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

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

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

An Anthropic engineer paid for my espresso at Sightglass... when he saw my screen I was running my Polymarket bot from the counter. He was next in line. Looked over my shoulder. Stopped scrolling. "That's not a normal trading app. What's it actually running on" I told him. Claude Code. Four repos. $25 a month. He sat down without asking. "I'm on the agent team. We stress test Claude for exactly this. You're letting it find its own edges" Not just edges. Wallets. 86 million trades. Every wallet. Every entry. Every exit. "You're feeding Claude raw wallet data and letting it identify who consistently wins. Then cloning them" He said it slowly. Like he was writing the threat model in his head. One prompt. Find every wallet with 100 plus trades and win rate above 70%. Rank by profit. Export top 50. Claude scanned 14,000 wallets in 4 minutes. Returned 47. The top 20 made more than the bottom 13,000 combined. "That's not a stat. That's a hit list" Exactly. "And you didn't write the scoring function" Claude did. I just wired it into an if-statement. Then I showed him the second repo. Official Rust CLI. No API key for reads. 500 markets, Claude scores them in minutes. Gap. Depth. Resolution window. 487 markets become 35 before a dollar moves. 93% killed before I even see them. A green fill landed on the screen. +$84. Copytrade wallet: He watched it hit. "How does it decide to actually enter" Three agents. Shared wallet. No shared memory. Arbitrage, convergence, whale copy. 2 agree, full size. 1 alone, half. Disagree, no trade. Consensus filter alone killed 40% of losing trades. "And the exits?" The 47 whales never hold to settlement. 91% exit early. 73% of max profit captured. Redeploy immediately. My bot cuts at 85% of expected move or on a 3x volume spike. "You built a whale copy bot that exits before the whales" Yeah. He put his espresso down. "How often does it trade" 10 a day on average. Most of them skipped before I look up from my coffee. My setup: Claude API - $20/mo VPS in Germany - $5/mo poly_data - free polymarket-cli - free Polymarket/agents - free $200 seed. 27 days ago. $14,300 now. Copytrade here: 271 trades. 74% win rate. Sharpe 2.47. I haven't touched it in 27 days. He stared at the screen for a long time. "This is literally what our red team simulates. Except you actually shipped it" He emailed me the next morning. "Any chance you'd take a call with our policy lead" I told him the article is the call. Read it twice. Too late to gatekeep.show more

🚨12 HOUR NEWS RECAP 1. Trump met the Team... USA gold medalists and joked with the goalie, dubbing him “the new Secretary of Defense” on camera. 2. Sec. Hegseth reportedly gave Anthropic CEO Dario Amodei until Friday to drop heavy safeguards on Claude for military use, with the Pentagon pushing for full unrestricted access. 3. Senate Democrats again blocked DHS funding, leaving agencies like TSA and the Coast Guard in limbo amid ongoing disputes over immigration tactics. 4. Trump just finished the longest State of the Union in American history, 1 hour 48 minutes, breaking the official record. 5. Los Angeles schools are borrowing another $250 million to settle sexual misconduct claims, adding to the $500 million already spent last year, a total settlement approaching three-quarters of a billion dollars. 6. Germany instructed its citizens in Israel to prepare for a possible U.S. strike on Iran, warning that if Iran retaliates and closes airspace, Germans may need to stay indoors for an extended period. 7. In the State of the Union, Trump took aim at Big Pharma: saying he is ending the inflated cost of prescription drugs. Other presidents tried but success has remained elusive. 8. An American Airlines 737 was found with an apparent bullet hole in is wing after landing in Medellín, Colombia. 9. Anti-ICE activists have flooded Minneapolis City Hall, occupying government buildings amid protests against federal immigration enforcement. 10. El Mencho, the most wanted cartel leader in Mexico, was reportedly found hiding at the Tapalpa Country Club, an ultra-exclusive retreat frequented by Guadalajara’s elite.show more

An Anthropic engineer watched my screen from the next... table at a cafe in SF. "Are you running Claude against live prediction markets right now" I told him yes. Then I showed him the stack. 214 trades. 74% win rate. +$9,437 in 19 days. Here's what actually happened: I gave Claude two repos and a simple job. Three commands. 500+ markets. No API key. Just a clean way to score the board fast. The system does not try to predict the world. It tries to find which wallets consistently exit better than the crowd, isolate the pattern, and only fire when the same structure shows up again. Main filter: captured value / expected value > 0.70 If a wallet wins often but leaks the move on exit, it gets ignored. If it captures most of the move and cuts losers fast, it becomes signal. Sizing uses Kelly: f* = (p*b - q) / b That is what stops the terminal from apeing into weak edges. Most of the time it does nothing. No edge - no position. Three trades from the run: > AMD Xilinx - entered 52c. Model said 59c. Closed +7c in 2h40m. > Artemis launch - entered 63c. Model said 85c. Closed +22c in 5h10m. > Derecho MW - entered 71c. Model said 87c. Closed +16c in 1h50m. When he saw the repo links and the live terminal, he stopped talking for a second. Then he said: "We tested something close to this internally." That was the whole joke. The data is public. The repos are public. The market is public. But most Polymarket traders still trade headlines, hold too long, and call it conviction. Polymarket does not reward the smartest story. It rewards the cleaner exit. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'CLAUDE' 2. Like and Retweet this post 3. Follow me Marry Evan (so i can DM you)show more

