when everything works they’re defined by how they behave when something goes wrong. Grateful to Pravin Mishra for shaping me and introducing me to the modern tech world, and to Praveen Pandey for following our progress and continuously pushing us toward our goals. A heartfelt thanks also to our co-mentors Ranbir Kaur, Tanisha Borana, and Egwu Oko for their constant support and follow-ups. Last but not least, a big thank you to Team Lead Pratyush Pahari, Goodness Ojonuba, Swaroopa Gajali, Ogbonna Nwanneka Mary for their effort and commitment, for the amazing demo on this module. #Docker #DevOps #CloudComputing #Containerization #LearningInPublic