Outreach

Outreach AI

Outreach AI

Outreach AI

Outreach AI

Outreach AI

Outreach AI

Outreach AI

Outreach AI

Outreach AI

Outreach AI

$4.2M SaaS founder. 8 months on claude. my honest read on which model to use for what.

Bay area. franchise ops SaaS. 8 years in. $4.2M ARR. 22 employees. 8 months into using claude across most of my workflow. wanted to share what i've actually learned about model selection because nobody at my level writes about this. my opinion. you should be using 3 different claude models for 3 different jobs. most founders i talk to are using one model for everything and it's hurting them. opus 4.7 (the new flagship). i use this for any work where the cost of being wrong is high. board memos. customer escalation responses. legal docs. acquisition outreach. work where i'd spend 4 hours writing and editing myself. opus produces a draft in 8 minutes that's 90% of where i'd end up after 4 hours. the cost saving is real. the marginal quality improvement over sonnet for high-stakes work is also real. sonnet 4.6. my workhorse for high-volume daily work. emails, summarizing meetings, drafting slack updates, processing customer feedback into themes. i probably hit sonnet 200+ times a week. cheaper, faster, and for "i need a competent draft i'll edit" work, it's the right tool. haiku 4.5. for repeated structured work. transcribing voice notes into action items, parsing customer support tickets into categories, batch-classifying things. haiku is what i'd use if i was building automation. nobody talks about haiku because it's not glamorous. it's the model i use most via API. my actual cost split. about $80/month on the claude pro plan (opus + sonnet via the app). about $140/month on API costs (mostly haiku for automation, some sonnet for batch work). what i learned that surprised me. using opus for everything is wasteful AND hurts your output. opus is over-thoughtful for low-stakes work. sonnet is faster and better-calibrated for "i just need a competent answer." the difference between opus and sonnet is most visible in writing tasks where TONE matters. legal docs, board memos, sensitive customer comms. for "summarize this meeting" tasks, sonnet is equally good. claude code is its own conversation. i use it for analysis tasks that touch files. running our customer cohort analysis. generating cohort retention reports. that's mostly opus inside claude code. submitted by /u/Strong-Reserve-3232 [link] [comments]

Thinking about upgrading to Claude Max—worth trying for a month?

Hey everyone, I’m currently on Claude Pro, but I hit limits really fast—especially when I’m using it for Google research or other tasks for my personal trainer business. I’m planning to push Claude Max to its full potential: editing my website, messaging and approaching people on social media, basically letting it handle a lot of my workflow. I’m thinking of trying it for one month to see how much it actually helps. Most reviews I’ve found are from coders or technical users, but I feel like there’s a lot of potential for casual users who want to do a lot of real-world tasks. Has anyone done this? How’s your experience using Claude Max for heavy business or outreach stuff if you’re not a coder? submitted by /u/No_Structure_1029 [link] [comments]

Idk how to code but I built my entire prospecting stack with Claude Code

I cant code at all. But i spent about a few hours over a weekend building a full outbound prospecting system with Claude Code and a couple of APIs. It replaced a very manual set up we had with multiple tools. Sharing the workflow because i think more people should know this is possible now without an engineering team. The setup: i have ICP criteria saved in a local text file on my desktop. Industry, headcount range, funding stage, target personas, the usual. Claude Code reads that file as context for everything it does. The workflow: Company search. Claude Code hits a data API with my ICP filters and pulls back matching companies. Headcount, funding, tech stack, hiring signals, all structured. I was using Exa before for web search but the data wasnt structured enough for this. People search within those companies. Filtered by persona, so i'm only pulling Directors of Sales, Heads of Revenue, VP Marketing, whatever matches my buyer. Contact enrichment. Emails and phones through a waterfall provider. Multiple sources checked, only pay for verified contacts. Personalization layer. Pull recent social posts and activity for each contact. Claude Code reads through their posts and drafts personalized openers referencing something specific they said or shared. This is where the AI part actually matters. Monitoring. Set up webhooks for job changes and hiring signals at target accounts. When someone new joins a company on my list or a company starts posting roles in my space, i get an alert and Claude Code auto-generates the outreach. The whole thing runs on three tools: Crustdata - company and people search, firmographics, hiring signals, social posts. API only so Claude Code queries it directly. FullEnrich - email and phone waterfall. 20+ providers, verifies inline, only charges for verified contacts. Also API based so it plugs straight into the workflow. Instantly - sending. Manages multiple inboxes and warming. Nothing fancy here, just needed something reliable for delivery. Some things I learned: Read the API docs carefully before you start building. i burned through a bunch of credits using the expensive realtime endpoint when the cached version would have been fine for 90% of my searches. 33x cost differnce. Claude Code is really good at chaining API calls together if you give it enough context about what you want. i just described the workflow in plain english and it built the scripts. The ICP file is key tho, without that context it doesnt know what to filter for. Its not perfect. Still iterating on the personalization quality and the webhook alerting sometimes fires on irrelevant job postings. But for a weekend build with zero coding ability, its replaced tooling thats very cumbersome and not as effective If you're a solo founder or small team running outbound and paying for 4-5 different tools, this is worth trying. Claude Code plus one good data API plus a sending tool is all you need imo submitted by /u/Unspoken_Table [link] [comments]

