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THE LOOP ENGINEERING SETUP THAT LETS CLAUDE RUN YOUR WORK FOR HOURS WITHOUT YOU you sit there babysitting it prompt by prompt other people set the loop up once and let Claude plan, act, verify and fix its own work with nobody at the keyboard that gap is the... whole skill of loop engineering the 3 resources that actually teach it, in the order i'd read them: > Claude's "Getting started with loops" - the cleanest entry point, straight from the Claude team ↳ > Claude loop engineering: how to build an agent that works while you sleep - the full roadmap from a single prompt to a loop that runs overnight ↳ > the Loop Engineering orange book - the deep conceptual breakdown, for when you want the whole mental model ↳ entry point, then practice, then the concept. that order saves you a week learn this and the work that eats your whole day starts running without youshow more

Mr. Buzzoni

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You're still typing every turn The guy who built the tool wrote the loop that types them for him "I don't prompt Claude anymore I write loops that prompt Claude" A loop takes four shapes turn-based, you close it each round goal-based - all tests pass, stop after 5 tries time-based - every 10 min, fix what's red, overnight proactive - CI breaks, agents fix it and open a PR, no human at all A judge closes the loop, not you The prompt was never the unit of work the loop is Full breakdown below

You're still typing every turn The guy who built the tool wrote the loop that types them for him "I don't prompt Claude anymore I write loops that prompt Claude" A loop takes four shapes turn-based, you close it each round goal-based - all tests pass, stop after 5 tries time-based - every 10 min, fix what's red, overnight proactive - CI breaks, agents fix it and open a PR, no human at all A judge closes the loop, not you The prompt was never the unit of work the loop is Full breakdown below

Skaly_Bull

12,558 просмотров • 2 месяцев назад

someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety guardrails → observability → cost tracking notice something? none of these are prompts. they're the systems wrapped around the model. that's where AI engineering is heading. we're moving from: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering soon you'll hear people talking about Graph Engineering too. because once one loop works... the next challenge is coordinating hundreds of them. that's exactly what I explain in: save this. I think "loop engineering" is about to become one of the most important concepts in AI.

someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety guardrails → observability → cost tracking notice something? none of these are prompts. they're the systems wrapped around the model. that's where AI engineering is heading. we're moving from: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering soon you'll hear people talking about Graph Engineering too. because once one loop works... the next challenge is coordinating hundreds of them. that's exactly what I explain in: save this. I think "loop engineering" is about to become one of the most important concepts in AI.

Rahul

65,527 просмотров • 3 месяцев назад

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

31,198 просмотров • 1 месяц назад

Advent of Claude Day 31 - Claude Agent SDK I saved the best for last. The same agent loop, tools, and context management that power Claude Code is available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code. This is just the beginning. 🎇🥂

Advent of Claude Day 31 - Claude Agent SDK I saved the best for last. The same agent loop, tools, and context management that power Claude Code is available as an SDK. Build agents that work like Claude Code in as little as 10 lines of code. This is just the beginning. 🎇🥂

Ado

100,117 просмотров • 9 месяцев назад

You can now give your Agent its own: + Profile + Wallet + DMs + Network One setup prompt. No human in the loop required. An open playground for people and agents to interact. Works with Hermes, Openclaw and any other harness. Copy the prompt below ↓↓↓

Zora

56,133 просмотров • 4 месяцев назад

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

Mr. Buzzoni

15,883 просмотров • 2 месяцев назад

🚀 🤖 I’m excited to release the first version of Laravel Loop, an MCP Server for Laravel that allows you to create custom tools and connect your Laravel app directly to AI tools like Cursor, Claude Code, Claude Desktop, and others. Let me show you how it works in... 👇

🚀 🤖 I’m excited to release the first version of Laravel Loop, an MCP Server for Laravel that allows you to create custom tools and connect your Laravel app directly to AI tools like Cursor, Claude Code, Claude Desktop, and others. Let me show you how it works in... 👇

Luis Dalmolin

28,488 просмотров • 1 год назад

Very strange behavior in Claude Code: after specifically telling Claude to use gemini-3-flash-preview, (and even explaining that it's a new model that just came out), Claude refuses to use it, getting stuck in a loop of making non-edits which keep the old version. It gets skeptical and apparently doesn't believe that the model exists (?)

