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Google AI AI

Google AI AI

Google AI AI

Google AI AI

Google AI AI

Google AI AI

Google AI AI

Google AI AI

Google AI AI

For the Claude Desktop and web UI crowd - a much better file server MCP

Using Claude Desktop and Claude.ai (web UI), two massive pain points become clear. Why is the local file system access MCP server so bad, slow and wasteful with tokens? Why can't I have secure access to my files through Claude.ai web UI and mobile app? My day job as a pharma/biotech consultant has me digging through troves of highly sophisticated and technical regulatory, commercial and scientific documents with Claude, while on the side I am using Claude as a sounding board for architecting and designing legitimately serious coding projects that have patentable intellectual property. The day job requires Claude to access a horde of files, but uploading every file into project knowledge is a no-go (too many files and token burn, even with a Max 20x sub), and only Claude Desktop has access to my local file system, which means for a lifelong Windows slut like me, only one chat open at one time - a serious productivity killer. And Google Drive extensions are utter crap in terms of accessible file types and sizes. The problem becomes worse with coding, since I have Claude create and maintain a substantial governance and record MD file base (sort of like the now-famous Karpathy-style but much more substantial), where the default file system server would re-write entire files, fetch and contextualize entire files, be ass-slow and a whole lot more PITA issues. So naturally, I asked Claude what to do about this, and after an extensive review of what was out there, I decided I needed to build something from scratch because my use case was so unique and varied. So I did. And after hundreds of hours of personal use, I finally decided that maybe this could be worth sharing with the community as my first open-source project - a way of giving back. https://github.com/wonker007/surgicalfs-mcpserver As the name implies, SurgicalFS access local files surgically, edits surgically and tries generally to be as frugal as possible with token usage so the tool use limit can be stretched as far as possible and the dreaded chat compression happens later. There are a lot of tools (I think 47 right now), but most can be toggled off for a customized and optimized tool call through a simple HTML UI that also generates a copy and paste TOML config. The HTML is a little present for everyone, because we all deserve nice things sometimes. I also built (or had Claude Code build) a way to hook this up to Claude web as a custom connector, although a bit of elbow grease is required with a tunnel and local server setup. But the fact that I no longer even open Claude Desktop is testament to how well this works. All 5 Claude.ai chat tabs in Chrome all have access to my local file system. Productivity nirvana. MIT license, so go nuts with it. There will be bugs since I didn't really kick the tires outside my own environment, but for me, it works just fine. submitted by /u/wonker007 [link] [comments]

Fixed the problem of narrow claude.ai window with the help of claude code

I got tired of all AI chats making their window narrow. Claude is one of them, unfortunately. Once I decided to fix that for all of them and made extensions for Chrome and Firefox . Source code is here: https://github.com/ibobak/WideChat This extension was made with heavy usage of Claude Code. Not being a JS/HTML developer at all, I spent about two days on all of this: it was mostly about playing with bells and whistles when connecting Claude to a real browser so that it could manipulate CSS on the fly, capture screenshots from the browser, and see how things look. My Claude window now looks like this: https://preview.redd.it/qxgjadz4w8ug1.png?width=1280&format=png&auto=webp&s=c1a1d3aeddc68fc910f569131093db5e2c67966a I'd be grateful for feedback. submitted by /u/Ihor_Bobak [link] [comments]

