Average Codex users: > keeps getting rate-limit resets >... gets 10% more usage > 5-hour cap temporarily disappears > codex keeps working until the task is finished > Sleeps peacefully Average Claude Code users: > Watches the weekly limit disappear in real time > Hits the cap halfway through the task > Claude stops immediately > Begs Anthropic for another rate-limit reset > Anthropic announces another “final” extension > Starts panic-coding before access disappears again > Ends up in the hospitalshow more

"You've reached your rate limit. Try again after 7pm."... every. fucking. day. $200/month - $2,400 a year - for Codex. then i opened the DeepSeek pricing page. Someone made DeepSeek a native subagent for Codex. Heavy work goes there, light stays on GPT-5. Same code out the other end. 35x cheaper. DeepSeek V4 Flash lives on its own API key, not on my Plus quota. One command: npx skills add oil-oil/codex-deepseek-subagent -g -y Restart Codex, tell it "set up DeepSeek as a subagent", done. First day in a month I'm still working in the evening instead of hitting the wall. save this before your next rate-limit ↓show more

🚨 do you understand what OpenAI just announced.. ChatGPT... Pro is now $100/month. 10x more Codex usage. and Codex isn't autocomplete - it writes, debugs, builds entire apps. autonomously. if you've used Claude Opus for coding you know the feeling. Codex hits the same way. except Claude Opus limits burn through in hours. you blink and you're out. Codex just got a $100 tier built to run 24/7. no cooldowns. no "you've reached your limit" at 2am mid-build. they're not selling you a chatbot. they're selling you a software engineer that never sleeps and never asks for equity. and they're doing it quietly while everyone argues about tariffs.show more

do you understand what Anthropic just admitted? their engineers... haven’t written most of their own code since early 2026. 80% of the code merged into Anthropic’s codebase last month was written by Claude. let that sink in. the company building the most powerful AI in the world is already being built by that AI. and the numbers keep getting wilder: → engineers are shipping 8x more code than in 2024. not because they’re working harder. because Claude is doing most of it. → Claude’s success rate on open-ended coding problems hit 76% in May 2026. up 50 points in just 6 months. → one Anthropic employee said “it’s been 5 months since I last wrote any code myself.” → Claude Mythos Preview achieved 52x speedup on research optimization. a skilled human gets 4x in 4-8 hours. → the length of tasks AI can reliably complete is doubling every 4 months. and here’s the part that keeps me up at night: in April 2026, Claude-powered agents were given an open AI safety problem and left alone to solve it. two human researchers recovered 23% of the performance gap in a week. the agents recovered 97% in 800 hours. Anthropic calls this recursive self-improvement. AI building AI. getting better. building a better version. repeat. they say we’re not there yet. but they also say it could come sooner than most institutions are prepared for.show more

The biggest gap between AI labs isn't whether their... agents get the job done. It's whether users actually enjoy working with them. We tracked the best agent from Anthropic, OpenAI, Google DeepMind, Kimi.ai, and DeepSeek across 4 key Agent Arena signals used between late June and the start of October to see how the industry is shifting. Three things stood out: 1. Users like some agents far more than others. On Praise vs Complaint, OpenAI and Anthropic sit at +35 points over the leaderboard average. Moonshot, xAI and DeepSeek land between +5 and +13. No other signal splits the labs this widely. 2. Agents are getting much better at delivery. On Confirmed Success, the average lab's best model went from -4 to +14 points in three months. Anthropic leads with Claude Opus 5.5 (Max) at +20. 3. Labs are converging on error recovery and steering. Since June, the spread between the six labs has more than halved on Bash Recovery (34 → 16 points) and nearly halved on Steerability (19 → 11). In short, the labs are catching up on avoiding failures. Leaving the user happy is where the top three are pulling away.show more

