Neo Kim

20 GitHub repos to elevate your AI engineering career (save this): 1 OpenClaw ↳ Runs a personal AI agent locally that can browse, plan & take actions on your device. 2 TensorFlow ↳ Provides a production-ready framework to build, train & deploy machine learning models at scale. 3 AutoGPT ↳ Automates multi-step tasks by chaining LLM reasoning into autonomous agents. 4 n8n ↳ Automates workflows with a visual builder that integrates APIs, data & AI tools. 5 Ollama ↳ Runs open LLMs locally with simple commands & optimized performance. 6 Stable Diffusion WebUI ↳ Generates images locally with a powerful UI for Stable Diffusion models. 7 Hugging Face Transformers ↳ Offers thousands of pretrained models for NLP, vision & multimodal AI tasks. 8 Langflow ↳ Builds & tests LLM pipelines visually using a drag-and-drop interface. 9 Dify ↳ Creates production-ready AI apps with built-in orchestration, prompts & APIs. 10 LangChain ↳ Orchestrates LLM workflows, tools, memory & agents in applications. 11 Open WebUI ↳ Delivers a self-hosted ChatGPT-style interface with local & API model support. 12 DeepSeek-V3 ↳ Provides a high-performance open-weight LLM optimized for reasoning and coding. 13 PyTorch ↳ Builds & trains deep learning models with flexible, research-friendly APIs. 14 Gemini CLI ↳ Interacts with Google’s Gemini models directly from the command line. 15 llama cpp ↳ Runs LLaMA-style models efficiently on CPUs & local hardware. 16 Whisper ↳ Transcribes & translates speech with high accuracy using deep learning. 17 ComfyUI ↳ Designs advanced image generation workflows using node-based pipelines. 18 CrewAI ↳ Coordinates multiple AI agents to collaborate on complex tasks. 19 RAGFlow ↳ Implements retrieval-augmented generation pipelines for enterprise search & QA. 20 Claude Code ↳ Assists coding with deep repository understanding & agent-style workflows. What else should make this list? 💾 Save this for later & RT to help other software engineers learn AI. 👤 Follow @systemdesignone + turn on notifications.

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20 GitHub repos to elevate your AI engineering career (save this): 1 OpenClaw ↳ Runs a personal AI agent locally that can browse, plan & take actions on your device. 2 TensorFlow ↳ Provides a production-ready framework to build, train & deploy machine learning models at scale. 3 AutoGPT ↳ Automates multi-step tasks by chaining LLM reasoning into autonomous agents. 4 n8n ↳ Automates workflows with a visual builder that integrates APIs, data & AI tools. 5 Ollama ↳ Runs open LLMs locally with simple commands & optimized performance. 6 Stable Diffusion WebUI ↳ Generates images locally with a powerful UI for Stable Diffusion models. 7 Hugging Face Transformers ↳ Offers thousands of pretrained models for NLP, vision & multimodal AI tasks. 8 Langflow ↳ Builds & tests LLM pipelines visually using a drag-and-drop interface. 9 Dify ↳ Creates production-ready AI apps with built-in orchestration, prompts & APIs. 10 LangChain ↳ Orchestrates LLM workflows, tools, memory & agents in applications. 11 Open WebUI ↳ Delivers a self-hosted ChatGPT-style interface with local & API model support. 12 DeepSeek-V3 ↳ Provides a high-performance open-weight LLM optimized for reasoning and coding. 13 PyTorch ↳ Builds & trains deep learning models with flexible, research-friendly APIs. 14 Gemini CLI ↳ Interacts with Google’s Gemini models directly from the command line. 15 llama cpp ↳ Runs LLaMA-style models efficiently on CPUs & local hardware. 16 Whisper ↳ Transcribes & translates speech with high accuracy using deep learning. 17 ComfyUI ↳ Designs advanced image generation workflows using node-based pipelines. 18 CrewAI ↳ Coordinates multiple AI agents to collaborate on complex tasks. 19 RAGFlow ↳ Implements retrieval-augmented generation pipelines for enterprise search & QA. 20 Claude Code ↳ Assists coding with deep repository understanding & agent-style workflows. What else should make this list? 💾 Save this for later & RT to help other software engineers learn AI. 👤 Follow @systemdesignone + turn on notifications.

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@vidigoat2011 appreciate it, thanks!

@systemdesignone This list is pure gold. OpenClaw and CrewAI are my daily drivers for agents — the shift from wrappers to real tools is happening fast. Saved it instantly.

