HeyGen

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

HeyGen AI

Heading to a GamesBeat panel Tuesday. What’s actually working with Claude in game dev?

Hey all. Topic is “Beyond the Hype: ML, GenAI, and What Actually Works in Games.” Im focused on that beyond-the-hype aspect and don’t want to get mired down in either vague optimism (or its dystopian twin). The broader games industry is in a rough place: 1/3 of US devs laid off in the past two years per the Game Developers Conference survey, voice actors just came off a year-long strike about AI replicas, and a lot of the displacement is related to “AI productivity.” Sentiment in r/gamedev is somewhere between skeptical and openly hostile, and a lot of it is earned. So I don’t want to walk on stage waving a flag for either side. I want to talk about what actually ships, what quietly got pulled, and where the tooling is genuinely useful versus where it’s still not ready for production… I’ve been using Claude to bring agentic engineering techniques to our precut development at Beamable. I’ve also used it for some gamedev side projects, including a multiplayer chess game (Chessmata) and resurrecting the MUD I made back in the 90’s (Legends of Future Past) so I come at this from the perspective of a builder, not a pundit. If you’re using Claude anywhere in a game dev workflow (code, design tools, content pipelines, playtest analysis, MCP servers, dialogue systems, agent behavior) I’d love to learn more about what you’ve done. What works? What doesn’t? How deep have you gone, and what have you learned? GamesBeat’s audience is heavy on execs, investors, and press, and most of them don’t hear directly from actual builders. If you’ve got something you want that room to hear, this is a vector for it. Attribution or anonymous, your call. Recap and call-outs here Tuesday. submitted by /u/jradoff [link] [comments]

We compiled 42 of the Generative & Agentic AI interview questions (and how to actually answer them).

Hey Everyone, The AI engineering job market has shifted massively in the last 6 months. Interviewers are no longer just asking "how does a transformer work?" or "how do you write a good prompt?" They want to know if you can architect production-grade multi-agent systems, prevent RAG hallucinations, and manage state across LLM calls. I’ve been building a visual learning sandbox for multi-agent workflows (agentswarms.fyi), and today I just launched a completely free AI Interview Prep Module inside it. I compiled 42 top interview questions specifically for GenAI and Agentic AI roles. But instead of just giving a generic answer, the module breaks down the "Standout Answer" and teaches you the mental model of how to answer it like a senior architect. Here are two examples from the list: Question 1: When would you use a Multi-Agent Swarm instead of a single LLM with multiple tools? ❌ The average answer: "When the task is too complex, multiple agents are better than one." ✅ The standout answer: "You use a swarm to prevent context dilution and enforce the Principle of Least Privilege. If you give one 'God Agent' 15 tools and a 4k-word system prompt, its reliability drops and hallucination risk spikes. By routing to specialized sub-agents with narrow instructions (e.g., separating the 'Data Extraction Agent' from the 'Customer Chat Agent'), you isolate failure points and allow for parallel execution." Question 2: How do you handle hallucinations in a financial RAG pipeline? ❌ The average answer: "I would lower the temperature to 0 and give it a better system prompt." ✅ The standout answer: "I would decouple data extraction from text generation. I'd use a deterministic node or a strict JSON-enforced agent to only extract the hard numbers from the retrieved context. Then, I would pass that structured data to a separate Synthesis Agent. Finally, I'd implement an 'LLM-as-a-judge' evaluation loop before returning the final output to the user." What's in the full list? The 42 questions cover: RAG Architecture & Vector Databases Agentic Routing (ReAct vs. Planner-Executor) Evaluation metrics for non-deterministic outputs Security (Prompt injection prevention in multi-agent loops) You can read through all 42 questions, answers, and the "how to answer" breakdowns right in the dashboard here: https://agentswarms.fyi/interview-questions For those of you who have interviewed for AI Engineering roles recently, what is the hardest system design question you've been asked? I'd love to add it to the list. submitted by /u/Outside-Risk-8912 [link] [comments]

5 enterprise AI agent swarms (Lemonade, CrowdStrike, Siemens) reverse-engineered into runnable browser templates.