BREAKING: Anthropic just dropped Opus 4.8—and it is a... MONSTER We've been testing for about a week Every 🪨 and our verdict is they could've just called it Opus 5, it's that good. Here's our vibe check: - Beats GPT-5.5 on Senior Engineer bench. On our toughest benchmark Opus 4.8 scores a 63—a hair higher than GPT-5.5's score of 62, and a full 30 points higher than Opus 4.7. It tackled a ground-up rewrite of a production codebase, and actually built something that works. HOWEVER: Coding performance varied a lot at different reasoning levels. We recommend using it on xhigh for best results. - Incredibly good writer. Opus 4.8 scored a 79.6 on our writing benchmark—measuring models on real-world writing tasks we do all of the time like essay writing, promo email writing, and more. It beats GPT-5.5 by 6 points. It produces well-written prose with fewer "AI-isms". It's also very good at writing in your voice given the right context. HOWEVER: Writing performance also varied with reasoning levels. Medium reasoning had higher incidence of AI-isms—we found best results with high. - Beast at knowledge work. Opus 4.8 is very good at general knowledge work tasks like report creation, research and more. It produced the best PowerPoint one-shot we've ever seen on our deck generation benchmark. - Emotionally intelligent, willing to question the frame. I've also found it to be quite good at talking through psychological or interpersonal issues. It has a high EQ, and it's also good at not glazing and helping to expand your perspective. Its thought process feels extremely rich and dynamic. THE BAD: These days a model is only as good as its harness, and Codex is still a far superior harness to the Claude Desktop app. This has kept me using Codex + GPT-5.5 as my daily driver, but I am flipping back and forth a lot more between Codex and Claude. Anthropic is back baby! Read the rest on Every 🪨:show more

This Claude Code Skills Pack is a cheat code... for ad creative teams 🤯 10 plug-and-play skills → competitor audits, creative briefs, 20 hook variations, ad copy, static ads, landing pages, & weekly performance reports. All inside Claude Code. Perfect for DTC brands and agencies who are still prompting Claude Code from scratch every time. If you're re-explaining your brand voice in every session, getting inconsistent output depending on who's prompting, and spending 30 minutes on tasks that should take 30 seconds... These skills eliminate the entire loop: → Competitor Ad Research Agent Drop a brand name, get back a full creative audit — hooks, messaging angles, ad formats, CTAs, and "steal this" angles. No more scrolling the Ad Library for an hour. → Creative Brief Generator One prompt, complete brief in your exact template. Hooks, concepts, visual direction, brand voice — all loaded from your own files. → Hook & Script Writer 15+ hooks categorized by type (curiosity, problem-agitation, result-first, social proof). Full 30-60s scripts with the hook → problem → mechanism → proof → CTA structure baked in. → Ad Copy Variation Engine Feed it one winning ad, get back 20 variations — each targeting a different persona and pain point. Same structure, different angles. Creative fatigue solved. → Weekly Report Writer Drop in your Meta ads CSV. Get back the narrative summary, anomaly flags, creative fatigue alerts, and recommended next steps. The report nobody wants to write, written in 60 seconds. → Creative Fatigue Detector Flags ads before they die. CTR trending down, frequency climbing, conversion rate dropping — caught in hours, not after three days of wasted spend. No prompting from scratch every time. No inconsistent output across your team. No re-explaining context in every session. I packaged all 10 as a free Skills Pack. Copy-paste the files into your Claude Code commands folder and they just work. Want the full Skills Pack? > Like this post > Comment "SKILLS" And I'll send it over (must be following so I can DM)show more

An Anthropic paid for my espresso at Sightglass when... he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.show more

I just vibe coded a Meta Ads creative analytics... tool in Claude Code 🤯 It plugs into your ad accounts, AI-analyzes every creative you've ever run, and tells you exactly what's working, what isn't, and WHY. Built 100% in Claude Code. Perfect for DTC brands and creative agencies who are sick of staring at Ads Manager trying to reverse-engineer why one ad scaled and another tanked. If you're pulling weekly reports that show you spend, ROAS, CTR, and hook rate but never tell you WHY any of it is happening — and you're stuck watching videos one by one, guessing at angles, and making kill/scale calls on gut feel... This tool runs the entire loop for you: → Connect your Meta ad accounts in one click → AI watches every video and analyzes every static → Auto-labels each ad by asset type, messaging angle, hook tactic, and funnel stage → Win rate analysis broken down by every category → Kill/scale recommendations segmented by TOF, MOF, and BOF → AI-generated iteration recommendations for every underperformer No manual video watching. No guessing at what's working. No spreadsheets to track creative performance. What you get: - A full creative analytics dashboard pulling live from your accounts - AI classification on every ad you've ever run - Iteration priorities ranked by ads with real spend behind them - Weekly reports surfacing top and bottom performers with AI insights Built 100% in Claude Code as a real tool, not a one-off script. I recorded a full walkthrough showing exactly how this works and what every feature does, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)show more