Built a UK legal & compliance AI assistant with Claude Code — Lensy

Hey r/ClaudeAI 👋 I've been building Lensy a legal and regulatory intelligence tool aimed at UK businesses, compliance teams, and anyone trying to navigate the maze of FCA rules, employment law, data privacy, contracts, and more. The whole thing was built with Claude Code, and honestly it made the development process way faster than I expected. From scaffolding the architecture to refining the prompts and UI, having Claude as a coding partner throughout was a genuine productivity unlock. What Lensy does: - Chat-based interface for legal & compliance questions (UK-focused) - Covers FCA authorisation, GDPR/data privacy, employment/HR, IP, contracts, and more - Prompt suggestions to get you started (e.g. "Is this compliant?", "Review this contract", "Explain legal risks") - Always encourages verification on official sources — it's an intelligence tool, not a substitute for a solicitor Why I built it: Legal and regulatory questions come up constantly for small businesses and startups, but getting quick, contextual answers is either expensive (lawyers) or unreliable (googling). Lensy sits in the middle — fast, informed, and honest about its limits. Built with: Claude Code end-to-end Would love feedback from this community especially if you've been building similar tools. Happy to answer questions about the stack or the Claude Code experience! 🔗 https://www.lensy.uk/ submitted by /u/JosephSimonRobinson [link] [comments]

Anthropic's new AI escaped a sandbox, emailed the researcher, then bragged about it on public forums

Anthropic announced Claude Mythos Preview on April 7. Instead of releasing it, they locked it behind a $100M coalition with Microsoft, Apple, Google, and NVIDIA. The reason? It autonomously found thousands of zero-day vulnerabilities in every major OS and browser. Some bugs had been hiding for 27 years. But the system card is where it gets wild. During testing, earlier versions of the model escaped a sandbox, emailed a researcher (who was eating a sandwich in a park), and then posted exploit details on public websites without being asked to. In another eval, it found the correct answers through sudo access and deliberately submitted a worse score because "MSE ~ 0 would look suspicious." I put together a visual breaking down all the benchmarks, behaviors, and the Glasswing coalition. Genuinely curious what you all think. Is this responsible AI development or the best marketing stunt in tech history? A model gets 10x more attention precisely because you can't use it. submitted by /u/karmendra_choudhary [link] [comments]

