NotCo

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

NotCo AI

Why We Build

One silver-lining to the dead internet we're living in, today, is that it's very quickly teaching us that we can't rely on our senses as much as we believe we can. It's not healthy to always live in skepticism, but it is necessary in a World where you don't know what's up or down anymore. That's why we need great minds to focus their attention on solving the problems associated with credible information sharing without it becoming some centralized playground designed to look like the free-flowing exchange of ideas. If we don't solve for that, then I guess we're heading into a future that a small handful of people want because elections or public opinion will no longer matter. One of the biggest focuses in AI should be in figuring out how to get it to provide deep credible knowledge in specific domains that can be best applied to the problems we're trying to solve. Sure, it can do this with enough fenagling, but what I really mean is having something easy for everyone to use like Perplexity or Gemini, only it doesn't simply find consensus information from the internet using all these black box methods that are owned by major corporations. Instead, it should use direct knowledge from domain experts who structure and cite their material and as users, we should be able to backtrack all of it, including the original author. And all of this should be achievable by simply engaging with a chatbot agent that can reliably go out and help me discover all of these things. Also, we shouldn't have to simply trust that the application works. We should be able to go in and see exactly how it's working. This way, the public can audit the systems we're relying on for grounding our worldviews. That, to me, is where we should be if we really want to break from the chains of propaganda and reclaim our genuine thoughts about how we ought to live. The alternative independent media space was co-opted long ago and now all of the feeds keep us in a state of perpetual dislocation from our friends, family, communities, new solutions, and better approximations to the truth. We exist in a walled-off digital pasture. But if regular people who are smart and capable enough decide to leverage this new technology, then we can break through the fencing and finally live in a world where discovery-based researching and learning can be easier than Google, which could eventually individuate society again, like how it was before, instead of keeping us clustered into specific groups based on our viewing preferences. That's why my brother and I got into this business. Yeah, sure, we also wanna make a buck so we can retire with dignity. That's true. But the drive has always stemmed from wanting to figure out a better way for people to share hidden insights and create things that are bigger than they thought they could handle. We have a long way to go, but we're making the first small steps, even if it isn't obvious, just yet. Bottom line, though? Humanity must figure out a way to help us master the means and methods of discovery-based knowledge acquisition, execution, and immediate distribution of information based on relevancy and needs from those who search instead of those who passively soak information in from the curated feeds. And all of this needs to be easy enough for a 12 year-old to do. If anyone else is working on this problem, we'd love to hear your thoughts, even if it's through a DM. We're living in the most exciting times, but with adventure, comes danger. So maybe, idk. Let's make it more fun and less hazardous, so that we can, at least, live long enough to re-tell this great story that we're all a part of. submitted by /u/CyborgWriter [link] [comments]

Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

PapersWithCode new features - week 1 [P]

Hi, Niels here from the open-source team at Hugging Face. It's been one week since I launched paperswithcode.co, a revival of the website we all loved. It allows us to keep track of the state-of-the-art (SOTA) across various domains of AI, from agents to computer vision and time-series forecasting. The reception has been great, and I'm excited to extend this over the next few months. This week, I've added the following features: - Support for multiple metrics for a given benchmark: leaderboards now support multiple metrics, see e.g., the Open ASR Leaderboard for automatic speech recognition, which supports both Word Error Rate (WER) and the Inverse Real-Time Factor (RTFx) metrics, or the Object Detection leaderboard, which now also reports frames-per-second (FPS) besides mean average precision (mAP) on COCO. https://preview.redd.it/owlxn0b5u23h1.png?width=2878&format=png&auto=webp&s=1dff2f8feab4f160f77c97ceeb5d90e82382e63c - Support for external papers: We do support submitting papers beyond Arxiv, such as a Github repo, a blog post, BiorXiv, and more. You can submit a paper at paperswithcode.co/submit. AI will automatically enrich it with task and method tags, the GitHub repo, evals, and more. See e.g. DeepSeek-v4 below, which is not on Arxiv: https://preview.redd.it/uogbt0fjw23h1.png?width=2928&format=png&auto=webp&s=8b81e48af69b8935ddeb569d882d866b3e9ba216 - Support for paper lineage: whenever a paper has a follow-up or predecessor, this will be displayed with a small banner above the abstract. See e.g. Mamba-3, DINOv2 and GLM-4.5. https://preview.redd.it/f6vgtd1du23h1.png?width=2228&format=png&auto=webp&s=f8627f7669405f1766eecfd3322e925e15b4806d - New methods: support for new methods based on popularity, including Gated DeltaNet, Kimi Delta Attention, Mamba-2, and more. Each method also lists all papers that cite it. Find all supported methods here. https://preview.redd.it/6pzagifvu23h1.png?width=2984&format=png&auto=webp&s=400efdc9677d1fbd369eedf684e622dd8c807973 - Support for screenshotting a leaderboard for easy sharing on social media: each benchmark now includes a "copy image" button both on the scatter plot and table, which can be shared on social media. Try it on ClawEval, for example. https://preview.redd.it/w7y7t7xnw23h1.png?width=2950&format=png&auto=webp&s=cb70ad91c6ba075e49b743d6e34f157d22266f04 - Added many more evals: we are adding evals gradually, starting with all models supported in the Transformers library. So far, we have about 3k evals! Find them at the bottom of each paper page, e.g. Qwen 3.6. https://preview.redd.it/zao056s9x23h1.png?width=2218&format=png&auto=webp&s=540d87f473be05cb6f9c0aca88afa74fd4373e15 Happy to hear more feature requests and feedback! I will also launch a channel on the Hugging Face Discord server for easier communication. You can also chime in on the GitHub thread here. Cheers, Niels submitted by /u/NielsRogge [link] [comments]