Hey everyone, There is a massive disconnect right now between what indie devs are building with AI (mostly simple customer support chatbots) and what enterprise companies are actually deploying in production (complex, multi-agent swarms). I wanted to bridge this gap, so I spent the last few weeks analyzing case studies from massive tech companies to understand their multi-agent routing logic. Then, I recreated their architectures as runnable visual node-graphs inside agentswarms.fyi (an in-browser agent sandbox I’ve been building). If you want to see how the big players orchestrate agents without having to write 1,000 lines of Python, I just published 5 new industry templates you can run in your browser right now: 1. 🛡️ Insurance: Auto-Claims FNOL Triage Swarm Inspired by: Lemonade’s AI Jim, Tractable AI (Tokio Marine), and Zurich GenAI Claims. The Architecture: A multimodal swarm where a Vision Agent assesses uploaded images of car damage, a Policy Agent cross-references the user's coverage database, and a Fraud-Detection Agent flags inconsistencies before routing to a human adjuster. 2. ⚙️ Manufacturing: Quality / Root-Cause Analysis Swarm Inspired by: Siemens Industrial Copilot, BMW iFactory, Foxconn-NVIDIA Omniverse. The Architecture: A sensor-data ingest node triggers a diagnostic swarm. One agent pulls historical maintenance logs via RAG, while a SQL Agent queries the parts database to identify failure patterns on the assembly line. 3. 🔒 Cybersecurity: SOC Alert Triage & Response Inspired by: Microsoft Security Copilot, CrowdStrike Charlotte AI, Google Sec-Gemini. The Architecture: The ultimate high-speed parallel routing swarm. When an anomaly is detected, specialized sub-agents simultaneously investigate IP reputation, analyze the malicious payload, and draft an incident response ticket for the human SOC analyst to approve. 4. 📚 Education: Adaptive Socratic Tutor & Auto-Grader Inspired by: Khan Academy Khanmigo, Duolingo Max, Carnegie Learning LiveHint. The Architecture: A strict "No-Direct-Answers" routing loop. The Student Agent interacts with the user, but its output is constantly evaluated by a hidden "Pedagogy Agent" that ensures the AI is guiding the student to the answer via Socratic questioning rather than just giving away the solution. 5. 📦 Retail/E-commerce: Returns & Reverse-Logistics Swarm Inspired by: Walmart Sparky, Mercado Libre, Shopify Sidekick. The Architecture: A logistics orchestration loop that analyzes a customer return request, checks inventory levels in real-time, determines if the item should be restocked or liquidated (based on shipping costs vs. item value), and autonomously issues the refund. How to play with them: You don't need to spin up Docker containers or wrangle API keys to test these architectures. You can load any of these 5 templates directly into the visual canvas, see how the data flows between the specialized nodes, and try to break the routing logic yourself. Link: https://agentswarms.fyi/templates submitted by /u/Outside-Risk-8912 [link] [comments]

Anthropic shipped 10 finance agent templates and the implications go way beyond finance

Anthropic dropped something massive this week and i think most people in this sub are going to scroll past it because the headline says financial services and assume it doesn't apply to them, it does. They released ten ready to run agent templates that work as plugins in cowork and claude code which are pitch builder, meeting preparer, earnings reviewer etc etc, each template bundles skills, data connectors and specialized subagents that handle pieces of the workflow independently. But the part that matters for everyone in this sub isn't in finance pecifically,its what this reveals about where claude is going. this is anthropic doing what they did with the creative connectors (adobe, blender, autodesk) but for an entirely different industry,this is the template for how claude enters every industry and if you're in legal, healthcare, consulting, real estate, whatever this is whats coming for your field. Agent templates bundling the specific skills and data connections your work requires, running as plugins inside the tools you already use. They're building infrastructure that makes claude essential to how work gets done across every function simultaneously The piece that connects to what a lot of us actually do dailyevery industry vertical is getting this treatment and the early movers in each category are the tools that plug into claude's ecosystem rather than fighting it. The creative production layer already consolidated around platforms like magichour ,kling, heygen. The outbound sales layer is consolidating around platforms like fuseai or salesforge or apollo that bundle data, sequences, dialer, linkedin under one login so now the finance layer is getting purpose built agent templates with native data connectors what's your read on how fast these industry specific templates roll out to other fields? submitted by /u/Jealous-Drawer8972 [link] [comments]

Animated pdf

Hello, do you have any ideas for combining tools to make a training PDF more engaging? For example: analysis by Claude AI + Heygen (creating an avatar that summarizes the key points in a video). Video output format for an LMS. The test I've done isn't bad, but it could be more impactful with better-designed content animations (flowcharts). The goal is to create training content that promotes understanding and engagement among the trainees. Thank you for your suggestions. submitted by /u/Glad_Focus_1925 [link] [comments]

