your opus context is too expensive for this sh*t stop filling it with transcripts from agents doing repetitive work claude code workflows let a script coordinate the run instead of making the main model babysit every step here's the setup i'd use: → opus designs the workflow → haiku handles read-only discovery → sonnet makes scoped changes → a separate verifier checks the evidence → opus reviews what actually survived the model split is something you configure, not a default to assume start here: claude --model opus --effort ultracode the important change isn't “spawn more agents” it's moving the execution loop into code the workflow holds the intermediate results the main session gets the useful outcome instead of every worker's running commentary for a large repo, structure the work like this: 1. discover the files in scope 2. scan them for a specific class of issue 3. collect findings with paths and evidence 4. reject duplicates and unsupported claims 5. patch confirmed issues in isolated worktrees 6. independently verify the changes 7. review the final diff before committing opus shouldn't need to reread 50 conversations to decide whether one patch is ready and if JEV is already part of your stack, keep it on the lightweight routing decisions rather than pretending every branch needs heavyweight reasoning paste this into claude code: “Propose a reusable workflow for this repository. Use Opus for architecture and final review, Haiku for read-only discovery, and Sonnet for edits where available. Give editing agents separate worktrees and clearly bounded file ownership. Require an independent verifier for every reported issue and proposed fix. Stop correction loops after two rounds without progress. Report unresolved failures instead of hiding them. Begin with one directory and report usage before expanding. Show the proposed workflow and configuration changes as diffs first. Do not edit or commit anything until I approve.” one thing worth remembering: cleaner context does not mean free compute parallel workers still consume tokens, so test the split on a small task before pointing it at the whole repo save this for the next audit or migration that would otherwise drown your main session in logs

this claude code setup is a f*cking goldmine stop making your most expensive model babysit every file in the repo give it the architecture let cheaper agents handle the repetitive work the setup: → opus writes the workflow → haiku handles read-only scans → sonnet handles fixes → a separate verifier checks the results that model split is the configuration to ask for, not something to assume is already enabled the important part is where the loop lives instead of dumping every worker conversation into the main chat, put the orchestration in a reusable script the script coordinates the work opus gets the findings that actually need its attention start with: claude --model opus --effort ultracode then describe the task and the model split you want for a repo-wide cleanup, i'd structure it like this: 1. scan a small directory first 2. collect findings with file paths and evidence 3. reject duplicates and unsupported claims 4. give each editor an isolated worktree 5. verify the patches independently 6. bring the surviving results back for review opus plans haiku searches sonnet patches and none of them gets to approve its own homework the JEV layer is optional: if you already have it wired in, use it for the routing decisions that don't need another heavyweight reasoning call paste this into claude code: “Design a reusable workflow for this repository. Use Opus for planning and final review, Haiku for read-only discovery, and Sonnet for edits where those models are available. Keep editing agents in separate worktrees. Require evidence for every finding and an independent verification pass for every proposed fix. Stop fix loops after two rounds without progress. Start with one directory so we can inspect quality and usage before scaling up. Show the workflow and proposed configuration changes first. Do not edit files or commit anything until I approve.” one catch: parallel agents still consume tokens less clutter in the main context does not mean free compute save the expensive reasoning for decisions that deserve it

claude can write your trading bot. the dangerous part is letting it trade without knowing when to stop. this shared blueprint is more interesting than another “paste a prompt and get rich” demo. it describes a system with four layers: → market regime detection identify whether conditions are quiet, ranging, volatile or falling sharply, then select the appropriate strategy → risk controls check a kill switch before every order and stop trading at a configured drawdown threshold, with 15% used in the example → testing before deployment evaluate the strategy across successive historical periods and keep it out of live trading if it fails the acceptance criteria → overnight review analyse failures and prepare proposed improvements, with a human deciding whether they get applied that’s a much better starting point than running the same strategy through every market. but the “one minute, no code, just paste” pitch skips the hard part. generated code still needs inspection. backtests can overfit. a 15% drawdown setting cannot guarantee losses stop at exactly 15%. let claude help build the system. test it in paper trading before giving it real money. a bot that knows how to place an order is easy to demo. proving when it should NOT place one is the actual work.