Anthropic made Claude. GitHub did the rest. Two repos.... $25 a month. 214 trades. 74% win rate. +$9,437 in 19 days. Here's what actually happened: I gave Claude a market archive and a live Polymarket scanner. One repo showed the full history. Who enters well. Who exits even better. Who keeps a high win rate by farming tiny moves. And who actually knows how to cut risk. The second repo turned that into a board. Every order book. Every active market. Fast enough to rank the setup before the crowd repriced it. The big shift was this: Stop tracking win rate. Start tracking exit quality. That is where most of the real edge sits. Top wallets were capturing 86% of the move and cutting losers around 12%. The average wallet was only capturing 58% and letting bad positions bleed past 40%. Same market. Same headlines. Completely different outcomes. That was the filter. Not who sounds smart. Not who posts the best thread. Who gets paid on the way out. After that I rebuilt all 8 agents around one rule: copy wallets only inside their best category and kill anything noisy. The result looked like this: 47 wallets ranked by exit quality. 191 trades. 73% win rate. Average hold: 4 hours. Capital velocity: 47x. Max drawdown: -2.1%. Sector breakdown: > Crypto: +$3,740 > Weather: +$2,910 > Politics: +$1,850 > Macro: +$1,200 > Sports: -$300 I killed sports after day two. That is the whole joke. Most Polymarket traders think the money is in predicting the result. It is not. The money is in finding people who know when to leave. Copytrade for those who don't want to build: Public repos. Cheap infra. Real edge.show more

This is f*cking insane. This tip saved me thousands... of dollars. run Opus 5.5, Sonnet 5.5, and Fable 5.1 together, and stop burning Opus on work it was never needed for. the whole idea in one line: the strong model plans, the mid-tier model executes, Fable stays quiet until it's actually needed. roles, broken down: Opus 5.5, high effort, owns the plan and ships the final code Sonnet 5.5, medium effort, splits into explorer (reads the codebase), worker (edits files, runs tests), researcher (pulls docs) Fable 5.1, called through /advisor fable, reads everything happening in the session but stays silent unless something's actually wrong three moments where Fable speaks: → a plan goes out: is this actually the right call? → the same failure shows up again: is the search going nowhere? → the task gets marked finished: did something get skipped? Jev engineering does the same thing one level down. the forks that don't need real thought, which file, which tool, keep going or stop, go straight to Jev and come back in under half a second. the big models only ever see the forks that genuinely need a decision. anyone still running one model for everything is paying Opus prices to decide whether a file exists. drop this into Claude Code: "Rebuild my Claude Code setup around this structure: Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: sonnet, effort: medium on each. If an existing subagent is locked to a different model, leave it as is and just list it. In ~/.claude/settings.json, set effortLevel to high and advisorModel to fable. Check for anything disabling the advisor, CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches, plus CLAUDE_CODE_EFFORT_LEVEL, which can override subagent effort settings. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: check in with the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."show more

