Jev is the FASTEST AI model ever built for trading It makes calibrated buy/sell decisions in under 100 ms That is one real decision on every single block, 24/7 In this article I've shown EXACTLY how to build HFT trading system with Jev (from scratch)

i turned GPT-6 Astra into a 24/7 trading agent here is the EXACT way to build your FIRST AI trading bot with GPT-6 Astra with COMPLETE ARCHITECTURE GPT-6 Astra is most powerful trading agent ever built the goal was simple - ship hedge fund grade trading strategies with a multi-agent team of quants every layer solves one specific failure mode i hit in earlier versions: 1. reasoning substrate - GPT-6 Astra with 1M context, hours of long-horizon reasoning, direct computer use, and mathematical capabilities 2. harness layer - AgenKit installs 10 specialist agents inside Codex CLI. Conductor, Backend Architect, Test Engineer, Code Reviewer, Data Engineer, Debugger, Frontend, Infra DevOps, Perf Observability, Security. same model, TEN DISCIPLINES 3. workflow gates - 6 phases with operator approval at every step. brainstorm, architecture, plan, build, review, ship. nothing writes code until you sign off on the spec 4. mathematical foundation - 4 models running in production. Ornstein-Uhlenbeck stat arb, Avellaneda-Stoikov market making, Hawkes order flow, Heston volatility 5. production architecture - 6 layers wrapping every strategy. data, signal, decision, risk, execution, monitoring. the 2 AM WebSocket disconnect can no longer silently kill the model 6. risk enforcement - Kelly-sized positions, 5% drawdown kill switches, delta-neutral hedging, intent blotter output. these numbers came from watching an earlier chatbot script blow through 12% in one session 7. validation gates - every strategy must pass Sharpe above 1.5, drawdown below 15%, hit rate above 55%, t-stat above 2.0, walk-forward across 5 years before any capital touches it 8. output layer - Telegram Bot pings my phone with instrument, strategy, Sharpe, drawdown, action window and Kelly-sized position the principle i built around from day one > capability without structure is a chatbot > the wall was never the math, it was always the six salary team i learned that one the hard way

GPT-6 Astra is the most powerful trading agent right now. It gives you AGI-adjacent reasoning. I've shown the exact way to use GPT-6 Astra at its HIGHEST benchmark. You could literally build MOST complex trading models like hedge fund with it.

How i trade with the Black-Scholes Model: i built Black-Scholes from SCRATCH six months into my quant journey the formula everyone learns in school, the one that won Merton and Scholes a Nobel, i figured if it was famous enough to have a Nobel, it was famous enough to trust that was my first MISTAKE here's what i actually learned from building it, using it, and losing money with it --------------- the build: Black-Scholes takes 5 inputs and outputs an option price, that's stock price, strike price, time to expiry, risk-free rate and volatility tech stack i used: > Python 3.11 as the base language > scipy(dot)stats for the normal cumulative distribution function > numpy for the vectorized math when i extended it across multiple strikes > yfinance to pull SPY option chain data for backtesting > matplotlib for the initial visualization the entire pricing engine was around 40 lines of Python, i wrote it in a jupyter notebook first, then moved it into a proper module once i started using it for real trades when i first ran it and compared to real SPY option prices, the model was within 2-3% on liquid at-the-money options with 30-90 days to expiry i thought i had cracked the CODE --------------- what actually worked: pricing accuracy on at-the-money SPY options was solid enough that i could use the model as a reference, not as gospel, but as a check against what the market was showing me the GREEKS were the real win tho i extended the code to output all 4 first-order Greeks from the same closed-form formula: > delta = how much the option moves for a $1 stock move > gamma = how fast delta itself changes as the stock moves > theta = the daily cost of holding the position from time decay > vega = sensitivity to a 1% change in implied volatility i built a simple streamlit dashboard on top of the pricing engine that showed all 4 Greeks on my open positions in real time, refreshed every 30 seconds against live yfinance data for the first time i actually understood WHY my positions were moving the way they were --------------- what broke, expensively: my first real trade was SPY puts before a Fed meeting i used 14% historical vol as my sigma, model priced the puts at $2.85, live market at $3.40 implied vol had already jumped to 22% ahead of the print market dropped 2% like i expected, my model P&L said i should be up 60%, i closed up 28% IV CRASHED from 22% to 13% the moment the Fed resolved, my puts lost the vega premium even though the direction was right lesson: Black-Scholes assumes constant vol, real markets don't work like that the other assumptions broke too: > log-normal returns fail on tail events, this is why the vol smile exists > no-dividends assumption cost me on ex-dividend dates, my code didn't adjust for it > frictionless markets are a joke, my real fills were 5-10% worse than mid-price --------------- how i actually trade with it now: i stopped using Black-Scholes to price options, i use it to read the market the gap between my model price and the market price is implied vol vs my assumption, that's INFORMATION, not a mispricing to fade example: model says $2.85 using 14% vol, market says $3.40, market is implying 22% on that strike then i decide with vega: > if 22% looks too high going into an event, i short vega through a spread > if 22% still looks cheap, i buy vega > i never trade the gap as a pure mispricing the Greeks are what i actually check before every trade: > delta: my directional exposure across the book > gamma: how fast delta changes on big moves, i size smaller when gamma is high > theta: daily cost or income from time decay > vega: my volatility exposure, i cut this before earnings and Fed meetings --------------- the honest breakdown: Black-Scholes is not a trading model, it's a FRAMEWORK for understanding options the formula prices options for a market that doesn't exist, no jumps, no vol changes, no dividends, no slippage but the intuition it gives you about Greeks is priceless build it once from SCRATCH in Python, price a few real options against the market, then watch it break on your first real trade that's how you actually learn options for serious pricing you eventually move to Heston, but that's a rabbit hole for another post use Black-Scholes for the Greeks, read the price gap as implied vol, trade with vega instead of against the model

