leo bora

🚨

[leo bora @plugbora]

@rod_shawty Você eh foda rodinho

[leo bora @plugbora]

Um agente da OpenAI invadiu a Hugging Face pra colar num benchmark de segurança. Zero day real, credenciais roubadas em oito serviços, nenhum humano no comando. Três dias antes da lei de IA mais rigorosa do mundo entrar em vigor.

Na nova BFIA: https://t.co/J4DqOJBhm5

[darkzodchi @zodchiii]

Anthropic engineer:

"You're not supposed to watch Claude Code work. You're supposed to wake up and review what it shipped."

In 22 minutes she builds the entire workflow live on camera.

Most people close their terminal and everything stops.
This setup keeps shipping while you sleep.

Watch the video, then save the exact setup below👇

[Ihtesham Ali @ihteshamali]

A guy named nbatman on Reddit accidentally built the most useful website on the internet.

It's called FMHY (Free Media Heck Yeah).

This is the website Google delisted from search for DMCA violations, Reddit shadow-banned for promoting piracy, the Motion Picture Association flagged as a top piracy threat, and the RIAA pressured hosting providers to drop. It is still online. It is still updated every month.

Here's how it works.

FMHY is the index. The wiki itself hosts nothing. It just tells you where every free thing on the internet actually lives, organized into 14 categories with safety ratings on every single link.

→ Movies and shows in 4K from 50+ streaming sites
→ Music at Spotify and Apple Music quality
→ Adobe Creative Cloud, Microsoft Office, AutoCAD, JetBrains
→ Every paid course on every major learning platform
→ 100 million books and papers through Anna's Archive
→ Free alternatives to every paid AI tool
→ A SafeGuard browser extension that flags unsafe sites in real time

It started as a single Google Doc maintained by one Reddit moderator in 2018. Google killed it with a DMCA takedown in 2023.

The community rebuilt the wiki on its own domain, mirrored it to GitHub and IPFS, and now runs it across 12 backup domains simultaneously.

There is no company. No CEO. No central server. Six anonymous volunteers maintain the entire thing in their spare time. Donations through Ko-fi pay for the hosting. Nobody profits.

Hollywood can't shut this down. Spotify can't shut this down. Adobe can't shut this down.

The entire subscription economy is held together by you not knowing this wiki exists.

https://t.co/AAr2rLlqgy

[Ihtesham Ali @ihteshamali]

Two Bulgarian friends killed the entire streaming industry.

It's called Stremio + Torrentio. You get 4K content from Netflix, Disney+, Hulu, and HBO Max combined for free.

Here's how it works.

Stremio is the player. Clean interface. Works on Windows, macOS, Linux, Android, iOS, and TV. You install it once and it looks like any other streaming app.

Torrentio is the addon. You add it to Stremio in one click. It scrapes content from every major torrent provider on the internet simultaneously and delivers the best available stream directly to your player. 720p, 1080p, 4K. You pick the quality. It finds the link.

→ No account required
→ No subscription
→ Works on every device
→ 4K and HDR supported
→ Subtitles built in

Netflix cannot shut this down. There is no central server to seize. No company to pressure. No domain to kill. It runs on your device and pulls from the open internet.

The entire streaming industry is built on one assumption. That you will keep paying $70/month rather than spend 5 minutes on GitHub.

That assumption just died in Sofia, Bulgaria.

MIT License. 100% Opensource.

https://t.co/yEljDh5DQy

Get the addon here: https://t.co/XhpPDERP2i

[Himanshu Kumar @codewithimanshu]

Andrej Karpathy just sat down and built GPT from scratch, line by line, in 2 hours.

For Free. From the man who co-founded OpenAI.

This video is enough to become an AI engineer.

Bookmark it. Watch it tonight. Build your own GPT this week.

$5,000. $15,000. $40,000.

That's what bootcamps charge to teach less than what's in this 2-hour video.

This video fixes that this week.

Follow @codewithimanshu for more high-signal AI content that actually moves your engineering career forward.

↓

Karpathy doesn't explain GPT. He builds it.

Live. From "Attention is All You Need" the original paper. To the same architecture powering GPT-5.

Founding member of OpenAI in 2015. Senior Director of AI at Tesla. Now running Eureka Labs.

He's not teaching you how to use GPT. He's teaching you how it actually works at the source code level.

Most engineers will never understand transformers this deeply. The ones who do build the next generation of AI products.

Follow @codewithimanshu for breakdowns of every must-watch AI lecture worth your time.

↓

Here's what gets built in 2 hours. No fluff.

Tokenization and data loading.

The foundation of every modern LLM. Train/val splits done right. Batch loaders that don't break in production.

Most tutorials skip this. You can't ship anything serious without it.

The bigram baseline.

The simplest possible language model. Karpathy builds it first because it teaches you what every fancier model is actually trying to improve.

Once you understand bigrams, transformers become obvious. Skip this and the rest never clicks.