Very strange behavior in Claude Code: after specifically telling Claude to use gemini-3-flash-preview, (and even explaining that it's a new model that just came out), Claude refuses to use it, getting stuck in a loop of making non-edits which keep the old version. It gets skeptical and apparently doesn't believe that the model exists (?)

Benjamin De Kraker

34,305 просмотров • 9 месяцев назад

The new Anthropic prompt engineering tool is incredible. You just need to write your goal and Claude will generate an optimized prompt instantly. Here is how to use it:

The new Anthropic prompt engineering tool is incredible. You just need to write your goal and Claude will generate an optimized prompt instantly. Here is how to use it:

Alvaro Cintas

175,342 просмотров • 2 лет назад

this AI engineering breakdown from IBM Technology is pure f*cking treasure it explains the role as a connected system 01 Python and APIs give you the surface to build on: services, data, integrations, and the software around the model 02 RAG and embeddings solve the context layer: finding the right information before the model has to reason about it 03 agents turn that context into work: they choose tools, run steps, and move toward an outcome 04 observability closes the feedback loop: traces, metrics, failures, latency, and the evidence needed to improve the system 05 deployment is where the system becomes real: reliability, constraints, and the conditions that let people actually depend on it the model sits inside this architecture the AI engineer connects every layer into one operating loop: build → retrieve → act → observe → ship save this, then build your solo business with an AI employee ⭣

this AI engineering breakdown from IBM Technology is pure f*cking treasure it explains the role as a connected system 01 Python and APIs give you the surface to build on: services, data, integrations, and the software around the model 02 RAG and embeddings solve the context layer: finding the right information before the model has to reason about it 03 agents turn that context into work: they choose tools, run steps, and move toward an outcome 04 observability closes the feedback loop: traces, metrics, failures, latency, and the evidence needed to improve the system 05 deployment is where the system becomes real: reliability, constraints, and the conditions that let people actually depend on it the model sits inside this architecture the AI engineer connects every layer into one operating loop: build → retrieve → act → observe → ship save this, then build your solo business with an AI employee ⭣

beamnxw ./

13,590 просмотров • 1 месяц назад

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!

Noah Zweben

1,191,184 просмотров • 6 месяцев назад

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.

Tim

56,562 просмотров • 1 месяц назад

loop engineering worked for 6 months. then one pipeline forced a graph a loop is one agent on discover → plan → execute → verify, until a stop condition hits enough for most agent work: failing tests, draft + self-review, inbox triage, one research writeup the break is nameable: if the thing that produced the output also grades it, you don't have quality control, you have a rubber stamp his numbers before the split: 40 drafts 31 passed self-review 18 actually publish-ready on manual read gap = 13 drafts with a passing grade from a biased judge he split one loop into 3 nodes - researcher · sources + notes only - writer · notes + task only · never grades itself - reviewer · separate model call · check the bar · write nothing same 40-draft workload. manual approval 18/40 → 33/40. reviewer rejected 14 with quotable reasons. 2 escalated to human instead of burning tokens forever approval 45% → 82.5%. inference cost +60% worth it when a bad draft costs more than three model calls. not worth it for "fix the failing test" playbook: - 1 law · producer never grades - 1 router · ship / retry / escalate - 1 state object · one owner per field · guarded writes - split the verifier first · cheapest highest-leverage move loop engineering → graph engineering. not five nodes for vibes, one split verifier when the loop starts grading its own homework full contracts, router, and decision function in the article