20 Claude Skills for Marketing, Launch and Sales built for technical people

Curated this list of 20 Claude Skills for devs to get help with marketing, sales, launch: Content human-tone: scans your copy against 18 GTM slop patterns and rewrites it. basically a linter for marketing language cook-the-blog: researches a company, extracts SEO keywords, writes a case study in MDX, generates a cover image, pushes to GitHub. one command noise-to-linkedin-carousel: paste rough notes or a voice transcript, get a carousel with hook and CTA. good for people who think faster than they write tweet-thread-from-blog: turns any blog post into a 7-10 tweet thread. optionally posts to X via Composio linkedin-post-generator: reads a GitHub PR or article, produces a post with the right hook and story arc Sales discovery: run a proper needs assessment before you pitch anything. most DevRels skip this and go straight to the demo. biggest mistake. objection-handling: "we already have something for this" and "our engineers will build it" are the two you'll hear constantly in developer sales. this is the one to internalize. storytelling: case studies and narratives move technical buyers more than feature lists. if you can make someone see themselves in a story, the sale is mostly done. qualifying-leads: not every inbound is worth chasing. knowing who to drop early saves more time than any outreach optimization. closing: DevRels are usually great at building trust and terrible at asking for the next step. this one bridges that gap. Intelligence gh-issue-to-demand-signal: give it a competitor's public GitHub repo. clusters open issues into demand categories, scores by engagement, outputs a GTM messaging brief. surprisingly useful for competitive research where-your-customer-lives: give it your ICP, it searches Reddit/HN/DuckDuckGo to find the actual communities your customers are in. per-channel entry tactics hackernews-intel: monitors HN for your keywords, Slack alert on match, no duplicates. runs on cron or GitHub Actions map-your-market: searches Reddit, HN, GitHub Issues, G2 for pain signals. outputs ICP definition and messaging angles competitor-pr-finder: finds where your competitors got covered, which journalist wrote it, and the angle that got them in. gives you a ready-to-send cold pitch Launch + Outreach show-hn-writer: drafts a Show HN post based on patterns from 250+ real HN submissions. generates 3 title variants, runs a review pass to catch anti-patterns before you post producthunt-launch-kit: taglines, listing copy, maker comment, tweet thread, LinkedIn post, 4-email sequence. all from one product description outreach-sequence-builder: buying signal in, 4-6 touchpoint sequence out across email, LinkedIn, phone cold-email-verifier: guesses, enriches, and verifies emails from a CSV autonomously npm-downloads-to-leads: give it npm package names, it pulls 12 weeks of download data, maps maintainers to GitHub/Twitter, outputs who to reach out to and what to say Link in comments 👇 submitted by /u/Sam_Tech1 [link] [comments]

AI agents fail in ways nobody writes about. Here's what I've actually seen.

