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Sprytix

@Sprytixl • 8,997 subscribers

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14 year old student from China ordered a Raspberry Pi Zero for $4, a PiSugar battery for $3, and a small 3.5" screen for $6. $13 total and a package from AliExpress. He soldered it together himself, installed Ollama, and loaded Gemma 2b - a model that runs entirely offline. No servers, no subscriptions, no API keys. Then he used that device to break into the GTA VI pre-alpha build. The one Rockstar keeps locked away from everyone. The debug console appeared on screen. Lucia's inventory. Water physics parameters. Things only the developers were supposed to see. Rockstar spent 10 years and keeps a team of 2,000 engineers to protect that build. The student spent $13 and a few evenings

14 year old student from China ordered a Raspberry Pi Zero for $4, a PiSugar battery for $3, and a small 3.5" screen for $6. $13 total and a package from AliExpress. He soldered it together himself, installed Ollama, and loaded Gemma 2b - a model that runs entirely offline. No servers, no subscriptions, no API keys. Then he used that device to break into the GTA VI pre-alpha build. The one Rockstar keeps locked away from everyone. The debug console appeared on screen. Lucia's inventory. Water physics parameters. Things only the developers were supposed to see. Rockstar spent 10 years and keeps a team of 2,000 engineers to protect that build. The student spent $13 and a few evenings

4,562,133 Aufrufe

A 14-year-old American girl bought a house in cash at 16 while her classmates were taking exams. It started with one Google Maps search - she found an HVAC company with 4.8 stars, 280 reviews and a website too embarrassing to show clients. Copied their data into ChatGPT 5.5 - 2 minutes later a complete brief, an hour later a live site with booking and a reviews section. Called the owner and showed him the preview. He signed the $900 invoice the same day. Then she stopped looking for clients by hand. Outscraper pulls 200 businesses from Google Maps in 10 minutes - name, email, rating, review count. ChatGPT 5.5 writes a personalized email for each one with their real business data. 500 emails a day, 3% respond, 10% of those become clients. Month one - $3,800. Month six - $22,000. Two years - $380,000 and a house. Five million businesses on Google Maps are still waiting for that call.

A 14-year-old American girl bought a house in cash at 16 while her classmates were taking exams. It started with one Google Maps search - she found an HVAC company with 4.8 stars, 280 reviews and a website too embarrassing to show clients. Copied their data into ChatGPT 5.5 - 2 minutes later a complete brief, an hour later a live site with booking and a reviews section. Called the owner and showed him the preview. He signed the $900 invoice the same day. Then she stopped looking for clients by hand. Outscraper pulls 200 businesses from Google Maps in 10 minutes - name, email, rating, review count. ChatGPT 5.5 writes a personalized email for each one with their real business data. 500 emails a day, 3% respond, 10% of those become clients. Month one - $3,800. Month six - $22,000. Two years - $380,000 and a house. Five million businesses on Google Maps are still waiting for that call.

1,064,541 Aufrufe

THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code: one model plans, a swarm executes, and nobody waits for anybody Opus 5.5 reads the task once, cuts it into 12 parallel lanes and then stops spending tokens entirely Haiku 4.5 takes everything mechanical, Sonnet 5.5 takes the few pieces that actually need judgment most setups die because the expensive model watches the work happen, and 12 agent transcripts in its context is how $12 disappears on a single task here the planner never reads raw output, every agent reports one line and the whole session stays under 9k of context a hard spend cap sits on the run, so one stuck agent cannot quietly eat the budget nothing gets reported until it passes its own check, and anything that fails re-runs in its own lane instead of landing back on the planner the planner comes back once at the end, reads 12 verified results and signs off, 2 minutes and 28 cents for the whole thing save it, this is the difference between burning your budget and finishing the job

THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code: one model plans, a swarm executes, and nobody waits for anybody Opus 5.5 reads the task once, cuts it into 12 parallel lanes and then stops spending tokens entirely Haiku 4.5 takes everything mechanical, Sonnet 5.5 takes the few pieces that actually need judgment most setups die because the expensive model watches the work happen, and 12 agent transcripts in its context is how $12 disappears on a single task here the planner never reads raw output, every agent reports one line and the whole session stays under 9k of context a hard spend cap sits on the run, so one stuck agent cannot quietly eat the budget nothing gets reported until it passes its own check, and anything that fails re-runs in its own lane instead of landing back on the planner the planner comes back once at the end, reads 12 verified results and signs off, 2 minutes and 28 cents for the whole thing save it, this is the difference between burning your budget and finishing the job

13,966 Aufrufe

14-year-old French girl made $1,500 in 7 minutes using ChatGPT 5.5 and didn't say a single word. Google Maps, "HVAC near me," a company with a 4.7 rating and a website from 2011. Reviews and services - into ChatGPT 5.5, a complete brief in 2 minutes, Lovable built the site in 5 minutes. Sent a link to the live preview. The owner bought it immediately. 5,000,000 businesses on Google Maps still without a proper website - one HVAC job pays $800, roofing $5,000-15,000, and they're all already paying clients to competitors just because those competitors have a better site. Realistically $2,500-3,400 in the first month - zero calls, zero experience, zero team.