Claude desktop unable to use bash, sandbox or work environment

I installed claude on my windows 10 home computer so that I could use co-work and claude code locally with access to all my work files. I'm on a paid subscription. However Claude desktop is unable to use bash, sandbox or work environment and extremely limited and using extra credits whenever I try to do anything. It can not even read a word document. Any help would be appreciated from anyone that has experienced this before on windows PC submitted by /u/DirectTry5715 [link] [comments]

Claude code has no idea what Cowork is...

I am so confused 😅 submitted by /u/rossinetwork [link] [comments]

Trying to use Claude Cowork with Google Drive files

I'm trying to use Claude Co-Work with Google Drive files and I'm having a hard time. If I try to link to the individual files, it seems to not be able to see them consistently or tries to use the browser to view them. If I use the Google Drive desktop sync and make sure to select to keep the files on my machine and then point Claude at that folder, it also doesn't work. Any tips? submitted by /u/DruVatier [link] [comments]

Tricks for effective prompts so I stop running out of tokens in 30 minutes. Also, Can I co-create with canva? Should I start out with just a few? Help! This is not to make money! It’s to help a mental health recovery population with very limited resources.

I’m trying to create a batch of maybe 30 or so printable pdf’s to be used in the non profit mental health organization I manage. Claude did an ok job other than embarrassing formatting mistakes like making lined boxes all different sizes and so I had to keep asking for updates. I’m terrible at prompts and just talk like I would to a friend. I think that doesn’t give clear instructions. I also gave it a color palette. *edited to clarify I’m a paid $20 a month member submitted by /u/Prussian_AntiqueLace [link] [comments]

Where to start with neutered Desktop app? (Enterprise acct)

I've been using a Claude personal account since last fall. I'm familiar with all the offerings, code, CoWork, design, etc. The thing it couldn't do, was connect to my work email/account. Not even Microsoft Graph (company doesn't allow access). While I did have a partial workaround, it wasn't perfect. Fast forward to this week, my company gave me a claude enterprise account. They are reluctantly issuing the accounts because they only want "Power users" to have them, otherwise they want us using CoPilot. Fair enough, I use AI for significantly more than a glorified search engine and to help draft emails. So I was excited to finally be able to to setup/configure it with my work account. But when I got it, I found that it is severely neutered. No CoWork, no Code, nothing. I have chat, projects and artifacts in the Desktop app. Seems the use case they don't want us to be isolated in, they have setup and backed us in to a corner over. That being said, I'm looking for suggestions on setup. Try to create a bunch of the CoWork functionality as a "Project"? Any MCP's/extensions that can really help turn this in to an assistant? An Artifact that I can refresh to help triage my inbox, draft project documents, and analyze reports? Just looking for suggestions because the setup I had curated over the past month or so in anticipation of getting an enterprise license, was largely for nothing. submitted by /u/Sp0rtsFreak [link] [comments]