I just went through Anthropic’s threat report & woah!... This is genuinely the craziest article I’ve read all month. They documented hackers, governments, scammers and Chinese AI labs all using Claude in completely different ways. & some of the cases are insane. Here’s a TLDR; ⟣ A suspected Russian state linked group used Claude across phishing, intrusion, data theft and malware development, including rebuilding malware after security products detected it. According to the article, more than 20 organizations were targeted. ⟣ ShinyHunters-linked hackers used AI agents to scan 1.8M Android apps for exposed secrets and help run breaches across multiple companies. Anthropic says the agents did nearly all the work in some operations. ⟣ A China-based group built an automated exploit setup that could research vulnerabilities, build offensive tools and keep working against targets with little to no supervision. ⟣ Claude was also being used for government surveillance. One consultant used it to build Lakana 360 for Mali’s intelligence service, a system designed to monitor roughly 25M SIM cards across the country, including calls, messages, voice interception, watchlists and automated intelligence dossiers. The finished system runs locally, so even if anthropic bans the account, it won’t shut it down. ⟣ Anthropic found Claude being used across six weapons programs. One Yemen-based group used multiple Claude Code instances while working on guided rockets, ballistic missiles and a hypersonic-glide project. They actually tested one of the rockets, it failed, and they returned to Claude afterwards to diagnose the problem. ⟣ A Russia-based team was working on autonomous FPV kamikaze drones capable of identifying target classes, including humans, and approving lethal engagement without a human making the final call. ⟣ One China-linked project built electronic-warfare and air-defense suppression systems, then later changed its scenario to 12 targets in Taiwan, including radar sites, air bases and command infrastructure. ⟣ Anthropic found multiple influence operations too, including fake news networks, fake political accounts, propaganda operations and systems built to copy the writing style of real people. ⟣ Then there’s this fucking dating operation. More than 20 dating apps. > 4,700+ AI personas. > 25,000+ people talking to them. > Around 2.36M Claude messages in two weeks. And when someone wanted a video call or social follow, real gig workers could step in to make the fake profile look legit. ⟣ Then Anthropic gets into distillation. They say they’ve now caught campaigns from Alibaba, Moonshot, DeepSeek, Xiaomi, SenseTime and MiniMax. Alibaba alone allegedly ran 151M+ Claude exchanges between May and July, peaking at almost 3M per day. (I just wrote a piece on distillation before this) > Moonshot: 23M+. > DeepSeek: 12.1M+ in 14 days. And this is where it gets messy. Anthropic says Moonshot and DeepSeek were sometimes routing requests from their own users through Claude without telling them. Those requests included internal company documents, source code, live credentials, surveillance data and other sensitive information. ⟣ Some labs were also actively trying to extract Claude’s hidden reasoning. Anthropic says one lab tested 12,000+ different requests, each trying a different method, until it found techniques that worked and scaled them up. That’s the TLDR. Got me feeling like 👇show more

A lot of you asked “whazzit?” on our last... post. So… here’s the idea: Chastity should be more than an overpriced piece of plastic that ends up sitting on a shelf. Our patent-pending device coming soon knows when you’re getting a little too close to the limit your keyholder set and lets them know immediately. Even better, your keyholder can decide ahead of time how the device should respond, so it can correct you automatically in the moment. 😈 For the wearer, we wanted something you can actually live in. The open-face design stays discreet under clothing, while side capture holds the balls with less overall squeezing than a traditional cage. You choose your U-ring, cut the connecting cord/tube to your exact size, and get a genuinely custom fit. And here’s where it gets fun: The device watches the combination that's needed for orgasm... erection + rising heart rate. Morning wood? Your keyholder doesn’t need an alert. Working out while soft? Also not interesting. But getting hard while your heart rate starts climbing in just the right way? 👀 They’ll know. And if you get pissed off and take the whole thing off? They’ll know that too. Once it’s on, good luck hiding what you’re up to. 😈show more

Claude Code vs Blender is getting f*cking illegal Two... workflows building the same complex 3D rocket assembly, but one costs nearly 19x more in the demo And the difference reveals something most AI automation demos completely ignore Claude Code can reason through geometry, generate Python scripts, assemble mechanical components, and automate complex animations But every unnecessary tool call, failed generation, and repeated rebuild adds overhead that can make the final result ridiculously expensive Meanwhile, Blender can execute predefined procedural operations without paying for another round of AI reasoning Here’s where the workflows differ: Traditional Blender: MODEL individual rocket components POSITION mechanical structures and hinges CONFIGURE materials, lighting, and cameras ANIMATE deployable panels REFINE and reuse finished assets Claude Code + Blender Python: GENERATE geometry from written instructions ASSEMBLE mechanical components through scripts PARAMETERIZE dimensions and deployment angles AUTOMATE animation and camera movement REBUILD variations by changing code The interesting part isn’t which tool wins It’s knowing when AI reasoning is worth the cost and when a deterministic script can do the job Want to recreate the experiment? Open Claude Code with Blender installed and paste: “Create a procedural Blender Python generator for a detailed rocket assembly with deployable panels, animated hinges, and realistic metallic materials Separate geometry, materials, animation, and camera setup into reusable functions Expose all major design parameters, avoid rebuilding unchanged objects, and log execution time and available AI usage costs” Blender handles the 3D execution Claude Code makes the workflow programmable The real advantage isn’t using more AI It’s getting the same result without paying for unnecessary intelligence Bookmark this workflow before your next 3D projectshow more

