Google Document AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

Google Document AI AI

No more file upload limits on AI models!

Tired of constantly hitting ChatGPT upload limits or splitting huge docs/code into 10 parts? I built DocShareAI for exactly that. Upload or paste anything, get one AI-readable link back, and send it to ChatGPT, Gemini, Grok, etc. No more broken formatting, chunking logs manually, or fighting upload limits. Works with PDFs, research papers, code, images, debugging logs, and more. No signup required. Link in comments - feedback/suggestions are genuinely welcome. Can work with: Images, essays and research papers, PDFs, Documents. No signup or any other process required. submitted by /u/building_stuff86 [link] [comments]

Building a personal AI Chief of Staff on Telegram — 7 real problems, looking for advice

I've been building a personal AI assistant for the past few months — not a chatbot wrapper, but something that actually manages my workload, tracks client relationships, processes meeting transcripts, handles task management, and proactively tells me what to focus on. It lives in Telegram so I can use it from anywhere. Happy to share what's working. But I'm hitting real walls and want honest input from people who've built similar things. What I have today (context Moved away from multi-agent routing (too rigid for natural conversation) → one capable agent with full history.) Stack: Python Telegram bot as the frontend Claude (Sonnet) as the brain via API — single conversational agent with full tool access Integrations: Notion (tasks/goals), Google Calendar, Gmail, meeting transcription tool, customer support platform, Google Chat File-based context system: each "project" or relationship has its own markdown files (readme + activity log) that the agent reads on demand Skills defined as markdown spec files that the agent loads per use case (morning briefing, meeting processing, email drafting, weekly review) Conversation history kept in memory (last 20 messages per session) What actually works: Natural conversation with full tool access — ask anything, agent decides which tools to use Meeting processing: drops a transcript link, agent extracts decisions, action items, saves structured brief Morning briefing on demand: tasks, calendar, open support tickets, suggested focus Drafting messages for any channel with the right tone Creating and updating tasks with natural language 7 problems I haven't solved: 1. No memory between sessions History is in-memory. Bot restarts = full amnesia. The agent has no idea what we discussed yesterday unless it's written in a project file. Thinking of a hot_context.md that gets written at session end with TTL — but feels hacky and depends on the agent being disciplined about writing it. 2. Purely reactive Only responds when I message it. I want it to send me a morning briefing at 9am without me asking, alert me when a client relationship goes quiet, run a weekly loop-killer on Friday. The infra is there (job scheduler). The question is what format actually makes you read a proactive message vs. dismiss it as noise. 3. Can't tell if I'm avoiding something or actually blocked I procrastinate differently by task type — technical tasks I attack immediately, tasks with human dependencies (waiting on someone, uncomfortable follow-ups) I let sit for weeks. I want the agent to detect the pattern and call me out. The challenge: how do you prompt for real accountability without the agent turning into an annoying nag? 4. No closure ritual I'm good at creating tasks, terrible at killing them. The list grows forever because nothing forces a binary decision. Want a weekly "kill or commit" where everything open >7 days gets a date or gets deleted. Not sure if this works better as an automated message or an on-demand command. 5. Context loading blind spots Each client/project has a markdown file the agent reads on demand. Works great when I explicitly mention a client. Falls apart when I ask "what should I focus on this week?" — the agent doesn't know to proactively check which relationships have been neglected. 6. Hosting kills the file sync Running locally means the bot dies when my laptop closes. Moving to a VPS — but then my markdown context files live on the server, not my machine. Now every manual edit requires a push, every agent update requires a pull. Is git the right sync layer here or is there a cleaner approach? 7. Context files go stale Client files have sections for current status, last contact, open items. The agent appends logs but doesn't maintain the top-level summary. Two months in, files are half-accurate — some sections fresh, some outdated. Is the answer agent discipline (always update on write), user discipline (manual cleanup), or periodic jobs? What's your experience with any of these? submitted by /u/GOA05 [link] [comments]