14-year-old French girl made $1,500 in 7 minutes using ChatGPT 5.5 and didn't say a single word. Google Maps, "HVAC near me," a company with a 4.7 rating and a website from 2011. Reviews and services - into ChatGPT 5.5, a complete brief in 2 minutes, Lovable built the site in 5 minutes. Sent a link to the live preview. The owner bought it immediately. 5,000,000 businesses on Google Maps still without a proper website - one HVAC job pays $800, roofing $5,000-15,000, and they're all already paying clients to competitors just because those competitors have a better site. Realistically $2,500-3,400 in the first month - zero calls, zero experience, zero team.

30,237 Aufrufe

Videos

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THIS IS ACTUAL F*CKING GOLD Anthropic just shared one of the best tricks for Claude Code. The idea: stop treating Claude like one worker. Make Opus 5.5 a commander and give it 12 agents in 4 squads. Same release, two ways: Opus alone was 2% through on one thread. The command center shipped the whole v2.0 in 2 minutes on 12 threads. You just launch Claude Code like this: claude --model opus --command-center 12 "ship v2.0" here's the split: → Opus 5.5 turns "ship v2.0" into 12 tasks across 4 squads → CODE and OPS squads run on Sonnet 5.5, TEST and DOCS on Haiku 5.5 → every agent reports back in one line, never raw logs → Opus only wakes up when something breaks or needs approval the part most people get wrong: the commander watches everything. read 12 agent transcripts and your expensive model becomes the slowest part of the room. here the commander stays at 8k of context the entire run. it plans, it waits, it reads 12 one-line reports, it opens the release gate. REA sits underneath on anything legacy: before a squad touches old code, REA reverse-engineers it into a spec, so agents build from the spec and never from 500 files nobody understands. Opus commands. Sonnet builds. Haiku checks. REA explains the old code. JEV engineering is the same move one layer down: the forks that need no thinker (which squad, which model, retry or stop) go to JEV in milliseconds, and the commander only sees the ones that actually split the full tree > Opus 5.5 runs as commander, splits the mission into tasks and squads > CODE squad on Sonnet 5.5 writes endpoints, refactors auth, migrates the db > TEST squad on Haiku 5.5 runs unit, e2e and load tests > DOCS squad on Haiku 5.5 writes changelog, api docs and release notes OPS squad on Sonnet 5.5 handles staging, health checks and canary rollout REA reverse-engineers any legacy module into a spec before a squad touches it every agent reports one line, the commander never reads raw output Opus 5.5 holds the release gate and waits for all 12 reports JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Run the main session on Opus 5.5 as a commander: it plans, assigns and approves, it never writes code. 2. Split work into 4 squads: CODE and OPS on Sonnet 5.5, TEST and DOCS on Haiku 5.5, 12 agents total. 3. Every agent reports a one-line structured result. Raw logs never enter the main session. 4. Before any squad touches legacy code, run REA on it first and have the squad build from the spec, not the old files. 5. Add one rule to CLAUDE.md: nothing ships until every squad has reported and the commander opens the release gate. 6. Route deterministic forks (squad choice, model choice, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ save it, you'll use this on every release from now on.

Sprytix

132,535 Aufrufe • vor 9 Tagen

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CHINESE CRYPTO TRADER POSTED A NEURAL NETWORK VISUALIZATION ON TIKTOK AND ACCIDENTALLY SHOWED THE SYSTEM MAKING HIS POLYMARKET TRADES FOR HIM IN REAL TIME Blue connection lines everywhere, hidden layers stacked vertically, neurons firing across the screen and a tiny label in the middle that most people ignored on the first watch - “Bitcoin XVIII”. He framed the video like a normal AI experiment. Virtual aquarium simulation. Reinforcement learning. “Teaching the network survival behavior.” That was the caption. Pause at 0:16 profil: The model was not learning fish behavior. The labels inside the hidden layer matched live Bitcoin prediction markets almost perfectly - price windows, directional probabilities and volatility ranges mapped directly into the network nodes while the simulation kept running in the background. Then people found the wallet. $367,385 profit in 30 days. 1,988 predictions. Biggest single win: $183,000. Almost every active position tied to Bitcoin range markets with entry prices between 94 and 98 cents - exactly the kind of low volatility spreads an automated system farms continuously without human input. The comments turned into a detective board within an hour. Someone slowed the TikTok to 0.25x, stitched together every visible frame of the neural network screen and started matching the hidden layer labels against the active Polymarket positions on the wallet. The timing matched too closely. While viewers thought they were watching an AI visualization, the model was quietly classifying live market conditions in the background and routing trades automatically into different probability buckets depending on short term BTC movement. The TikTok got 11K views. The repost showing the wallet crossed 600,000 overnight. By morning people were already cloning the interface, rebuilding the network layout and trying to figure out why almost every position on the account sat between 96 and 99 cents with unusually high sizing. The original creator deleted nothing. The wallet is still active. Fastest way to copy-trade:

