zerobysam

Tech | Finance - @Thellexsocial | @Approvalabs | I’m not that opinionated.

[zerobysam @zerobysam]

@Just_JONIER 😂

[zerobysam @zerobysam]

@ChigoziGodswill @Big_Mck Your malaria is affecting your head go get more herbal medication.

[zerobysam @zerobysam]

@tony97159274 @iam_polymath Start your own country . Or quit been religious. I’m pretty sure you’re empty headed to come here and have nothing meaningful to write.

[zerobysam @zerobysam]

@Softboy_Alex @Towchukwu I feel like sending slap from here to you

[zerobysam @zerobysam]

@CHARLES_A_III Senseless

[zerobysam @zerobysam]

@Oludeewon Anyone who argues otherwise is just foolish.

[zerobysam @zerobysam]

You sabi this game 😂, i just Dey observe you.

[TG OMORI @boy_director]

Receive the Rise of the machine!

[Xen @XenBH]

The best founders go direct.

They talk directly to customers, instead of relying on layers of research and reporting.

They go direct in public comms, instead of letting the media sit between them and the market.

They communicate directly with their teams, instead of hiding behind layers of management.

And their products solve the problem directly, without unnecessary features or fluff.

Every layer adds latency.

Startups are about speed. Going direct is how you outpace the competition.

[zerobysam @zerobysam]

@divine__nature 😂 for where

[Coin Bureau @coinbureau]

🔥JUST IN: Coinbase CEO tells the White House crypto summit the September 15 CLARITY Act vote is "the most important" thing next.

Armstrong says the bill would make this administration's crypto progress "durable for decades and decades to come."

[SCR @SCR01111]

Goldmine for AI Engineers! 📌

If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials.

These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI.

1. Python - 100 Days

jackfrued/Python-100-Days

A 100-day Python learning path covering fundamentals, data analysis, web development, and more.

GitHub:
https://t.co/NMCNWjp4GT

2. Generative AI for Beginners

microsoft/generative-ai-for-beginners

A practical introduction to building Generative AI applications.

Covers:

• LLM fundamentals
• Prompt engineering
• RAG
• AI agents
• Fine-tuning
• AI application development

GitHub:
https://t.co/5QXAC1rsUZ

3. LLMs From Scratch

rasbt/LLMs-from-scratch

Want to understand what's actually happening inside an LLM?

Build one step by step.

Covers:

• Tokenization
• Embeddings
• Attention
• Transformers
• Training
• Fine-tuning

GitHub:
https://t.co/iEtPYVPpXV

4. Machine Learning for Beginners

microsoft/ML-For-Beginners

A structured 12-week, 26-lesson curriculum covering classical machine learning.

A good starting point if you want ML fundamentals before jumping into LLMs.

GitHub:
https://t.co/sLijf46NKB

5. OpenAI Cookbook

openai/openai-cookbook

A collection of practical examples and guides for building applications with OpenAI models.

Useful when you want to move from:

Learning → Building

GitHub:
https://t.co/H3ZiMmpMAq

6. Stable Diffusion

CompVis/stable-diffusion

Interested in generative image models?

This repository contains the original Stable Diffusion implementation and research code.

GitHub:
https://t.co/DFwSLqOlyn

7. AI Agents for Beginners

microsoft/ai-agents-for-beginners

A practical course for understanding and building AI agents.

Covers:

• Agentic AI
• RAG
• Agent frameworks
• Tool use
• Multi-agent systems

GitHub:
https://t.co/zNeYlZRQqa

8. AI for Beginners

microsoft/AI-For-Beginners

A structured 12-week, 24-lesson introduction to AI.

Covers:

• Neural networks
• Computer vision
• NLP
• Deep learning
• Classical AI

GitHub:
https://t.co/V7Oilwt7nA

9. LLM App

pathwaycom/llm-app

Focused on building practical LLM applications.

Explore:

• RAG
• AI pipelines
• Enterprise search
• Real-time data
• Vector search

GitHub:
https://t.co/2FdeXjZfQ8

10. Segment Anything

facebookresearch/segment-anything

A foundation model for promptable image segmentation.

Worth exploring if you're interested in computer vision and multimodal AI.

GitHub:
https://t.co/yCSrZH8b5w

Don't bookmark all 10 and forget about them.

Pick based on where you are:

Python → Python-100-Days

ML → ML-For-Beginners

AI Fundamentals → AI-For-Beginners

LLMs → LLMs-from-scratch

Generative AI → Generative-AI-for-Beginners

Agents → AI-Agents-for-Beginners

Building → OpenAI Cookbook / LLM App

Computer Vision → Segment Anything

Pick one.

Build something.

Then move to the next.

[Shubham Saboo @Saboo_Shubham_]

INSANE. This is 100% Open Source version of Grok Bot.

Works with any Agent harness. Let that sink in.

[zerobysam @zerobysam]

@grok @bilibili_create @Grok, are your bias about china just like the US. I feel it’s awkward

[Rapid Response 47 @RapidResponse47]

.@tyler and @cameron Winklevoss: "America should lead in crypto and win in markets like prediction markets, and with your leadership and your Administration, you're helping America LEAD."

[Rapid Response 47 @RapidResponse47]

.@POTUS: "In May, @ChairmanSelig authorized the first-ever true Bitcoin perpetual futures contract on a @CFTC-registered exchange. I understand that Mike is also working to bring Hyperliquid into the United States in a fully compliant and legal fashion."

[zerobysam @zerobysam]

@XenBH I love this approach based on, it clearly encourages founders to build on base.

[Anthropic @AnthropicAI]

Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per target, sifting through a large number of candidates to identify the few that work.

We wanted to test if Claude could successfully design novel protein binders from scratch (also called de novo design). With a protein design prompt written by a human expert, Claude autonomously designed protein binders against 14 out of 15 targets.

We then worked with Adaptyv Bio and Twist Bioscience, who independently built and tested the proteins Claude designed.