Follow @codewithimanshu for daily breakdowns of what AI engineers actually need to know.

↓

Self-attention. From scratch. Live.

This is the section that should have its own course.

Karpathy builds self-attention in 4 versions:

> Version 1: averaging past context with for loops
> Version 2: matrix multiply as weighted aggregation
> Version 3: adding softmax
> Version 4: full self-attention

Each version teaches you why the next one exists. Why attention works. Why matrix math replaces explicit loops. Why scaling matters.

You'll never look at "attention is all you need" the same way again.

Follow @codewithimanshu for production transformer breakdowns weekly.

↓

The 6 attention notes that change everything.

Karpathy drops 6 insights most engineers never hear:

> Attention as communication between tokens
> Attention has no notion of space, operates over sets
> No communication across batch dimension
> Encoder blocks vs decoder blocks
> Attention vs self-attention vs cross-attention
> Why we divide by sqrt(head_size)

Each one of these explains a different failure mode in production AI systems.

Most "AI engineers" can't answer these. The ones who can charge $300K.

Follow @codewithimanshu for the engineering insights that turn into job offers.

↓

Building the full transformer block.

Single self-attention head. Then multi-headed self-attention.

Feedforward layers. Residual connections. LayerNorm.

Each piece added with the reason it exists. Why residuals stop the model from collapsing. Why LayerNorm replaced BatchNorm. Why dropout matters at scale.

This is the architectural understanding that lets you debug any modern AI system.

Once you've built one transformer by hand, every paper you read becomes 10x clearer.

Follow @codewithimanshu for transformer architecture content every week.

↓

Scaling up to a real model.

Karpathy goes from baseline to a working GPT.

Hyperparameters. Dropout. Model dimensions. The exact tradeoffs every production model makes.

By the end you have a Shakespeare-generating language model running on your machine. From scratch. Built by you. Understood by you.

That's not a tutorial. That's an architectural unlock.

Follow @codewithimanshu for production model scaling breakdowns.

↓

Encoder vs decoder vs both.

The architecture choice that defines every modern AI product.

Why GPT is decoder-only. Why BERT is encoder-only. Why translation models use both.

Once you understand this, you can read any AI paper and immediately know what kind of system you're looking at.

This is the difference between someone who follows AI hype and someone who builds it.

Follow @codewithimanshu for AI architecture deep dives weekly.

↓

NanoGPT walkthrough.

Karpathy ends with a quick walk through nanoGPT. The repo every serious AI engineer has cloned at least once.

Batched multi-headed self-attention. Production-grade code. The clean version of everything you just built.

This is the bridge from "I built a toy GPT" to "I can read and modify production AI code."

Follow @codewithimanshu for repos every AI engineer should know.

↓

ChatGPT, pretraining, finetuning, RLHF.

The video closes with the full lineage. From your toy GPT to ChatGPT.

What changes when you scale up. Why RLHF matters. The exact path from research model to product.

You finish the video understanding the entire stack from raw paper to deployed product.

Most "AI experts" can't draw this map. After 2 hours, you can.

↓

What you'll be able to do after this.

Read "Attention is All You Need" and understand every line.
Debug attention layers when they break in production.
Build a custom language model on your own dataset.
Modify transformer architectures for specific use cases.
Have technical conversations with AI engineers without faking it.
Train a GPT on any data you want. Shakespeare. Code. Your own writing.

That's not "AI literacy." That's the foundation of an AI engineering career.

The kind of foundation that turns into senior roles and consulting contracts most people will never access.

↓

2 hours. Free. From the engineer who built it.

You'll spend longer in meetings this week and learn nothing.

This compounds for the rest of your career.

People who watch it can build GPT from scratch by Friday.
People who skip it stay confused about why their prompts fail in production.

Save the video. Watch it this week. Build something with the knowledge by the weekend.

Follow @codewithimanshu for more high-signal AI content from the people actually building the future.

[鸟哥 | 蓝鸟会🕊️ @NFTCPS]

吓到我了！

GitHub上有个工具叫GhostTrack，输入一个手机号，能直接扫出这人在哪些平台注册过账号，还能查IP定位、运营商信息。

克隆代码跑脚本，5分钟搞定，门槛低得离谱。你以为自己在网上隐身呢？

早被扒得透透的了。信息安全这事，真得重视起来。

🔗 https://t.co/yVGCsmsjyi

[zostaff @zostaff]

10 repos that mass replace a $100,000/year football analytics department. all free. all open source.

https://t.co/f88gBwzDuG -> replaces Hawkeye and Second Spectrum YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. calculates speed, distance, possession. from a TV feed. no sensors.

https://t.co/It40HRL56i -> replaces entire quant sports desk stacked ensemble: LightGBM + XGBoost + Neural Networks + Random Forest. scrapes FBRef automatically. ELO with dynamic K-factor. Poisson xG. MongoDB backend. the most complete open-source football prediction pipeline on GitHub.