Not theory. Things that broke on me running real workflows. Context bleed. Agent carries memory from a previous task into the next one. Outputs start drifting. By step 6 of 10, it's confidently wrong in ways that are hard to catch. Confident wrong answers. Agents don't say "I don't know." They fill gaps. In outreach automation this means sometimes writing a personalised message that references something that doesn't exist. The model just invented a plausible detail. This is the one that costs the most with clients. The human review queue nobody designed for. You build 90% autonomous. The 10% that needs review piles up silently. Two days later, 47 things are waiting and the whole pipeline is stalled. The workflow needed a notification system before it needed the AI. None of these are model problems. They're systems problems. The AI part is usually the least broken part of an AI agent. What failures have you seen that aren't on this list? submitted by /u/Scary_Historian_9031 [link] [comments]

AI - IT TAKES ONE TO CATCH ONE: ChatGPT as Sheriff in the Digital Wild West

Mixed feelings abound surrounding Artificial Intelligence (AI). I am a fan and use it daily to search. I rarely seek an opinion but rather pose questions that I could likely answer myself if I had the time and access to more databases. This is the story of coming uncomfortably close to being scammed by AI and then being saved by AI from (at the very least) embarrassment and (potentially) financial and reputational harm. As someone who meets definitions of both 1. “Old and At Risk” – i.e., 79, and 2. “Educated and Should Know Better” – i.e., Having successfully completed 23rd Grade I offer this cautionary tale: The Story: As the 2024 author of an underappreciated (and definitely under-selling) book, I was surprised to receive an extremely well-written email via my book’s website. Politely describing my less-than-bestselling effort as “in the early stages of reaching that wider audience,” the writer offered a “brief conversation to share a few ideas on how books like yours often gain traction with the audiences who need them most.” They described themself as one who “works with authors . . . helping position them so they connect more effectively with readers who are actively looking for answers.” Sucked in with well-written flattering prose, I replied, agreeing to the suggested “brief conversation”. After a second prompt and flattering email and offering availability to talk, the words “Perhaps too good to be true” crossed my mind. I searched for the writer on Google and found nothing, so I queried ChatGPT: MY QUESTION to ChatGPT: “A writer and their ‘team’ have emailed offering to help me with promoting my book. What can you find about them?” Milliseconds later came the: “SHORT ANSWER”: "I cannot find any credible, verifiable person or legitimate company behind [this letter writer] doing book promotion—and that’s actually a red flag in itself. What I can find strongly suggests this fits a very common, ongoing scam pattern targeting authors.” “FINAL ASSESSMENT" by ChatGPT after reviewing the letter. If I had to categorize this: • High likelihood: mass outreach template • High likelihood: paid marketing pitch coming next • Moderate risk: scam or zero-value service • Low likelihood: legitimate, high-quality publicist The Safest Option is: “IGNORE COMPLETELY.” Reference Provided: Anne R. Allen blog on AI book marketing scams submitted by /u/ResearchAware7810 [link] [comments]

Claude helped us get into the Partner Network, now it's helping us solve the 10 person problem

We're a two person AI consultancy that's been building with Claude for the past year. Agent workflows, MCP server integrations, full-stack AI products for clients. Claude has honestly been central to how we operate. When the Partner Program opened up, we used Claude to help write the application, structure our pitch, and figure out positioning. Got the acceptance email and were pumped. Then we hit the 10 person requirement. For context, there are two of us. We're not a big agency. We deliver real implementation work but we don't have 10 people sitting around. So we've been using Claude to help solve that problem too. Writing outreach, identifying what specialties we're missing, even drafting the LinkedIn post we boosted to find people (which actually worked, we've gotten solid responses from experienced independent devs and fractional CTOs). What we're building is basically a bench of certified independents. Different specialties like full-stack, DevOps, healthcare AI, security, agent architecture. Everyone gets through the four Anthropic Academy courses (Building with Claude, Prompt Engineering, Tool Use, Claude for Enterprise), and then we've got a certified group that can go after implementation work together that none of us could land solo. We're making progress but it's been a grind. Curious about a few things: Has anyone else pulled together outside independents to meet the 10 person requirement? How did that go? For those who completed the training courses, how long did it take and what did you think of the content? Is there any kind of partner community or Slack where people are sharing notes on this process? Would love to connect with others going through the same thing. submitted by /u/Correct-Alfalfa3427 [link] [comments]

With just one prompt, AI successfully found and emailed 200 potential investors for my startup.