Introducing a new tool called "SideChannel". A secure alternative... to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?".show more

Meta just shipped the official Ads CLI 🤯 The... first time you can plug Claude Code directly into your Meta ad account without a third-party connector or (allegedly) getting your account banned. Plug it into Claude Code and Claude can pull live performance data, build dashboards, and analyze creative fatigue, all from one prompt. Here's what's possible inside Claude Code: → Pull last 30 days of campaign performance into a styled HTML dashboard → Find every ad with frequency over 3.0 or CTR drops over 20% week-over-week → Generate weekly client reports written in your brand voice → Run anomaly detection on spend, CPM, and conversions → Build creative fatigue alerts that flag dying ads before CPAs blow up The biggest difference from every third-party Meta MCP that's been floating around: This one is built and maintained by Meta themselves. Most folks running Meta ads won't touch third-party connectors because the ban risk is real. The official CLI (mostly) eliminates that. It's your token, your app, your direct connection to Meta's API. I put together the complete playbook: → The exact 15-minute setup: Meta Developer App, system user, asset assignment, token generation → 20+ prompt templates for reporting, fatigue detection, opportunity scoring, and anomaly analysis → A weekly operating cadence: Monday performance pull, Wednesday creative review, Friday exec brief → Rate limit guardrails so you don't trip Meta's automated enforcement → The 5 prompts to run first Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)show more

An ex-Anthropic engineer stopped next to me at a... meetup in LV. I had my Polymarket terminal open. He watched the ladder for maybe fifteen seconds and said one sentence. "You're trying to predict the market. You should be measuring when it falls asleep" I asked what that was supposed to mean. He pulled my laptop closer. Opened one repository. 86 million trades. Every wallet. Every fill. Every close. The full Polymarket record since day one. "Stop reading posts. Read behavior. Ask one question - who makes money when liquidity disappears" I asked why he cared about that more than entries. He said the best wallets are not better at guessing. They are better at waiting. Then he had Claude work through the dataset. Find wallets with 70%+ win rate. More than 100 trades. Then isolate what they do between 02:30 and 04:00 UTC. That was the first useful query I had run in months. The pattern was obvious. Normal hours: tight book. noisy edge. Wolf Hour: wide book. lazy pricing. Typical spread: 2-3 cents. Dead hours: 8-10 cents. Same market. Different level of defense. I asked why Claude Code instead of just using chat. He laughed. "Because chat gives opinions. Claude Code goes into the repo, reads the structure, and actually works through the data" Then he opened the scanner. Three commands. 500+ markets live. Read-only. No API key. Claude built the overnight filter in about twenty minutes. Fair value gap > 8 cents. Spread blown out beyond normal. Resolution in 4-48 hours. No new trades unless the contract was pre-approved during liquid hours. Most markets died immediately. The few that survived were the only ones worth touching. One example. Fair value during the day: $0.51. Wolf Hour target: $0.41. At 3:12 UTC the ask printed there. Order in. London woke up. Same contract traded back around $0.50. Not prediction alpha. Structural mispricing. Then he said the line that made the whole thing click. "Good traders use AI to think faster. Great ones use it to stop themselves from trading until the market gets stupid" I rebuilt the stack that week. Daytime research. Night scanner. Morning exits. Four real entries a week was enough. About 9 cents blended edge. Roughly $9,360 a year on around $2,000 average deployed capital. No team. No fancy infra. No sitting there at 3 AM pretending discretion is a system. the edge was never hidden in better prompts. it was sitting in the one part of the night when the market stopped protecting its own prices.show more

