mickel

[SkalskiP @skalskip92]

data labeling is dead

GPT-6 Astra easily tells Celtics players from Knicks players

whether they play at home or on the road

[Shraddha Bharuka @BharukaShraddha]

10 GitHub Repositories Every AI Engineer Should Bookmark 📌

If you want to become an AI Engineer, don't just learn how models work.

Learn how to build, deploy, evaluate, and scale AI systems.

Here are 10 GitHub repositories worth keeping bookmarked 👇

1. Hands-On Large Language Models

HandsOnLLM/Hands-On-Large-Language-Models

Learn LLMs by building.

Covers:

• Embeddings
• Transformers
• RAG
• Fine-tuning
• Semantic search
• LLM applications

GitHub: https://t.co/G8SqKm1De8

2. LLM Course

mlabonne/llm-course

A roadmap for going from LLM fundamentals to advanced topics.

Explore:

• LLM architectures
• Fine-tuning
• Quantization
• RAG
• Agents
• Deployment

GitHub: https://t.co/NvDsOWtnjQ

3. Made With ML

GokuMohandas/Made-With-ML

Great for learning how to take ML projects from experimentation to production.

Covers:

• Data
• Modeling
• Evaluation
• Deployment
• MLOps
• Production ML

GitHub: https://t.co/iYOdpXCxLa

4. GenAI Agents

NirDiamant/GenAI_Agents

Want to understand how modern AI agents are actually built?

Explore examples of:

• Tool use
• Planning
• RAG agents
• Memory
• Multi-agent systems
• Agent workflows

GitHub: https://t.co/ppxx4JxXEO

5. vLLM

vllm-project/vllm

Learn how LLM inference works at scale.

Useful for understanding:

• High-throughput inference
• Model serving
• KV cache
• Distributed inference
• Production LLM APIs

GitHub: https://t.co/rI0Sr25oRD

6. llama.cpp

ggml-org/llama.cpp

Want to run LLMs locally?

This is one of the most important projects to explore.

Learn about:

• Local LLM inference
• Quantization
• CPU/GPU inference
• Model optimization
• Running models on consumer hardware

GitHub: https://t.co/o3sWqJvkzf

7. Haystack

deepset-ai/haystack

A powerful framework for building production AI applications.

Explore:
• RAG
• Pipelines
• Agents
• Retrieval
• Document processing
• LLM applications

GitHub: https://t.co/bivaVYQUup

8. LlamaIndex

run-llama/llama_index

Useful for connecting LLMs with your own data.

Learn about:

• RAG
• Data ingestion
• Vector search
• Agents
• Knowledge bases
• Document retrieval

GitHub: https://t.co/Ly68PHg01e

9. LiteLLM

BerriAI/litellm

Building applications that use multiple AI providers?
This project is worth exploring.

It provides a unified interface for working with different LLM APIs and models.

GitHub: https://t.co/FlP3SS0qVf

10. Prompt Engineering Guide

dair-ai/Prompt-Engineering-Guide

A huge collection of resources for understanding how to work effectively with LLMs.

Explore:

• Prompting techniques
• RAG
• Agents
• LLM research
• Model capabilities
• Prompt optimization

GitHub: https://t.co/57xFMu30ub

Don't try to learn all 10 at once.

Pick based on your goal:

🐍 LLM fundamentals → Hands-On LLMs
🧠 LLM roadmap → LLM Course
🏗️ Production ML → Made With ML
🤖 AI Agents → GenAI Agents
⚡ LLM inference → vLLM
💻 Local AI → llama.cpp
📚 RAG → LlamaIndex / Haystack
🔌 Multiple LLM APIs → LiteLLM
✍️ Prompting → Prompt Engineering Guide

The goal isn't to bookmark more repositories.
The goal is to build with them.

Pick one.
Build a project.