$508k PnL on Polymarket. the claimed edge is a straight line and a noise filter. Daniro credits a Grok Bot-built system with +$508,183 over 125 days. that averages roughly $4,065 a day across the period, not a guaranteed daily payout. the reported setup targets crypto “Up or Down” markets: → around $258 per average trade → a claimed frequency of 33 trades an hour → regression to measure direction → R² to check how closely prices follow that trend here’s the actual idea. 1. measure the move fit a line through recent BTC or ETH prices: slope = Cov(time, price) / Var(time) positive slope suggests upward momentum. negative slope suggests downward momentum. the magnitude measures how quickly price is moving over the chosen window. 2. check the fit R² = 1 − SSE / SST a higher R² means the line explains more of the price variation in that sample. a lower R² means price is moving less consistently around the trend. the described rule combines directional momentum with a stronger fit before taking a trade. the account named in the post is mo-money. listed winning examples: $1,666 → $4,175 $1,120 → $3,556 $587 → $2,609 interesting as f*ck, but the missing details matter. the post doesn’t establish that this exact algorithm produced the account’s results. a high R² describes past data, not the probability of winning the next market. entry odds, latency, fees and losing trades still decide whether the idea actually makes money.

stop asking AI to draw monsters. make it build their bones first. this blender workflow is f*cking interesting because it starts with the part nobody puts in the thumbnail: the skeleton. the shared setup describes three stages: → FRAME define the limbs, joints and balance before creating the mesh → FLESH build muscle and body volume around that structure → WARP distort the working anatomy to make the creature look unnatural that order matters. a creature can look incredible in an image while giving you a nightmare when you try to rig it. cool silhouette, joints that make no sense. starting with the skeleton gives the design something functional to deform. lengthen a pair of limbs. change the curve of the spine. shift the proportions. you’re modifying an existing structure instead of asking the model to invent a completely new body every time. and because the frame is parametric, variations can come from changing values rather than rebuilding everything. the underrated detail is weight distribution. it affects how the creature stands, which changes whether it reads as powerful, unstable or barely alive. you still need to check the rig and motion. a procedural skeleton doesn’t magically guarantee good animation. but “build something that can move, then make it weird” is a much better brief than “generate a terrifying monster.”

stop paying opus to do haiku’s homework. this claude code setup is f*cking gold. keep sonnet doing the work. bring opus in when the decision actually needs it. launch with: claude --model sonnet --advisor opus then build the rest of the setup around that split: → sonnet 5.5 runs the main session and edits code → read-only haiku 5.5 subagents handle repo searches and documentation → opus 5.5 acts as the advisor for difficult decisions the advisor gets the full conversation when consulted, including tool calls and results. three useful moments to ask for it: → before committing to a substantial plan → after the same failure keeps returning → before declaring a long task finished those are instructions you give it, not three hard-coded triggers. claude decides when to call the advisor. and no, the second opinion isn’t free. advisor calls consume tokens too. the point is to avoid running the expensive model through every file lookup and routine edit. if you already use JEV, deterministic routing can sit underneath this setup. it’s a separate layer, not something the advisor flag installs. paste this into claude code: “Propose a setup with Sonnet as the main model and Opus as advisor. Create three read-only Haiku subagents: search for repository structure grep for references and call sites docs for official API documentation Add guidance to CLAUDE.md to request advisor input before major plans, after recurring failures, and before completing long tasks. Check existing configuration first. Preserve conflicting settings and report them. Show all proposed changes as diffs. Do not edit until I approve.” opus for judgment. haiku for lookup work. sonnet keeps the task moving. the advisor feature is experimental, so check support in your environment before relying on it.

anthropic ran AI on $55 TV boxes. the self-replication tests are the f*cking scary part. their red team connected 15 cheap android boxes and ran a 26B model across them. not fast. just 0.39 tokens per second. in separate tests: → Qwen3.5-9B set up inference on 8 of 9 hardware types → GLM-5.2 completed the replication chain in a simplified, isolated environment → smaller models still failed to reliably put every step together important: they haven’t demonstrated a fully autonomous AI worm spreading through real homes. but the hardware side is already working. using a previous 2-million-device botnet as a hypothetical baseline, they estimated: → roughly 133,000 distributed model copies → up to about 40 H100s worth of inference → around 1–2 days per replication for a 9B model slow doesn’t mean harmless. a distributed worm wouldn’t have one central server you could switch off. containment would mean finding and isolating infected devices. everyone is arguing about who controls the datacenters. meanwhile, the cheap box behind your TV is starting to look like compute.