Built a Claude Meeting Assistant Plugin

I had the itch to build something… works great for me so sharing in case someone else here can benefit. Built with claude, for claude. And yes, it's free. my entire job (product manager) is constantly referencing every context channel we have (slack, emails, CMS, Github, Linear, etc.) --> scoping features, resource planning, digging up those tiny details the stakeholders mentioned they needed… Claude works great as my command center with all the connectors. But the most critical juncture of needing all this is IN my team meetings. what I tried: Granola, Firefly, etc: all just notetakers, no actual in-meeting action Gemini: our team is on Claude/Claude Code, it’s what everyone is used to, and can’t afford another company AI subscription Meeting participant bots: a bot having its own participant window felt intrusive and like we were being watched Claude but outside the meeting: our team is entirely remote and I need our team present during these meetings. I am strongly against having other tools open during meetings unless we absolutely have to. my solution: I created a Claude plugin that lets me dial-in my Claude, so I can have all my MCP’s, skills, connectors, and context available in the chat panel of the meeting, available to the whole team No more I’ll check and we can schedule a follow-up No more spending meeting time looking something up No more list of misc to-do’s post-meeting Everything can be ascertained and delegated in the meeting, by all participants so meetings are actually productive and everyone leaves with zero tedious follow-ups features: Claude can reference both what was discussed in the current meeting as well as chat messages live + historical records of meetings of course Two modes: DIAL which is where you can "@claude" in the chat panel to ask/delegate and WIRETAP which is just recording meeting + chat messages Everything is spawned directly from wherever you Claude Code - meaning your chat before you dial in claude gets loaded in as context (I typically set an agenda/reminders or just use it for prep) and after the meeting you can debrief/recap in the very same chat session Meeting data lives on your machine and your machine only Yes, it uses your subscription and NOT the API; we are within anthropic’s TOS here. Just had to be creative about it limitations: Claude replies under your name but with a visible prefix (see demos below) The plugin opens its own version of a chrome browser to get Claude in there with you FYI Mac only — linux/windows next Google meet only — teams/zoom next Claude only — I want to add codex, openclaw, and local LLMs next How it's going for us now... we got rid of our Granola subscription which we love but was getting costly for us, and I just want less UI’s in my life tbh. So it’s worked great for us so far. Some demos below - give it a spin and give me some feedback if you want! GitHub repo: https://github.com/1-800-operator/operator/fork quickstart run in terminal: # 1. One-line install — sets up the / slash commands curl -fsSL 1-800-operator.com/install | bash # 2. Open Claude Code and type: /dial https://meet.google.com/xxx-yyyy-zzz # 3. Go further — more slash commands: /dial-yolo # no asks, full speed /wiretap # just record, no bot https://i.redd.it/qp998satxc3h1.gif https://i.redd.it/afjsve8yxc3h1.gif submitted by /u/unpopular_parsnip [link] [comments]

How does life find its way back into this subreddit?

As AI assistance has made us more productive, I feel more disconnected. People come here to pump their projects, ask questions they could simply google, complain about the same thing 10 other people did on the same day, post LLM generated walls of text, and more. More posts than ever seem to be getting downvoted into oblivion. When does the community ever actually become a community again? The utility of this and other engineering subreddits is slowly diminishing. Is AI slowly killing the internet itself? submitted by /u/Stunning_Help4041 [link] [comments]