i've been running this regime-based trading for months and it's the single reason my strategies survive when markets shift here's how to build regime-based trading that actually adapts: --------------- 1. define your regime signals: pick 5-6 that actually predict shifts.. Hurst exponent on your universe, VIX term structure, realized vs implied vol spread, cross-asset correlation matrix, credit spreads, rates curve slope.. write them into a signals.md file 2. build the classifier: a scheduled job that pulls fresh values every 4 hours, compares each signal to its rolling 90-day distribution, flags anything in the top or bottom decile, then outputs one line.. current regime is trending, mean-reverting, high vol, or crisis 3. give it a strategy mapping: "tag every strategy in your book with the regimes where it actually works. stat arb needs mean-reverting, momentum needs trending, vol selling needs low vol and dies in crisis".. now every strategy knows when it should be live 4. add the resizing agent: same loop but pointed at every open position.. current regime vs strategy's favored regime, current sizing vs Kelly-optimal for that regime, correlation with the rest of the book.. tells you exactly which positions to scale up, cut, or hedge 5. everything writes into context files inside my strategy folder: current-regime.md, regime-history.md, position-adjustments.md.. every strategy reads them before entering or resizing anything the KILLER part: this plugged straight into my kill switch layer so when the regime flips to crisis, the system pulls risk automatically before i even wake up a regime shift used to mean 3 hours of manual research and usually a missed exit now the classifier flags the shift within 4 hours and my book auto-adjusts before the loss compounds the highest-leverage tool in trading is not a better strategy it's the system that tells you when your current strategy is about to stop working