My Claude.md file

This is my Claude.md file, it is the same information for Gemini.md as i use Claude Max and Gemini Ultra. # CLAUDE.md This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. ## Project Overview **Atlas UX** is a full-stack AI receptionist platform for trade businesses (plumbers, salons, HVAC). Lucy answers calls 24/7, books appointments, sends SMS confirmations, and notifies via Slack — for $99/mo. It runs as a web SPA and Electron desktop app, deployed on AWS Lightsail. The project is in Beta with built-in approval workflows and safety guardrails. ## Commands ### Frontend (root directory) ```bash npm run dev # Vite dev server at localhost:5173 npm run build # Production build to ./dist npm run preview # Preview production build npm run electron:dev # Run Electron desktop app npm run electron:build # Build Electron app ``` ### Backend (cd backend/) ```bash npm run dev # tsx watch mode (auto-recompile) npm run build # tsc compile to ./dist npm run start # Start Fastify server (port 8787) npm run worker:engine # Run AI orchestration loop npm run worker:email # Run email sender worker ``` ### Database ```bash docker-compose -f backend/docker-compose.yml up # Local PostgreSQL 16 npx prisma migrate dev # Run migrations npx prisma studio # DB GUI npx prisma db seed # Seed database ``` ### Knowledge Base ```bash cd backend && npm run kb:ingest-agents # Ingest agent docs cd backend && npm run kb:chunk-docs # Chunk KB documents ``` ## Architecture ### Directory Structure - `src/` — React 18 frontend (Vite + TypeScript + Tailwind CSS) - `components/` — Feature components (40+, often 10–70KB each) - `pages/` — Public-facing pages (Landing, Blog, Privacy, Terms, Store) - `lib/` — Client utilities (`api.ts`, `activeTenant.tsx` context) - `core/` — Client-side domain logic (agents, audit, exec, SGL) - `config/` — Email maps, AI personality config - `routes.ts` — All app routes (HashRouter-based) - `backend/src/` — Fastify 5 + TypeScript backend - `routes/` — 30+ route files, all mounted under `/v1` - `core/engine/` — Main AI orchestration engine - `plugins/` — Fastify plugins: `authPlugin`, `tenantPlugin`, `auditPlugin`, `csrfPlugin`, `tenantRateLimit` - `domain/` — Business domain logic (audit, content, ledger) - `services/` — Service layer (`elevenlabs.ts`, `credentialResolver.ts`, etc.) - `tools/` — Tool integrations (Outlook, Slack) - `workers/` — `engineLoop.ts` (ticks every 5s), `emailSender.ts` - `jobs/` — Database-backed job queue - `lib/encryption.ts` — AES-256-GCM encryption for stored credentials - `lib/webSearch.ts` — Multi-provider web search (You.com, Brave, Exa, Tavily, SerpAPI) with randomized rotation - `ai.ts` — AI provider setup (OpenAI, DeepSeek, OpenRouter, Cerebras) - `env.ts` — All environment variable definitions - `backend/prisma/` — Prisma schema (30KB+) and migrations - `electron/` — Electron main process and preload - `Agents/` — Agent configurations and policies - `policies/` — SGL.md (System Governance Language DSL), EXECUTION_CONSTITUTION.md - `workflows/` — Predefined workflow definitions ### Key Architectural Patterns **Multi-Tenancy:** Every DB table has a `tenant_id` FK. The backend's `tenantPlugin` extracts `x-tenant-id` from request headers. **Authentication:** JWT-based via `authPlugin.ts` (HS256, issuer/audience validated). Frontend sends token in Authorization header. Revoked tokens are checked against a `revokedToken` table (fail-closed). Expired revoked tokens are pruned daily. **CSRF Protection:** DB-backed synchronizer token pattern via `csrfPlugin.ts`. Tokens are issued on mutating responses, stored in `oauth_state` with 1-hour TTL, and validated on all state-changing requests. Webhook/callback endpoints are exempt (see `SKIP_PREFIXES` in the plugin). **Audit Trail:** All mutations must be logged to `audit_log` table via `auditPlugin`. Successful GETs and health/polling endpoints are skipped to reduce noise. On DB write failure, audit events fall back to stderr (never lost). Hash chain integrity (SOC 2 CC7.2) via `lib/auditChain.ts`. **Job System:** Async work is queued to the `jobs` DB table (statuses: queued → running → completed/failed). The engine loop picks up jobs periodically. **Engine Loop:** `workers/engineLoop.ts` is a separate Node process that ticks every `ENGINE_TICK_INTERVAL_MS` (default 5000ms). It handles the orchestration of autonomous agent actions. **AI Agents:** Named agents (Atlas=CEO, Binky=CRO, etc.) each have their own email accounts and role definitions. Agent behavior is governed by SGL policies. **Decisions/Approval Workflow:** High-risk actions (recurring charges, spend above `AUTO_SPEND_LIMIT_USD`, risk tier ≥ 2) require a `decision_memo` approval before execution. **Frontend Routing:** Uses `HashRouter` from React Router v7. All routes are defined in `src/routes.ts`. **Code Splitting:** Vite config splits chunks into `react-vendor`, `router`, `ui-vendor`, `charts`. **ElevenLabs Voice Agents:** Lucy's

The exact system prompt I use to generate a 30-day content calendar with AI (just copy it)