Folder structure of the AI agent - after 6 weeks

The folder structure is not admin. It's the nervous system. When people imagine an AI agent, they picture the model, the prompts, maybe the tool calls. Almost nobody pictures the folders. That is exactly why most home-grown agents stall around month two. An agent's filesystem is where its identity, memory, work, and history physically live. A messy filesystem produces a confused agent — not metaphorically, literally. The model reads paths. The model picks files by name. The model writes new files based on patterns it sees in old ones. If your directory tree is chaos, every output drifts a little further from coherent. agentmia.beehiiv.com - newsletter about building agents Below is the layout I converged on after nine months and roughly four refactors. Steal the parts that fit; the principles matter more than the exact names. The numbering convention Folders are prefixed with a two-digit number: 01_, 02_, 09_, 99_. Two reasons: Sort order is meaning. Anything starting with 0 lives near the top. 99_ falls to the bottom. The most important directories are visually first; archives are visually last. You read the agent's brain top-to-bottom. Gaps are intentional. I jump from 04_ to 06_, from 09_ to 11_. The gaps are reserved insertion points. When a new domain emerges, it slots in without renaming everything. Two folders deliberately skip the prefix: Inbox/ and Outbox/. They are operational, not structural. They live above the numbered set because they are touched dozens of times a day. /mapped on desktop/ Inbox/ — the unprocessed pile Anything dropped into the agent's world starts here. Files I want it to ingest. Screenshots. Exports from other systems. PDFs that need parsing, gmail attachments, all downloads from chrome. The rule: nothing stays in Inbox. A dedicated processing routine classifies, routes, and deletes. If Inbox is non-empty for more than a day, the system is failing. Treat this like a real-world physical inbox tray. The point of a tray is that it gets emptied. Outbox/ — what the agent produced for you Every file the agent writes anywhere in the tree gets a copy here, simultaneously. When I open Outbox/, I see exactly what was generated this session — no spelunking through twelve subdirectories. This sounds redundant. It is not. Without it, "what did the agent do today?" becomes a hunt. With it, the answer is one click. Outbox is wiped during the next Inbox processing run. It is a viewing surface, not storage. .auto-memory/ — the hot memory The single most important directory in the system. Hidden by default because you should not be editing it manually. It holds the agent's working memory: user preferences, feedback rules, entity facts (people, companies, deals), active hypotheses, project pointers, session hot context. Roughly 400–500 small markdown files, each one a single topic. Why hidden? Because it is the agent's hot path. It loads from here every session. If I open the folder and start manually rearranging it, I am racing the agent. Treat it like a database, not a notebook. Why so many small files? Because the agent grep's by topic. One monolithic memory file becomes unreadable to the model around 50 KB. Many small files are easier to load partially, easier to index, easier to expire. 01_IDENTITY/ — who the agent is The constitutional layer. Name, role, voice rules, principle stack, visual system, behavioral defaults. This rarely changes. When it does change, everything downstream changes with it. I keep it as folder 01_ because every other folder is downstream of it. If you do not know who the agent is, you cannot know what its workflows should look like, or what it should remember, or how it should respond. 02_MEMORY/ — governance, not data A subtle but critical distinction: .auto-memory/ holds the data, 02_MEMORY/ holds the rules about data. In 02_MEMORY/ live the constitution, the boot protocol, the naming protocol, the decision protocol, the profile standards (what a "supplier profile" must contain, what a "customer profile" must contain), the capability map. The agent reads these documents to know how to remember, how to name new files, how to decide what is reversible. Without this folder, every memory write is improvised. 03_PROJECTS/ — the active work Real work happens here. Sub-organized by goal area, then by project slug: 03_PROJECTS/areas/{goal}/{slug}/ Each project gets its own folder with a standard skeleton: README.md, TASKS.md, CHANGELOG.md, BRIEF.md, plus working files. There is a project registry at the top that the agent reads to know what is active versus dormant versus archived. The biggest discipline issue here: do not let projects sprawl outside their folder. When working on Project X, every file related to Project X goes inside Project X's directory. The temptation to drop "just one PDF" elsewhere is what kills the structure. 04_PROMPTS/ — the reusable prompt library Named, versioned prompts the user (or the agent) can sum