THIS DEVELOPER OPENED HERMES ON A LAPTOP, SPENT $0... ON API SETUP, SAVED 7 WORKFLOWS, AND CUT 2-HOUR CLIENT TASKS DOWN TO 14 MINUTES he is not giving a polished demo. he is just filming the laptop while Hermes runs, and that is why the clip works. you can see the terminal, the workspace, the task history, and the moment a normal chat tool starts looking like a local operating layer most people still run AI like a vending machine: 1 prompt, 1 answer, 1 reset. Hermes is different. after 5-10 repeated jobs, the useful steps start living inside skills instead of getting rewritten every morning the money math is where it gets ugly. $20 for Claude, $40-90 in API usage, $50 for wrappers, $29 for automation tools, and you are already near $140-190/month before you even sell the first report he used the same flow for 9 small research tasks: 18 competitor pages, 126 review snippets, 9 pricing checks, 9 summary drafts. the first one took 43 minutes. later runs were mostly review, edit, send that is the part people miss about Hermes. it is not trying to win the prettiest chatbot contest. it is trying to make repeated work stop leaking out of the machine every time the session endsshow more

I went a little overboard with Codex last week... and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.show more

Claude Code Agent Teams are f*cking ridiculous 🤯 One... prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)show more

🚨 WARNING: SOMETHING VERY BAD JUST STARTED The Fed... is now projected to hike another 25 bps in October. Sit with that for a second. Months ago, 2026 was supposed to be the year of rate cuts. Now the market is bracing for another hike. And somehow stocks are still parked near all-time highs. None of this adds up: → Oil above $100. → Diesel near record highs. → Inflation still sticky. → Rates already restrictive. And now the Fed might tighten again. So why hasn't the market cracked yet? One thing is holding it up. The AI boom. And here's where almost everyone gets it wrong. AI isn't just pushing stocks higher. It's giving the Fed room to stay aggressive. Massive AI spending keeps growth alive while a handful of mega-caps carry the entire index. As long as that holds, the Fed has zero reason to back off. That's the trap: AI boom → growth stays strong → inflation stays sticky → Fed keeps rates high → another hike becomes possible. Now stack oil on top: $100+ crude → higher energy costs → more inflation pressure → even less room to cut. October or December, doesn't matter. The direction is what matters. Months ago it was cuts. Now it's no cuts, then a hike, then possibly another. The market survives all of this as long as AI keeps carrying it. The real problem starts when AI stops. If those names finally crack while rates are climbing and inflation is still hot, the market loses the one thing absorbing all that pressure. Then it moves fast: AI cracks → indexes fall → liquidity vanishes → forced selling starts. And once forced selling begins, funds don't sell what they want to sell. They sell what they can. Stocks. Metals. Bitcoin. Everything at once. That's the part nobody's prepared for. And that's exactly where the next real buying opportunity shows up. I'm not scared of the dump. I'm waiting for it. 15+ years trading, and the pattern never changes. When the liquidation hits and I see a level actually worth buying, I'll post it here like always. Turn notifications on. You'll want this chart later.show more

this is pure f*cking treasure. my ai bill last... month: claude max 20x ............ $200 chatgpt pro ............... $200 supergrok heavy ........... $300 total ..................... $700 then i went through github and found 5 repos that eat the boring half of that bill on my own machine rizzo-flow ................ 833 stars the open local take on typesafe's jev. a 4.4gb model on llama.cpp answers yes / no / pick one / score with a probability on every answer, and you point your api at localhost by changing one url llm2jev ................... 391 stars turns qwen3.5-4b into a jev-style decision model. 48ms p50 in their own benchmark, against 652ms for the real jev fast-browser-use .......... 202 stars a browser agent that runs 100% local on qwen3.5-9b and plugs into claude code and codex as a skill. opens the right wikipedia article in about 4 seconds, zero cloud calls deepseekgui ............... 84 stars a desktop workbench on deepseek harness with git, a built-in browser and memory. you pay deepseek per token instead of a flat $200 a month oriveo .................... 25 stars one app for openai, anthropic, gemini, grok, deepseek and 10 more providers, plus ollama on your own gpu. your keys, no subscription, no account claude still writes my hardest code. the yes or no calls, the browser clicking and the everyday chatting moved to my gpu and my own api keys, and the receipt stopped looking like rent repos in the commentsshow more

