Claude Code tip: before you ship a vibe-coded site, let Opus 5.5 send 𝟱 𝘀𝘂𝗯𝗮𝗴𝗲𝗻𝘁𝘀 through 𝘁𝗵𝗲𝘀𝗲 𝟮𝟬 𝗰𝗵𝗲𝗰𝗸𝘀 and fix everything in one pass: 1. design that holds together • write a DESIGN file first and tie every color, font size, spacing and radius to it • keep the palette tight and the type hierarchy clear • same kind of button, card or input = same look • flat page? add emphasis. crowded page? cut • text stays readable on dark and light backgrounds 2. mobile • nothing scrolls sideways or spills off screen • add a real mobile menu • buttons big enough for a thumb • the layout survives enlarged text 3. every state • loading, empty and error each get a proper screen • every button gets its own hover, pressed and disabled look • form errors show up next to the field, plus submitting, success and failure messages • modals, dropdowns and tabs switch with transitions 4. click it like a real user • run sign-up and checkout from start to finish • hunt down dead buttons and broken links • make sure the whole site works with just a keyboard 5. pre-launch • the home page says what you do in one line • each page gets one main button • every page has a title, a description and a favicon • delete every bit of placeholder text on effort: Anthropic's cost guide puts medium on well-scoped daily work, so the default covers these 20 fixes raise it only for the hard ones - Thariq's tests showed higher effort mostly buys extra verification and edge-case testing paste this whole post into Claude Code and add this at the end 👇 "Run a pre-ship check on this repo. You: main session, Opus 5.5, medium effort. Spawn 5 read-only subagents, one per group: design (medium) - DESIGN file, styles/globals.css, tailwind.config.ts, components/ui/ mobile (medium) - app/layout.tsx, components/nav/, every page at 375px states (medium) - components/forms/, every loading, empty and error view real user (high) - app/signup/, app/checkout/, every link and button launch (medium) - app/page.tsx, page metadata, public/favicon.ico Each subagent: - checks only its group - edits nothing - returns issue, file:line, severity Then: 1. merge all five lists into .claude/preship/issues.json, grouped by file, no duplicates 2. stop and show me the list - no edits until I confirm 3. after I confirm, fix everything yourself in one pass, so no two agents touch the same file 4. save before and after screenshots to .claude/preship/screens/"

this guide is a f*cking jackpot how to make OpenAI Dots pay for itself (full setup + day-one prompt) you sleep, it clears your inbox, Slack and GitHub in the right hands it's a chief of staff you never hire:

Codex tip: once GPT-6.1 Sol is your main model, stop running Astra on every turn keep Astra on call as an architect agent Sol writes every line of code Astra gets spawned at exactly three moments: -> before a plan: is this even the right approach? -> when the same error returns: am I digging in the wrong spot? -> before "done": what did I skip? Astra reviews. Sol ships Jev engineering does the same thing one layer lower: forks that need no thinker (which file, which tool, retry or stop) go to Jev in under half a second, and the big models only see the forks that actually split the full tree: > GPT-6.1 Sol on high runs the main session > explorer reads the code on Luna > worker edits and runs tests on Sol > researcher pulls the docs on Luna > all three on medium > Astra on call as the architect > auto_review checks every approval paste the tree and this prompt into Codex 👇 "Rebuild my Codex setup around this tree. 1. Agents - scan ~/.codex/agents and .codex/agents for anything that already fits explorer, worker or researcher - draft TOML only for missing roles: explorer and researcher on gpt-6-luna, worker on gpt-6.1-sol, all at model_reasoning_effort medium - explorer and researcher get a read-only sandbox, only worker can write - skip agents pinned to another model and list them 2. Architect - add an architect on gpt-6-astra, model_reasoning_effort high, read-only sandbox - its only jobs: review plans, repeated errors and finished work - feed it the plan, the diff and the failing log, never the full transcript - force one output shape: verdict (proceed / revise / stop), top 3 risks, one cheaper path, under 200 words - cap it at 3 calls per task 3. Main config - in ~/.codex/config.toml set model to gpt-6.1-sol, model_reasoning_effort to high, approvals_reviewer to auto_review 4. Overrides - find anything that would beat this: active profiles, flags in shell aliases, agents.default_subagent_model - report it, change nothing 5. Rules - add to the AGENTS file in the repo root: spawn the architect before a large plan, when the same error signature shows up twice, and before calling a long task done - log every architect call with its trigger and verdict to .codex/architect-log.jsonl Check that every TOML file parses, then show me all changes as one diff. No edits until I say go." ↳ developers.openai.com/codex/…