I’m a solo founder, and fundraising outreach used to drain me — scraping emails, checking duplicates, writing personalized cold emails, and logging everything to Notion. Hours of grind per batch. So, I built one prompt that does all of it. I paste it into any AI agent (Claude Code, Cursor, Windsurf, whatever), and it: Searches the web for relevant investors, partners, or customers. Checks my Gmail + Notion to ensure no one is contacted twice. Writes a personalized email for each one (no generic templates). Sends every email individually via my SMTP. Logs everything to Notion with thread IDs. Auto-corrects itself if something fails. Yesterday, it found and emailed 200 targets while I made lunch. Zero duplicates. Full audit trail in Notion. Multiple replies already. This works for investors, customers, B2B partners, job applications — anything that requires personalized mass outreach. The entire skill file is open-source: 👉 github.com/samihalawa/swarm-massive-outreach-skill Just drop it into your AI agent, plug in your SMTP + Notion creds, edit the 5 lines about your startup, and run it. One prompt. Done. Happy to answer questions in comments. submitted by /u/BlacksmithHot17 [link] [comments]

AI is getting better at doing things, but still bad at deciding what to do?

i've been experimenting with AI workflows/agents over the past few weeks, and sth keeps coming up that i cant quiet figure out. on one hand, AI is incredibly good at execution like writing content, summarizing, even handling multi step workflows, but the failures i keep seeing arent really about capability. they're about small decisions like: - choosing the wrong context - missing edge cases - continuing when it should stop and ask for clarification - applying the right logic in the wrong situation whats weird is these arent hard problem, they're the kinds of judgement calls human make without thinking. a simple example i ran into was i tried automating basic lead qualification + outreach flow using AI. it worked great on clen data, but as soon as inputs got messy (incomplete info, slightly ambiguous intent) the system didnt fail loudly, it just kept executing, incorrectly. it feels like execution is mostly solved, but decision making inside workflows is still very fragile. i recently came across approaches like 60x ai that seem to focus on structuring context and decision layers around workflows, rather than just improving prompts or chaining tools. im curious how people think about this. do u see the main bottleneck now as: - improving model outputs (better prompts, better retrieval) or - improving how decisions are made across a system (context, logic, orchestration)? would love to hear from people who've tried building or running these in real world scenarios submitted by /u/Tough_Daikon_4321 [link] [comments]

I built an AI biz dev assistant that keeps my pipeline alive while I'm heads-down on client work — here's exactly what it does (Studio of One, Ep. 3)

Quick context if you missed the earlier posts: I run a one-person 3D animation studio and built 6 specialized AI team members using Claude Cowork plugins. Not chatbots — persistent, role-specific assistants that know my business. I'm documenting the whole thing in a video series called Studio of One. Episode 3 just went up. This one is about Reid — my biz dev assistant. And it's the role that probably saves me the most money. The problem Reid solves: When you run a creative business solo, the work and the finding of the work cannot happen at the same time. You're either making the thing or marketing yourself — never both. Every freelancer knows the feast-or-famine cycle that comes from this. But there's a second layer: even when you DO have time for biz dev, the research alone kills you. You can't blast generic emails. You have to dig into a brand, find the right person, figure out the angle. That's hours per lead. What Reid actually does (the plugin architecture): 1. Prospect Research + Outreach. Before writing anything, Reid pulls company data, finds the decision-maker, looks at their recent campaigns, and identifies a specific angle — a weak product render, a new launch that needs visualization, something real. Then drafts a 4-6 sentence email that leads with that observation. No "I hope this finds you well." No portfolio dumps. 2. Follow-Up Tracking. This is the one that saved me. I'm terrible at follow-ups — not because I don't know they matter, but because by the time I remember, it's been three weeks and I convince myself the moment is gone. Reid tracks what's outstanding, drafts follow-ups that don't sound desperate, and is honest when a lead is dead ("send one clean final message or close it out"). 3. Pitch Prep. When someone agrees to a call, Reid builds a pre-call brief: who you're talking to, what they care about, where your work is relevant to them, five smart questions, things to avoid. The difference between winging a call and showing up prepared is the difference between being treated as a vendor vs. a peer. 4. Strategy + Positioning. Broader questions — pricing, retainers vs. project work, which communities matter, when to walk away. Not replacing gut instinct, but giving me something informed to push against. The honest part: Reid can't build relationships. He can't tell me which projects to take. He can't replace the instinct that comes from years of doing this. But the pipeline doesn't go dark anymore. Follow-ups happen on time. New conversations start before old projects end. I'm not writing cold emails at 10 PM that I should have sent three weeks ago. How it's built (technical): Same plugin architecture as the other roles — a Cowork plugin with skills for each task type. The outreach skill has my voice guide, portfolio context, and constraints about how I approach clients. The research skill connects to web tools. The follow-up skill tracks state across conversations. The key design choice: Reid has a persona that's strategic, slightly blunt, and willing to tell me a deal is dead. That constraint shapes everything he outputs. It's not "write me a cold email" — it's a role with a perspective. If you want to start from the beginning, Episode 1 is the overview and Episode 2 is a full build-along for your first AI employee. Happy to answer questions about the biz dev plugin architecture specifically. submitted by /u/markyc120 [link] [comments]