Someone built a free tool that turns real-world map data into manga backgrounds. A street you know can become the setting for your next panel. Enter a place name or coordinates, and Rokehan uses map information to recreate the location as a manga-style background. The appeal is obvious: start with an actual place instead of inventing an entire city from scratch. Your neighborhood. A street in Tokyo. The location your story is set in. Worth exploring for comics, storyboards and scene planning. One catch: traffic has already overwhelmed the tool. The creator temporarily restricted search, although locations such as Shibuya remain available. So don’t expect unrestricted access to every location right now. tools.oyasumi-gamedev.com/ro…

someone put an AI pentesting team on GitHub and it’s f*cking open source meet PentAGI you describe an authorized target and a goal specialized agents plan the work, run security tools and turn the findings into a report 20+ tools including nmap, Metasploit and sqlmap execution inside a Docker sandbox persistent memory so it can reuse what it learned supports cloud models and local setups through Ollama the wild part is the coordination agents delegate research and technical tasks instead of making you babysit every step self-hosted too, so you control the deployment small catch: open-source software doesn’t mean zero running costs cloud model APIs and infrastructure can still cost money use it on your own systems or targets you have permission to test github.com/vxcontrol/pentagi

wtf you can stack internet connections for a single download Plexo turns your Wi-Fi + USB-tethered phone into a download setup that uses both at once it splits files into chunks of up to 8 MB and fetches them across the networks you select the faster connection grabs more chunks instead of waiting for the slower one near the finish, idle streams can race slow chunks so one lagging connection doesn’t hold the whole file hostage and it’s more than a basic downloader: → direct links, magnets and torrents → pause and resume across restarts → replace expired download links without starting over → separate speed limits and data caps for each network → overnight download scheduling → live charts showing which connection is doing the work runs on Windows, macOS and Linux open source under MIT no VPN needed for the download engine one catch people will absolutely miss: Wi-Fi + Ethernet on the SAME router won’t magically double your speed you need separate internet connections, like home broadband + mobile data servers that don’t support partial downloads fall back to a single stream too your phone can finally help download the file instead of just distracting you while it loads github.com/anmolkapil/plexo

AI tool calling is getting f*cking dangerous a bad answer is one thing. a bad answer with permission to execute is a different problem. 7 github repos to check before your LLM app gets anywhere near production tools ↓ attack it before someone else does 01 garak probe models for prompt injection, data leakage, jailbreaks and other failure modes. github.com/NVIDIA/garak 02 PyRIT run structured red-team campaigns against AI systems instead of testing five prompts and calling it secure. github.com/Azure/PyRIT 03 promptfoo turn evaluations and red-team checks into repeatable tests you can run in CI. github.com/promptfoo/promptf… check what goes in and what comes out 04 LLM Guard scan inputs and outputs for risks like exposed secrets, malicious URLs and suspicious instructions. github.com/protectai/llm-gua… 05 Rebuff a layered prompt-injection detection prototype. worth studying, but the repo is archived, so don’t treat it as a maintained production shield. github.com/protectai/rebuff put rules around the interaction 06 NeMo Guardrails add programmable guardrails to conversational workflows. useful as a layer, not a replacement for tool permissions. github.com/NVIDIA-NeMo/Guard… test whether the controls actually work 07 Inspect AI build controlled evaluations for model capabilities and behavior, then rerun them when your setup changes. github.com/UKGovernmentBEIS/… the loop: map the risks → attack → inspect failures → add controls → retest → monitor prelaunch: garak + promptfoo + Inspect AI runtime: input/output checks + guardrails + tool-side permission checks a clever system prompt isn’t a security boundary. keep credentials scoped. validate tool arguments outside the model. require approval for consequential actions. if the model gets confused, your app should deny the action. not execute it and apologize afterward.

what the f**k.. someone turned a single Grok Bot prompt into a blueprint for a 30-agent engineering company the funniest part? the agent in charge isn’t allowed to touch the code. it plans the work and delegates through managers instead of trying to do every job itself the setup is designed to keep agents working on client projects and SaaS features while you’re offline. an actual hierarchy, not 30 bots arguing in one chat give it a build request and the prompt lays out how the team should organize around it save this if your current “AI team” is just you opening another tab

what these guys put on github is actually f*cking ridiculous a free, open-source intelligence dashboard called Osiris. think Palantir-style world monitoring in your browser. it pulls public data into one interactive globe: → commercial flights, private jets and available military aircraft data → satellites and space weather → public CCTV and traffic cameras → earthquakes, wildfires and extreme weather → mapped conflict zones then there’s a built-in recon toolkit: → port scanning → DNS and WHOIS lookups → SSL certificate inspection → IP location and threat reputation so instead of jumping between flight trackers, webcam sites and disaster maps, you can explore the feeds in one interface. open a location. switch on the layers. see what’s happening around it. the repo now has over 10k stars. it’s public-source intelligence, not access to classified systems or a full Palantir replacement. but the fact you can run something that looks like a geopolitical command center from a github repo is still wild repo below github.com/simplifaisoul/osi…