this is certified f*cking gold a 23-year-old from China runs 300 AI agents at once with Jev engineering, and none of them can lie to him he opens the dashboard live: 300 Kimi K3 agents firing in parallel Jev checking every single output against its source in about 100 milliseconds pointed at 100 companies in the EV market: > pass 1 - 12 failed: wrong revenue, dead citations, empty fields > pass 2 - 3 failed > pass 3 - zero. every row filed straight into an Obsidian vault the whole trick is the split Kimi K3 does the work, Jev decides if each claim actually matches the source Jev never writes a second opinion. it answers yes or no with a confidence score, at $0.042 per million tokens in and the loop refuses to stop while anything is still wrong an analyst team bills weeks for 100 verified company profiles. he gets them from one run one model writes, a cheaper one decides, and nothing ships until it says yes

ok this is a f*cking cheat code for coding agents how to make them 200x faster and 400x cheaper with Jev engineering Jev founder Diogo Amogo put the whole harness into a 12-page PDF, 10 steps: 1. meet Jev the LLM writes, the harness executes, Jev decides what each turn sees, where it routes and whether it runs 2. ask the question that breaks every agent how would you build one if LLMs had no KV cache? 3. quit blind routing Claude Opus 5.5 -> Sonnet 5.5 -> Opus costs 6.19, Opus alone 4.15, because every handoff reprocesses the full context 4. follow the tokens reading and searching eat 56.2% of tool turns and 46.5% of tokens, writing code stays under 10% 5. score each chunk per query hide it, summarize it or keep it whole, and compress once the question is known 6. reveal tools in tiers one-line snippets for hundreds of tools, full schema on demand 7. load instructions by condition a *.tsx edit pulls the style guide, billing/ pulls its gotchas file, compaction can't wipe either 8. route by trust as well as difficulty secrets and infra stay on first-party frontier models, public docs go to the cheapest one 9. reuse one retrieval pass review, evals, explainers and progress pages all run read-only in the background 10. gate every command allow / ask / deny policies read the whole script before it runs hand this PDF and the article below to Claude Code or Codex and start shipping faster

Claude Code tip: before you ship a vibe-coded site, let Opus 5.5 send 𝟱 𝘀𝘂𝗯𝗮𝗴𝗲𝗻𝘁𝘀 through 𝘁𝗵𝗲𝘀𝗲 𝟮𝟬 𝗰𝗵𝗲𝗰𝗸𝘀 and fix everything in one pass: 1. design that holds together • write a DESIGN file first and tie every color, font size, spacing and radius to it • keep the palette tight and the type hierarchy clear • same kind of button, card or input = same look • flat page? add emphasis. crowded page? cut • text stays readable on dark and light backgrounds 2. mobile • nothing scrolls sideways or spills off screen • add a real mobile menu • buttons big enough for a thumb • the layout survives enlarged text 3. every state • loading, empty and error each get a proper screen • every button gets its own hover, pressed and disabled look • form errors show up next to the field, plus submitting, success and failure messages • modals, dropdowns and tabs switch with transitions 4. click it like a real user • run sign-up and checkout from start to finish • hunt down dead buttons and broken links • make sure the whole site works with just a keyboard 5. pre-launch • the home page says what you do in one line • each page gets one main button • every page has a title, a description and a favicon • delete every bit of placeholder text on effort: Anthropic's cost guide puts medium on well-scoped daily work, so the default covers these 20 fixes raise it only for the hard ones - Thariq's tests showed higher effort mostly buys extra verification and edge-case testing paste this whole post into Claude Code and add this at the end 👇 "Run a pre-ship check on this repo. You: main session, Opus 5.5, medium effort. Spawn 5 read-only subagents, one per group: design (medium) - DESIGN file, styles/globals.css, tailwind.config.ts, components/ui/ mobile (medium) - app/layout.tsx, components/nav/, every page at 375px states (medium) - components/forms/, every loading, empty and error view real user (high) - app/signup/, app/checkout/, every link and button launch (medium) - app/page.tsx, page metadata, public/favicon.ico Each subagent: - checks only its group - edits nothing - returns issue, file:line, severity Then: 1. merge all five lists into .claude/preship/issues.json, grouped by file, no duplicates 2. stop and show me the list - no edits until I confirm 3. after I confirm, fix everything yourself in one pass, so no two agents touch the same file 4. save before and after screenshots to .claude/preship/screens/"