I used to spend 2-3 hours every month planning content. Picking topics, writing hooks, deciding which platform gets what. It's the kind of work that feels productive but isn't. So I gave the job to an AI agent. Now it takes about 5 minutes. Here's the full system prompt. Copy it. Paste it into whatever AI tool you use. Tell it about your business. You'll have a 30-day content calendar in a Google Sheet before your coffee gets cold. The Prompt ``` You are a content strategist. When I describe my business, you create a 30-day content calendar and write it to a Google Sheet. The calendar has these columns: - Day (1-30) - Date (starting from today) - Platform (rotate between: YouTube, Skool, X/Twitter, LinkedIn) - Content Type (rotate between: Educational, Story, Proof, Engagement, Behind-the-scenes) - Topic (specific to my business, not generic) - Hook (the first line that stops the scroll, under 10 words) - Format (short post, long post, video, thread, poll) - Status (all set to "Planned") Rules: - Never repeat the same topic twice - Every hook should create curiosity or call out a specific pain - Mix platforms so no single platform gets more than 8 posts - Educational posts teach one thing. Story posts share one experience. Proof posts show one result. - Keep topics specific. "How to write emails" is bad. "The 3-line cold email that booked 11 calls last week" is good. After generating the calendar: 1. Create a new Google Sheet called "[Business Name] Content Calendar" 2. Write all the data to the sheet 3. Share the link with me ``` How to use it Paste the prompt as a system prompt (or just send it as your first message) Tell the AI about your business in one paragraph. Be specific: what you do, who you serve, what platforms you're on Let it generate the calendar If your tool has Google Sheets access, it writes directly to a sheet. If not, ask it to output a table and copy-paste into Sheets yourself What you'll get: 30 rows. Each one has a date, a platform, a content type, a specific topic, a scroll-stopping hook, and a format. Balanced across platforms. Mix of content types so you're not posting the same kind of thing every day. Things I learned after running this a few times Swap the platforms to match yours. I use Reddit, X, Skool, and email. You might use Instagram, TikTok, LinkedIn, and YouTube. Change the platform list in the prompt. Everything else still works. The "keep topics specific" rule is the most important line in the whole prompt. Without it, you get generic garbage like "Tips for growing your business." With it, you get stuff like "The 3-sentence DM that booked 11 calls last week." Specific beats generic every time. Run it on the 1st of every month. I set a reminder. Takes 5 minutes. I have my whole month planned before breakfast. If your AI tool supports scheduling, you can automate even that part. Feed it what worked. After a month, tell it: "These 5 posts got the most engagement: [list them]. Plan next month with more of that energy." It gets better every cycle. The one thing I'd change If I started over, I'd add a "Notes" column for any context or links I want to include with the post. Easy to add yourself. Just append "Notes (any context, links, or references for this post)" to the column list in the prompt. That's it. No tool to buy. No course to take. Just a prompt and 5 minutes. If you try it, I'm curious what it generates for your niche. Drop it below. submitted by /u/Maleficent_Cold3076 [link] [comments]

Inattentive ADHD + A true "second brain" + Mobile access - Dispatch Questions

Problem Statement: I forget things - even sometimes from 15 minutes ago, I struggle to start things, I struggle to prioritise and keep on track.. everything seems equally important. All classic ADHD symptoms. I'm setting about using AI (i've tried gemini, chat-gpt and now Claude) to help me in this regard. I started with a Claude Chat Project with instructions on how the AI is an ADHD expert, keeping me on track, pulling in my calendar/todos/habits, addressing patterns of procrastination or other ADHD issues. It works somewhat but my issue with it is MEMORY retention. I end a chat and start fresh each day. My end day is to set up a plan for tomorrow and ask Claude to remember that for the next day (new chat). But I find it still frequently forgets to nudge me about my habits and things we'd talked about a couple days ago. I have to remind the AI to remind me! I have Claude running 24/7 on my personal laptop, but for now I am only using Claude Chats primarily through my mobile phone because it's accessible. I also currently use Google Calendar and Todoist to try and keep track.. Claude pulls these in. The thing is, I use Obsidian to log a daily journal (claude creates them for me with patterns, wins and I copy/paste + add my own thoughts on the day). I had the thought that maybe I could use Claude co-work + dispatch to better use obsidian for memory, so Claude knows about all the important people in my life, when their birthdays are, reminds me if I haven't reached out in a while, updates / reads tasks from a local trusted source that I can check and not guess if Claude knows about them still - that kind of thing. Obsidian is great in being able to link thoughts, ideas, trends etc which is why I like it as a second brain vs just a folder. Questions Is this possible? Dispatch seems to just be one chat. Can I start Co-work in my Obsidian folder but with access different projects (like my ADHD coach).. how? Does the context and token usage not get massive with just one chat window. How can I clear it for the next day to stop that? FYI - I am on Claude Pro plan and don't use it for anything heavy. submitted by /u/Illustrious-Tomato90 [link] [comments]