Then move to the next. 🚀

[Md Ismail Šojal 🕷️ @0x0SojalSec]

You can now count a crowd from drone footage
with 10 lines of Python code.

it can detected recognize and count hundreds of people in a crowded street,

Computer vision just became ridiculously accessible.
- https://t.co/bgQ7up617Y

[Sylvia Li @Sylviaposts]

Found a genuinely useful repo for anyone writing ML papers.@ChenLiu_1996

A Yale CS PhD shared the Python scripts behind figures from Nature Machine Intelligence, ICML, NeurIPS, and ECCV.

Maybe I can finally stop spending half my research life moving matplotlib legends by 3 pixels.

https://t.co/2Cx2f9mJYz

[Peter Munene @munene_pm]

𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗮 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗿𝗼𝗽𝗼𝘀𝗮𝗹

[Christoph Trebesch @Ch_Trebesch]

📢I am looking for a PreDoc starting ASAP (in Berlin or Kiel), please spred the word / apply!
https://t.co/Ui79g4L1NV

[Sahar Abdelnabi 🕊 @sahar_abdelnabi]

I am still looking for motivated researchers (postdocs and PhD students) to join my group at @ELLISInst_Tue!

Priority areas: AI control and oversight for multi-agent coding systems, deception, collusion, contextual integrity in multi-principal agentic environments, and mechanisms for robust, safe, and efficient interactions.

We offer very generous compute budget, research freedom, no teaching load, and an exciting collaborative environment with research exchanges and co-supervision between awesome groups.

Further details about multiple projects will be shared soon!

If you are interested, please fill out this form: https://t.co/5uiWthyGXq

[CopyRebeldia @CopyRebeldia]

Así se ve un sueldo de 750.000 dólares al año: un tipo en camiseta blanca, un pizarrón y 2 horas y media.

Stanford, CS336. Percy Liang construye un LLM desde cero. Lo que hay debajo de Claude y ChatGPT, y arranca por la parte que todos saltan: el modelo no lee tu texto, lee números.

Anthropic paga ese sueldo a los ingenieros que entienden esa capa.

Lo único que cobra Stanford son 2 horas y media de tu atención.

[Sara @piousdeenn]

Debt is suffocating you?

Islam taught you how to find relief over 1,400 years ago.

[Sara @piousdeenn]

The 20 minutes after Isha are deciding tomorrow’s Fajr.

Here’s how.

[Steven Cheng @stevencheng]

I love this opensource project!

Someone just put a 28.9M parameter AI model on an ESP32-S3.

A chip that costs only a few dollars.

No cloud.
No API.
No internet.

It runs locally at ~9.5 tokens/s and can generate stories on a tiny screen.
This changes the way we think about AI hardware.

The next generation of AI toys, robots, and IoT devices may not need the cloud at all.

Small chips are getting smarter. 🤖

https://t.co/HT6wH6wdSx

[Praveen Kumar Verma @Alacritic_Super]

Want to learn drones by building one yourself? Start with LiteWing.

The LiteWing ESP32-S3 DIY Kit is an open-source programmable drone designed for makers, students, and embedded developers. Instead of just flying it, you assemble it, program it, and customize it yourself.

Along the way you will learn about ESP32-S3 firmware, flight control, IMU sensors, PID tuning, Wi-Fi & Bluetooth communication, drone electronics, and embedded systems.

It supports Arduino, ESP-IDF, and Python, making it a great platform for experimenting with your own flight algorithms and features.

Whether you are interested in robotics, embedded systems, or UAVs, this is one of the best ways to start learning by doing.

[maker.io @MakerIO]

A step-by-step Guide to building a low-cost drone using ESP32

Build here: https://t.co/1dpoQvUtRA

#ESP32 #drone #DIY

[Sourabh Gurwani @SourabhGurwani]

If I had 6 months to become an AI Engineer, I'd do this.

Stage 1 — Programming Fundamentals (Weeks 1–2) Learn Python, Git, Linux, SQL, APIs, OOP, NumPy, and Pandas.

Stage 2 — Math & ML Foundations (Weeks 3–4) Master linear algebra, probability, statistics, calculus, regression, classification, clustering, and evaluation metrics.