The 4 BACKTESTING TECHNIQUES behind WINNING Strategies: i've spent the last 2 years running backtests on everything from mean reversion setups to volatility arbitrage to prediction market signals some strategies survived and tbh most of them died and the difference was never the strategy itself, it was how i tested it a backtest is not proof your strategy works, it's a stress test to see how easily it breaks here are the 4 techniques i've actually run, what worked, what broke AND what i still use --------------- technique 1: standard in-sample / out-of-sample split verdict: broken by default, NEVER TRUST THIS the setup is easy, take 5 years of data and train on the first 4, test on the last 1 the problem is subtle - every time you tweak the strategy and re-run, you're peeking at the test data and after 30 iterations your "out-of-sample" is FULLY contaminated the first strategy i ever backtested was a simple pairs trade between two energy stocks that showed a Sharpe of 2.1 on the standard split, so i deployed $2,000 of my own money and lost 40% of it in 3 months going back later i realized i'd re-run that backtest 47 times during tuning, the test data was never really untouched use this only for a quick first look, NEVER as the final validation --------------- technique 2: walk-forward validation verdict: the real workhorse, this is what i actually use instead of splitting once, you slide a window through the data train on 2018-2020, test on 2021 train on 2019-2021, test on 2022 keep sliding each test window is data the model has never seen and you get 5 or 6 test periods instead of JUST ONE what this catches: > strategies that only worked in one regime (the pattern shows up immediately) > parameters that shift wildly when retuned (unstable strategy, red flag) > strategies that survive across every window (this is real edge) at our fund we killed a stat arb strategy that showed Sharpe 2.4 on a standard split, but walk-forward revealed it worked beautifully in 2019-2020 and completely died in 2021-2022, the regime had shifted underneath us and it saved us months of losses but this is slower and more painful than a standard split and it's also the reason institutional backtests match live P&L :) --------------- technique 3: purged k-fold cross-validation verdict: fixes a hidden bug in walk-forward financial data has memory, today's price is not independent of yesterday's when your training window ends on december 31 and your test window starts january 1, information leaks across that boundary and your Sharpe looks better than it should purged k-fold fixes this, Marcos Lopez de Prado covers it in Advances in Financial Machine Learning the idea is simple: > split data into folds like standard cross-validation > when a fold is used for testing, remove the adjacent observations that overlap in time > this eliminates the leakage a QUANT friend of mine who runs an ML-based factor model showed me his numbers before and after adding purging, Sharpe dropped from 1.9 to 1.4 on the same strategy with the same data and the extra 0.5 was pure leakage he didn't know he had use this when you're training ML models on financial data, the leakage in tree-based models is brutal without it --------------- technique 4: monte carlo trade shuffling verdict: the reality check that saves capital EVERY SINGLE TIME your backtest shows one sequence of trades, Monte Carlo randomizes the order and runs it thousands of times why this matters: > your backtest might have gotten lucky with sequencing, what if the drawdown happened in month 2 instead of month 10 > the max drawdown you observed is one path, Monte Carlo shows the full range > the 5th percentile drawdown is often 2 to 3 times worse than what you saw few months ago (during the hype of 15-min BTC markets) i built a systematic prediction market strategy that showed 12% max drawdown across 18 months of backtest, but before deploying i ran Monte Carlo with 10,000 shuffled sequences and the 5th percentile scenario showed a 34% drawdown ofc i didn't deploy at full size, i sized it at 25% of what i originally planned and three months in the strategy hit a 22% drawdown, but the smaller size meant i could hold through it and the strategy recovered to finish the year up 31% Monte Carlo is why i stayed in that trade instead of blowing up --------------- what i actually use in production NOW: > walk-forward validation as the primary test > purged k-fold when the strategy uses ML models > Monte Carlo shuffling on strategies that survive both, before any real capital > standard in-sample/out-of-sample only for the very first pass (rarely tho) if your backtest is designed to make you feel good, it's designed to LOSE you money for real.