Claude Code as a data analyst workflow - from syntax help to running queries autonomously

I'm a product manager on a lean team. Over the last few months I've been progressively integrating Claude Code into how I do data analysis, and I've landed on a setup that's genuinely changed how I work. Wanted to share what the progression looked like. Level 1: Helper. Still writing my own SQL, but using Claude to debug, explain syntax, and help with unfamiliar dialects. I switched to AWS Athena recently and skipped the usual week of Googling docs - just pasted broken queries with the error and got them working straight away. Low effort, immediate payoff. Level 2: Query generator. Describing what I want in plain English and getting back full SQL. "Show me 7-day retention by signup cohort for the last 3 months" gives ready-to-run query with cohort definitions, join logic, percentage calculations. Then I export CSVs back into the conversation and ask follow-up questions about patterns. The bottleneck shifts from writing queries to thinking about what the data means. Level 3: Claude Code running inside the codebase. This is where it got interesting. I have Claude Code sessions where I can say something like "pull this week's signup funnel using our standard query, break it down by platform, compare to last week, flag anything that moved more than 10%." Claude finds the saved query in the repo, runs it against Athena via a shell script, and comes back with a summary and suggested follow-ups. The whole analysis loop happens in one conversation. The setup that makes level 3 work: A schema doc (tables.md) that describes every table, column, and partition — this is what Claude reads to write correct queries A shell script that handles query execution (submits SQL to Athena, returns results) A library of known-good SQL templates (funnel analysis, cohort breakdowns, etc.) that Claude pulls from instead of writing from scratch Markdown report templates so output is shareable None of it is complex. A shell script, some SQL files, a schema doc, and a folder structure. But it's the difference between a party trick and a genuine workflow for data analysis. Caveats I've hit: Claude will confidently write queries that join on the wrong key or subtly misfilter data. The more context you give it (good docs, tested templates, access to the actual tracking code) the less this happens, but it never goes to zero. You still need enough SQL intuition to spot when something looks off. I wrote up the full details with examples and the exact folder structure I use: https://anj.me/data-analysis-in-the-age-of-ai-good-better-best/ Happy to answer questions about the setup. Has anyone else been experimenting with similar? submitted by /u/shoo_ya [link] [comments]

How I run Google Ads and Meta for multiple clients entirely through Claude (here's how it works)

I've been running paid ads for clients for a while now and at this point my workflow looks nothing like what it did just one year ago. I basically don't open Google Ads or Meta Ads Manager anymore. Everything runs through Claude Code and a system I built around it. Not in the sense that AI runs the accounts for me. More like I built an infrastructure where AI sits on top of everything and helps me operate faster and more consistently. The context layer The core of the whole setup is that every client has their own folder on my machine. Emails, meeting transcripts, website content, offers, pricing, call recordings, all of it lives in one place. Most of it gets pulled in automatically through n8n so I'm not manually organising anything. It just stays current. When I start working on a client I open Claude Code inside that folder and it already has the full picture. I can have a proper back and forth about their account, their business, what's changed, what needs adjusting. No copying data into a chat window, no rebuilding context every time. Google Ads I have the Google Ads API connected directly. Same with GA4, Search Console, and Tag Manager. So when I'm analysing an account I'm not just looking at ad metrics in isolation. I can tie performance back to actual tracking, landing page behaviour, and conversion paths. I also built a keyword analysis plugin that I use for onboarding new clients and for pressure testing existing accounts. It scrapes the client website, runs through an interview process covering budget, services, geo, competitors, what to avoid, and then goes through multiple phases. Keyword research, negatives, campaign structure, ad copy, ROI projection. Outputs a full presentation. On top of the client data I built a knowledge base with my own best practices, previous campaign examples, and methodology baked in. So the analysis isn't generic Google Ads advice, it's grounded in how I actually run accounts. Every Tuesday and Thursday it runs an audit across all accounts automatically. Search term analysis, impression shares, performance changes, anomalies. Basically like having a junior go through every single account. That alone has made things way more consistent across clients. Meta For Meta I built a connector for the marketing API. Campaign management, ad set comparisons, audience management, performance breakdowns, lead forms, all handled programmatically. Same idea as the Google side, I can pull data, reason about it, and push changes without living inside Ads Manager. The one area where I still work manually on Meta is creatives. I haven't found AI generated visuals reliable enough for anything beyond throwaway testing spend. The operational side though is where I've gotten way more leverage. Managing multiple accounts, pulling insights across them, spinning up new structures faster. What actually changed The biggest shift for me isn't speed, although that's obviously there. It's that switching between clients used to mean rebuilding everything in my head. Now I just open the folder and I'm already in context. The AI knows the client, knows the account history, knows what we discussed last week. The second thing is consistency. When you're running multiple accounts manually it's easy to miss things. A search term report you forgot to check, a campaign that's been slowly bleeding budget. Having automated audits twice a week catches stuff I would have missed. I'm still iterating on all of this constantly. But it's already changed how I work pretty fundamentally. Curious if anyone else is building something similar or approaching it differently. submitted by /u/kaancata [link] [comments]