CLAUDE OPUS 5.5 + FABLE 5.1 + JEV ENGINEERING = THE CHEAPEST CODING LOOP YOU CAN RUN one real multi-file refactor in Claude Code hid 31 checks inside it did it pass, is it the right file, merge, retry or flag a person the core idea: 25-40% of model calls in any agent loop with a review step are checks Claude writes the code. Jev makes the calls who owns which job: > read a diff, write a review - Claude > does this pass, is this the right file - Jev > write the fix - Claude > merge, retry or flag a person - Jev > enforce the exact rule - your own code the numbers: > 348 ms per check > 93.3% of broken rules caught > TypeSafe's best case: 200x faster, 400x cheaper the playbook: 1. audit one real transcript and mark every yes-or-no moment 2. write the schema down: questions, answers, confidence lines 3. move one decision first, keep the rest of the loop as is 4. below the threshold, hand it back to Opus 5.5 or Fable 5.1 5. npx skills add typesafe-ai/skills --skill typesafe-ai the key insight: Jev can't hallucinate text because it never writes any but a bad schema lets it pick the wrong option with clean confidence so set the threshold per decision and spot-check the sure ones the engineer who cuts the review bill and keeps the code quality is the one who gets the raise

I can't believe this is f*cking free how to build your first AI agent (full guide, with a Jev decision layer) a year ago I burned two weeks on my first agent. this guide gets you there in an afternoon in the right hands it makes one builder ship like a small team:

Tavus gave me early access to Griffin, the first human interaction model you talk to it face to face, over video, in real time 48% of people thought it was a real human previous systems got under 3% #1 on NVIDIA's full-duplex video benchmark backed by Sequoia and YC, $75M raised what you can do with it: > tell a story, it laughs and asks questions mid-sentence > show it something, it works it into the chat > solve a Rubik's cube, it coaches and waits while you think > share good news, its face lights up I tried it on a sick day and talked way longer than planned congrats @tavus and @hassaanraza tavus.io/griffin

ok this one is a f*cking goldmine 9 jobs where Jev engineering replaces a pricey LLM call (TypeSafe's full list) Jev is TypeSafe AI's first System One model. 100x faster and cheaper than frontier LLMs each of these 9 is money you're paying Claude for right now in the right hands the work you skipped as too slow or too pricey gets you noticed:

Claude Code tip, and it's absolute free f*cking gold: run Opus 5.5, Sonnet 5.5 and Fable 5.1 as one team and stop burning Opus tokens on routine work the setup in one line: plan on high, delegate on medium, keep Fable on call • who does what > Opus 5.5 on high - plans and ships the code > Sonnet 5.5 on medium - explorer reads code, worker edits and runs tests, researcher pulls docs > Fable 5.1 via /advisor fable - reads the whole session and speaks up only when it matters • when Fable 5.1 steps in -> before a plan: is this the right approach? -> when an error repeats: am I digging in the wrong place? -> before "done": what did I miss? Jev engineering takes it one layer lower: which file, which tool, retry or stop all go to Jev in under half a second, so the big models only see the real forks paste this into Claude Code ↓ "Rebuild my Claude Code setup: 1. Find subagents in ~/.claude/agents and .claude/agents that fit explorer, worker and researcher. Draft only the missing ones. Set each to model: sonnet, effort: medium. List any that pin a different model and leave them 2. In ~/.claude/settings.json set effortLevel to high and advisorModel to fable. 3. Report anything that disables the advisor (CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, flag-fetching blockers) and CLAUDE_CODE_EFFORT_LEVEL. Change nothing. 4. Add to ~/.claude/CLAUDE.md: consult the advisor before a large plan, when an error repeats, and before calling a long task done. Show every change as a diff. No edits until I say go." ↳ code.claude.com/docs/en/advi…

HEYGEN VIDEO 1.0 + CLAUDE CODE = AN INSANE VIDEO STUDIO THAT LIVES IN YOUR TERMINAL I made an entire anime short with it the prompts, the renders and the final cut, all from one window > a tiny train skimming a shallow sea at sunset > a cottage walking off on wooden legs > a red seaplane touching down in a hidden cove and @HeyGen returns every shot with its own sound from the same call: > wind through grass > bacon sizzling > rain tapping an umbrella • my run, in numbers > 58 shots rendered, all 58 came back > about 15 seconds for a typical shot > 16 anime shots in 67 seconds, 8 running at once • two tricks worth stealing > chain shots off the last frame: sketch -> paint -> alive > one picture -> the same clay creature in anime and in a real café Claude Code wrote every prompt in HeyGen's structured format and built the motion design around the clips one person with a terminal can now make the kind of short that used to take a whole animation team which scene would you type first?

