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I'm deleting every paid document extraction tool because of this. Zipstack built Unstract and it turns any PDF, scan, or image into clean structured JSON using LLMs you already pay for. You point it at a document (an invoice, a bank statement, a KYC form, a tax return, a... claims form) and it automatically pulls the fields you asked for and hands back a JSON object ready to drop straight into your database. The difference from every other document extraction tool is the setup. Most tools want a model per vendor or regex per template. This works from a single natural language prompt that handles every variation. → Extracts structured JSON from PDFs, scans, and images → Lets you write extraction schemas in plain English through Prompt Studio → Deploys as a REST API or ETL pipeline in minutes → Ships an MCP server so Claude and other agents can extract documents directly → Connects to S3, GCS, Snowflake, BigQuery, Postgres, and every major destination → LLM-agnostic: bring OpenAI, Anthropic, Bedrock, Gemini, Mistral, Ollama, or any provider you already use Basically: you have thousands of documents nobody has time to read. Unstract turns your documents into clean, structured JSON that you can run in production. This is what document extraction should have been all along.show more

Hasan Toor

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Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Akshay 🚀

58,152 views • 8 months ago

Extracting structured outputs with LLMs is easy. But doing large-scale extraction with precise citations and bounding boxes back to the source documents is way harder. With our latest release in LlamaExtract, we extract citation bounding boxes along with every single key and value within a document. You can see this in the UI. Hover over any k:v pair and you’ll be able to see the corresponding highlights in the source doc. If you’re a human reviewing a million docs (resumes, IDs, invoices, claims, contracts), this will help you 5x your ability to verify values and make sure things are correct. Check out these new extraction upgrades in LlamaCloud:

Extracting structured outputs with LLMs is easy. But doing large-scale extraction with precise citations and bounding boxes back to the source documents is way harder. With our latest release in LlamaExtract, we extract citation bounding boxes along with every single key and value within a document. You can see this in the UI. Hover over any k:v pair and you’ll be able to see the corresponding highlights in the source doc. If you’re a human reviewing a million docs (resumes, IDs, invoices, claims, contracts), this will help you 5x your ability to verify values and make sure things are correct. Check out these new extraction upgrades in LlamaCloud:

Jerry Liu

23,044 views • 5 months ago

I just vibe-coded a Shopify Reviews Scraper that swipes all your competitors' customer reviews instantly. 🤯 Paste any competitor's product URL → pull every review in under 30 seconds → export to CSV. Built 100% in Codex. Perfect for brands and agencies who want to mine competitor voice-of-customer data at scale and turn it into winning ad creative. Here's how it works: → Drop a Shopify product URL into the tool → Set your review limit (50, 100, 500+) → Hit scrape — it launches a browser, finds the review widget, pulls every review → Export clean rows: rating, body, author, date → Feed the CSV to Claude and ask "what do customers love and hate about this product" → Turn the output into ad angles, hooks, and headlines No more paying $99/mo for clunky review scraping SaaS that locks you behind credits. What you get: - Any Shopify store's reviews in 30 seconds - Clean structured data (rating, body, author, date) - CSV or JSON export — drops straight into Sheets or an LLM - Unlimited scrapes, no per-review fees - A voice-of-customer firehose you can turn into ad creative on demand This is essentially a competitor review intelligence engine in a box. I'm giving away the full GitHub repo so you can clone it and run it yourself for free. Want the repo? > Like this post > Comment "SCRAPE" And I'll send it over (must be following so I can DM)