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE... 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5show more

Instagram Reels. 22 years old. Lamborghini. Penthouse. Watches worth... more than most cars. 4.3M views. Every comment asking the same thing Parents money? He never answered. Just pinned one comment with a wallet address: 0x93C22116E4402C9. I clicked thinking it's some scam token. Instead I found $382,997.94. Made in 90 days. From $500. → Wallet: 11,326 trades. Almost all green. Biggest single position: $15,400. Currently active: $91,000. This person does not trade Bitcoin direction. They trade crowd panic. Here is the actual strategy. Polymarket has YES and NO on every event. Prices should add up to $1. Basic math. But when fear hits, math breaks. News drops. One side spikes to 62 cents. Other crashes to 33 cents. Together: 95 cents. But one outcome MUST pay $1. Guaranteed. This wallet waits for these exact moments. Buys both sides. Spends 95 cents. Gets $1 back. Keeps 5 cents. Zero risk. Sounds small until you see the volume: 300 trades daily x 5 cents = $15 minimum per day. When real panic hits? Spreads blow to 10-15 cents. That's $3,000+ in one session. No prediction needed. No insider info. Just math and patience. While everyone was commenting daddy's money? on his Reels, he was collecting 5 cents at a time from crowd mistakes. $383K in three months. The Lamborghini is real. The strategy is simpler than you think. Markets panic every single day. Math errors happen every single hour.show more

Since TermMax V2 rolled out the new Roll feature,... I’ve been thinking DeFi lending is finally getting serious about managing time. The worst part of fixed-rate positions was never opening them—it was those brutal few days before expiry. You’re stuck in meetings all day, topping up margin at night, jumping chains for liquidity at 3 a.m., watching rates while praying nothing blows up. A lot of positions didn’t die from volatility; they died right there in that 48-hour window. When I saw what TermMax | Fixed Rate Borrowing & Lending just shipped, my first reaction was that on-chain borrowing finally feels like actual debt management. Besides straight repayment, you can now roll your position two ways: straight into a new fixed-rate term market to lock the rate again, or over to Morpho’s floating market if you want flexibility. A lot of people are calling it “just rolling over,” but it’s really changing how we handle time. Fixed rates used to lock the interest but left time broken—expiry hit and you had to decide everything from scratch again. The real stress wasn’t the APR; it was the panic questions like “what if I don’t have cash that day” or “what if the market flips.” V2 stitches that gap shut. Inside the rollover pop-up you pick the next term—like USDC/wstETH to 30SEP2026—and you see the APY instantly. The real win isn’t the yield; it’s finally being able to plan your next cash flow ahead of time. If you want stability, rolling to the next fixed market is like building your own debt calendar—next due date, cost of funds, everything crystal clear so you don’t scramble at the last second. Want to keep options open? Flip to Morpho and stay flexible if rates move. That’s what makes this update feel mature. It doesn’t decide for you—it hands the duration choice back to the user. The Maturity Watch plus the unified Positions view is the most underrated detail. Expiry pressure used to hit like an alarm clock out of nowhere; now you can actually see your full funding timeline. Lately the community can’t stop talking about “control.” XHUNT’s last 7-day stats show TermMax sitting at 86.7% positive sentiment. People aren’t just chasing APY anymore—they’re praising the certainty of fixed rates, the clean dashboard, Range Orders, and that new feeling of not having to put out fires at the last minute. This shift is bigger than it looks. Most on-chain users used to live in the “today” lane—what’s pumping, what’s the rate, any quick moves? Now with Roll, some are already thinking three months out. That’s not a trading habit anymore; it’s turning into a real money habit. Sure, it’s not perfect yet. We still need more real-world rollover data, rates will keep moving, and there are edge cases like zero-debt positions that can’t roll. The TGE delay frustration is real too, but that’s separate from the product itself. Still, this V2 Roll just turned DeFi’s most ignored stress—from pure expiry panic into something you can actually schedule. With DeFi rotating hard and Bitcoin pulling back a bit, people are craving exactly this kind of certainty. Once users start managing the future properly, fixed-rate lending finally starts feeling like a real credit market. Have you noticed? A lot of us aren’t just asking “is the APY good?” anymore. We’re asking whether this money will still fit in our plans when it comes due.show more