The final nail in the coffin for entry level creative freelancers just dropped

Anthropic just officially released the blender mcp connector today alongside adobe ,splice and sketchup, you can now type "create a low poly beach scene with palm trees and sunset lighting" into claude and watch it build the entire thing in blender in real time tadaaa. They even became an official blender development fund patron This is claude directly controlling blender through the python api which means it can modify existing scenes, debug your node setups, batch apply changes across objects and even add custom tools to blender's interface. IT's not replacing blender, it's becoming a copilot inside blender. The implication for entry level freelancers is brutal and i say this as someone who was an entry level freelancer five years ago. The gigs that used to be bread and butter for junior 3d artists, simple product renders, basic scene compositions, low poly assets for games, architectural visualization blockouts those are now achievable by a marketing manager with claude desktop and zero blender experience. The blender mcp page literally says "amateur users who barely know blender can use natural language to describe models". And the part that made me actually pause is that it connects to the broader creative pipeline in a way that makes the whole thing end to end so claude writes your script and plans your content in the chat while blender mcp builds your 3d scene or product render and you take that output and run it through something like magichour or heygen for face swap or lip sync and remotion assembles the final edit programmatically . The entire creative pipeline from concept to finished deliverable can now be orchestrated by one person through claude and a handful of connected tools. Two years ago that pipeline required a 3d artist , a video editor,one motion graphics person plus someone managing the face swap and lip sync tools. Today it's one person with claude desktop and 200/mo in tool subscriptions producing comparable work for straightforward projects. what do you think this means for creative freelancing? Are you guys connecting the same dots ?? submitted by /u/Legitimate_Aerie_606 [link] [comments]

two years ago this sub had 12k members asking "is claude better than chatgpt for writing" and now the company is worth a trillion dollars

I joined this sub when claude 3 opus dropped and it was a completely different world in here, small group of people who'd stumbled onto something that felt genuinely different from chatgpt and couldn't shut up about it. The posts were stuff like "did anyone else notice claude actually admits when it doesn't know something" and "i think anthropic might be onto something here" loll yesterday google committed $40 billion, amazon committed $25 billion the same week and revenue went from $1 billion to 30 billion in fifteen months which is apparently the fastest growth in american tech history. Secondary market says a trillion dollars and eight of the fortune 10 are customers, the tool we were geeking out about in a tiny subreddit is now arguably the most important ai product in the world and i'm still processing that I'm not trying to brag about being early because being early got me exactly nothing except a tool i love using and talk about too much at dinner parties. I'm writing this because i think this community deserves a moment and this sub was one of the first places where people figured out what claude could actually do in practice, people here were sharing creative pipelines, coding workflows and research systems openly before the enterprise market caught on. My own story is tiny compared to some of yours but it means everything to me, i do video content production and when i found this sub someone here posted about using claude to redesign their creative workflow and i tried the same thing and ended up in a conversation where claude basically told me my problem wasn't my tools it was my architecture,it helped me audit everything i was paying for separately across runway, topaz, heygen, kling, a headshot tool i used twiceand consolidate most of it into magichour, then connect the pipeline to remotion for automated editing. That single conversation saved me roughly $120 a month and cut my production time by 40%. I went from billing $3k a month doing everything manually to $14k a month as a one person studio and claude was involved in almost every step of that growth But honestly my story isn't the pointm hundreds of people in this sub have stories like this and collectively those stories are part of why anthropic is where it is today, the use cases now generating $30 billion in revenue started as experiments shared in communities exactly like this one. The part of the news i care about most as a daily user isn't the valuation it's the 10 gigawatts of new compute capacity. Every single person in this sub has hit rate limits midthought and wanted to throw something, if $73 billion in combined investment means i stop seeing "you've reached your limit" during a client deadline then the entire deal is justified and i will personally write dario a thank you letter haha I m trying not to get ahead of myself about what this means long term because historically when startups become megacorps the product changes and not always for the better but right now in this moment i just feel grateful i found this tool and this community when i did what's your claude story, curious when you joined and what changed for you because i think today's a good day to share those submitted by /u/Jealous-Drawer8972 [link] [comments]

Released a Claude Code skill that drives a full ML fine-tuning platform end-to-end from one conversation