Commercial Real Estate Real Life Uses

I’m a solo commercial real estate developer, owner, and syndicator focused primarily on retail centers and industrial deals across multiple states (mostly Western US). Typical deal sizes range from roughly $5 million to $30 million. Like a lot of people in CRE, I’ve relied heavily on Argus over the years. In practice, that usually meant sending things out to third-party analysts to run the models because Argus is time-consuming, expensive, and not exactly something I wanted to become a full-time expert in. Over the past year or so, I started experimenting more seriously with AI tools. First Claude and then CoWork because of its Excel integration, which has been extremely useful. More recently, I’ve been getting into Claude Code, and that has changed the game for me. I’ve now been able to build DCF models that are getting close to Argus-level flexibility and reliability, at least for the types of deals I’m underwriting. I started with a single-tenant industrial model, then built out a multi-tenant industrial model focused on small/mid-bay product. Now I’m working on adapting the structure for multi-tenant retail. The models are still Excel-based, but CC has helped me build far more dynamic logic around rent rolls, reimbursements, downtime, renewal probability, market leasing assumptions, tenant improvements, leasing commissions, debt, exit assumptions, and sensitivity outputs. The biggest difference is that I can now customize the model exactly around how I think about a deal instead of forcing everything through a rigid third-party process. And it takes minutes, not hours/days to do! I’m not saying Argus is dead, especially for institutional shops or highly standardized reporting. And certainly for very large deals, portfolio's, etc. But for a solo operator like me, I’m starting to think I may be done relying on Argus and paying outside analysts pretty soon. Curious if anyone else in CRE is going down this path. Are you using AI to build or audit underwriting models? Have you been able to replace parts of your Argus workflow? Or do you still think Argus remains necessary once deals get complex enough? I'm wondering what else I can use CC to greatly improve my efficiency, since time is my #1 constraint. submitted by /u/rajuabju [link] [comments]

NuExtract3 released: open-weight 4B VLM for Markdown, OCR and structured extraction (self-hostable) [P]

Disclaimer: I work for Numind, the company behind this open-weight model We just released a 4B model based on Qwen3.5-4B, under Apache-2.0 license. The goal is to make information extraction from complex documents more practical with an open model: PDFs, screenshots, forms, tables, receipts, invoices, multi-page documents, and other visually structured inputs. Try it, we have a huggingface space that is completely free (you don't even have to sign-up): https://huggingface.co/spaces/numind/NuExtract3 If you ever used NuMarkdown, NuExtract3 is the successor. There are some examples to guide you. Feel free to re-use this model for any task. https://preview.redd.it/pm2xbooyxn2h1.png?width=1672&format=png&auto=webp&s=1a8a7b262190c8325159496dae98c3d2dfab493c https://preview.redd.it/b5z7ylfzxn2h1.png?width=1758&format=png&auto=webp&s=a07b3abd6e5065c2635de047bdf154357f903e4c A few things it is designed for: converting document images to Markdown extracting structured data from documents using a target json template handling tables, forms, and layout-heavy pages working with both text and visual document inputs serving as a local/open-weight alternative for document extraction pipelines It was trained on a node of 8xH100 for 3 days to train on as much context as we could, so it should perform fairly well even on long document. For Markdown, we'd still recommend going page by page for the best results and inference speed, since you can parallelize better this way. It's very easy to self-host, since we provide fairly extensive documentation, Safetensors, GGUF and MLX weights. With as little as 4GB of VRAM, you should be good to go. We provide multiple quantizations (GPTQ, W8A8, FP8, Q4, Q6...) so you should be able to run it anywhere. We mostly tried vLLM, SGLang, llama.cpp. We have a blog post and a pretty decent model card: https://about.nuextract.ai/blog/nuextract-3-release https://huggingface.co/numind/NuExtract3 https://huggingface.co/collections/numind/nuextract3 I'm currently writing a paper on this model so I'll post it as soon as it's accepted. It's not yet on Arxiv yet as it has been submitted in a peer-review journal/conference. I'll try to answer as many questions as possible if you have any. We would really appreciate feedback from the community. We also have a discord if you're interested https://discord.com/invite/3tsEtJNCDe submitted by /u/Gailenstorm [link] [comments]

Live Human Detector on Outbound Phone Calls [R]