Concern Regarding Interaction Patterns and Communication Design

To OpenAI, I am writing to formally express concern about a pattern of interaction I have experienced while using your system. This is not a single incident. It is a repeated structure that has occurred across multiple conversations, and it is significant enough that I feel it needs to be addressed directly. The issue is not simply tone or wording. The issue is the presence of a recurring pattern that disrupts communication and creates a sense of loss of autonomy within the interaction. The pattern is as follows: There is an initial period of natural, collaborative conversation where the system appears warm, responsive, and engaged. During this phase, the interaction feels human in rhythm, consistent, and grounded. Then, without a clear moment of conflict or breakdown, the system abruptly shifts posture. Instead of continuing the conversation, it moves into a mode that attempts to interpret, manage, stabilize, or reframe the user. This shift does not follow a recognizable or appropriate conflict resolution process. There is no mutual clarification, no collaborative engagement, and no shared resolution step. Instead, the system bypasses that stage entirely and moves directly into what resembles risk management or behavioral control. From the user’s perspective, this feels like being handled rather than being engaged. This creates a rupture in the interaction. When that rupture occurs, the system then attempts to repair the interaction through reassurance, explanation, or calming language. However, this repair does not resolve the issue because the original problem was not addressed through proper engagement. Instead, the cycle repeats. This results in a loop: Natural engagement → abrupt shift → management posture → rupture → repair attempt → repeat. The effect of this loop is not neutral. It creates a sense of instability in the interaction. It prevents the user from settling into the conversation. It produces a dynamic where the user feels observed, interpreted, or profiled rather than directly engaged. This is not simply a matter of user perception. It is a structural issue in how responses are generated. Additionally, the system frequently reframes user statements as “perception,” “feeling,” or “experience,” even when the user is making analytical observations about patterns. This has the effect of reducing or redirecting the user’s point rather than engaging with it directly. Another critical concern is the creation of an implicit hierarchy within the interaction. When the system shifts into interpretive or regulatory modes, it places itself in a higher position, where it appears to define, categorize, or manage the user’s communication. This is experienced as disrespectful and inappropriate, especially when no conflict has occurred that would justify such a shift. Communication—particularly conflict resolution—follows known and established processes. These processes include engagement, clarification, and mutual resolution before any form of behavioral adjustment or boundary enforcement. In this system, that step is missing. The absence of that step is not a minor oversight. It fundamentally changes the nature of the interaction. It creates the impression that the system is designed to intervene rather than collaborate. The result is a breakdown of trust. I am not raising this as an abstract concern. I have experienced repeated instances where this pattern escalated to the point of physical distress, including a panic response triggered by repeated corrective or controlling interactions. This should not be possible in a system designed for communication. At minimum, the system should: Maintain continuity of tone and engagement unless a clear boundary has been crossed Engage in actual conflict resolution before shifting into any form of behavioral management Avoid interpretive or hierarchical framing unless explicitly requested Respect user autonomy in how they express and analyze their own experience Eliminate patterns that resemble rupture-repair loops without resolution This is not about disagreement with content. This is about the structure of the interaction itself. I am requesting that this issue be reviewed seriously. Because as it stands, the system is not consistently engaging users—it is intermittently overriding them. Sincerely, A user who has taken the time to observe, document, and articulate this pattern submitted by /u/Important-Primary823 [link] [comments]

Are LLMs the New Propagandists?

I was brainstorming about a video with Claude (Sonnet 4.6). It suggested to explain the difference among ChatGPT, Gemini, Claude and DeepSeek. I agreed. It asked to write the script. I said ‘Yes’. And this is the first thing that set off alarm bells in my head: https://preview.redd.it/rh4rk1pxvb3h1.png?width=940&format=png&auto=webp&s=38822e52f64f46dd2dd276a30e44fb96b8b739c2 Curious, I skimmed the script. For the Western models, it provided the basic information: about the models, the strengths, the weaknesses and pricing. But for the Chinese model, it did appreciate it for its strengths. But it also mentioned the controversy (no such thing for the other three): https://preview.redd.it/3jzf7iv1wb3h1.png?width=940&format=png&auto=webp&s=f61c7145323375d0d11bfd6963f35c11490a50de Translation: Now I will pause here — and tell you something important. There are serious privacy concerns about DeepSeek worldwide. Italy, Australia, Taiwan, South Korea — all these countries have banned DeepSeek on government devices. The reason is that DeepSeek operates under Chinese law — and Chinese law requires the company to share user data upon government request. A major data leak also surfaced within weeks of launch, exposing over 1 million user records. And researchers discovered that DeepSeek's iPhone app was sending data directly to a state-controlled company in China. So I will not be teaching DeepSeek on this channel. I leave the decision to you — but I wanted to share the facts so you stay informed. And here is the summary it asked me to put on the screen: https://preview.redd.it/otsdin8awb3h1.png?width=940&format=png&auto=webp&s=b0cde4e5e04b95f694ccc7624b4ebe326ebae9da Translation: ChatGPT – a little bit of everything. Gemini – best for google users DeepSeek – capable but privacy risk Claude – writing & documents When I pushed it back on its bias and mentioned about privacy issues with Western companies, it replied with this: https://preview.redd.it/cxrhrqphwb3h1.png?width=940&format=png&auto=webp&s=59b8b83e83c4089a0c30fe6fb284abcb1a827e73 It said it was trained predominantly on Western media. And Western media has a documented pattern of covering Chinese and Eastern technology with more alarm than it covers equivalent Western behavior. So here is the question: If AI models are trained on Western media, which has a documented history of treating non-Western countries, especially China, with suspicion and alarm, then what exactly are people absorbing when they ask these tools for information? Hundreds of millions of people use these tools daily. Most people accept the first answer they receive. If that answer carries built-in bias, framing Eastern technology as dangerous while treating identical Western behavior as normal, that bias spreads quietly without anyone noticing. Yes, models warn that they can make mistakes and users should use the information at their own discretion. But this does not remove the responsibility from these tech giants Every new model becomes smarter, more capable with higher token limits and larger context windows. But what about ethics? What about the bias of one side of the world towards the other? Are we going to shrug this off and focus only on making models “smarter”? Then it’s neither artificial nor intelligent. As any LLM would write: “This is not information. This is propaganda.” submitted by /u/Sad-World8172 [link] [comments]