I just vibe-coded a Shopify Reviews Scraper that swipes all your competitors' customer reviews instantly. 🤯 Paste any competitor's product URL → pull every review in under 30 seconds → export to CSV. Built 100% in Codex. Perfect for brands and agencies who want to mine competitor voice-of-customer data at scale and turn it into winning ad creative. Here's how it works: → Drop a Shopify product URL into the tool → Set your review limit (50, 100, 500+) → Hit scrape — it launches a browser, finds the review widget, pulls every review → Export clean rows: rating, body, author, date → Feed the CSV to Claude and ask "what do customers love and hate about this product" → Turn the output into ad angles, hooks, and headlines No more paying $99/mo for clunky review scraping SaaS that locks you behind credits. What you get: - Any Shopify store's reviews in 30 seconds - Clean structured data (rating, body, author, date) - CSV or JSON export — drops straight into Sheets or an LLM - Unlimited scrapes, no per-review fees - A voice-of-customer firehose you can turn into ad creative on demand This is essentially a competitor review intelligence engine in a box. I'm giving away the full GitHub repo so you can clone it and run it yourself for free. Want the repo? > Like this post > Comment "SCRAPE" And I'll send it over (must be following so I can DM)

Mike Futia

29,783 views • 2 months ago

SOMEONE VIBE CODED AN APP THAT TURNS ANY PHOTO INTO A REAL PHYSICAL STAMP YOU CAN ORDER you snap a photo, it cuts it into a clean stamp design, and you can order the actual physical stamp to press onto anything. > take or upload any photo or image > it turns it into a proper stamp design automatically > all your designs are saved and sorted by date, so you can go back to any of them > order the real physical stamp right from the app and it ships to you > press it onto letters, packaging, cards, whatever you want so instead of paying a custom shop and waiting, you make your own stamp from a photo in a couple taps, then order it without ever leaving the app. the whole thing is dead simple and does one thing well, which is exactly why people love it

SOMEONE VIBE CODED AN APP THAT TURNS ANY PHOTO INTO A REAL PHYSICAL STAMP YOU CAN ORDER you snap a photo, it cuts it into a clean stamp design, and you can order the actual physical stamp to press onto anything. > take or upload any photo or image > it turns it into a proper stamp design automatically > all your designs are saved and sorted by date, so you can go back to any of them > order the real physical stamp right from the app and it ships to you > press it onto letters, packaging, cards, whatever you want so instead of paying a custom shop and waiting, you make your own stamp from a photo in a couple taps, then order it without ever leaving the app. the whole thing is dead simple and does one thing well, which is exactly why people love it

Om Patel

100,110 views • 13 days ago

Claude Design + Shopify is f*cking ridiculous 🤯 You can now publish pages from Claude Design → Claude Code → Shopify. Built 100% with Claude Design, Claude Code, and the Shopify CLI. Perfect for DTC brands and agencies who want to skip the design → dev handoff entirely. Here's how it works: → Design any landing page in Claude Design → Export as a zip and drop it into Claude Code → Install the Shopify + Shopify AI Toolkit plugins → Prompt Claude to convert the HTML into a Shopify page template + push to live theme → Claude uploads the images, deploys the files, and creates a published page No more handing designs off to a dev and waiting 2 weeks for a Shopify page. What you get: - A workflow that turns any Claude Design page into a real Shopify page template - Editable sections so your marketing team can swap copy, images, and CTAs without code - Images uploaded straight to Shopify Files automatically - A files-only deploy that only touches what's new in your live theme - A repeatable pipeline you can use every time you design a new landing page This is essentially the design-to-deploy pipeline brands have been waiting for. I put together a step-by-step playbook for going from Claude Design → published Shopify page. Every install, every plugin, every command, and the exact prompt that runs the whole thing. Want the playbook for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Claude Design + Shopify is f*cking ridiculous 🤯 You can now publish pages from Claude Design → Claude Code → Shopify. Built 100% with Claude Design, Claude Code, and the Shopify CLI. Perfect for DTC brands and agencies who want to skip the design → dev handoff entirely. Here's how it works: → Design any landing page in Claude Design → Export as a zip and drop it into Claude Code → Install the Shopify + Shopify AI Toolkit plugins → Prompt Claude to convert the HTML into a Shopify page template + push to live theme → Claude uploads the images, deploys the files, and creates a published page No more handing designs off to a dev and waiting 2 weeks for a Shopify page. What you get: - A workflow that turns any Claude Design page into a real Shopify page template - Editable sections so your marketing team can swap copy, images, and CTAs without code - Images uploaded straight to Shopify Files automatically - A files-only deploy that only touches what's new in your live theme - A repeatable pipeline you can use every time you design a new landing page This is essentially the design-to-deploy pipeline brands have been waiting for. I put together a step-by-step playbook for going from Claude Design → published Shopify page. Every install, every plugin, every command, and the exact prompt that runs the whole thing. Want the playbook for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Mike Futia