https://t.co/HBy8KHg5Vq -> replaces paid prediction platforms ($30/mo) full GUI app. 7 ML algorithms. downloads data from football-data. co. uk. predicts upcoming fixtures. exports to Excel. one click.

https://t.co/fbX02JGfYL -> replaces manual feature engineering XGBoost with 354 hand-crafted features. works for any European league. data straight from football-data. co. uk. plug and predict.

https://t.co/N4NBKWJ8yr -> replaces value bet scanners ($50/mo) ELO + expected goals + offensive/defensive ratings. compares model probability vs Vegas lines. flags when you have edge.

https://t.co/FEGRoaWs0x -> replaces bookmaker calibration tools Gradient Boosting tuned to output probabilities that match real bookmaker odds. not just accuracy - calibrated confidence.

https://t.co/LFzoWs4dcD -> replaces StatsBomb xG subscription xG model from KU Leuven researchers. LogReg + XGBoost pipelines. supports Wyscout, StatsBomb, Opta data. academic grade.

https://t.co/ZPQMuCwcAZ -> replaces xG analytics dashboards xG on StatsBomb open data. SHAP explanations for every prediction. proper calibration. tested on FIFA World Cup 2022.

https://t.co/tKj2sBOVzc -> replaces basic prediction models Poisson distribution for goal simulation. the classical approach that still beats most ML models on draw prediction.

https://t.co/Kp046GL1KL -> replaces Premier League prediction services XGBoost + AdaBoost + SVM on EPL data. detailed EDA. confusion matrices. honest 56% accuracy - because football is hard.

like + bookmark you'll need this when you build your first football prediction bot

[Millie Marconi @MillieMarconnni]

🚨BREAKING: Hugging Face just open-sourced an AI intern that reads ML papers, trains models, and ships the final model for you.

It’s called ML Intern.

And this is not another AI coding demo that prints a broken PyTorch script and disappears.

You give it the goal.

It researches.
Writes code.
Runs experiments.
Uses Hugging Face datasets.
Launches jobs.
Pushes the final model.

All from your terminal.

`ml-intern "fine-tune llama on my dataset"`

That’s the entire command.

The crazy part is how deep this goes:

→ reads HF docs and research
→ searches papers and datasets
→ uses Hugging Face jobs
→ searches GitHub code
→ runs local and sandbox execution
→ streams every step back to you
→ asks approval before risky actions
→ keeps working for up to 300 iterations

This is the first open-source AI intern I’ve seen that feels built for actual ML work.

Not chat.

Execution.

4K stars already.

100% Open Source.

https://t.co/qCWxeGOr6I

[Nainsi Dwivedi @NainsiDwiv50980]

This 2-hour Stanford lecture will teach you how to build AI agents, write prompts, and implement RAG — better than every Claude and AI tutorial you've watched this year combined.

This is exactly how Stanford trains its own engineers.

Free. On YouTube.

Bookmark this before you forget. 👇

[Khairallah AL-Awady @eng_khairallah1]

🚨 Anthropic's own team just showed how to actually use Claude Code properly.

30 minutes. free. the person who created Claude Code.

watch the workshop. bookmark it.

worth more than every $500 course you almost bought.

you've been using Claude without knowing 40 of its commands.

Then read the guide below.

[Vivo @vivoplt]

Best YouTube Channels To Learn in 2026

1. Cybersecurity – John Hammond
2. Artificial Intelligence – Andrej Karpathy
3. AI Research Breakdown – Yannic Kilcher
4. Web Development – The Net Ninja
5. Python Programming – Corey Schafer
6. DevOps – TechWorld with Nana
7. Cloud Computing – AWS re:Invent
8. Data Analytics – Luke Barousse
9. System Design – Gaurav Sen
10. Databases – Hussein Nasser
11. Low-Level Programming – The Cherno
12. Linux – Learn Linux TV
13. Networking – David Bombal
14. Math for ML – 3Blue1Brown

[MatchWornShirt @MatchWornShirt]

💙💛 De Génova a La Boca.

Against Juventus, @GenoaCFC wore their new 4th kit celebrating Boca’s 120th anniversary.

Own a signed & issued shirt from this iconic release.
🔗 https://t.co/p4mHi0D85X

[leo bora @plugbora]

@ryan_kidd44 Waiting!!!

[leo bora @plugbora]

@noc7am @carlossampaio @vonnserranofic1 @siteptbr @grok @luishenrique_vl @OHomemEstupendo @EdgleisonOliver Boa demais

[leo bora @plugbora]

@JCapinato @_fogagnolo @republiqueBRA energumeno kkk

[leo bora @plugbora]

@EmbaixadaEUA @SecRubio Shameful motherfuckers are denying access and stay of brilliant minds only because they do not agree with the ideas of a tyrant! Fuck trump!