Where to start and is Cowork the appropriate tool

Starting off by saying im very new to the AI world outside of using ChatGPT or claude chat. I run a few different businesses and trying to better understand if Cowork is the best solution for my business. I run an influencer marketing company managing both creators and brand campaigns where I get hundreds of emails a day and just sifting through the junk (my clients get so much outreach) is a challenge. Outside of that my biggest pain point is contract building and review (building about 60-80 basic agreements a month via templates i have for each brand client). Additinonally I run all accounts receivable and payable via Bill (AP) and Quickbooks (AR). Paying out on 80-150 deals a month as well. My other job is a concert production company where I put on about 10-20 concerts per month across multiple venues in different cities. All of this is tracked inside Monday.com and all of our offers/settlements/contracts are housed and built inside dropbox via excel docs. It feels like cowork is the move. Are there any good places to look on how to learn and utilize it? Maybe im having bad luck but on YouTube most of the "how to use claude" videos have been not the most helpful. submitted by /u/lennytha3rd [link] [comments]

Considering testing my human–AI collaboration system in Claude — looking for advice

⚠️ Long post incoming ⚠️ ✅ The gist: I’m exploring Claude more seriously and considering a limited portability test of a human–AI collaboration system I’ve been building primarily in ChatGPT. Before I do that, I’d love to hear from people with deeper Claude experience, especially anyone who has tested Claude across long-running workflows, Projects, artifacts, or portability between model families. The core question I’m trying to answer is: Which parts of my system are model-agnostic, and which parts are overfit to ChatGPT-style interaction? 🤓 The deep dive: My use case is not mainly content generation or “better prompting.” I use AI as a structured collaboration partner: a calibration tool, workflow stabilizer, externalized structure layer, and continuity system across long-running professional, creative, and personal projects. I’ve also started pressure-testing portability for end-user adaptability through AI-assisted prompting. So far, I’ve successfully tested aspects of the system with one other human user, and I’m working toward testing it with additional people. That is part of why I’m interested in Claude: I want to understand not only whether the system works for me, but whether parts of it can transfer across users, models, and external knowledge architectures. A few concrete examples: Veterinary reasoning → client communication I’m a veterinarian, and I use AI to help structure clinical interpretation before translating it into client-facing communication. The AI is not making the medical judgment. I am. Its value is in helping me clarify what the data does and does not mean, identify what remains unresolved, avoid premature certainty, and turn that reasoning into clear communication. For example, in bloodwork, urinalysis, imaging, or other diagnostic interpretation, the useful pattern is often: what is reassuring what remains unresolved what this finding does not prove what home-history question would actually change weighting what the next most useful step is That has been one of the strongest examples of AI as a calibration partner rather than a replacement for human judgment. Protocol-based operational workflows I also use AI for recurring operational workflows like schedule parsing, invoice generation, clinical communication, and outreach. These are not just individual prompts. They function more like protocol-based workspaces with input rules, output contracts, edge-case handling, correction loops, and migration/reseed logic when a thread becomes too degraded or overloaded. One important lesson has been that a correct answer in the wrong interface shape can still be a failed output. For some workflows, the output format matters as much as the reasoning because the result has to be immediately usable. Executive routing and cross-thread architecture The system also has an executive / Control Room layer that does not primarily generate content itself. Its role is to assess where things are, route work to the right specialized thread, and give directives to other layers with my collaborative input. Below that, I use specialized working threads for different domains, intake threads for absorbing raw material, an Evolution layer for extracting durable lessons, and a more canonical reference layer for material that has been promoted. I also use external source material as part of the architecture rather than relying entirely on chat memory. Google Docs function as source frameworks, canonical references, migration packets, and system seeds that can be copied into new threads when needed. GitHub, Substack, and my personal websites serve as additional reference layers for public specifications, longer-form writing, cross-reference, and public visibility. That is one reason Claude interests me: I recently learned that Obsidian plus Claude may serve a similar role, and may even be better suited for a system that depends on externalized structure, versioned source material, public/private reference layers, and portable continuity. That distinction matters because not every insight should become a rule. I try to label things by status: candidate lesson, local preference, validated pattern, external input, portable protocol, or canon. This is one of the places where the system feels less like ordinary prompt engineering and more like governed continuity. Writing and signal-preserving calibration I use AI heavily for writing and public communication, but not to replace authorship. The recurring distinction is: audience-fit adaptation is useful mechanism flattening is not clarity is useful losing the human-owned judgment, voice, or meaning is not So part of the system is about using AI to improve legibility while preserving authorship and signal. Creative systems and artistic calibration I use AI in creative work, but not mainly to generate finished art for me. One example is DJ/music curation. I’ve used AI to help develop symbolic curation lenses like I Am T

