rody

@0x_rody • 4,930 subscribers

AI tools analyst | Code & Consciousness | @zscdao

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you can call me crazy but.. it's now obvious: grok bot + jev is going to create more millionaires in 2026 than crypto ever did. idea → grokbot → jev decisions → grokbot execution → $$$$$ don’t scroll past this if it hits your timeline. just send this article to grok bot. connect it to jev. thank me later.

you can call me crazy but.. it's now obvious: grok bot + jev is going to create more millionaires in 2026 than crypto ever did. idea → grokbot → jev decisions → grokbot execution → $$$$$ don’t scroll past this if it hits your timeline. just send this article to grok bot. connect it to jev. thank me later.

182,040 Aufrufe

I genuinely don't understand why every developer isn't doing this yet. Elon Musk's Grok Bot can run your entire workflow like a $100,000/month software agency. here's the exact setup (takes 15 minutes): 1. install GrokBot (Grok Bot) and make it your Head of Product 2. give it the guide below with all workflows and prompts 3. give it access to your APIs, IDE and tools you are done. thank me later.

I genuinely don't understand why every developer isn't doing this yet. Elon Musk's Grok Bot can run your entire workflow like a $100,000/month software agency. here's the exact setup (takes 15 minutes): 1. install GrokBot (Grok Bot) and make it your Head of Product 2. give it the guide below with all workflows and prompts 3. give it access to your APIs, IDE and tools you are done. thank me later.

55,175 Aufrufe

laugh now, screenshot this for later: grok bot + jev will make more people money in 2026 than every memecoin season combined. idea → grok bot → jev picks the route → grok bot executes → profit 77 jobs on autopilot, just 16 handed to a human, and jev decided all of it in 182ms. saw this on your feed? it's not an accident. drop this article into grok bot. hook it up to jev. you'll thank me in a month.

laugh now, screenshot this for later: grok bot + jev will make more people money in 2026 than every memecoin season combined. idea → grok bot → jev picks the route → grok bot executes → profit 77 jobs on autopilot, just 16 handed to a human, and jev decided all of it in 182ms. saw this on your feed? it's not an accident. drop this article into grok bot. hook it up to jev. you'll thank me in a month.

32,021 Aufrufe

call me insane but.. i will keep saying that: grok bot + jev is going to create more millionaires in 2026 than crypto ever did. idea → grokbot → jev decisions → grokbot execution → $$$$$ don’t scroll past this if it hits your timeline. just feed this entire thing into grok bot. thank me later.

call me insane but.. i will keep saying that: grok bot + jev is going to create more millionaires in 2026 than crypto ever did. idea → grokbot → jev decisions → grokbot execution → $$$$$ don’t scroll past this if it hits your timeline. just feed this entire thing into grok bot. thank me later.

23,580 Aufrufe

I couldn't f*cking believe this Stanford AI research team gave Grok Bot a human brain setup. JEV is the fast gut brain. It scans everything in milliseconds and makes the easy calls. Grok is the slow thinking brain. It only wakes up for the hard ones. Here are the commands that wire the two together: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp → Grok gets Jev as tools: classify, score, check npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project → ask what the code does, get the right files back. the author measured ~30% lower cost on the same tasks npx skills add altryne/jevify --skill jevify → teaches your agent to hand the bulk yes/no work to Jev on its own > and fast-jev-compaction swaps Grok bot’s summary for Jev decisions: it drops stale tool calls and keeps everything else word for word. Faster runs, smaller bills, barely any compute wasted. Save this before your next session, then read the full guide below:

I couldn't f*cking believe this Stanford AI research team gave Grok Bot a human brain setup. JEV is the fast gut brain. It scans everything in milliseconds and makes the easy calls. Grok is the slow thinking brain. It only wakes up for the hard ones. Here are the commands that wire the two together: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp → Grok gets Jev as tools: classify, score, check npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project → ask what the code does, get the right files back. the author measured ~30% lower cost on the same tasks npx skills add altryne/jevify --skill jevify → teaches your agent to hand the bulk yes/no work to Jev on its own > and fast-jev-compaction swaps Grok bot’s summary for Jev decisions: it drops stale tool calls and keeps everything else word for word. Faster runs, smaller bills, barely any compute wasted. Save this before your next session, then read the full guide below:

13,649 Aufrufe

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call me crazy but.. I will keep saying this… Grok Bot + Jev is going to make more millionaires in 2026 than crypto ever did. prompt → GrokBot → Jev SEO decision → GrokBot action → $$$$$ don’t bookmark this if it shows up on your feed. Just paste this entire thing into Grok Bot. thank me later.

call me crazy but.. I will keep saying this… Grok Bot + Jev is going to make more millionaires in 2026 than crypto ever did. prompt → GrokBot → Jev SEO decision → GrokBot action → $$$$$ don’t bookmark this if it shows up on your feed. Just paste this entire thing into Grok Bot. thank me later.