We released a Claude Code skill today that turns a conversation with Claude into a deployed fine-tuned SLM. Point Claude at a folder of production traces, answer a few questions, approve a handful of checkpoints, and come back to a managed-endpoint deployment (or download the weights and self-host). The skill drives the process and distil labs platform does the heavy lifting under the hood (platform is free for open-source projects — details at the bottom). Concrete walkthrough from our release: 327 noisy production traces from a restaurant-reservation agent (the kind of data you can't train on directly) turn into a fine-tuned Qwen3-1.7B for multi-turn tool-calling. Install ``` curl -fsSL https://cli-assets.distillabs.ai/install.sh | sh distil signup /plugin marketplace add https://github.com/distil-labs/distil-cli-skill /plugin install distil-cli@distil-cli-skill ``` Then point Claude at your traces. What the skill actually does When I asked Claude "what are we about to do?" at the start, it laid out the 5-step workflow without me prompting for any of it: ``` Hey Claude, we will be training an SLM for multi-turn function calling based on the production logs in scenario-2-noisy-labels/. Can you explain the process on a high level? ⏺ 1. Prep. Inspect the traces, then write a converted traces.jsonl (OpenAI message format), a job_description.json, and a config.yaml. Upload & clean. The platform filters low-quality traces and uses a committee of teachers to relabel noisy outputs. Teacher eval. Verify the teacher can actually solve the task before spending hours on training. Train. Teacher generates synthetic data, student learns from it. Analyze & deploy. Compare the tuned student against the original production model, then deploy. ``` Every expensive step has a checkpoint where Claude stops for approval. Training burns credits that aren't easily refundable, so the confirmation checkpoint looked like this: ``` ⏺ Step 8: training confirmation checkpoint. Here's what will run: base.task: multi-turn-tool-calling-closed-book base.student_model_name: Qwen3-1.7B base.teacher_model_name: zai.glm-5 trace_processing.convert_to_single_turn: false trace_processing.teacher_model_name: zai.glm-5 Duration: ~6 hours Cost: burns credits, not easily refunded once started Pipeline: teacher eval → synthetic data gen → student finetune Reply "go" to kick off, or tell me what to change first. lets go! ``` The verdict-before-training gate refuses to even reach that checkpoint if teacher eval can't clear 0.70 on the generated test set. In that case, the skill walks you through iterating the task description instead. The skill also knows task-specific configs. For multi-turn tool-calling, I didn't have to look up that convert_to_single_turn: false is required. Claude flagged it as part of a config sanity check. Every checkpoint leaves a structured markdown analysis report (original-model-analysis.md, teacher-eval-analysis-iter-1.md, training-analysis-iter-1.md). Git-committable, reviewable three weeks later when someone asks why you picked this teacher. What came out A Qwen3-1.7B fine-tuned on ~10k synthetic examples grounded in the noisy traces. Model LLM-as-a-Judge staged_tool_call Function match Qwen3-1.7B (base, untuned) 0.513 0.535 45/78 GLM-5 (744B teacher) 0.808 0.695 69/78 Qwen3-1.7B (tuned) 0.846 0.769 76/78 Deployment Managed OpenAI-compatible endpoint (one-line swap in existing OpenAI client code), or download weights + Modelfile for llama.cpp or vLLM. Skill drives either path. Why it works as a skill Most skills I've seen wrap a few CLI commands but this one is end-to-end: reads your data, writes custom scripts, orchestrates an external platform, interprets the results, and leaves artifacts behind that persist past the conversation. The pattern that worked: Knows the workflow end-to-end and walks you through it Catches edge cases by re-reading the platform's own docs mid-conversation Stops for explicit approval on expensive operations Leaves structured artifacts that outlast the conversation Caveats Training is ~6 hours per run and burns credits (not refundable once started, which is why the confirmation gate exists). Happy to dig into how the checkpoints work, the config-sanity-check logic, or what building a purpose-built skill looked like. submitted by /u/party-horse [link] [comments]

I Built a desktop app for generating LLM fine-tuning datasets — started it a week ago while learning FT

