Murtazah

Building Production-Ready LLMs & Multi-Agent Systems | Travelholic ✨

[Cristiano Ronaldo @Cristiano]

Curiosity is a requirement for greatness. You win when you keep asking new questions every day. That’s why I am proud to announce my investment in Perplexity.

Perplexity is powering the world’s curiosity, and together we will inspire everyone to ask more ambitious questions.

https://t.co/xhCeLuW51J is just the beginning!

[Murtazah @beingmurtazah]

🚨 OpenAI is launching an AI-powered browser and it could be Chrome’s biggest threat yet.

✅ Built on Chromium
🤖 Includes ChatGPT-style interface & autonomous agent
🔒 No ads. No tracking. Just pure AI

Google makes $100B+ from ads. This changes everything.
#OpenAI #AI #Chatgpt

[Murtazah @beingmurtazah]

Chand Raat Mubarak to All Muslims 🌙

[Murtazah @beingmurtazah]

A 23-year-old asked Mozart, "How do I write symphonies?" Mozart replied, "You're too young."
The man protested, "But you wrote symphonies at 10!"
Mozart answered, "Yes, but I didn’t ask others how."

Lesson: Start, learn, and create—don’t wait for permission.

[Murtazah @beingmurtazah]

Just wrapped up an exciting machine learning project! 🚀 Built and deployed a predictive model using Python and TensorFlow, delivering key insights and driving data-driven decisions. On to the next challenge!
#MachineLearning #AI #DataScience

[Murtazah @beingmurtazah]

Building a predictive model for customer churn was a game-changer. After weeks of refining, we reduced churn by 20%. The power of data in action! 🚀

#DataScience #ML #AI #CustomerSuccess

[Murtazah @beingmurtazah]

Do you remember when you joined Twitter? I do! #MyTwitterAnniversary

[Murtazah @beingmurtazah]

Freelancing taught me that flexibility, creativity, and resilience are the keys to success. I enjoy having the freedom to choose my own schedule, express my own ideas, and overcome any obstacles. #intlfreelancerday #fiverr @fiverr @FiverrSupport

[Murtazah @beingmurtazah]

Data is the new oil, but it’s only useful when refined. 🔍
Building clean, well-structured datasets is the foundation of every ML project.
Remember, garbage in = garbage out! #DataScience #MachineLearning #AIart

[Mushtaq Bilal, PhD @MushtaqBilalPhD]

ResearchGPT is a custom GPT especially designed for researchers.

It has a database of 282 million research articles and it answers your questions with references to published articles.

No fake citations.

Here's how to use it:

[Fiverr @fiverr]

What has freelancing taught you? 🤔 Share a tweet completing the prompt “Freelancing taught me….” for a chance at a $200 gift card. Get creative, and be sure to tag us and use the hashtag #intlfreelancerday

Submissions close at 11:59pm PST. Terms and conditions apply: https://t.co/ZAHWCsCNOv.

[Murtazah @beingmurtazah]

Freelancing taught me that flexibility, creativity, and resilience are the keys to success. I enjoy having the freedom to choose my own schedule, express my own ideas, and overcome any obstacles. #intlfreelancerday #fiverr @fiverr @FiverrSupport

[Ofra Amir @ofraam]

I'm recruiting PhD students through the ELLIS PhD program this year to work on explainable AI and human-AI interaction. The application deadline is Nov 15. Feel free to reach out if interested

[ELLIS @ELLISforEurope]

The portal is open: Our #ELLISPhD Program is now accepting applications! Apply by November 15 to work with leading #AI labs across Europe and choose your advisors among 200 top #machinelearning researchers!

#JoinELLISforEurope #PhD #PhDProgram #ML https://t.co/Bk8eWGvwRX

[Global Index @TheGlobal_Index]

Foreign born CEOs of American companies:

🇮🇳 Alphabet Google
🇮🇳 Adobe
🇲🇾 Broadcom
🇬🇧 Coca-Cola
🇮🇳 IBM
🇫🇷 Instacart
🇵🇱 Honeywell
🇪🇸 Johnson & Johnson
🇵🇰 KFC
🇩🇪 Mastercard
🇮🇳 Micron Technology
🇮🇳 Microsoft
🇹🇼 Nvidia
🇮🇱 Oracle
🇮🇳 PepsiCo
🇬🇷 Pfizer
🇮🇳 Starbucks
🇮🇪 Stripe
🇿🇦 Twitter, Tesla, SpaceX
🇬🇧 UnitedHealth
🇮🇳 World Bank
🇨🇳 Zoom

[Abhinav Upadhyay @abhi9u]

Just announced LPython - a high performance compiler for Python with multiple backends.