holy sh*t... GPT-6.1 Sol, Claude Opus 5.5 and Gemini 4 Argon in one coding stack sounds ridiculous until you give each one a different job most people still use agents like overpriced interns dump the whole repo into a chat ask for a fix watch it retry the same broken approach get a confident “done” while the tests are still red then blame the model and buy more tokens the architecture in this breakdown takes a different approach: > repo state: track file changes and cache tool results so agents don’t keep rereading unchanged code > parallel execution: split genuinely independent tasks across specialist agents, then join their outputs before integration > isolated patches: let the coding worker explore changes in separate sandboxes without trashing the working branch > verification loop: run tests and linters after each candidate patch. a failed check sends it back with the actual error, not another vague prompt > adversarial review: give a separate reviewer the diff and make it look for bad assumptions, missing edge cases and tests that pass for the wrong reason the proposed model split: Gemini 4 Argon handles repo context GPT-6.1 Sol writes candidate patches Claude Opus 5.5 challenges the result but the useful part isn’t collecting three expensive model names it’s keeping implementation and verification separate a worker saying “fixed” is a claim a diff shows what changed a test run shows what was actually checked and even green tests don’t prove the feature works if nobody tested the thing that broke the original post throws around huge benchmark numbers. without the underlying evaluation, i wouldn’t build a budget around them the workflow is still worth stealing: scope -> split -> patch -> test -> review -> integrate parallelize independent work keep shared-file edits coordinated cache unchanged results make failures visible stop letting the agent grade its own homework otherwise you’ve just built a faster way to generate broken PRs save this before your next “the agent said it was done” moment

this is a f*cking goldmine 20 GitHub repos to turn Claude into a motion-design machine instead of another chatbot you argue with. launch videos, animated demos, captions, sound, camera moves. here’s the stack: THE SKILLS 1. claude-motion adds visual direction, frame checks and audio generated through code. github.com/whaleyxbt/claude-… 2. claude-motion-design builds animations with HTML, Playwright and ffmpeg. no Remotion required. github.com/howseen-ai/claude… 3. motion-graphics-skills 13 skills focused on turning product launches into actual videos. github.com/charlie947/motion… 4. claude-remotion-skill a prompt-driven Remotion workflow for captions, B-roll and sound. github.com/haidrrrry/claude-… 5. klik-anim-skill-creation gives the agent animation and camera rules instead of letting it freestyle every frame. github.com/t3knobox/klik-ani… 6. hyperframes-motion-reel-skill builds a motion reel around a 120 BPM beat. github.com/Sunwood-ai-labs/h… 7. product-launch-motion a launch-film workflow covering voice-over, camera movement and color grading. github.com/AbubakrChan/produ… 8. animate keeps a short animation inside one canvas file, with storyboarding along the way. github.com/cth9191/animate 9. motion-design-skills guidance for typography, logos, color and timing in Remotion. github.com/iart-ai/motion-de… 10. hyperframes-student-kit a learning kit with 14 skills and 406 motion-graphics cards. github.com/nateherkai/hyperf… THE RENDERING LAYER 11. hyperframes takes HTML-based compositions and exports video. github.com/heygen-com/hyperf… 12. remotion build videos using React components. github.com/remotion-dev/remo… 13. remotion skills official instructions that help your coding agent work with Remotion. github.com/remotion-dev/skil… 14. GSAP the animation toolkit for sequencing movement and controlling timelines. github.com/greensock/GSAP THE EXTRA SAUCE 15. motion-design-skill motion-design principles without locking you into one rendering engine. github.com/LottieFiles/motio… 16. awesome-motion-design-agent-skills a directory covering skills for GSAP, Remotion, Lottie and Rive. github.com/frankxai/awesome-… 17. motion-graphics a workflow for creating B-roll from a transcript. github.com/Barty-Bart/motion… 18. motion-dev-animations-skill for animated websites using Motion. different job from exporting an MP4. github.com/199-biotechnologi… 19. motion the animation library previously known as Framer Motion. github.com/motiondivision/mo… 20. lottie-web plays Lottie animations on the web when video isn’t the right format. github.com/airbnb/lottie-web you don’t need all 20. pick a skill for the kind of video you want, pair it with a compatible engine, then make Claude build a short test. watch the frames. fix the timing. render again. that’s a much better starting point than “make it cinematic” and praying. save this before it disappears into your bookmarks graveyard.

this guy claims Grok turned $100 into $5,423 in 48 hours he stopped picking memecoins himself and let it watch what whales were buying instead 42,518 Solana wallets scanned 16 made the cut minimum 70% win rate the idea is simple: track the wallets finding coins before CT starts shilling them one tracked whale reportedly made $510k in 30 days with an 83% win rate on $FLURB, 12 of 14 monitored wallets bought while the market cap was just $61k his reported trade: $223.17 entry 3.6x exit $533.82 profit another whale reportedly cleared $178k in 30 days with an 85% win rate it bought $8,600 of $TOOTZ at a $106k market cap before copying the trade, the setup checked the token burned LP revoked mint authority team holding 1.3% of supply risk score of 5/100 his reported entry was $632.55 sold half at 2x held the rest to 2.9x claimed profit: $1,105.70 FOMO was used to filter the wallets according to the post, each entry used 20% of the bankroll and the average hold was 17 minutes 214 trades 78% win rate 37 rugs supposedly avoided that's the claimed $100 → $5,423 run obviously these are self-reported numbers, not verified returns. a wallet's win rate or a low risk score doesn't make the next trade safe but the wallet-first approach is worth looking at less sitting in telegram waiting for someone to drop a ticker more watching where the money actually moves because by the time a KOL tells you you're early, someone else might already be looking for an exit

i don’t get why more people aren’t using this ur Claude might be following instructions written for a model you stopped using months ago Anthropic has a prompt-audit workflow for exactly that with the claude-api skill installed, open Claude Code and run: /claude-api prompt-audit it checks ur prompts, skills and CLAUDE.md for: → outdated instructions that make newer models overthink → aggressive rules that trigger tools unnecessarily → conflicting instructions across project files → stale assumptions the code has already outgrown then gives you an audit report and proposed changes the goal isn’t deleting everything until the prompt is tiny it’s removing the instructions that are actively making the model worse before buying more tokens, check what ur already feeding it bookmark this for the next time Claude turns a simple fix into a side quest

holy sh*t. Anthropic has an advisor mode for Claude Code. stop making Opus do work Haiku could handle while Sonnet builds ur strongest model doesn’t need to babysit every file search keep it on call for the decisions that actually matter the setup: → Sonnet 5.5 runs the main session writes the code, applies diffs, runs tests → Haiku 5.5 handles scoped repo searches finds files, pulls docs, reports back without dumping the whole codebase into the main chat → Opus 5.5 acts as the advisor gets called when the lead needs a stronger second opinion start a session with: claude --model sonnet --advisor opus or enable it inside an existing session: /advisor opus the three checkpoints worth asking for: 1. before committing to a big plan are we about to build around the wrong assumption? did we miss an auth rule or a schema contract? 2. when the same error comes back are we fixing the cause or just producing increasingly creative patches? 3. before calling the task done does the full diff actually hold up? what breaks outside the happy path? Sonnet keeps moving Haiku does the legwork Opus checks the expensive mistakes one catch people will skip: the advisor receives the full conversation and uses additional tokens it isn’t a free background reviewer and those checkpoints are instructions, not guaranteed automatic triggers Jev fits the same idea one layer lower the original post describes using it for narrow routing decisions: which file, which tool, retry or stop that’s a separate layer, not something /advisor magically installs the useful principle is simple: don’t spend frontier-model reasoning on every mechanical branch paste this into Claude Code: “Review my current Claude Code setup and propose a lead / researcher / advisor workflow. 1. Inspect user settings, project settings and existing subagents. Identify anything that overrides the selected models. 2. Propose Sonnet as the lead with high effort where supported. Use Haiku for scoped, read-only file discovery and documentation tasks. Configure Opus as the advisor using supported settings. 3. Add instructions to consult the advisor: - before committing to a substantial multi-file plan - when the same test or compiler error fails twice - before declaring a long task complete 4. Keep researcher outputs compact: relevant file paths, findings, source links and unresolved questions. No unrelated edits or giant context dumps. Verify every setting against the installed version. Do not invent CLI flags or configuration keys. Show every proposed change as a diff. Do not edit anything until I approve.” save this before ur next debugging session turns into Opus reading the same folder for the seventh time