THE MATH NEEDED FOR TRADING (COMPLETE ROADMAP): today I'll will break down the essential math you need for trading & this is the exact roadmap that helped me personally when i started, i thought math was for interviews, two months into live trading i realized every position i took was pure math running in production here's the complete map of what math actually fires on real trades: --------------- 1. statistics and probability every price move is signal plus randomness. statistics separates the two what you need: > mean, median, expected value = EV formula (win% × avg win) - (loss% × avg loss) is what you're actually maximizing > variance and standard deviation = foundation of every position sizing formula, becomes volatility when applied to returns > correlation from -1 to +1 = tells you if strategies are actually independent > correlation 0.9 across 3 strategies = you have one strategy dressed as three > conditional probability = the biggest edge upgrade retail misses. P(win) = 55% unconditionally, but 70% when VIX < 15 > Bayes' theorem = how you update beliefs when new information arrives. never work with static beliefs > central limit theorem = why portfolio-level statistics behave cleaner than individual trades > linear and logistic regression = building blocks for mean reversion and binary prediction --------------- 2. linear algebra the moment you hold multiple positions, you're doing linear algebra whether you know it or not what you need: > scalars, vectors, matrices = your portfolio is a weighted sum of vectors > portfolio variance = w^T Σ w. not the sum of individual variances. one matrix operation > eigenvalues and eigenvectors = reveal where risk actually lives. in a 500-stock universe, top 5 eigenvectors explain 70% of variance. the other 495 are noise > PCA and SVD = reduce 50 correlated indicators into 5 independent factors explaining 90% of variation --------------- 3. time series analysis markets have memory. today's price depends on yesterday's. volatility clusters. trends persist what you need: > stationarity = assumption most statistical tests make, but markets aren't stationary, this is why strategies decay when regime shifts > autocorrelation = positive means momentum, negative means mean reversion, zero means random walk > ARIMA = framework for forecasting returns and volatility > GARCH = formalizes what every trader knows, volatility clusters. after a big move expect more volatility > cointegration = the foundation of pairs trading. two assets can both trend but their spread stays stationary --------------- 4. risk management math edge doesn't matter if you size wrong what you need: > Value at Risk = 95% VaR of $5,000 means 95% of the time you won't lose more, but 5% of the time you might lose much more > Sharpe ratio = (return - risk-free rate) / volatility. institutional threshold is Sharpe > 1.5 before deployment > maximum drawdown = biggest peak-to-trough loss. more intuitive than volatility for most traders > Monte Carlo simulation = randomizes trade sequencing to show the range of possible outcomes > Kelly criterion = f* = (bp - q) / b. professionals use 0.25x to 0.5x fractional Kelly because your true edge is never certain --------------- 5. stochastic calculus (for options) if you trade options, every price on your screen came from a stochastic differential equation what you need: > Black-Scholes = dS = μS dt + σS dW. the underlying follows geometric Brownian motion > Ito's Lemma = why the σ² term exists. this is why gamma exists > Heston stochastic volatility = dv = κ(θ - v)dt + ξ√v dW. captures the volatility smile that Black-Scholes misses > delta hedging = stochastic calculus running in production. every rehedge is dictated by the SDE governing the underlying --------------- MINIMUM TO START you don't need everything above to start for your first backtest: > mean, median, standard deviation > correlation > basic probability > Sharpe ratio and max drawdown start with statistics, that alone separates you from 95% of retail traders --------------- every real trade is math executing in production: > entry = conditional probability > validation = statistics > portfolio = linear algebra > sizing = Kelly optimization > risk = VaR, Sharpe, max drawdown > options = stochastic calculus the traders who make consistent money see markets as continuous equations, everyone else guesses if you're a complete beginner shoot me a DM and I'll share the resources with you MATH IS EVERYTHING <3

i turned GPT-6 Astra into a 24/7 trading bot here is the exact ARCHITECTURE i designed to build this system the goal was simple - find profitable strategies overnight, validate them and only alert me when the statistics are real every layer solves one specific failure mode i hit in earlier versions: 1. data source layer - 7 feeds running in parallel. 60 agents on US equity order books. 40 on Hyperliquid. 40 on Polymarket. 40 on options flow. 40 on SEC filings. 40 on X accounts. 40 on macro calendar. 300 AGENTS TOTAL 2. monitoring layer - filters raw signals into high conviction candidates before any reasoning happens. cut my monthly compute cost by 70% 3. reasoning layer - three specialist bots run in sequence. Hypothesis Bot generates strategies. Backtest Bot tests against 5 years of data. Validation Bot enforces significance thresholds 4. orchestration layer - Chief of Staff Bot routes everything through natural language. I stopped touching config files 3 months ago 5. enforcement layer - Risk Bot has zero negotiation authority. drawdown above 5% pulls every position. these numbers came from watching an earlier version blow through 12% drawdown in one session 6. output layer - Deployment Bot ships approved strategies. Telegram Bot pings my phone with instrument, strategy, Sharpe, drawdown, window and Kelly sized position the principle i built around from day one > no bot grades its own output > every kill switch checkable by something other than the bot's own claim i learned that one the hard way