Hey, I've been building side projects with Claude Code for a few months, but I'm completely new to fine-tuning — started experimenting maybe a week ago. From day one I wanted a GUI for the dataset side of the workflow, so this desktop app grew alongside my very first FT attempts. I know there are similar apps out there, but I wanted something simple that non-technical users could run with open-source models end-to-end. To sanity-check whether the datasets were actually useful I fine-tuned Qwen2.5-Coder-7B-Instruct on them and ran HumanEval / HumanEval+ (pass@1, 5 runs). Picked these benchmarks because they match the dataset's focus and run fast on my machine: I know it's not much but know now that app work :) - Base: 55.5% / 49.0% - FT V2 (1135 samples from the app): 60.0% / 54.0% Error bars don't overlap so it's at least not noise. Obviously HumanEval is only one slice — YMMV with other categories / criteria. https://reddit.com/link/1srz5aq/video/zubr426holwg1/player Stack: Next.js 16 + FastAPI + SQLite, packaged as standalone binary (Win/Linux). Code: https://github.com/AronDaron/dataset-generator Fine-tuned model: https://huggingface.co/AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 Datasets: https://huggingface.co/datasets/AronDaron/dataset-gen-v1 / https://huggingface.co/datasets/AronDaron/dataset-gen-v2 Happy to hear feedback, especially if something doesn't work on your setup or if the approach misses something obvious — this is my first finetune llm tool release. submitted by /u/AronSan [link] [comments]

HyperFrames — OSS framework for AI agents to author video as HTM

Been building this with my team at HeyGen for a while and today we are releasing it to the world. HyperFrames is an open-source HTML-to-video framework where the authoring format is plain HTML with a few data attributes, and the renderer outputs deterministic MP4. The reason for "HTML as the format" is specifically agents: every LLM writes HTML fluently, so a composition is a 60-line file the agent can emit in one shot. The CLI installs skills for Claude Code / Cursor / Gemini CLI as slash commands (npx skills add heygen-com/hyperframes). The agent learns the schema on install and can generate correct compositions from prompts like: ▎ Using /hyperframes, create a 10-second product intro with a fade-in title, background video, and background music. or take existing context and turn it into a video: ▎ Summarize the attached PDF into a 45-second pitch video using /hyperframes. Under the hood the renderer pauses the composition and drives Chrome via BeginFrame, seeking frame by frame and capturing pixel buffers. Output is byte-identical across runs, so CI caching and shard-parallel rendering work. There is a frame-adapter pattern that lets GSAP, Lottie, CSS, Three.js, and (experimentally) Remotion coexist in one composition. Each runtime has a small adapter that translates HyperFrames' seek into the runtime's native API. On the "why not Remotion" question: Remotion is great, but the authoring model (React component tree, durations in frames) is a lot for an agent to get right on the first try. Plain HTML with data-start / data-duration is the smallest schema I could find that still produces correct video. This is something we built inside HeyGen as part of our work on video generation, and we decided to open source it because we think the agent-first authoring model is useful for the whole community, not just for us. Limitations: no real-time collab, no keyframe editor, no effect graph. It is a headless renderer plus a small studio for preview. Repo: https://github.com/heygen-com/hyperframes Docs: https://hyperframes.heygen.com Apache 2.0. Node 22+, FFmpeg required. Happy to answer questions about the agent workflow, BeginFrame capture, the adapter pattern or use cases in the comments! submitted by /u/Miguel07Alm [link] [comments]

I built Synapse AI: An open-source, DAG-based orchestrator for AI agents.

Hey Everyone, For the past three months, I’ve been building an open-source orchestration platform for AI agents called Synapse AI. I started this because I found existing frameworks (like LangChain or AutoGen) either too bloated or too unpredictable for production workflows. Letting agents freely "chat" with each other often leads to infinite loops, high API costs, and debugging nightmares. I wanted strict, predictable control. The Architecture: Instead of conversational routing, Synapse AI relies on a Directed Acyclic Graph (DAG) architecture. You define the work, strictly control the hand-offs between agents, and get a completed task on the other side. Under the Hood: Tool Agnostic: Build custom tools from scratch (Python/webhooks) or instantly plug in existing Model Context Protocol (MCP) servers. Local-First Emphasis: Full native support for Ollama so you can run routing and tasks entirely locally. (It also supports Gemini, Claude, and OpenAI for the heavy lifting). CLI Integration: Just shipped a community-requested feature to connect Claude Code, Gemini CLI, Codex CLI, and GitHub Copilot CLI directly to your agents. Frictionless Setup: A 1-step installation process across macOS, Windows, and Linux. What I'm looking for: I am currently maintaining this solo and rolling it out for an early pilot phase. I would love for this community to take a look under the hood. Specifically: Code Review: I’d love brutal feedback on the DAG implementation and overall architecture. Contributors & Collaborators: If you find the project worthwhile, I am actively looking for people to team up with! Whether it's adding new LLM providers, fixing UI quirks, or improving the 1-step installer, PRs are incredibly welcome. Repo: https://github.com/naveenraj-17/synapse-ai If you bump into any bugs, please drop an issue so I can patch it. Would love to hear your thoughts! submitted by /u/WabbaLubba-DubDub [link] [comments]

Sora is dead. What's everyone actually using now?