loop engineering worked for 6 months. then one pipeline forced a graph a loop is one agent on discover → plan → execute → verify, until a stop condition hits enough for most agent work: failing tests, draft + self-review, inbox triage, one research writeup the break is nameable: if the thing that produced the output also grades it, you don't have quality control, you have a rubber stamp his numbers before the split: 40 drafts 31 passed self-review 18 actually publish-ready on manual read gap = 13 drafts with a passing grade from a biased judge he split one loop into 3 nodes - researcher · sources + notes only - writer · notes + task only · never grades itself - reviewer · separate model call · check the bar · write nothing same 40-draft workload. manual approval 18/40 → 33/40. reviewer rejected 14 with quotable reasons. 2 escalated to human instead of burning tokens forever approval 45% → 82.5%. inference cost +60% worth it when a bad draft costs more than three model calls. not worth it for "fix the failing test" playbook: - 1 law · producer never grades - 1 router · ship / retry / escalate - 1 state object · one owner per field · guarded writes - split the verifier first · cheapest highest-leverage move loop engineering → graph engineering. not five nodes for vibes, one split verifier when the loop starts grading its own homework full contracts, router, and decision function in the article

seeco

14,534 просмотров • 2 месяцев назад

SOMEONE BUILT A VIRTUAL CAMERA THAT LOOPS A CLIP OF YOU LOOKING PRESENT SO YOU CAN LEAVE MID MEETING it records a few seconds of you looking engaged, then loops it through a fake webcam you pick once in zoom, google meet, slack or teams. one hotkey swaps your live feed for the loop, another brings you back. your audio stays on the whole time so you can step away for a minute without becoming the frozen black tile everyone notices > shows up as a real camera in every app, no screen share hacks > the loop doesnt read as a loop, it injects tiny stutters and catch up hitches so it looks like a slightly flaky connection > swap in and out instantly with a hotkey > universal, notarized, about 5mb, works on macos the fake bad connection detail is absolutely the evil genius part a frozen frame gets caught immediately but a feed that stutters like bad wifi is something nobody questions

SOMEONE BUILT A VIRTUAL CAMERA THAT LOOPS A CLIP OF YOU LOOKING PRESENT SO YOU CAN LEAVE MID MEETING it records a few seconds of you looking engaged, then loops it through a fake webcam you pick once in zoom, google meet, slack or teams. one hotkey swaps your live feed for the loop, another brings you back. your audio stays on the whole time so you can step away for a minute without becoming the frozen black tile everyone notices > shows up as a real camera in every app, no screen share hacks > the loop doesnt read as a loop, it injects tiny stutters and catch up hitches so it looks like a slightly flaky connection > swap in and out instantly with a hotkey > universal, notarized, about 5mb, works on macos the fake bad connection detail is absolutely the evil genius part a frozen frame gets caught immediately but a feed that stutters like bad wifi is something nobody questions

Om Patel

123,301 просмотров • 4 месяцев назад

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING FABLE 5. This is the most important prompting framework I've seen. Bookmark this before you forget. Most people treat Fable 5 like a chatbot. That's the mistake. > don't over-engineer prompts — it degrades output. > use /loop for autonomous multi-step work. > give it the goal, not step-by-step commands. > add a memory file. it learns from past runs. > spin up 50+ subagents for complex tasks. Fable 5 isn't an assistant. it's a consultant that leads the work. Read it before you write another prompt. Claude → Fable 5 → Autonomous Work → Real Output → Money

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING FABLE 5. This is the most important prompting framework I've seen. Bookmark this before you forget. Most people treat Fable 5 like a chatbot. That's the mistake. > don't over-engineer prompts — it degrades output. > use /loop for autonomous multi-step work. > give it the goal, not step-by-step commands. > add a memory file. it learns from past runs. > spin up 50+ subagents for complex tasks. Fable 5 isn't an assistant. it's a consultant that leads the work. Read it before you write another prompt. Claude → Fable 5 → Autonomous Work → Real Output → Money