@_jaydeepkarale 🤝

@systemdesignone Ollama 🤝 Deepseek

@RaulJuncoV love minimax

Most AI models get trained, shipped, and forgotten. MiniMax M2.7 trained itself. One person. Four days. Zero hand-written code. The model built its own Agent Harness from scratch: - CI - testing - code review Then it used that harness to build a Research Agent on top. Data pipelines, training loops, evaluation, memory. The model builds its own tools, then uses those tools to get better. 9 Gold Medals on MLE-bench Lite. 56% on SWE-Bench Pro. 97% skill adherence across 50+ complex skills running simultaneously. Second only to Opus-4.6 (75.7%) and GPT-5.4 (71.2%) The model not only follows instructions well but also updates its own memory, builds new skills autonomously, and feeds its results back into its own learning. I ran a quick experiment with their API. Gave it: “Build a GitHub issue triage agent.” It came back with architecture, agent workflows, and working components. Pretty good results. MiniMax calls it self-evolution. You should probably pay attention. 👉 MiniMax Token Plan 12% OFF: platform.minimax.io/subscribe/codi…

@RaulJuncoV good one, thanks!

@systemdesignone Another one Haystack → great for production-grade RAG pipelines

@RitikaAgrawal08 love this visuals

push(), pop(), shift() & unshift() in JavaScript 👇 These are basic array methods used to add or remove elements from the beginning or end of an array. ✨ Important pointers : ▪️ push() → adds elements to the end of an array ▪️ pop() → removes the last element ▪️ shift() → removes the first element ▪️ unshift() → adds elements to the beginning Remember that all these methods modify the original array (they are mutable).

@sevalla_hosting VSCode, Vi

What's your preferred code editor?

@e_opore nice overview Dhanian

How AWS Handles Key Management Introduction → AWS manages encryption keys using AWS Key Management Service (KMS) → Provides secure creation, storage, and control of cryptographic keys → Fully managed with high availability and durability AWS KMS (Key Management Service) → Central service for managing encryption keys → Supports symmetric and asymmetric keys → Integrated with most AWS services (S3, EBS, RDS, Lambda) → Automatically handles key storage and lifecycle Key Types → Customer Managed Keys (CMKs) Full control over key policies and lifecycle → AWS Managed Keys Automatically created and managed by AWS services → AWS Owned Keys Fully managed by AWS with no user control Encryption Process → Data is encrypted using a data key → Data key is encrypted using a master key (KMS key) → Only encrypted data keys are stored → Decryption requires access to KMS Envelope Encryption → AWS uses envelope encryption for performance → Encrypt large data with data keys → Encrypt data keys with KMS master keys → Reduces direct KMS usage and improves speed Access Control & Security → Controlled using IAM policies and key policies → Fine-grained permissions for key usage → Supports grants for temporary access → Integrated with AWS CloudTrail for auditing Key Rotation → Automatic yearly rotation for KMS keys → Manual rotation supported for more control → Ensures long-term cryptographic security Integration with AWS Services → S3 → Server-side encryption (SSE-KMS) → EBS → Encrypted volumes → RDS → Encrypted databases → Lambda → Secure environment variables → Secrets Manager → Secure secrets storage Monitoring & Auditing → All key usage logged in CloudTrail → Monitor access and detect anomalies → Ensure compliance and security governance High Availability & Durability → Keys stored across multiple Availability Zones → Designed for 99.999% durability → No single point of failure Why It Matters → Protects sensitive data → Enables compliance (GDPR, HIPAA, etc.) → Centralizes encryption control → Reduces risk of data breaches Grab the AWS Handbook; codewithdhanian.gumroad.com/l/tbpasf

@swapnakpanda @Trae_ai A win for Linux users

There exists a great app, but how will you feel if that's not available on Linux? I have been using @Trae_ai for a long time but on Mac. Finally now, it's available on Linux. The OS which is the default home for many developer communities. Not only that, Minimax M2.7 is also embedded with it. From idea to shipped code, all in one place.

@sevalla_hosting only fear is when we have to clean up & review AI code ^

The best programmers don’t fear AI. They integrate it.

@Shefali__J saving this for the weekend

14 Essential React Concepts You Should Know🔥 Open this🧵 Bookmark for later🔖

@swapnakpanda not much into CP, but love this

"Competitive Programmer’s Handbook" It covers 30 important topics from DSA. A MUST for tech interviews. Download this FREE book → cses.fi/book/book.pdf

@sysxplore RED, RED

@csaba_kissi all the best!

🙌 "Is ready for launch?" is the #1 product on Uneed today so far. Thanks for your support, guys!

@saviour_4110 you're welcome + all the best

@systemdesignone This super educational and resourceful I'll love to make a thread on each of these topics for a deeper understanding... Thanks for sharing

15 posts that'll teach you 15 system design concepts:

If you want to turn vague ideas into agent-ready prompts, copy this: