Content creator | AI researcher & builder | AI insights from 2030 | @beyond_xai

San Francisco
Three engineers from SpaceXAI, DeepMind and Thinking Machines just gave a free 1-hour session of how to actually build an AI career, from 0% to 100%: • 10% → 12:26 - what actually gets you hired at tier-1 AI labs, from someone who's been through it • 30% → 20:05 - the billboard method: applied everywhere, became the youngest intern the NYT ever had • 55% → 28:00 - 300 applications, all rejected except three. that's how you get a PhD-level role without a PhD • 80% → 44:02 - less than 1% of the world has touched Cursor or Claude Code - that gap is the opportunity • 100% → 1:00:58 - how much code they write by hand today by tier 1 engineers: 0%, 0-5%, 5-10% most people are optimizing one perfect application - they were maximizing surface area watch it today - then read below on how to rebuild a business supply chain with AI from scratch ↓
I just rebuilt the business entire supply chain with Drex. From order to outcome. Every decision visible. This is Drex from Nace. ai running a real business process end to end: > Order comes in → documents get classified, checked & reconciled > Every checkpoint is one Drex decision: pass, repair or escalate > Something breaks mid-flow? It replans sourcing and revalidates instead of stalling > Only the real edge cases go to a human > Case closes with a full decision trace you can export 8 stages, one flow: intake → commercial controls → procurement → fulfillment → trade docs → transport → delivery → settlement & audit Synthetic documents, real model decisions Drex doesn't write essays. It scores the options and picks one, fast enough to sit inside every step of a workflow Link below
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JEV + OPUS 5.5 is an insane engine for live content factories... One brief in. 1 explainer + 3 shorts out. Full production ship in 48s. → Opus 5.5 draws 10 scenes, frame by frame → Jev QA's each scene: text, contrast, layout → 3 scenes failed. Opus patched, re-check passed → Jev cuts shorts while Opus keeps drawing → Hooks and captions tested live → 40s explainer + 3 shorts queued 13 QA checks. 3 fixes. 0 manual edits. The business math: → Shorts pay ~$0.03 to $0.10 per 1K views → 3 briefs a day = 270 shorts a month → 20K views each = 5.4M views a month → That's $160 to $540/mo from ads alone → Unlock: 1K subs + 10M views in 90 days Ads are the floor. The factory is a funnel: brand deals, affiliate, long-form. Not a slop farm. YouTube cut pay for template spam. Jev's QA is the difference.
Drex just took #1 from Jev on the Decision Index... So I made them play the same game: Drex vs Jev, flappy-style, head to head Same seed, same course, same prompt. Every flap is one typed decision: flap or glide. Only the model changes. what Drex is: > A small decision model from Nace.AI, under 6B params > Returns a probability for every option in > #1 on Decision Index 0.2: 51.73 vs Jev 1.13.0 at 51.67 > Wins 23 of the 40 benchmarks scored so far > 70 tokens per decision vs 367 for Jev, 5x fewer > ~25 decisions per second on a single H100 My bench (simulated, latency profile from Nace): > Drex ~136ms vs Jev ~204ms per decision > Faster reply = fresher state = fewer crashes > 20 seeds: Drex won 19, 1 draw Built for decisions, not chat: agent routing, tool selection, reranking, doc classification, compliance checks, guardrails, risk detection 250M free tokens for the first 10,000 builders Link below
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The zero manual edits part is the real flex This is what content production at scale looks like
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yeah, now its time to connect it to youtube account
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I just rebuilt the business entire supply chain with Drex. From order to outcome. Every decision visible. This is Drex from Nace. ai running a real business process end to end: > Order comes in → documents get classified, checked & reconciled > Every checkpoint is one Drex decision: pass, repair or escalate > Something breaks mid-flow? It replans sourcing and revalidates instead of stalling > Only the real edge cases go to a human > Case closes with a full decision trace you can export 8 stages, one flow: intake → commercial controls → procurement → fulfillment → trade docs → transport → delivery → settlement & audit Synthetic documents, real model decisions Drex doesn't write essays. It scores the options and picks one, fast enough to sit inside every step of a workflow Link below
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Try Drex on your own workflow: nace.ai/drex
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I’ve set aside $20M because I want more startups to grow with us. Higgsfield crossed a $1B revenue run-rate today. We’re celebrating this with 100% cashback on Higgsfield API spend with Genjutsu and Cinema Studio available. When you're a small startup, early support can change how quickly you build. Special thanks to @gregisenberg for showing how to get started with GPT-6 Astra + Higgsfield API, step by step. I hope this helps you ship faster and grow into one of our biggest customers.
We're announcing 100% cashback on every model on the Higgsfield API platform. Seedance 2.5, Kling 3.0, MiniMax H3, Wan 3.0, and more. Spend on the API and get your cashback instantly, up to $100,000 per business. $20,000,000 cashback pool. First come, first served. You helped us reach a $1B run rate. We’re celebrating by putting $20M back into what you build next. Unused cashback expires on September 30.