Sprytix

2,285,921 Aufrufe • vor 5 Monaten

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THIS IS ACTUAL F*CKING GOLD Anthropic just shared one of the best tricks for Claude Code. The idea: stop configuring Claude Code by hand. One prompt, 44 seconds, and your whole .claude/ folder writes itself. 7 files, 162 lines, 2 cents. you typed one line and wrote zero of them. You just launch Claude Code like this: claude --model opus "set up this repo: ..." here's the split: → 3 Haiku 5.5 agents scan the repo before a single file gets written → one reads the stack, one reads the npm scripts, one hunts for risky paths → Opus 5.5 takes those three summaries and writes the actual config → 7 files land: CLAUDE.md, 3 agents, permissions, a format hook and a /ship skill the part everyone gets wrong: they write CLAUDE.md from memory. a config written without reading the repo is a guess, and you live with that guess for months. here the risk scan came back with 5 dangerous paths and those went straight into the permissions file, so the agent is blocked from them before it ever runs once. the scripts scan found 5 npm scripts, and the agents got wired to the ones that actually exist in this repo instead of generic placeholders. one prompt from you, 7 files from it, 0 lines written by hand. Haiku reads. Opus writes. You commit. JEV engineering is the same move one layer down: 1,867 forks this run - which file, which agent, allow or ask - resolved in code at 0.97 sharpness, and the split ones (0.59 on "allow or ask") come back to you instead of being guessed the full tree ▸ you paste one prompt describing the setup you want ▸ 3 Haiku 5.5 agents scan stack, scripts and risky paths in parallel ▸ every agent returns a summary, never raw files ▸ Opus 5.5 writes CLAUDE.md, the agents, permissions, the hook and the skill ▸ anything touching permissions comes back to you before it is written ▸ JEV resolves micro-forks in code and escalates only the split ones paste the tree and this prompt into Claude Code ↓ "Set up this repo for me: 1. First scan it with 3 Haiku 5.5 agents in parallel: one reads the stack and manifests, one reads the build and npm scripts, one finds paths that are dangerous to touch. 2. Each agent returns a summary only, never raw file contents. 3. Then write CLAUDE.md, 3 subagents, a permissions file, a format hook and a /ship skill, all based on what the scan actually found, not on defaults. 4. Every risky path from the scan goes into the deny list before anything runs. 5. Ask me before writing anything that grants permissions. Show me every file as a diff first. No writes until I say go." ↳ save it, this is the first thing you run in every new repo.

Sprytix

24,378 Aufrufe • vor 4 Tagen

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THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: keep one expensive brain and make everything else cheap. Opus 5.5 thinks, Haiku 5.5 does the grunt work, and your bill drops 79%. Same task, two ways: all-Opus would have cost $1.13. This run cost $0.23. You just launch Claude Code like this: claude --model opus # agents: model: haiku here's the split: → Opus 5.5 plans, decides and reviews, and burns 40k tokens for $0.21 → 12 Haiku 5.5 agents fan out across read, grep and test lanes → those 12 agents chew through 180k tokens for $0.02 → Opus never reads raw agent output, only 3 summaries at 6k tokens the part most people get wrong: they let the expensive model do the reading. Haiku is 40x cheaper per token. mapping 48 files cost $0.009. doing that on Opus is where the whole budget goes. one catch worth knowing: past 100k of context Haiku jumps to 5x its own price, so every agent is kept under 100k on purpose. that is the whole design. the brain stays small and expensive, the hands stay huge and cheap, and nothing raw ever crosses between them. Opus plans. Haiku reads. Opus reviews. JEV engineering is the same move one layer down: 1,301 forks this run - which file, which agent, retry or stop - resolved in code at 0.90 sharpness, and only the split ones (0.48 on "which agent") ever reach Opus the full tree > Opus 5.5 runs the main session, plans the lanes and decides what happens next > 12 Haiku 5.5 agents fan out across read, grep and test, each kept under 100k context > every agent returns a summary, never raw files or logs > Opus 5.5 reads 3 summaries at 6k tokens and reviews the result > JEV resolves 1,301 micro-forks in code and escalates only the split ones paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Run the main session on Opus 5.5 for planning, decisions and review only. 2. Set every subagent to Haiku 5.5 and fan the grunt work out across read, grep and test lanes. 3. Keep each agent under 100k of context, split the work further if it goes over. 4. Agents return summaries only, raw files and logs never enter the main session. 5. Add one rule to CLAUDE.md: if a task is reading, searching or running tests, it never runs on Opus. Route deterministic forks (file choice, agent choice, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ save it, it'll come in handy.