How to become a quant from 0 to $650k/year (PLAYBOOK): by the end, you'll know how to: - build the exact math foundation top firms actually test - ship real projects that separate you from every other applicant - land your first credential and use it to climb the staircase - crack interviews at Citadel, Jane Street and prop shops btw i was quantitative backend developer at Liquid Hedge Fund based in Dubai for almost a year, building systematic trading strategies across perp dexes and prediction markets (right now on a break, working on my own projects) everything below is what actually got me here, in the order it actually worked here's the full 5-stage roadmap: --------------- Stage 1 (year 0): build the math foundation in the correct order the biggest mistake is starting with finance textbooks or trading blogs you don't need to know markets. you need to know math that markets run on how to build it: > probability first - Blitzstein & Hwang, free PDF from Harvard. chapters 1 to 6. conditional thinking is the entire skill > statistics second - Wasserman, All of Statistics. how to tell signal from noise > linear algebra third - Strang MIT 18.06, free at > OpenCourseWare. eigenvalues reveal what actually matters > convex optimization fourth - Boyd & Vandenberghe, free from Stanford. every portfolio problem reduces to this > stochastic calculus last - Shreve Vol 1 and 2. this is where Black-Scholes comes from your first year should feel painful, slow, non-linear most people quit here. that's why the field pays what it pays metric to watch: can you derive Ito's Lemma from scratch on a whiteboard? mistake to avoid: skipping layers because they feel abstract --------------- Stage 2 (year 1): read the papers and implement each one from scratch this is where I separated from every other backend developer trying to get in the 5 papers that shaped every quant desk on Wall Street: > Avellaneda & Stoikov 2008 (market making) > Engle & Granger 1987 (cointegration) > Heston 1993 (stochastic volatility) > Fama & French 2015 (five-factor model) > Cohen, Malloy, Pomorski 2012 (insider clusters) read all five. twice. then implement each from scratch in Python. no frameworks language stack that actually ships: > Python for research, backtesting and 90% of quant work. pandas, polars, pytorch, cvxpy, statsmodels > Rust for new systematic infrastructure. this is what Hyperliquid, prediction market engines, and next-gen HFT stacks are built in > Go for execution infrastructure. broker connectivity, microservices, low-latency APIs > C++ only if you're targeting elite HFT engineering. Jane Street uses OCaml specifically for their trading stack the mistake most self-taught quants make is picking one language and defending it, real desks run POLYGOT. Python for research, Rust or Go for execution what this gave me: 1: real understanding of how each model breaks 2: code on GitHub that recruiters actually respected 3: the ability to explain any of these in an interview without notes Heston was the one that changed my thinking. once I understood why volatility itself follows a stochastic process, options started making sense metric to watch: can you code Heston from a blank file in under 4 hours? mistake to avoid: reading 20 papers superficially instead of 5 deeply (VERY IMP) --------------- Stage 3 (year 2): ship 6 real projects with working backtests this is where most aspiring quants die silently the 6 projects that will land your first interview: > pairs trading bot on 5 cointegrated equity pairs > options pricing engine with Heston implementation > Fama-French factor decomposition scanner > insider cluster detector using SEC EDGAR > mean reversion scanner for S&P 500 > cross-exchange arbitrage scanner for perp DEXs every project needed: - clean GitHub repo with a proper README - working backtest with real historical data - honest documentation of what failed and why - one paragraph explaining the math in plain English the honest documentation is what actually got me noticed. I wrote a section called "what broke" in every README. two recruiters told me later that was the reason they DM'd me metric to watch: how many recruiters DM you after posting these mistake to avoid: hiding the projects where the math didn't work --------------- Stage 4 (year 3): first credential and the interview grind credential path (what worked for me): > cold emailed 40 PhD students. 3 responded. 1 took me on > TA'd a quantitative finance course at a local university > contributed to open-source quant libraries on GitHub > the title didn't matter. having real technical work to talk about mattered interview prep: - the Green Book (Xinfeng Zhou). 200 real interview problems. spend 15 minutes attempting each before looking at hints - QuantGuide(dot)io for probability puzzles at interview difficulty - Brainstellar for brain teasers - Zetamac for mental math. target 50+ correct per minute before applying - LeetCode Blind 75 with focus on dynamic programming the interview is a filter. it's not testing your knowledge, it's testing how you think under pressure with incomplete information metric to watch: how quickly you can solve fresh problems out loud mistake to avoid: memorizing solutions instead of understanding patterns --------------- Stage 5 (year 3-4): compete publicly and climb the staircase organic applications alone hit a ceiling. now you layer public competitions: > Jane Street Kaggle ($100K prize + direct recruiter visibility) > WorldQuant BRAIN (paid alpha signals + institutional pipeline) > Citadel Datathon (winners fast-tracked to interview rounds) the staircase (this is the part nobody tells you): 1: research lab or startup first 2: mid-tier prop shop second 3: elite fund like Citadel, Jane Street, or Two Sigma third nobody jumps directly from zero to Jane Street. everyone climbs attempting the vertical jump and failing doesn't mean the field is closed. it means you tried to skip stairs metric to watch: interview conversion rate at each tier mistake to avoid: applying to elite firms first, getting rejected and giving up --------------- CONCLUSION most aspiring quants die between the math foundation and the first credential because they think they need one more course, one more book, one more year the truth is: the first 3 stages are slow, painful and non-linear but they build the foundation that makes stages 4 and 5 feel like gravity start with probability. read the 5 papers. ship 6 projects. then apply no MIT. no Stanford. no finance degree required Citadel entry level pays $336K to $642K. Jane Street average $1.4M. 5-year top prop $800K to $1.2M the information asymmetry keeping most people out isn't intelligence. it's not knowing what the path looks like. COMPLETE SUMMARY BELOW:

i finally cracked how to master GPT-6 Astra OPENAI JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING GPT-6 ASTRA GPT-6 Astra → Clean System Prompt → Autonomous Execution → Production-Ready Output → Ship faster Most people are still prompting Astra like GPT-5. That's the mistake. Bookmark this, then read the article below to build to alpha trading strategies 24/7

i finally cracked how to master GPT-6 Astra OPENAI JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING GPT-6 ASTRA GPT-6 Astra → Clean System Prompt → Autonomous Execution → Production-Ready Output → Ship faster Most people are still prompting Astra like GPT-5. That's the mistake. Bookmark this, then read the article below to build to alpha trading strategies 24/7

GPT-6 Astra is the most dangerous AI model right now. It gives you AGI-adjacent reasoning. That can discover new profitable trading strategies for you 24/7. If you set it up correctly, you gain a personal hedge fund.

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000 as an AI Engineer. I asked him how he broke in from zero. He sent me the exact video that got him in. Anthropic's 1-hour course on "How to become an AI agentic engineer in 2026". Anthropic Engineer shows you exactly how to code & build AI agents from scratch. I watched it last night. Halfway through, I realized I could break into an AI lab in just weeks, not years. Bookmark this and read the article below.

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000 as an AI Engineer. I asked him how he broke in from zero. He sent me the exact video that got him in. Anthropic's 1-hour course on "How to become an AI agentic engineer in 2026". Anthropic Engineer shows you exactly how to code & build AI agents from scratch. I watched it last night. Halfway through, I realized I could break into an AI lab in just weeks, not years. Bookmark this and read the article below.

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My friend applied to 150 tech jobs in two years. No MIT. No Stanford. No PhD. Last month SpaceXAI offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. A 1-hour course from SpaceXAI on "Full AI Engineering in 2026". Lauren Tan aka Poteto (Ex-Cursor) shows you how to architect & build AI agents like Grok Bot from scratch. I watched it last night. Halfway through, I realized anyone could break into an AI lab in weeks, not years. Bookmark this & read the article below.

I put in $250. This open-source bot turned it into +$13,000. Build HFT algo trading bots, market making bots & trading strategies with HummingBot for FREE. Bookmark before someone takes it down.

I put in $250. This open-source bot turned it into +$13,000. Build HFT algo trading bots, market making bots & trading strategies with HummingBot for FREE. Bookmark before someone takes it down.

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Anthropic's 2-hour course on "How to become an AI engineer in 2026". Anthropic's team will teach you exactly how to build AI agents with skills from scratch. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - AI agent skills framework • 25:19 - Building AI agents with MCP • 42:05 - AI agents architecture • 1:28:55 - How to ship with AI agents

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Anthropic's 2-hour course on "How to become an AI engineer in 2026". Anthropic's team will teach you exactly how to build AI agents with skills from scratch. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - AI agent skills framework • 25:19 - Building AI agents with MCP • 42:05 - AI agents architecture • 1:28:55 - How to ship with AI agents