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a browser agent built for speed ↳ github.com/browser-use/jev-u… 2. Fast-JEV-Compaction - squeezes your context down ↳ github.com/tamaratran/fast-j… 3. JSON-Render - UI generated on the fly ↳ github.com/vercel-labs/json-… 4. Typesafe-MCP - plugs Jev into any client ↳ github.com/itsmostafa/typesa… 5. JEV-MCP - a toolkit for judgment calls ↳ github.com/burnigtm/jev-mcp 6. Semdecide - a classifier right in your terminal ↳ github.com/sharziki/semdecid… 7. JEV-Codex-Router - sends every task to the model that fits it ↳ github.com/0xNatoshi/jev-cod… 8. Winnow - clears the junk out of your context ↳ github.com/GhalebDweikat/win… 9. JEV-Review - sorts code reviews by what needs eyes first ↳ github.com/devagrawal09/jev-… 10. Blink - finds your way around any repo ↳ github.com/ellipsis-dev/blin… 11. Agent-Desktop - runs your desktop for you ↳ github.com/lahfir/agent-desk… 12. Typesafe-Mario - an agent playing Super Mario ↳ github.com/fhshaik/typesafe-… 13. JEV-Drone - flies a drone ↳ github.com/RomanSlack/jev-dr… 14. OneVOneJev - a shooter in your browser ↳ github.com/emrickgarrett/One… 15. JEV-Trader - high-frequency market making ↳ github.com/buberlo/jev-trade… 16. Prism - spots liquidity signals ↳ github.com/irfndi/prism-liqu… 17. Neo4Jev - walks a knowledge graph ↳ github.com/jexp/neo4jev 18. JEV-Curate - screens training data ↳ github.com/AkashPriyadarshii… 19. Canny - confirms a task is really finished ↳ github.com/qkal/Canny 20. KillMyIdea - scores a startup idea before you sink time into it ↳ github.com/monteduro/killmyi… start where your work is: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone pick one, build on it this week, and you'll be the person on your team who actually knows Jev engineering when it gets asked for

CLAUDE + JEV ENGINEERING = THE HARNESS SKILL THAT GETS AI ENGINEERS PROMOTED IN 2026 1,000 decisions on a cold frontier model cost $605 the same 1,000 through this harness cost $0.17 the core idea: Claude proposes, Jev answers, code decides every step leaves a receipt the loop: > propose - Claude writes plans and patches, and decides nothing > filter - code drops the routes your machine can't run before Jev ever sees them > answer - Jev picks from code's menu with a probability, or abstains > re-check - code checks the pick against live state. stale goes to fallback > act - the tool runs through the normal approval path > receipt - every step logged and replayable against a new policy the numbers: > 0.2s median per call > ~$0.0002 per desktop step > 49 of 49 WebMCP tasks passed the key insight: "I don't know" is a real answer here when Jev is only 38% sure, a fallback written in advance fires instead of a guess worth stealing even if you never touch Jev: > one decision the harness can check > the exact options code prepared > a defined path for when the model isn't sure > a way to measure what actually happened the engineer who can walk into a meeting and turn a $605 line into 17 cents is the one who gets asked to design the next system the model got the words, Jev got the choices, and code kept the final say

> pov: Claude Opus 5.5 just finished your whole app > you open the cloud console to deploy it > it suddenly hits you > the agent writes code in an hour and you've been doing its DevOps by hand for days > then you see InstaCloud > the agent deploys it, scales it, and tests on a full copy with the data > and that's when it lands

paste this Jev prompt into Claude and let it audit you it installs Jev, studies how you actually work, and shows you exactly where the hours and the money slip away then it rebuilds your whole setup around the leaks it found same $20 subscription and you end up running it better than 95% of the people paying for the same model the full prompt 👇

this is pure f*cking gold for anyone doing Jev engineering the skill that teaches Claude Code to build on Jev the right way, from TypeSafe themselves without it, coding agents send Jev one question at a time and guess the field names with it, the order is set before a single line gets written: docs -> behaviour -> judgments -> one request -> code decides the two-page breakdown of every rule: > the failure each of its 11 instructions is there to prevent > Choice, Noul or Score, picked at a glance > a prompt skeleton you paste straight into your agent 12.2x that's how far the bill fell in TypeSafe's cookbook when 13 questions went out in one call one short file, and your agent stops paying for decisions it should have batched