I tested Claude for my freelance business for 30 days — here are the 5 prompts that actually moved the needle

I've been using Claude daily for client work and wanted to share what actually works vs what sounds good in theory. The proposal prompt "You are a senior consultant. Write a project proposal for [client type] with this scope: [scope]. My rate is $[X]. Make it professional, outcome-focused, and under 400 words." Result: Cuts proposal writing from 45 min to 5 min. Clients can't tell the difference. The scope creep response "A client is asking for [extra work] outside our original agreement of [scope]. Write a professional response that acknowledges their request, reminds them of our scope, and offers it as a paid add-on at $[rate]." Result: Never feel awkward about scope creep again. The rate increase email "Write an email to a long-term client announcing my rate is increasing from $[X] to $[Y] effective [date]. Warm but confident tone." Result: I raised my rates 30% last month with zero pushback. The cold pitch "Write a cold outreach email to [company type]. My service: [X]. Their likely pain point: [Y]. Keep it under 120 words, no fluff." Result: 3x better response rate than my old templates. The weekly plan "I have these active projects: [list]. Help me prioritize my week and create time blocks for deep work vs admin." Result: Stopped losing hours to context switching. Happy to share more if useful. What Claude prompts are you using for work? Edit: a few people asked for more — I packaged all 50 prompts into a kit organized by business area. Get here. $17 price this week launch submitted by /u/KingEnough49 [link] [comments]

How Claude is helping me overcome the early talent shortage

Building dexity — an AI skills sprint platform — as a lean team. no design hire, no content team, no dedicated researcher, no marketing ops. at the 0-1 stage that's normal. but the work doesn't wait for headcount. here's where Claude is covering the gap: web pages and edits — sprint landing pages go from a brief to a live page without a front-end bottleneck. copy, structure, updates — handled. research — before building anything, i need to know if the market wants it. what the audience is saying, what competitors are doing, what's missing. Claude orchestrates the research layer. i review the synthesis and make the call. copy and content — every post, brief, outreach sequence, and GTM angle gets drafted with Claude. grounded in real audience signals, not assumptions. lead nurturing — outreach sequencing, follow-up logic, segmentation. workflows that would need a dedicated person now run leaner. video and creative — this one surprised me the most. i needed a youtube channel trailer. 45 seconds, animated, branded. i'm a PM — i've never opened after effects. the last design tool i used with confidence was powerpoint. i built it with Claude Design. here's how that actually went: before Claude touched a single frame, i worked through four script iterations. each pass forced a sharper answer — what's the hook, who's watching in the first 4 seconds, what do they need to feel, what does the CTA need to do. the script thinking was the hard part. once the script was locked, i handed it to Claude Design scene by scene. 9 scenes, 45 seconds, fixed timeline. i'd specify what each scene needed to communicate, Claude handled the visual execution. i reviewed, flagged what wasn't landing, iterated. total production time: 2 hours. https://youtu.be/_VEhuD1tSKE not perfect. but it's a real branded asset — and i built it without a motion designer, a creative agency, or a brief that went three rounds over two weeks. that's what the talent shortage looks like when you lean into Claude properly. not replacement — coverage. the gaps that would stall a small team at the 0-1 stage become workflows instead of blockers. what's Claude covering in your stack right now? submitted by /u/avrawat [link] [comments]

Earnestly using Claude to create a shared drive hierarchy and manual maintenance plan = LOL