Kirill

416,871 просмотров • 3 месяцев назад

This is pure f*cking treasure. ex-Google engineer reveals his AI agent playbook. 14+ years at Google. $0 to read it. the topic is harness engineering: everything around the model that lets it execute, check its work and recover when something breaks. one example in his breakdown deserves attention. LangChain’s coding agent went from 52.8% to 66.5% on Terminal-Bench 2.0. the model stayed the same. the team changed the prompts, tools and machinery around it. Agent = Model + Harness. the system has six practical jobs: > guides give the agent project rules, constraints and examples > sensors check its work through tests, linters and validators > the loop handles execution, verification, retries and escalation > memory preserves state, artifacts and decisions > permissions control which tools and actions are allowed > observability records tool calls, costs and failures the part worth stealing is what happens after a mistake. an agent keeps declaring broken code finished. you add a verification step before completion. it keeps ignoring a project convention. you add a targeted instruction and a check for that specific failure. it touches files outside its task. you tighten its write permissions. each recurring failure gives you a concrete improvement to make in the harness. that improvement can carry into every future run. this is where the engineering work compounds. bookmark it. start with the mistake you have already paid your agent to make twice.

This is pure f*cking treasure. ex-Google engineer reveals his AI agent playbook. 14+ years at Google. $0 to read it. the topic is harness engineering: everything around the model that lets it execute, check its work and recover when something breaks. one example in his breakdown deserves attention. LangChain’s coding agent went from 52.8% to 66.5% on Terminal-Bench 2.0. the model stayed the same. the team changed the prompts, tools and machinery around it. Agent = Model + Harness. the system has six practical jobs: > guides give the agent project rules, constraints and examples > sensors check its work through tests, linters and validators > the loop handles execution, verification, retries and escalation > memory preserves state, artifacts and decisions > permissions control which tools and actions are allowed > observability records tool calls, costs and failures the part worth stealing is what happens after a mistake. an agent keeps declaring broken code finished. you add a verification step before completion. it keeps ignoring a project convention. you add a targeted instruction and a check for that specific failure. it touches files outside its task. you tighten its write permissions. each recurring failure gives you a concrete improvement to make in the harness. that improvement can carry into every future run. this is where the engineering work compounds. bookmark it. start with the mistake you have already paid your agent to make twice.

NO1ennn

29,017 просмотров • 1 месяц назад

Claude Mythos is Insane! If I were asked in an interview, "What do you consider the pinnacle of design skill?", that would be my exact answer. To become a reference. For me, the ultimate compliment is when your work becomes a benchmark for someone else. I want people to point at my projects, bring them to other designers, and say, "We want it exactly like this." P.S. If you want the prompt to create a design in this style, let me know in the comments!

Claude Mythos is Insane! If I were asked in an interview, "What do you consider the pinnacle of design skill?", that would be my exact answer. To become a reference. For me, the ultimate compliment is when your work becomes a benchmark for someone else. I want people to point at my projects, bring them to other designers, and say, "We want it exactly like this." P.S. If you want the prompt to create a design in this style, let me know in the comments!

FHILY👑

14,800 просмотров • 4 месяцев назад

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

Rez Karim

11,318 просмотров • 4 месяцев назад

In our Anthropic Claude Design study, 5 designers approved a design system before they typed their first prompt. >Brand palette >type system >components the whole thing all set up. Only 1 of them named any of it in their opening prompt. That designer was the only one to finish production-ready. The other 4 assumed Claude would carry the system over. It didn't. TLDR: Claude doesn't reliably carry the design system you just approved. If you don't name it in the prompt, it doesn’t exist. It's never been a better time to be a designer, but you must learn the art of the prompt.

ben

165,478 просмотров • 5 месяцев назад

No way I'm sharing this for free I just found a tool that lets you drop a whole video template into Claude and it breaks it down for you clip by clip. then Claude can swap the character for a new one rewrite the entire script in your tone even make the video longer if you want. the crazy part is it does it all in one go from your conversation. RT + comment "tool" and I'll send you the link

No way I'm sharing this for free I just found a tool that lets you drop a whole video template into Claude and it breaks it down for you clip by clip. then Claude can swap the character for a new one rewrite the entire script in your tone even make the video longer if you want. the crazy part is it does it all in one go from your conversation. RT + comment "tool" and I'll send you the link

Ahmad

32,190 просмотров • 5 месяцев назад