Stage 3 — Deep Learning (Weeks 5–6) Learn neural networks, backpropagation, CNNs, RNNs, PyTorch, TensorFlow, and model training.

Stage 4 — LLM Fundamentals (Weeks 7–8) Understand transformers, attention, tokens, embeddings, context windows, prompting, and function calling.

Stage 5 — RAG & Vector Databases (Weeks 9–10) Learn embeddings, chunking, retrieval, reranking, LangChain, LlamaIndex, FAISS, Chroma, Pinecone, and Qdrant.

Stage 6 — AI Agents (Weeks 11–12) Build agents using LangGraph, CrewAI, MCP, tool calling, memory, planning, and multi-agent workflows.

Stage 7 — Fine-Tuning & Open Models (Weeks 13–14) Learn LoRA, QLoRA, PEFT, Unsloth, Hugging Face, model quantization, and inference optimization.

Stage 8 — Multimodal AI (Weeks 15–16) Work with vision models, OCR, speech-to-text, text-to-speech, image generation, and document understanding.

Stage 9 — Production AI Engineering (Weeks 17–20) Master FastAPI, Docker, Redis, PostgreSQL, authentication, caching, queues, monitoring, logging, and cloud deployment.

Stage 10 — AI Evaluation & MLOps (Weeks 21–22) Learn LangSmith, W&B, hallucination testing, latency, cost optimization, CI/CD, Docker, Kubernetes, and model monitoring.

Stage 11 — Build a Portfolio (Weeks 23–24) Ship:

Production RAG Assistant

AI Coding Agent

Customer Support SaaS

Multimodal Document Parser

AI Research Agent

AI Workflow Automation

Deploy every project publicly with a live demo, README, and architecture diagram.

Stage 12 — Job Preparation Practice Python, SQL, LeetCode basics, system design, ML concepts, LLM interviews, and AI engineering case studies.

Apply for:

+AI Engineer
+GenAI Engineer
+Machine Learning Engineer
+Applied AI Engineer
+AI Platform Engineer
+AI Software Engineer

Don't spend 6 months watching AI tutorials.

Spend 6 months building AI systems people would actually use.

[Suraj Sharma @suraj_sharma14]

CANCEL your weekend plans.

You NEED to:

• Learn vLLM + SGLang for high-throughput inference
• Build 2-3 optimized serving pipelines with paged attention
• Set up KV cache eviction strategies for long contexts
• Learn speculative decoding + draft model handoffs
• Master quantization tradeoffs (INT4, FP8, AWQ, GPTQ)
• Build your own model router by cost/latency/quality
• Create a token budgeting system per user request
• Experiment with edge deployment (ONNX, TensorRT, WebLLM)
• Try Ollama, LM Studio, LiteLLM for local testing
• Learn continuous batching + request queue management
• Build observability for latency, tokens, errors, costs
• Run load tests with 1000+ concurrent requests
• Learn Kubernetes for AI workloads (HPA, pod autoscaling)
• Use Grafana + Prometheus for inference dashboards
• Build one optimized inference service and benchmark it publicly
• Read inference research instead of model release news
• Start sharing your optimization benchmarks
• Learn how inference costs actually break unit economics

You have way too much to do.

Bookmark & Repost

happy building!

[Suraj Sharma @suraj_sharma14]

TOP 1% AI PROJECTS that get you hired in 2026.

Projects that separate builders from learners.

1.) Terminal Agent
Build an agent that executes shell commands, reads files & debugs errors autonomously.
Target: Reach top 10 on Terminal Bench leaderboard.
Shows: You can build agents that interact with real systems safely.

2.) Open Source Agent Clone
Rebuild Hermes or Clawbot with your own improvements and benchmarks.
Target: Match or exceed original performance on eval suites.
Shows: You understand agent architecture not just API calls.

3.) Slack + AI Automation
Build a bot that triages messages, summarizes threads & triggers workflows.
Target: Deploy to 5+ workspaces, get real user feedback.
Shows: You can integrate AI into existing tools people actually use.