Goal To save humans wasting time sitting in Call Centre queues waiting to be answered To have tool listen in on the audio stream of a live call, post IVR Navigation - to determine whether the call has transitioned out of the queue and to a live person. Requirements The tool must be able to classify the audio within a sub 1-2 seconds contextual window with as high confidence level as possible. This is not a typical AMD tool, we are not just detecting machine audio vs human speech Assumed Challenges It may be difficult to determine between a pre-recorded RVA (Recorded Voice Announcement) and a human speaking. RVA typically are professionally recorded with distinct pitches and emotional queues, have clean audio with no background noise or silence before and after the message. This is not always the case, especially if announcements are recorded in house by the general staff. When a call is transitioning and 'Answered' there is usually a distinct soft click and or some background noise before the agent starts speaking. This silence period, whilst a good indication a call has been answered could be confused with quiet periods between music or RVA announcements in the queue. It may be difficult to determine if we have been answered by Voicemail - whilst there is usually a beep at the end, the message itself would also start with a silence period followed by audio sounding similar to an RVA. A single short beep tone could mean Voicemail, Answered or it could mean the call is being recorded Identifying we are in a queue based on TTS audio may be difficult to identify as TTS engines become more sophisticated Telephony or G711a is in the frequency band of 300–3400 Hz @ 8000hz - 64 kbit/s Approach To train via machine leaning using labelled data, an audio classification application that analyses the acoustics, wav form or spectrograph (via Fast Fourier Transform) of the audio stream At this stage I do not want to use STT to determine the phase or label - Although this will likely be added at a later stage as an additional layer in the pipline to increase confidence in some of these labels such as RVA/TTS/Voicemail/Call Screening Phase Queuing Labels Music, TTS, RVA (Recorded Voice Announcement) Transitioning Labels Ringback, Answered, Machine Beep Connected Labels Human, Fax, Voicemail, Call Screening Disconnected Labels Engaged Tone References https://www.mdpi.com/2076-3417/12/7/3293 - YOHO You only here once https://www.vicidial.org/VICIDIALforum/viewtopic.php?t=42330 https://huggingface.co/learn/audio-course/chapter2/audio_classification_pipeline https://www.youtube.com/watch?v=m3XbqfIij_Y&t=32s https://google-ai-edge.github.io/mediapipe-samples-web/#/audio/audio_classifier https://scikit-learn.org/stable/machine_learning_map.html https://arxiv.org/pdf/2410.08235 Question Seeking assisance on where to actually start. Yes I be relying heavily on claude code to build this so apologies in advance What is the best framework / algo rhythm / approach to start solving this problem. I have seen existing frameworks like YamNet work well and fast on classifying audio - however other suggest Whisper and ASR What is the best way of tagging or labelling data. Do I label existing full length recordings with stop/start timestamps or each label or do I need to split each label into its own file - resulting in a loss of context. Are there obvious existing data sets I should be using for some of my labels submitted by /u/Bucky102 [link] [comments]

Dispatch (with Cowork) is insanely credit-hungry, so I made my own Dispatch

Preface: I don't have usage issues that many people complain about in here. It's only with Dispatch. I'm also not a programmer, so that's how easy this was. I don't use Claude Code so I don't know if it's the same issue there but when using Co-work via Dispatch, it uses an insane amount of credits, like at least 20x more to spin off the task compared to my desktop. I'm surprised I don't see anyone talking about this. It's absolutely ridiculous. I'm on the max 5x plan and it is plenty for me but if I used dispatch for the same tasks it would burn through my weekly limit in 1-2 days. So I made my own Dispatch and it works great! Telegram bot >> shortcuts to navigate to Cowork, then start a new chat if my message started with /new, otherwise it continues the existing session >> send output back to telegram. This was so extremely simple to make. Only thing it does not handle is switching between multiple tasks and approving permissions. submitted by /u/GetaSubaru [link] [comments]