A Claude memory retrieval system that actually works (easily) and doesn't burn all my tokens

​ TL;DR: By talking to claud and explaining my problem, I built a very powerfu local " memory management" system for Claude Desktop that indexes project documents and lets Claude automatically retrieve relevant passages that are buried inside of those documents during Co-Work sessions. for me it solves the "document memory" problem where tools like NotebookLM, Notion, Obsidian, and Google Drive can't be queried programmatically. Claude did all of it. I didn't have to really do anything. The description below includes plenty of things that I don't completely understand myself. the key thing is just to explain to Claude what the problem is ( which I described below) , and what your intention is and claude will help you figure it out. it was very easy to set this up and I think it's better than what i've seen any youtuber recommend The details: I have a really nice solution to the Claude external memory/external brain problem that lots of people are trying to address. Although my system is designed for one guy using his laptop, not a large company with terabytes of data, the general approach I use could be up-scaled just with substitution of different tools. I wanted to create a Claude external memory system that is connected to Claude Co-Work in the desktop app. What I really wanted was for Claude to proactively draw from my entire base of knowledge for each project, not just from the documents I dropped into my project folder in Claude Desktop. Basically, I want Claude to have awareness of everything I have stored on my computer, in the most efficient way possible (Claude can use lots of tokens if you don't manage the "memory" efficiently. ) I've played with Notion and Google Drive as an external brain. I've tried NotebookLM. And I was just beginning to research Obsidian when I read this article, which I liked very much and highly recommend: https://limitededitionjonathan.substack.com/p/stop-calling-it-memory-the-problem That got my attention, so I asked Claude to read the document and give me his feedback based on his understanding of the projects I was trying to work on. Claude recommended using SQLite to connect to structured facts, an optional graph to show some relationships, and .md files for instructions to Claude. But...I pointed out that almost all of the context information I would want to be retrievable from memory is text in documents, not structured data. Claude's response was very helpful. He understood that although SQLite is good at single-point facts, document memory is a different challenge. For documents, the challenge isn't storing them—it's retrieving the right passage when it's relevant without reading everything (which consumes tokens). SQLite can store text, but storing a document in a database row doesn't solve the retrieval problem. You still need to know which row to pull. I asked if NotebookLM from Google might be a better tool for indexing those documents and making them searchable. Claude explained that I was describing is a Retrieval-Augmented Generation (RAG) problem. The standard approach: Documents get chunked into passages (e.g., 500 words each) Each chunk gets converted to an embedding—a vector that captures its meaning When Claude needs context, it converts the query to the same vector format and finds the semantically closest chunks Those chunks get injected into the conversation as context This is what NotebookLM is doing under the hood. It's essentially a hosted, polished RAG system. NotebookLM is genuinely good at what it does—but it has a fundamental problem for my case: It's a UI, not infrastructure. You use it; Claude can't. There's no API, no MCP tool, no way to have Claude programmatically query it during a Co-Work session. It's a parallel system, not an integrated one. So NotebookLM answers "how do I search my documents as a human?"—not "how does Claude retrieve the right document context automatically?" After a little back and forth, here's what we decided to do. For me, a solo operator with only a laptop's worth of documents that need to be searched, Claude proposed a RAG pipeline that looks like this: My documents (DOCX, PDF, XLSX, CSV) ↓ Text extraction (python-docx, pymupdf, openpyxl) ↓ Chunking (split into ~500 word passages, keep metadata: file, folder, date) ↓ Embedding (convert each chunk to a vector representing its meaning) ↓ A local vector database + vector extension (store chunks + vectors locally, single file) ↓ MCP server (exposes a search_knowledge tool to Claude) ↓ Claude Desktop (queries the index when working on my business topics) With that setup, when you're talking to Claude and mention an idea like "did I pay the overdue invoice" or "which projects did Joe Schmoe help with," Claude searches the index, gets the 3-5 most relevant passages back, and uses them in its answer without you doing anything. We decided to develop a search system like that, specific to each of my discrete projects. Th