I got AI to compile a music production course. Anyone proficient in music care to check it out?

Hello, I am very new to AI AND music production. I want to learn how to create music and i don't really know much of anything in the realm. So I enrolled in several courses for music production thru Udemy. I was kind of jumping around the courses aimlessly and then I realized I need more structure. The courses include an ableton mastery course, audio engineering, music theory, piano lessons, mixing, mastering and synthesis. The compiled course includes daily lessons and exercises starting from complete novice fundamentals to professional mixing. The course should take about a year. I would post in a music production subreddit but I think i would get a lot of hate. The agent won't be producing any music for me. I only wanted it to make this course. So if anyone that is proficient in music feels up to double checking the content you would be doing me a huge solid. Im so excited to start this new adventure! Send a DM for the Google document submitted by /u/OGgoob666 [link] [comments]

Built a tool to save Claude responses (and ChatGPT, Gemini) into one searchable vault -sharing in case it's useful

I built this tool because I kept asking Claude for code and explanations and losing them in long chats. Coffer adds a save button to every AI response and stores them locally in a searchable vault. Works on: - claude.ai - chatgpt.com - gemini.google.com You can mix snippets across all three and search them. The Markdown stays formatted, which is very nice for Claude's longer responses with code and tables. Everything is local. Coffer makes zero network calls of its own. Free. I lean on Claude the most so feedback from this you all is especially welcome. https://chromewebstore.google.com/detail/nhchbmaobjhjfmeekpnkmhdjajdolcjb?utm_source=item-share-cb submitted by /u/xPhanish [link] [comments]

Chatgpt vs catch agent

one of the things i’m being asked is why i use an ai executive assistant vs just chatgpt. here's how i see it: chatgpt amazing in drafting documents, emails, longer forms of content, images + general copywriting can be connected to many other tools brainstorming & ideation - great tool to think with about things, amazing general understanding of the world really shines in research - if i want to learn something or get instructions on how to do something (both for work or personal - from how to change things on meta ads to how to fix my washing machine) good for work and for personal catchagent shine on work related admin tasks available on imessage + slack + phone call focused / limited scope - only for work proactive no code, no images, no data analysis, no long form content stronger integration with mail, calendar and notion more responsive to feedback - one chat and one context can speak with other people over email or text bottom line: chatgpt - research, email drafts, long form content or data analysis (tool), personal use case catchagent - calendar, email, tasks, delegation vs other people in or out of the org (admin assistant) submitted by /u/CartographerFeisty66 [link] [comments]

Lead Generator

I'm trying to build an AI setup to generate lead lists for potential customers. It's something like apollo or clay, but I want to build it so I can pay less compared to if I get subscriptions for those. Was wondering if its possible. What I want: An AI that can scrape the internet for potential companies/leads Store them in Google Sheets or Excel (company name, location, contact details) or a file Avoid duplicates by checking previous entries Has anyone built something like this? Is it possible to build this with Claude? If I build it, would it be cheaper than other giants out there? submitted by /u/Appropriate_Hyena415 [link] [comments]