267,857 Aufrufe • vor 19 Tagen

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist: 1. agent-desktop Desktop automation. Read the system's accessibility tree, judge which button, menu, or input field to click next. 2. typesafe-mario Have Jev play Super Mario. No screenshots—just read the structured state in the emulator's RAM, then decide to run, jump, or dodge. 3. jev-drone Use Jev to control a drone. The underlying flight control still handles stability and safety; Jev just does higher-level judgments like climbing, braking, and navigating obstacles. 4. OneVOneJev 1v1 FPS in the browser. Every decision tick, judge movement, view angle, aiming, firing, and jumping. 5. jev-trader High-frequency market making on Monad testnet. Jev judges the next buy or sell based on spreads and trade direction, with model latency around 81ms. 6. Prism Doesn't directly have Jev place orders. It judges states like toxic flow, market pressure, mean reversion, etc., then hands off to the original strategy. 7. neo4jev Stuff Jev into a knowledge graph. At each node, judge the most worthwhile edge to take next, then follow it all the way. 8. jev-curate Use Jev to screen training data. For JSONL / Parquet, first judge quality, relevance, and risk, then decide which ones go into the next training round. 9. Canny Prevents Coding Agents from stubbornly claiming they're done. Look at tool outputs, code diffs, and test results, then judge if the completion claim is reliable. 10. killmyidea Input a startup idea, and Jev scores it from multiple angles, finally giving you KILL, FIX, or SHIP. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist: 1. agent-desktop Desktop automation. Read the system's accessibility tree, judge which button, menu, or input field to click next. 2. typesafe-mario Have Jev play Super Mario. No screenshots—just read the structured state in the emulator's RAM, then decide to run, jump, or dodge. 3. jev-drone Use Jev to control a drone. The underlying flight control still handles stability and safety; Jev just does higher-level judgments like climbing, braking, and navigating obstacles. 4. OneVOneJev 1v1 FPS in the browser. Every decision tick, judge movement, view angle, aiming, firing, and jumping. 5. jev-trader High-frequency market making on Monad testnet. Jev judges the next buy or sell based on spreads and trade direction, with model latency around 81ms. 6. Prism Doesn't directly have Jev place orders. It judges states like toxic flow, market pressure, mean reversion, etc., then hands off to the original strategy. 7. neo4jev Stuff Jev into a knowledge graph. At each node, judge the most worthwhile edge to take next, then follow it all the way. 8. jev-curate Use Jev to screen training data. For JSONL / Parquet, first judge quality, relevance, and risk, then decide which ones go into the next training round. 9. Canny Prevents Coding Agents from stubbornly claiming they're done. Look at tool outputs, code diffs, and test results, then judge if the completion claim is reliable. 10. killmyidea Input a startup idea, and Jev scores it from multiple angles, finally giving you KILL, FIX, or SHIP. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

349,882 Aufrufe • vor 1 Monat

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

204,901 Aufrufe • vor 1 Monat

This is absolutely insane. A Stanford AI research group joints JEV with Claude Code to sort 100+ billion data points every 15 minutes. the trick is stupidly simple: JEV runs a cheap first pass on everything. Claude only gets the hard cases. so instead of: everything -> Claude -> $$$ it’s: everything -> JEV filters -> hard stuff -> Claude thinks the boring stuff never touches the expensive model. Claude gets a tiny pile that actually deserves deeper analysis. faster. cheaper. way less compute burned. basically, JEV sorts the mail so the genius only opens what matters. LLMs think, JEV decides Read the full Jev setup guide in the article below:

This is absolutely insane. A Stanford AI research group joints JEV with Claude Code to sort 100+ billion data points every 15 minutes. the trick is stupidly simple: JEV runs a cheap first pass on everything. Claude only gets the hard cases. so instead of: everything -> Claude -> $$$ it’s: everything -> JEV filters -> hard stuff -> Claude thinks the boring stuff never touches the expensive model. Claude gets a tiny pile that actually deserves deeper analysis. faster. cheaper. way less compute burned. basically, JEV sorts the mail so the genius only opens what matters. LLMs think, JEV decides Read the full Jev setup guide in the article below:

114,820 Aufrufe • vor 1 Monat

Anthropic engineer James Brady: "Every agent in production lies. We measured it. The good ones lie less, the great ones catch the lie before the user does." In 29 minutes, he walks through the verification stack he built and the patterns the Claude Code team adopted to keep agents honest at scale. Watch the full talk, then save the config below👇

Anthropic engineer James Brady: "Every agent in production lies. We measured it. The good ones lie less, the great ones catch the lie before the user does." In 29 minutes, he walks through the verification stack he built and the patterns the Claude Code team adopted to keep agents honest at scale. Watch the full talk, then save the config below👇

345,146 Aufrufe • vor 4 Monaten

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced plug-and-play infrastructure layer that lets you run any harness under a single interface for free, like: - Codex - Claude code - Hermes - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, the article below explains how to set up Jev from scratch, check it out ↓

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced plug-and-play infrastructure layer that lets you run any harness under a single interface for free, like: - Codex - Claude code - Hermes - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, the article below explains how to set up Jev from scratch, check it out ↓

88,298 Aufrufe • vor 1 Monat

Anthropic's main manager: "Nobody types prompts from scratch. The commands should be live in the project." In 26 minutes, she walks through how Anthropic runs Claude Code, including the command library every new dev inherits on day one. Watch the full talk, then save the config below👇

