HE GOT OFFERS FROM META, GOOGLE, AMAZON, APPLE, AND ROKU THEN GAVE AWAY HIS ENTIRE PREP FRAMEWORK FOR FREE for most engineers, one faang offer is the goal he cracked five and instead of keeping the playbook private, he open-sourced every note, every pattern, every system he used to get there the repo has 6 chapters: ▫️ general coding (algos + data structures) ▫️ ml coding from scratch ▫️ ml fundamentals & breadth ▫️ ml system design ▫️ agentic ai systems (added 2025) ▫️ behavioral prep no paid course... no youtube grind... just a clean, structured guide built from real interview rooms at the world's biggest tech companies he recently added an entire chapter on agentic ai systems because the interview landscape is shifting & he knew it before most people did 8.2k engineers have already starred it github → github.com/alirezadir/mac…

Jane Street Quant ~$650K showed his code in the fund's trading system that makes $3500 per SECOND - 27-min and you will learn how to make tricks that a $20.5B/year fund uses every minute bookmark & watch - these are skills most engineers never learn outside of a hedge fund

To build a CS skillset that can't be replaced by an AI, you have to learn fundamentals. That lets you do things that an AI still can't, because your brain learn how to think and work. If you want to get better at fundamentals, read these 16 articles: (links below) ↓

i made a web server in c using sockets, and honestly, i learned so much about how the web works

Jane Street Quant ~$7M/year showed the fund's code that analyzes ALL MARKET data every millisecond - the library that made $20.5B/year and nobody talks about 33-min guide on the code a tier-1 fund has been running for ~16 years to stay on top bookmark & watch - instead of Netflix to learn how to do the same!

Introducing React​ Review Agents write terrible React, this helps you fix it Just paste your GitHub repo! No signup required

Godfather of AI: "If you sleep well tonight, you may not have understood this lecture." This 47-minute lecture is the best thing I saw about AI in the last few months. It will definitely help you understand how it actually works and where it's going. Geoffrey Hinton built the neural networks behind every AI alive, then quit Google to warn the world about it. The part nobody wanted to hear: > AI is already developing abilities its creators didn't intend > in most cognitive tasks it's already ahead of us > the question is no longer if it surpasses us but when > the only decision left is which side of that line you're on Right now the average person opens Claude, types something, gets an answer, closes the tab. They think they're using AI. they're using maybe 10% of it. I went through his entire lecture, built a practical system from what he was describing. 18 steps to actually use Claude the right way, with copy-paste prompts that work today. Full guide in the post below.

A self-taught Quant just published the exact technique that separates real trading edge from data mining - permutation tests on backtested strategies in Python. Quant Twitter quietly knows about him. Quants juniors send each other his videos in DMs. Bookmark it tonight before the algorithm pushes him mainstream. Then read the article, I built the AI quant system that runs thousands of these tests per week.

Python notebooks for building S&P 500 trading strategies github.com/nickmccullum/a…

The most mass-complete list of CS video courses on the internet. cs-video-courses. 78K+ stars. MIT. Stanford. Berkeley. Harvard. CMU. IIT. Princeton. Caltech. All free. All video lectures. All in one repo. Topics covered: → Data Structures and Algorithms → Operating Systems → Distributed Systems → Database Systems → Computer Networks → Machine Learning → Deep Learning → Natural Language Processing → Computer Vision → Computer Graphics → Security → Quantum Computing → Robotics → Blockchain From beginner (CS 50) to advanced (6.824 Distributed Systems). The curriculum is free. The commitment is yours. GitHub link in comments.

Andrej Karpathy: "90% of your AI coding bill is paying for context you didn't need to send" Here are 10 things senior AI engineers stopped wasting tokens on: 1. Auto-context loading 50 files for a 30-line fix: $1.20/turn for tokens you'll never read. 80% input waste, every session 2. Running Opus on lint, format, and rename tasks: $0.60 for what Haiku nails at $0.02. 30x overpay on the cleanup tier 3. Tool call loops that re-send the full repo on every retry: 5x context cost per agentic flow. fixing these alone cuts 30-50% of bills 4. Sonnet as the default model: Kimi 2.6 matches its quality on most coding tasks at 1/6 the cost. defaulting to Sonnet in 2026 is leaving 60-70% on the table 5. Streaming responses on stable-prefix workflows: kills your prompt cache. you pay 10x for tokens that should have cost cents 6. "Just in case" file includes: 80,000-token prompts that should be 3,000. context bloat is the silent budget killer 7. Per-session knowledge rebuilding: 10 min writing a SKILL.md once vs paying agents to re-figure out your environment every run. $4 vs $0.30 per execution 8. Single-model setups: premium tier on every task is the most expensive mistake in AI coding right now 9. Asking 10 small questions one at a time: 10 separate input prefix charges vs one batched call. 70-90% savings on routine workflows 10. Buying Claude Pro + ChatGPT Plus + Cursor Pro: you seriously use one. the other two are habit, not utility what actually compounds instead: - context discipline (grep before fetching, always) - prompt caching on every stable prefix - multi-model routing (Kimi 2.6 default, Opus for the 10%) - graduated skills via SKILL.md files - profiling tool calls before optimizing prompts - the routing mindset (right model for right task) in 12 months, the gap between developers shipping on $200/month and $4,000/month budgets won't be skill it'll be how well they route study this.