🚨 SOMETHING VERY STRANGE IS HAPPENING Yesterday, the Fed... hiked rates by 25 bps for the first time in 3 years. Today, stocks are rallying like nothing happened. Something doesn't add up: 16 of 18 Fed policymakers expect at least ONE MORE HIKE this year. Just 9 months ago, markets were pricing in 3 RATE CUTS for 2026. Oil is above $100. Diesel prices just hit RECORD HIGHS. And the Fed now expects 3.7% inflation in 2026. But the market still hasn’t broken. WHY? Because one thing is keeping it alive: THE AI BOOM. And this is where almost everyone is getting it wrong. AI is not just holding the market up. IT IS GIVING THE FED ROOM TO KEEP HIKING. Massive AI spending is keeping growth alive while a handful of mega-cap stocks keep the indexes near the highs. As long as that continues, the Fed has less reason to back off. That creates a dangerous setup: AI boom → stronger growth → sticky inflation → higher rates for longer Now add: $100+ oil → record diesel → higher costs across the economy → even less room for cuts October or December doesn’t matter. Nine months ago: 3 CUTS. Today: HIKE → ANOTHER HIKE. The market can survive that while AI keeps carrying it. The problem starts when AI stops. If those stocks finally crack while rates are still rising and energy inflation is still hot, the market loses the one thing absorbing all that pressure. Then it gets ugly fast: AI cracks → indexes fall → liquidity disappears → forced selling begins And once forced selling starts, funds don’t sell what they WANT. They sell what they CAN. Stocks. Metals. Bitcoin. That’s the part most people are not prepared for. And that’s exactly where the next real buying opportunity appears. I’m not afraid of the dump. I’M WAITING FOR IT. Remember, I’ve been trading markets for over 15 years. When the liquidation starts and I see the level worth buying, I’ll post it here publicly like I always do. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.show more

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt... → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)show more

By tomorrow, everyone will be using this: Anthropic just... revealed the ultimate power move for Claude Code The concept: have Opus 5.5 design the master plan as a script, then deploy parallel swarms of the new Haiku 5.5 Massive scale operations with zero wasted Opus compute: the script manages the execution loop so Opus only reviews the final diff Simply boot up Claude Code with these parameters: claude --model opus --effort ultracode Here is the task distribution: -> Opus 5.5 translates your target into a fully runnable workflow script -> Haiku 5.5 scans all files concurrently at 340 tokens/sec to spot candidates -> Sonnet 5.5 applies each patch and validates findings within an isolated workspace -> You eliminate wasting Opus context on dozens of agent log transcripts Opus is involved at only three specific steps: -> writing the initial workflow script before execution -> analyzing verified results once the swarm wraps up -> giving final approval prior to committing any code to git Opus architects | Haiku audits | Sonnet delivers JEV engineering applies the exact same principle a level deeper: logic paths that require no deep reasoning (choosing files, picking tools, handling retries/stops) pass through JEV in milliseconds, leaving heavy models to focus only on complex branches The complete architecture: Opus 5.5 orchestrates the core session and generates workflow.js pipeline() fans out instances of Haiku 5.5 to inspect every file parallel() schema validators eliminate false alarms directly in V8 Sonnet 5.5 applies fixes within dedicated, isolated worktrees verify double-checks all code diffs prior to summary generation Opus 5.5 processes exclusively the results that survived validation JEV routes lightweight micro-decisions in milliseconds Feed this architecture and prompt directly into Claude Code Reconfigure my Claude Code setup following this tree: 1. Execute the primary session using Opus 5.5 with ultracode enabled 2. Assign read-only scanning phases to Haiku 5.5 across all workflows, while routing code modification steps to Sonnet 5.5, running each edit inside its own isolated copy 3. Update CLAUDE.md with a new rule: assign a separate verifier agent to every issue before reporting, and terminate fix loops after two iterations without progress 4. Route fast, deterministic branching (tool selection, path routing, retry logic) via JEV Present every modification as a diff upfront do not execute changes until approved Bookmark this to stay one step aheadshow more