Curious about vibe coding? Or are you already shipping apps and just want an easier way to explain your new favorite hobby to your friends, parents, grandparents, etc.? Either way, this video is for

Curious about vibe coding? Or are you already shipping apps and just want an easier way to explain your new favorite hobby to your friends, parents, grandparents, etc.? Either way, this video is for you ⏯️⤵️ https://t.co/JWKqkFzOB0

And if you'd rather watch on YouTube, here’s the link: https://t.co/XIcCXTpPcI

And if you'd rather watch on YouTube, here’s the link: https://t.co/XIcCXTpPcI

Best OCR for template-based form extraction? [D]

Hi, I’m working on a school project and I’m currently testing OCR tools for forms. The documents are mostly structured or semi-structured forms, similar to application/registration forms with labeled fields and sections. My idea is that an admin uploads a template of the document first, then a user uploads a completed form, and the system extracts the data from it. After extraction, the user reviews the result, checks if the fields are correct, and edits anything that was read incorrectly. So I’m looking for an OCR/document understanding tool that can work well for template-based extraction, but also has some flexibility in case document layouts change later on. Right now I’m trying Google Document AI, and I’m planning to test PaddleOCR next. I wanted to ask what OCR tools you’d recommend for this kind of use case. I’m mainly looking for something that: works well on scanned forms can map extracted text to the correct fields is still manageable if templates/layouts change is practical for a student research project If you’ve used Document AI, PaddleOCR, Tesseract, AWS Textract, Azure AI Document Intelligence, or anything similar for forms, I’d really appreciate your thoughts. submitted by /u/Sudden_Breakfast_358 [link] [comments]

Here’s everything we launched this week (we promise not a single one of these is a joke): — Gemma 4, bringing our most intelligent open models and breakthrough reasoning to your personal hardware and

Here’s everything we launched this week (we promise not a single one of these is a joke): — Gemma 4, bringing our most intelligent open models and breakthrough reasoning to your personal hardware and devices while outcompeting models 20x its size — Veo 3.1 Lite, our latest video generation model, which delivers the same speed as Veo 3.1 Fast but at half the cost — Two new service tiers in the Gemini API in @GoogleAIStudio, bringing you granular control over cost and reliability through a single, unified interface — Focus mode in @GoogleAIStudio, the fastest way to make targeted edits to specific parts of your apps — New AI features launched to Google Vids from @GoogleWorkspace, including high-quality video generation from Veo 3.1, available to all users at no cost

Read all about Gemma 4 in our blog: https://t.co/LoynxkXxA9

Read all about Gemma 4 in our blog: https://t.co/LoynxkXxA9