Sprytix

17,928 Aufrufe • vor 5 Tagen

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THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: never let build logs touch your main session. One tiny agent runs the build and hands back 3 lines. Same build, two ways: inline it is 40k tokens and $0.09 every single run. Through the builder it is 3 lines and nothing. You just launch Claude Code like this: claude --model sonnet # builder.md: model: haiku here's the split: → Sonnet 5.5 edits code in the main session and never sees a log → a Haiku 5.5 builder runs npm build, tsc and the test suite → it reads all 2k log lines, 227 files and 18 tests itself → it returns 3 lines: what ran, what failed, where the part everyone gets wrong: they run the build in the main session. 40k tokens of log noise per run, and every later message in that session carries it. you pay for that log again on every turn until the context resets. the builder agent is 10 lines of markdown. name, model haiku, tools Bash, and one rule: run the build, fix nothing, return 3 lines. that "fix nothing" is the whole trick. the cheap agent never tries to be clever, it just reports, and the lead decides what to do. one catch worth knowing: past 100k of context Haiku jumps to 5x its own price, so the builder is kept deliberately small. main context grows by +0 instead of +40k. that is the entire win. Sonnet edits. Haiku builds. The log never leaves the agent. JEV engineering is the same move one layer down: 1,301 forks this run - which agent, retry or stop, fix or report - resolved in code at 0.90 sharpness, and only the split ones ever reach Sonnet the full tree ▸ Sonnet 5.5 runs the main session and edits code only ▸ a Haiku 5.5 builder agent runs build, typecheck and tests ▸ the builder reads every log line and returns 3 lines, never the log ▸ the builder fixes nothing, it only reports ▸ Sonnet 5.5 reads those 3 lines and decides the next edit ▸ JEV resolves micro-forks in code and escalates only the split ones paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Run the main session on Sonnet 5.5 for editing code only. 2. Create a builder subagent with model: haiku and tools: Bash. 3. The builder runs build, typecheck and tests, reads all output itself, and returns at most 3 lines: what ran, what failed, where. 4. The builder never fixes anything, it only reports. 5. Add one rule to CLAUDE.md: build output never enters the main session, no exceptions. 6. Route deterministic forks (which agent, retry or stop, fix or report) through JEV. Show me every change as a diff first. No edits until I say go." ↳ save it, it'll come in handy.

Sprytix

10,928 Aufrufe • vor 4 Tagen

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THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: let Opus 5.5 write the plan as a script, and let 12 cheap agents run it while Opus sits idle at zero tokens. Same task, two ways: Opus solo was 49% done and still running. The swarm finished in 9 minutes on 165k tokens. You just launch Claude Code like this: claude --model opus --swarm haiku,sonnet here's the split: → Opus 5.5 writes plan.js, then stops reading anything → 6 Haiku 5.5 agents audit every file in parallel → 4 Sonnet 5.5 agents patch each fix in its own isolated copy → 2 Haiku 5.5 agents cross-check, 8 of 9 findings survive Opus only shows up at two moments: → writing the plan before the run → signing off on the verified fixes after it Opus plans. Haiku scans. Sonnet ships. JEV engineering is the same move one layer down: 3,767 forks this run - which model, which lane, retry or stop - resolved in code, and only the genuinely split ones ever reach Opus the full tree > Opus 5.5 runs the main session on xhigh and writes plan.js > scan audits each file on Haiku 5.5, six in parallel > patch applies each fix on Sonnet 5.5 in its own isolated copy > verify cross-checks every finding on Haiku 5.5 > Opus 5.5 reads only what survived, then signs off > JEV resolves micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: Run the main session on Opus 5.5 with effort xhigh and swarm haiku,sonnet. Have Opus write the plan as a script: scan on Haiku 5.5, patch on Sonnet 5.5 in isolated copies, verify on Haiku 5.5. Let the phases overlap - each finding moves on as soon as its own stage finishes, no waiting for the batch. Never surface raw agent output to the main session, only verified results. Route deterministic forks (model choice, lane choice, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ save it, it’ll come in handy

Sprytix

19,727 Aufrufe • vor 10 Tagen