So OpenAI finally pulled the plug on Sora. Can't say I'm shocked honestly. The writing was on the wall for a while with how they handled access and the whole vibe around it felt off. Anyway, doesn't really matter now. Point is a lot of people (myself included) were holding out hoping Sora would be "the one" and now we gotta figure out what actually works. I've been testing pretty much everything over the past few days so figured I'd share what I've landed on(Actually hoping if you guys could guide me better ) For text-to-video (cinematic/realistic stuff): Kling 2.0 looks genuinely impressive for the price Motion quality is wild. Runway Gen-3 still has the edge on pure quality but you'll burn through credits insanely fast. Veo 2 from Google is worth watching but access is still weird For image-to-video / animating stills: Luma Dream Machine works well for quick generations. Magic Hour has been solid for me too, especially for product shots and turning AI images into clips. Not as flashy as Runway but the credits stretch way further which matters if you're actually producing volume. For face swap / lip sync: Honestly here i need your help .For me HeyGen looks fine but i think there might be some better alternative out there For stylized / video-to-video: Kaiber still works. Pika is fun for experimental things(not a fan of their ui) and Kling handles this decent too. Stuff I gave up on: Pika for anything serious (too inconsistent), waiting for any OpenAI video product at this point Curious what everyone else has migrated to. Feels like the landscape just shifted again and I'm probably missing some newer tools. submitted by /u/Healthy-Challenge911 [link] [comments]

Built a presentation orchestrator that fires n8n workflows live on cue — 3 full pipelines in the repo

Finally built something worth sharing. I run AI workshops. The biggest pain point wasn't the content — it was live demos breaking mid-talk. API timeouts, HeyGen rendering for 8 minutes, webhooks failing in front of 40 peopl. The fix I landed on: separate generation from runtime entirely. BEFORE the talk: - System reads every slide from the PPTX - Claude writes narration scripts per slide - Edge TTS (or HeyGen) synthesises all audio/video - Everything cached with a manifest DURING the talk: - Orchestrator reads the manifest - Plays audio, auto-advances slides - At configured slides (e.g. slide 8), fires n8n webhook automatically - Final slide opens a mic — audience asks questions, Claude answers aloud - Zero API calls at runtime. Fully deterministic. The three n8n workflows I built for the live demos: Email Pipeline — Gmail trigger → Claude classifies + drafts reply → escalation router → Gmail + Sheets log Meeting Pipeline — paste any transcript → Claude extracts action items, decisions, risks → Gmail to all attendees + Slack + Sheets Evidence Intelligence Engine — research question → Claude decomposes into sub-queries → dual Perplexity search (web + academic) → quality evaluation → iterates if needed → Google Doc brief + Slack All three are importable JSON files in the repo. Reconnect your credentials and they work out of the box. Architecture diagram and full setup in the README. Repo: https://github.com/TrippyEngineer/ai-presentation-orchestrator Happy to answer questions on the n8n side specifically — the webhook trigger timing and the agentic research loop were the trickiest parts to get right. submitted by /u/Trippy666_ [link] [comments]

Claude Code workflow for a multi-device setup?

Hey y'all, total Claude Code noob here, so please go easy on me! I’m still figuring out the ropes. 😅 I’ve been using Claude Code on my MacBook while I’m out, but when I get home, I want to be able to jump onto my Mac Mini and pick up exactly where I left off without losing the flow. A couple of questions for the pros: Syncing: Is it possible to seamlessly sync the workspace AND the AI context/chat history between two machines? How are you guys handling this? Folder Structure: Right now, I’ve got a master folder in iCloud called Claude_Code. Inside, I have my .claude.md (for custom instructions) and a .env file for my credentials. Whenever I start something new, I just pop a sub-folder in there. Current setup looks like this: Claude_code/ Scraper-Tool/ Image-Gen/ Web-Dev/ HR-Tool/ Random/ (for those "ooh, shiny" ideas that don't have a home yet) Am I doing this wrong? These projects are all for my business but they aren't really related to each other. The Goal: I'm trying to build enough custom tools to finally cancel all my expensive SaaS subscriptions. Also, if anyone has tips on security/preventing getting hacked while doing this, I'm all ears. Don't want to accidentally leak my API keys to the void. Thanks in advance! submitted by /u/sgcircle [link] [comments]