Anthropic's main manager: "Nobody types prompts from scratch. The commands should be live in the project." In 26 minutes, she walks through how Anthropic runs Claude Code, including the command library every new dev inherits on day one. Watch the full talk, then save the config below👇

264,804 Aufrufe • vor 4 Monaten

Jev + Grok Bot... i can't understand why everyone isn't wiring this up yet the forks alone drop from about $110 a day to $4 once jev takes them over. right now it's the best way i know to run grok what jev handles: -> scores which project notes matter before the context even loads -> hands the easy steps to a faster worker -> picks a recovery path when a tool fails, so nothing retries blind -> runs the focused checks before the full test suite Grok does the hard reasoning. jev chooses from options the harness already prepared and validated that's jev engineering in one line: the expensive model thinks, the cheap one decides Ckeck out the full guide on Jev in the article below ↓ ↓

Jev + Grok Bot... i can't understand why everyone isn't wiring this up yet the forks alone drop from about $110 a day to $4 once jev takes them over. right now it's the best way i know to run grok what jev handles: -> scores which project notes matter before the context even loads -> hands the easy steps to a faster worker -> picks a recovery path when a tool fails, so nothing retries blind -> runs the focused checks before the full test suite Grok does the hard reasoning. jev chooses from options the harness already prepared and validated that's jev engineering in one line: the expensive model thinks, the cheap one decides Ckeck out the full guide on Jev in the article below ↓ ↓

34,246 Aufrufe • vor 16 Tagen

At Anthropic's event, Metaview engineer: "We stopped fixing our prompts. The system reviews its own output and rewrites its own instructions now." In 16 minutes, he shows the Claude Code loop running in production on thousands of reviews, not in a demo. Watch the talk, then grab the full loop setup below👇

At Anthropic's event, Metaview engineer: "We stopped fixing our prompts. The system reviews its own output and rewrites its own instructions now." In 16 minutes, he shows the Claude Code loop running in production on thousands of reviews, not in a demo. Watch the talk, then grab the full loop setup below👇

213,524 Aufrufe • vor 4 Monaten

Top 9 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 9 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. It captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: If you want to dive deeper, check out the full guide on Jev below ↓

Top 9 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 9 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. It captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: If you want to dive deeper, check out the full guide on Jev below ↓

47,630 Aufrufe • vor 1 Monat

Your JEV agent knows the answer. It doesn't know whether it should act on it every call returns two things: the answer, and the confidence behind it. most setups only read the first one. confidence comes from the spread. same 4 options, one distribution concentrated at 0.91, one flat at 0.22. identical answer, completely different decision. the fix is a bar that rises with the stakes: read-only 0.5 → refund 0.7 → escalation 0.8 → destructive 0.9 below 0.5 the ticket goes to a human. no guessing. two traps most people miss. a noul of 0.5 means "as likely yes as no", not "medium". and a confident "done" doesn't prove the file was saved. confidence is not permission, check the outcome. pin the version. aliases move, and thresholds tuned on one version quietly break on the next. check out the full article on Jev below ↓ ↓

Your JEV agent knows the answer. It doesn't know whether it should act on it every call returns two things: the answer, and the confidence behind it. most setups only read the first one. confidence comes from the spread. same 4 options, one distribution concentrated at 0.91, one flat at 0.22. identical answer, completely different decision. the fix is a bar that rises with the stakes: read-only 0.5 → refund 0.7 → escalation 0.8 → destructive 0.9 below 0.5 the ticket goes to a human. no guessing. two traps most people miss. a noul of 0.5 means "as likely yes as no", not "medium". and a confident "done" doesn't prove the file was saved. confidence is not permission, check the outcome. pin the version. aliases move, and thresholds tuned on one version quietly break on the next. check out the full article on Jev below ↓ ↓

30,276 Aufrufe • vor 1 Monat

Anthropic's Claude Code engineer Sid Bidasaria: "Stop babysitting your agents. If you're watching Claude work, you've already lost the leverage." In 37 minutes, he shows how to set up agents that run without supervision, and the config below takes it to your phone. Watch the talk, then grab the full setup below👇

Anthropic's Claude Code engineer Sid Bidasaria: "Stop babysitting your agents. If you're watching Claude work, you've already lost the leverage." In 37 minutes, he shows how to set up agents that run without supervision, and the config below takes it to your phone. Watch the talk, then grab the full setup below👇

95,901 Aufrufe • vor 4 Monaten

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

18,089 Aufrufe • vor 1 Monat

Anthropic engineer Arnaud Doko: "Saying 'make it better' to Claude Code is the most expensive mistake anyone can make." In 31 minutes, he walks through the exact prompt patterns, planning workflow, and verification setup Anthropic uses in-house. Watch the full talk, then save the config below👇

Anthropic engineer Arnaud Doko: "Saying 'make it better' to Claude Code is the most expensive mistake anyone can make." In 31 minutes, he walks through the exact prompt patterns, planning workflow, and verification setup Anthropic uses in-house. Watch the full talk, then save the config below👇

59,480 Aufrufe • vor 5 Monaten

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