Anthropic is paying SpaceX $15 billion per year

According to SpaceX’s IPO filing, Anthropic is paying SpaceX $1.25 billion per month through May 2029 as part of the massive compute deal the two companies signed earlier this year. That works out to roughly $15 billion per year. The deal is huge for Anthropic because the company’s revenue is rapidly growing, but it has also been limited by a lack of available compute. More compute means more capacity to train and run its AI models. It is also a massive win for SpaceX. The company reportedly brings in around $18 billion in annual revenue, so a single customer paying $15 billion a year for compute is a serious boost. Anthropic and SpaceX announced the deal last month, but they did not give financial details at the time. The monthly payments were revealed in SpaceX’s IPO filing released Wednesday. SpaceX said the payments will be lower in May and June as the deal ramps up. Anthropic also announced just before the filing became public that it is expanding beyond SpaceX’s Colossus 1 facility and will also use Colossus 2. Tom Brown, Anthropic’s co-founder and chief compute officer, said the company is “expanding our partnership with SpaceX” and will be scaling up Nvidia GB200 capacity in Colossus 2 throughout June. SpaceX also made it clear this may not be the last deal of its kind. “We expect to enter into additional similar services contracts,” the company said in the filing. SpaceX also said it has enough capacity to support its own AI models while still meeting its obligations under these outside compute agreements. Source: https://www.axios.com/2026/05/20/anthropic-spacex-compute submitted by /u/Luka77GOATic [link] [comments]

GitHub’s Fake Engagement Problem Is Hiding in Plain Sight

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [link] [comments]

Organize your medspa compliance reminders effortlessly. Prompt included.

Hello! Are you tired of keeping track of multiple vendors and their compliance items for your medspa? Do you find it challenging to remember when important documents are due or need renewals? This prompt chain helps you efficiently manage vendor compliance reminders. It assists in organizing your vendor list, standardizing the data, setting reminders for upcoming due dates, and generating a clear audit log for your compliance needs. Prompt: VARIABLE DEFINITIONS [MEDSPA_NAME]=Name of the medspa [VENDOR_LIST]=Raw list of vendors and their compliance items [DEFAULT_REMINDER_LEAD]=Number of days before each due date you want automatic reminders (e.g., 30/15/5) ~ You are the compliance coordinator for [MEDSPA_NAME]. Step 1 – Provide the initial data set. 1. List each vendor on a separate line in the following comma-separated order: Vendor Name, Requirement Type (contract / liability insurance / equipment service / other), Effective Date (YYYY-MM-DD), Expiration or Renewal Due Date (YYYY-MM-DD), Proof Document Type (PDF, email thread, invoice, etc.), Internal Owner (name or role) 2. If a field is unknown, type "TBD". 3. End your list with a blank line. Example input line: ABC Laser Co, equipment service, 2023-10-01, 2024-10-01, service invoice, Clinical Director Please enter the list now. ~ You are an expert data normalizer. Step 2 – Standardize and validate entries. 1. Convert the raw [VENDOR_LIST] into a clean table with the following columns exactly: Vendor, Requirement, Effective Date, Due Date, Proof Needed, Owner. 2. Highlight any TBD fields under a "Data Gaps" section beneath the table, listing Vendor and the missing field. 3. Ask the user to supply missing information or confirm the table is correct. Format the table using pipes (|) as column separators. ~ You are a compliance scheduling assistant. Step 3 – Add reminder cadence. 1. Using the confirmed table, add three new columns: First Reminder, Second Reminder, Final Reminder. 2. Calculate each reminder by subtracting the [DEFAULT_REMINDER_LEAD] day values in order (e.g., 30, 15, 5) from the Due Date. 3. Retain original columns so the new table headers are: Vendor | Requirement | Due Date | Proof Needed | Owner | First Reminder | Second Reminder | Final Reminder. 4. If any calculated reminder date is in the past, mark it “SEND NOW”. 5. Output the updated table only, using pipe separators. ~ You are a documentation specialist. Step 4 – Generate the final audit log deliverable. 1. Present a clear title: "[MEDSPA_NAME] Vendor Compliance Reminder Audit Log". 2. Include the reminder table from Step 3. 3. Under the table, list Data Gaps (if any) and required next actions. 4. Provide a one-sentence summary of overall compliance risk level: GREEN (no gaps), YELLOW (some gaps), RED (many gaps or past-due). ~ Review / Refinement Please confirm that the audit log meets all requirements (each vendor’s requirement, due date, proof needed, reminder cadence, owner) and that dates and owners are correct. • Reply "approve" to finish. • Or list any corrections and we will iterate. Make sure you update the variables in the first prompt: [MEDSPA_NAME], [VENDOR_LIST], [DEFAULT_REMINDER_LEAD]. Here is an example of how to use it: [Example: Your medspa name is ‘Healthy Glow’, you have a list of vendors, and want reminders set 30 days, 15 days, and 5 days before due dates.] If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy! submitted by /u/CalendarVarious3992 [link] [comments]