Jane Street pays $750k/ year for quants who can answer how to use Stochastic Process and Markov Chains in quant trading. This 1-hour MIT lecture on probability gives you the same insights quants get paid $60K/month for. Bookmark & watch today. Then read the article below.

As a Principal Backend Engineer with over 12 years of experience, I can tell you quite certainly that if you're still getting rejections in system design interviews after good efforts, I think your fundamentals are not strong... Dedicate 2-3 months to mastering these design fundamentals, then practice designing a few systems(and do plenty of mock interviews). Scaling & Architecture ↬ CDN ↬ Caching ↬ Sharding ↬ Queueing ↬ Replication ↬ Partitioning ↬ API Gateway ↬ Rate Limiting ↬ CAP Theorem ↬ Microservices ↬ Load Balancing ↬ Fault Tolerance ↬ Database Scaling ↬ Service Discovery ↬ Consistency Models ↬ Eventual Consistency ↬ Distributed Transactions ↬ Monolith vs Microservices ↬ Leader Election Databases & Storage ↬ Leader-Follower Replication ↬ WAL (Write Ahead Log) ↬ Asynchronous Processing ↬ Transaction Isolation ↬ Read/Write Patterns ↬ Consistent Hashing ↬ Redis/Memcached ↬ Backup & Restore ↬ Hot/Cold Storage ↬ Data Partitioning ↬ Object Storage ↬ SQL vs NoSQL ↬ Data Retention ↬ Data Modeling ↬ OLAP vs OLTP ↬ ACID & BASE ↬ Bloom Filters ↬ File Systems ↬ S3 Basics ↬ B+ Trees ↬ Indexing Communication & APIs ↬ JWT ↬ CORS ↬ OAuth ↬ Throttling ↬ Serialization ↬ API Security ↬ Long Polling ↬ WebSockets ↬ API Gateway ↬ Idempotency ↬ Service Mesh ↬ Retry Patterns ↬ REST vs gRPC ↬ API Versioning ↬ Circuit Breaker ↬ API Rate Limits ↬ Fan-out/Fan-in ↬ Protocol Buffers ↬ Message Queues ↬ Dead Letter Queue Reliability & Observability ↬ Metrics ↬ Alerting ↬ Failover ↬ Logging ↬ Rollbacks ↬ Monitoring ↬ Heartbeats ↬ Retry Logic ↬ Autoscaling ↬ SLO/SLI/SLA ↬ Load Testing ↬ Error Budgets ↬ Health Checks ↬ Circuit Breaker ↬ Incident Response ↬ Chaos Engineering ↬ Distributed Tracing ↬ Canary Deployments ↬ Graceful Degradation ↬ Blue-Green Deployment

The founder of Renaissance Technologies - the hedge fund that makes 66% a year, runs the most secretive trading floor on Earth, and has never accepted outside money - once stood in front of 500 mathematicians and explained exactly how he did it. Jim Simons. The Einstein Lecture. American Mathematical Society. 1 hour 20 minutes.They left it on a small university channel. Almost nobody knows it exists. No one's talking about it. Bookmark it before they do. Then read the article below.

A lot of people are arguing that HTML burns more tokens than markdown. It's true but you can save at least 40% by externalizing the CSS to a template with . This style.css is your formatting so the LLM will never output CSS again. I tested on a 12116 token HTML article and it dropped to 6,723 tokens so -44%! You'll have this: Instead of this:

Take this course by @pmavrin youtube.com/playlist?list=…

/goal is f*cking insane. You can literally get your AI agents to work for HOURS without manual intervention. Already active in Claude Code and Codex - you need to use it now. Use this prompt and your agents will complete any task on autopilot:

USE THE PROMPT BELOW IN CODEX/CC TO PROTECT YOUR SYSTEM AND CODEBASE FROM NPM SUPPLY CHAIN ATTACKS (LIKE TANSTACK TODAY): """ set up npm supply-chain protection on this machine. do all four steps. 1. edit ~/.npmrc. keep every existing line (auth tokens etc), append: min-release-age=7 minimum-release-age=10080 save-exact=true 2. edit ~/.bunfig.toml (create if missing). keep existing content, append: [install] minimumReleaseAge = 604800 3. in this project, open package.json and pin every dependency: strip ^ and ~ from every version under dependencies, devDependencies, and peerDependencies. exact versions only. 4. commit the lockfile (bun.lock / package-lock.json / pnpm-lock.yaml) so the resolved tree is locked in git. then report: files changed, deps pinned, anything unexpected. """ the cooldown makes every package manager refuse any version published in the last 7 days. attack chains usually only last a couple hours, but this protects you long term and for any future attacks... which at this rate will keep happening

Collection of GPT jailbreaks, prompt injections, and super prompts github.com/CyberAlbSecOP/…