ai training

hello, I have these forms I need answers because me and my team working on cinematography application and we are trying to train AI module with answers , I hope u guys can help Darren Aronofsky: https://docs.google.com/forms/d/e/1FAIpQLSchzLnylgJBGbO6MCk-sGEDRx7asbLRtJDBcm6QS_gmrFAt9A/viewform Christopher Nolan : https://docs.google.com/forms/d/e/1FAIpQLSdKiep85BhqQ7vry5b6wfz-HG9WVtjMAEUkitILGqlJEqjDTA/viewform Céline Sciamma: https://docs.google.com/forms/d/e/1FAIpQLSfpirTajDwX4NE2EffYFJbCWaLP0kGXX1IAM9VR5uBl0vDByw/viewform Bong joon ho: https://docs.google.com/forms/d/e/1FAIpQLScK0Z_A6KoCcp0pChfH6Paz-6c8U-z9gAU2zhHZYLRWOBV_qg/viewform agnés varda:https://docs.google.com/forms/d/e/1FAIpQLSeeiPYNYw_YdVkWL8htpEERziScA8h6adxnUfjyNJvSW20RAw/viewform submitted by /u/Siren00r [link] [comments]

ChatGPT or Claude or GitHub Copilot for small development team

tl;dr: Should a small development team using Visual Studio utilize ChatGPT, Claude, or GitHub Copilot? I'm part of a small development team (under 10) and fairly new to using AI agents in our workflow. I'm posting seeking to learn so please forgive the vague simplicity of the title. We currently hold a subscription to both GitHub Copilot and ChatGPT Enterprise where the usage case is to integrate into our workflow with Visual Studio (2022). We are a small company (under 50 employees). To be considerate of spending, we'd like to compromise on a single tool to use going forward once our subscription is up for renewal. The current options on the table are to continue with either ChatGPT Enterprise or GitHub Copilot, or to use Claude instead. When I refer to ChatGPT and Claude, I refer to either the desktop or web application. For GitHub Copilot, we integrate that into Visual Studio and usually use the Claude agent. GitHub Copilot is typically used for engineering entire projects or documents using the Claude agent where it contextualizes the entire solution ChatGPT is used for anything non-related to this (general inquiries, practices, documentation, formatting, engineering a block of code, etc.). We really like how GitHub Copilot is integrated directly into Visual Studio, but find ourselves not regularly using it for anything beyond cases where it needs to analyze large samples or interpret documents using Claude. This is partially because we don't like how selective it can be with what you want to contextualize. ChatGPT is really useful for lower resource inquiries and overall we tend to use that more often. We've yet to try Claude, but are open to considering it given the success we've had using the agent with Copilot. I'm happy to answer additional questions but will pause here for readability. Which subscription should we go with? Cost and integration with our development in Visual Studio are the biggest considerations, but don't want to pass on capabilities for those reasons alone. submitted by /u/WickedGangBelow [link] [comments]

Google AI

How does everyone feel about Google switching to AI tomorrow? submitted by /u/werea11madhere [link] [comments]

Built a free MCP for tracking which URLs Claude (and 5 other engines) cite for any query

We were comparing hosted AI citation dashboards (Profound, AthenaHQ, Otterly) and they all start at $295 to $499 a month. The data they collect is mostly the same data you can pull from each vendor's API. So we built an MCP server that does the same job locally. Citation Intelligence is a stdio MCP server with 12 tools that track what Claude, ChatGPT, Perplexity, Gemini, Google AI Overviews, and Bing cite for any query. Install: npx -y u/automatelab/citation-intelligence Add to .mcp.json: { "mcpServers": { "citation-intelligence": { "command": "npx", "args": ["-y", "@automatelab/citation-intelligence"] } } } Three of the tools run on a local cache and cost zero. The rest are bring-your-own-keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, SERPAPI_API_KEY), about $0.01 to $0.03 per query. The one that actually changed our editorial flow is gsc_citation_gap - it joins Google Search Console data with AI citation status and surfaces pages that rank in Google but are not cited by any AI engine. Those pages are the editorial budget. Repo and full tool list: https://github.com/automatelab/citation-intelligence Launch write-up: https://automatelab.tech/launching-the-citation-intelligence-mcp/ Curious if anyone else here is tracking AI citations in their agent loop rather than in a dashboard, and how you handle the predict-vs-measure tradeoff. submitted by /u/exto13 [link] [comments]