On a less serious (but perhaps profound?) note: Some guys I know recently decided to use AI for the first time in their lives, while setting up a new company. By golly, they were determined to create the best, most organized shared drive ever for the new company. They spent a couple weeks with Claude on this effort. They were positively thrilled with the results (the Maintenance SOP at the end is peak!): CRE Debt Fund Drive Build-Out SOP Standard operating procedure for building and maintaining the shared drive hierarchy for the Growth Market Investors debt fund platform. 1. Rules Before Building · Use numbered folders so they remain fixed in order and can scale as the platform grows. · Create folders first and migrate files second. Do not drag legacy files into the new structure until the hierarchy is complete. · Each primary folder must contain a 00_Shared Templates folder specific to that business function. · Do not mix investor reporting, live loan files, corporate records, and marketing materials across the three main folders. 2. Top-Level Folder Build Create the root folder and the three primary folders exactly as shown below. Growth Market Investors├── 01_Sales & Marketing├── 02_Corporate & Admin└── 03_Operations 3. Build Sheet — 01 Sales & Marketing Purpose: this folder holds all outward-facing activity, including loan sourcing, investor sourcing, track record packaging, and market-facing content. 01_Sales & Marketing├── 00_Shared Templates├── 01_Loan Sourcing├── 02_Investor Sourcing├── 03_Presentations & Marketing Materials├── 04_Track Record & Case Studies├── 05_Origination & Relationships└── 06_Market-Facing Content Folder Purpose / Contents 00_Shared Templates Outreach templates, teaser templates, investment summary templates, track record slide templates, and standardized email language. 01_Loan Sourcing Deal intake forms, broker outreach, sponsor outreach, referral sources, market visit notes, and pipeline tracker inputs tied to origination. 02_Investor Sourcing Investor target lists, family office outreach, capital partner materials, investor meeting notes, and fundraising support files. 03_Presentations & Marketing Materials Firm overview deck, debt strategy decks, loan product summaries, one-pagers, branding assets, and presentation drafts. 04_Track Record & Case Studies Track record summaries, realized case studies, tombstones, performance summary materials, and portfolio highlight slides. 05_Origination & Relationships Broker contacts, sponsor database, title companies, legal contacts, lender-side market contacts, and relationship mapping files. 06_Market-Facing Content Articles, newsletter drafts, LinkedIn materials, conference support materials, and thought leadership content. 4. Build Sheet — 02 Corporate & Admin Purpose: this folder holds firm-level infrastructure only. No live investment underwriting, draw files, or servicing files should be stored here. 02_Corporate & Admin├── 00_Shared Templates├── 01_Corporate Documents├── 02_Legal & Compliance├── 03_Finance & Accounting├── 04_Banking & Treasury├── 05_Internal Operations└── 06_Strategic Planning & Board Materials Folder Purpose / Contents 00_Shared Templates Board memo templates, internal memo templates, legal request templates, recurring report templates, and administrative checklists. 01_Corporate Documents Entity formation documents, governance records, insurance, service provider files, and permanent corporate records. 02_Legal & Compliance Regulatory filings, compliance policies, insurance requirements, loan document templates, intercreditor templates, guaranty templates, and pref/mezz agreement templates. 03_Finance & Accounting Accounting, AP, AR, audit and tax support, fund-level financial statements, recurring close files, and administrative finance workpapers. 04_Banking & Treasury Bank account records, treasury controls, authorized signer information, banking instructions, and internal treasury reference materials. 05_Internal Operations Team compensation, internal operating memos, HR/admin procedures if applicable, and back-office process documentation. 06_Strategic Planning & Board Materials Strategic planning files, annual plans, board materials, partner meeting materials, and internal planning outputs. 5. Build Sheet — 03 Operations Purpose: this folder is the execution engine for the debt fund platform. It houses fund platform files, live and closed loans, underwriting, construction administration, asset management, portfolio reporting, and research. 03_Operations├── 00_Shared Templates├── 01_Fund Platform├── 02_Investment Strategy & Templates├── 03_Deal Pipeline├── 04_Underwriting & Risk├── 05_Construction Loan Administration├── 06_Investor Reporting├── 07_Portfolio Management└── 08_Market Research Folder Purpose / Contents 00_Shared Templates Master deal checklists, IC memo templa