LPython is a novel, fast, and retargetable Python compiler that can compile type-annotated Python code to optimized machine code. It offers several backends such as LLVM, C, C++, WASM, Julia, and x86. LPython features quick compilation and runtime performance, as demonstrated by the benchmarks in their blog.

LPython also offers Just-In-Time (JIT) compilation and seamless interoperability with CPython. It is designed to be compatible with existing Python libraries and tools. LPython is released as an alpha version, meaning that users may encounter bugs when using it.

One of the key features of LPython is its ability to generate optimized machine code from type-annotated Python code. It uses the Abstract Semantic Representation (ASR) shared with LFortran, which allows for independent optimizations at the ASR level. The goal of LPython is to produce a compiler that runs exceptionally fast and generates fast code.

LPython provides several backends, including LLVM, C, C++, and WASM. It uses a multi-phase compilation process, transforming input code into an Abstract Syntax Tree (AST), then into an Abstract Semantic Representation (ASR), which preserves all semantic information. The ASR undergoes several ASR-to-ASR passes, where abstract operations are transformed into concrete statements. Finally, the final ASR is sent to the selected backend to generate code.

LPython supports Ahead-of-Time (AoT) compilation, which allows users to compile their Python code to optimized machine code before execution. It also supports Just-In-Time (JIT) compilation, where functions can be decorated with @lpython to enable JIT compilation. Currently, only C backend is supported for JIT compilation, but support for LLVM and other backends is planned for the future.

LPython offers interoperability with CPython, allowing users to access any library implemented using CPython. This is achieved through the @pythoncall decorator, which allows calling functions in CPython libraries. Users can also access libraries like NumPy, TensorFlow, PyTorch, and Matplotlib using LPython.

The benchmarks provided in the blog demonstrate the performance of LPython in various scenarios. For example, LPython's JIT compilation is compared to Numba on tasks like summation of elements in an array, pointwise multiplication of arrays, insertion sort on lists, and the Dijkstra shortest-path algorithm. LPython shows competitive performance in all these scenarios.

LPython looks like a promising project to aid applications which require high performance, such as AI. Mojo is also adopting a similar approach, however they are not only building a compiled Python, but also extending it to offer systems programming constructs as first class features. It would be interesting to compare the two.

Here's the full blog post covering their alpha release: https://t.co/jdHHgnJ1dL

[Ocean Recipes @oceanrecipes]

🔥 Get Ready to Master the Art of Smoking Meat! 🔥
🍖 Calling all meat lovers! 🍖
🌊 Dive into the world of smoky goodness with our ultimate guide on how to smoke meat for the first time!
🔥🔥🔥 Discover the secrets of achieving mouthwatering, tender, and flavorful results!🔥🔥🔥

[World of Statistics @stats_feed]

Trillion-dollar companies:

$1 Trillion
2007: 🇨🇳 PetroChina
2018: 🇺🇸 Apple 🇺🇸 Amazon
2019: 🇸🇦 Saudi Aramco 🇺🇸 Microsoft
2020: 🇺🇸 Alphabet
2021: 🇺🇸 Meta 🇺🇸 Tesla
2023: 🇺🇲 NVIDIA

$2 Trillion
2019: 🇸🇦 Saudi Aramco
2020: 🇺🇸 Apple
2021: 🇺🇸 Microsoft 🇺🇸 Alphabet

$3 Trillion
2022: 🇺🇸 Apple

[Murtazah @beingmurtazah]

Do you remember when you joined Twitter? I do! #MyTwitterAnniversary

[Jaun Elie (Fan Club) @FanJaun]

Pls look into it properly @FiverrSupport @fiverr