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SpaceXAI engineer, Nick Miller: "99% of people run one GrokBot and call it done, only 1% build teams of bots that talk to each other. i'm running a team of 20+ GrokBot agents in a loop & graph. I have a Chief of Staff bot, PM bot, QA bot and 15+ workers - that's the new stack" In a 27-minute demo, a SpaceXAI engineer explains how to build a team of GrokBot agents from scratch worth more than a $500 agentic engineering course skip Netflix today and watch this, then read how to build a fleet of GrokBot agents in the article below
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brilliant grokbot stage !
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yeah, so true Movez ! thanks
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JEV + Opus 5.5 is insane for live design... I built a live site redesigner with JEV + Opus 5.5 Paste any link → press Start → scroll, and Jev + Opus 5.5 rebuild every section of the site in front of you Full production ship in 20 seconds: 1. IntersectionObserver fires when a section is 30%+ in the viewport 2. Jev returns one typed decision in ~0.1s: { layout, copy, drop, type, palette, p } 3. Opus 5.5 writes the component (TSX) + a CSS patch for the chosen style 4. The new section wipes in with clip-path, the old one blurs out 5. Next section enters the queue, one at a time, no race conditions Output: 8 sections of a 2015 hosting site rebuilt in ~20s, streamed line by line in the terminal 3 styles, one renderer: orthographic globe + lambert shading → ASCII / 2-color halftone / ink stipple Scroll yourself and it redesigns whatever you land on Jev decides fast, Opus designs it
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wow, this is crazy demo bro !
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Codez retweeted
SpaceXAI engineer just released a 1-hour workshop on building an effective team of GrokBot agents from scratch: • 10% → 8:48 - GrokBot effective team blueprint: CloseBot, ProdBot, StalkBot, ProtoBot • 30% → 9:34 - building graphs, loops with GrokBot + automations • 55% → 18:19 - designing Chief of Staff agent from scratch • 80% → 27:30 - training a team of GrokBot agents, based on your data • 100% → 28:55 - building a “cleaner” GrokBot for context managing One agent saves you an hour - a squad of them replaces the staff function you can’t afford to hire. Watch it today - then read the full bot orchestration playbook below ↓
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JEV + OPUS 5.5 is insane... I built a viral post prediction analyser with JEV + Opus 5.5 Paste any X link → press Start → Jev compares it to 800 posts that went viral and gives it a virality score Full production ship in 9 minutes: > Jev parses 800 viral posts from X as a live baseline > Groups them by hook type: build demo, receipt, launch, contrarian... > Runs 12 typed checks on every post (hook, numbers, media, CTA) > Opus 5.5 explains why each one spread > Your post gets scored against its hook type + the 5 most similar viral posts Output: score /100, expected likes and views, and % of the viral baseline it beats Works on drafts too, so you know before you post Hover any tile and you see the full post with its media, the checks and the score Jev decides fast, Opus explains why
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this looks like a crazy setup bro !
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Codez retweeted
TyperSafe Founder, Diogo Almeida just gave the best 2-hour breakdown on how to actually master Jev, from 0% to 100%: • 10% → 21:43 - building an eval for your own workflow with JEV • 30% → 55:49 - choice, score, noul - new types of code mapping with JEV • 55% → 1:12:36 - cascade: confident answers ship, uncertain ones go to a bigger model • 80% → 1:37:12 - JEV use cases: dark data, real-time calls, verifying every LLM in your stack • 100% → 2:11:50 - building coding agents with JEV this is worth more than another $500 course on building agents with Claude watch it today - then read the full Jev playbook below
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TypeSafe just released a Blueprint for building x200 and x400 cheaper Agentic Loops with Jev I collected the best tips in a structured 14-page PDF on "How to ship an effective agent JEV LOOP": step 1 → meet Jev: a System One model. It doesn't generate text. It takes state + questions and returns typed answers with probabilities step 2 → learn the three question types: Choice picks one option, Score places it on a scale, Noul returns the probability something is true step 3 → batch questions per state: every question runs in parallel in one call, so extra questions barely add latency step 4 → split the loop: the LLM thinks and writes, tools act, Jev takes every bounded fork in between step 5 → route models with Jev: fast model for lookups, powerful model for architecture, picked from the latest message step 6 → guard every tool call: AutoModeMiddleware scores bash calls for risk and blocks them before they run step 7 → replace LLM-as-judge: correct, grounded and complete scored in parallel on the same trace step 8 → check the test: 5 frozen runs, 100 repeats per judge. Jev matched the human oracle on 500/500 decisions. Terra 99.8%, Luna 96.4%, Claude Sonnet 4.6 80% step 9 → do the math: 0.44s and $0.00035 per call. $0.34 total vs $28.17 for Claude Sonnet 4.6, with 92-913x lower variance step 10 → keep thresholds in code and a human in the loop: stable doesn't mean right. Claude gave the same wrong verdict every single time the result: the expensive model only does the work that needs it, and every decision around it runs in under half a second Send this PDF to your LLM before running your next agentic workflows, then explore how to become a Jev-native engineer in the article below
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It’s time to build an efficient harness with Jev, finally!