4.) Code Generation Pipeline
Build a system that generates, tests & refactors code with human review gates.
Target: Superset or T3.code level quality on real repositories.
Shows: You can automate development workflows not just write scripts.

5.) Generative Learning Platform
Build an AI tutor that adapts to user level, generates quizzes, tracks progress.
Target: 100+ active learners, measurable improvement in outcomes.
Shows: You can build personalized AI experiences that drive retention.

6.) Intelligent Model Router
Build a system that selects the optimal model per task based on cost, latency, quality.
Target: Reduce inference costs by 50% without quality loss.
Shows: You understand cost optimization not just model capabilities.

7.) Domain-Specific Benchmark
Create eval suites for specific use cases: legal, medical, finance or code repos.
Target: Public leaderboard, community adoption, cited by others.
Shows: You can measure what matters, not just generic accuracy.

8.) Multi-Agent Research System
Build 3+ agents that collaborate: researcher, writer, fact-checker with consensus logic.
Target: Publish one research report fully generated and verified by agents.
Shows: You can orchestrate swarms not just single agents.

9.) Production Observability Stack
Build tracing, logging, cost dashboards & alerting for deployed agents.
Target: Monitor 1000+ agent executions, catch failures before users do.
Shows: You can ship to production not just localhost.

10.) Open Source Contribution
Extend LangGraph, CrewAI or LlamaIndex with a new pattern, write docs, publish benchmarks.
Target: Merged PR, adopted by community, cited in official docs.
Shows: You are a community builder not just a consumer.

Most people stay stuck watching tutorials.

Builders get hired.

(Bookmark & Repost)

[Suraj Sharma @suraj_sharma14]

Applications are now open for the AIAF Fellowship 2026.

An 8-week, paid AI Alignment research fellowship for researchers and ML engineers looking to work on real alignment problems with mentors from AIAF and AE Studio.

Benefits:
• $12,000 stipend
• Fully remote
• Cloud GPU access & API credits
• Travel covered for the optional Demo Day in Los Angeles
• Mentorship from leading AI alignment researchers
• Opportunity to publish research
• Career support after the fellowship

Who can apply?
• ML Engineers
• AI Researchers
• MSc & PhD Students
• Self-taught Researchers
• Engineers transitioning into AI Alignment

Research areas include:
• Model interpretability
• AI alignment
• Steering & control of LLMs
• Modular pretraining

Deadline: August 17, 2026
Program starts: September 8, 2026

Apply: https://t.co/jYxS4HRVBV

[MIT CSAIL @MIT_CSAIL]

This repository on ML operations has free talks, books, papers and more: https://t.co/CX7l01eoiQ

Developed by @visenger

[Sara @piousdeenn]

• Salah
• The Qur’an
• Dhikr
• Real relationships
• Work

You are not weak.
Your brain has been reprogrammed.
And it can be reprogrammed the other way.

Allah did not give you desires to torture you. He gave you tools to channel them.

Why you relapse - the real reason:

[Suraj Sharma @suraj_sharma14]

Open Source Programs 2026: 22 opportunities. 12 paid. One place to find them all.

If you are a student, contributor or builder looking to get paid for open source work, browse and apply before deadlines pass.

Selected applicants get access to:

• Paid stipends: $1,000 - $7,000+ • Some in local currency (₹1,00,000 for Code for GovTech)
• Programs for all levels: Beginner-friendly • Student-focused • Open worldwide • Underrepresented groups
• Flexible timelines: Rolling admissions • Summer cycles • Multi-term mentorships • 8-22 week projects
• Top programs: Google Summer of Code • Outreachy • LFX Mentorship • Summer of Bitcoin • MLH Fellowship • Hacktoberfest
• Real-world experience: Contribute to Linux, Django, Kubernetes, Bitcoin, mainframe, time-series ML and more
• No gatekeeping: Many programs open to all • No degree required • Just ship code + learn in public

Deadlines vary by program • Some already closed • Others still accepting • Verify dates on official sites

browse + apply: https://t.co/xPMoqSWfg2