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1 day left to lock in up to 50% OFF Higgsfield API. Build your own AI app with our product endpoints, including: • Higgsfield Genjutsu • Cinema Studio 4.0 • Higgsfield Soul Plus all frontier video and image models through the same API.
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Codez retweeted
SpaceXAI engineer, Lauren Tan: "99% of people use GrokBot just for 1% of its real power. They run 1 agent without "loop" & "graph" last month my team of 30+ GrokBot agents shipped 2000+ PRs, fully autonomous. Chief of Staff, two PM agents, 20+ coding agents" in 38-minutes, a SpaceXAI engineer explained how to build a team of GrokBot agents that work while you sleep this is worth more than a $500 agentic engineering course skip Netflix today and watch this, then read how to build a fleet of GrokBot agents in the article below
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Ops engineer energy: "99% celebrate $140M ARR in 90 days and still use AI like a search box for contracts. only 1% run agents that watch a lawyer redline once, then enforce the playbook on the next 200." That's the loop.
EXCITED TO LAUNCH: Akai (akai.run) Deel added >$140M ARR in 90 days without increasing headcount by automating~600 Full Time Employees' equivalent in work with Akai. Akai was an internal tool to automate our painfully repetitive operations in Finance, HR, Accounts Payable, and Compliance, etc. We never intended to make this a product. But we watched revenue per employee grow from $130K to $215K We built >8k agents that do the work of ~600 employees It had such a dramatic impact on our business that today we are launching it for everyone. How it works: Say you're automating payment reconciliation: 1. Record your screen while manually matching a messy transaction and Akai will capture your screen, voice, server requests 2. Akai will see that you pulled unformatted wire transfer info from an archaic bank portal, put it in some excel sheet, checked NetSuite invoices, payment history, and put a ticket on Zendesk 3. Akai reads between the lines and build a workflow + steps + conditional guardrails. It learns tacit edge cases, like resolving malformed invoice references without you writing a single regex 4. Simply connect NetSuite, your ledger, Zendesk, PSPs, and even legacy bank portals with zero API access 5. Run the workflow and tell it what to adjust in plain English: "strip slashes on wire memos and auto-apply partial payments." It adapts instantly 6. Once it works for you, add 100s of colleagues. Your entire payment ops team forks and extends the workflow for new PSPs, secondary ledgers, or regional settlement rules 7. We automated 85% of our payment reconciliation end to end, eliminating 500+ hours of soul-crushing manual grunt work every single week. Claude Code/Codex can't do this in multiplayer mode. Every person rebuilds the same skill from scratch in their own way. Deel built Akai to: 1. Understand backend operations edge cases (it had to work for our 7000 person team first) 2. Collaborative across 1000s of employees 3. Self-Learning from millions of runs 4. Optimises cost and gets cheaper every run We're so confident that we're announcing an Automation Guarantee: If our engineers can't automate a thousand of hours of work in your first 30 days, you get a full refund. Book a demo: akai.run if you're an exec at a company with hundreds of employees
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SpaceXAI engineer, Lauren Tan: "99% of people use GrokBot just for 1% of its real power. They run 1 agent without "loop" & "graph" last month my team of 30+ GrokBot agents shipped 2000+ PRs, fully autonomous. Chief of Staff, two PM agents, 20+ coding agents" in 38-minutes, a SpaceXAI engineer explained how to build a team of GrokBot agents that work while you sleep this is worth more than a $500 agentic engineering course skip Netflix today and watch this, then read how to build a fleet of GrokBot agents in the article below
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Two thousand PRs is nuts
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yeah, this is crazy numbers bro !
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GPT-6 ASTRA JUST TURNED ONE AGENT CREW INTO 20+ BUSINESS WORKFLOWS one coordinator can route research, sales, support, compliance and engineering without spinning up a separate agent stack for every job. objective → specialist → tools → guardrails → human review → output multi-agent runs can burn roughly 15× more tokens than normal chat, but parallel research has cut complex task time by up to 90% when the branches are actually independent. one real sourcing system replaced about $12,000 of analyst work with roughly $200 in compute, while its internal evals climbed from 25% → 60% → 85%. the interesting shift is not “more agents.” it is one reusable operating layer that changes skills, tools and permissions depending on the job instead of rebuilding the workflow every time.
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wow, this looks crazy bro !
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