56,432 views • 2 months ago

Web Scraping is dead. Web Agenting is here. Writing selectors (div > .class > span) breaks every time a site updates. Building custom bots for every new target is a waste of engineering hours. TinyFish turns the entire real-time web into a single API. Input: Natural Language ("Find availability for X"). Target: 1 or 100 URLs. Output: Structured JSON. This isn't a simulation. It visits the Real-Time Web. 1./ One API, Many Sites - Same contract whether you hit 1 URL or 50. You focus on the Goal (Business Logic). TinyFish handles the How (Navigation, Clicks, Inputs). 2./ Real Automation - It doesn't just "read." It interacts. It fills forms, navigates multi-step flows, and handles dynamic JS content. 3./ Production Ready - This is the same infrastructure used by large enterprises, now exposed as a clean developer primitive. Logs, error handling, and structured data are built-in. The web is finally a proper API.

Web Scraping is dead. Web Agenting is here. Writing selectors (div > .class > span) breaks every time a site updates. Building custom bots for every new target is a waste of engineering hours. TinyFish turns the entire real-time web into a single API. Input: Natural Language ("Find availability for X"). Target: 1 or 100 URLs. Output: Structured JSON. This isn't a simulation. It visits the Real-Time Web. 1./ One API, Many Sites - Same contract whether you hit 1 URL or 50. You focus on the Goal (Business Logic). TinyFish handles the How (Navigation, Clicks, Inputs). 2./ Real Automation - It doesn't just "read." It interacts. It fills forms, navigates multi-step flows, and handles dynamic JS content. 3./ Production Ready - This is the same infrastructure used by large enterprises, now exposed as a clean developer primitive. Logs, error handling, and structured data are built-in. The web is finally a proper API.

Tech with Mak

119,568 views • 5 months ago

TrendTrack API & MCP are live Connect it to Claude, GPT, Gemini or any tool you already use. What you can pull : Winning ads before they saturate Scaling stores before everyone spots them Email campaigns from your competitors Hooks that are converting right now Everything you see on TrendTrack can now be extracted and processed by AI. How to use it ? API → integrate TrendTrack data directly into your tools, dashboards, or custom apps MCP → connect TrendTrack to any LLM. Claude, GPT, Gemini, Perplexity. Ask a question, it pulls the data itself, responds in real time Start building now →

TrendTrack API & MCP are live Connect it to Claude, GPT, Gemini or any tool you already use. What you can pull : Winning ads before they saturate Scaling stores before everyone spots them Email campaigns from your competitors Hooks that are converting right now Everything you see on TrendTrack can now be extracted and processed by AI. How to use it ? API → integrate TrendTrack data directly into your tools, dashboards, or custom apps MCP → connect TrendTrack to any LLM. Claude, GPT, Gemini, Perplexity. Ask a question, it pulls the data itself, responds in real time Start building now →

Trendtrack

177,020 views • 2 months ago

$I built a content engine that runs on telegram. Two commands... /discover: sends out to 9 sources across HackerNews, Reddit communities covering AI automation, prompt engineering, vibe coding, and specialist newsletters. Pulls everything published in the last 24 hours, runs each item through an AI extraction layer that scores it against 100+ niche keywords, deduplicates, and drops the relevant ideas into a Notion database. Takes about 90 seconds. Costs fractions of a cent. /ideas: this command pulls the top scored ideas from that database, randomizes the selection so you're not seeing the same ones every time, and sends them to you in a clean numbered list. You reply with /write 3 or whatever you choose, and the system researches the topic using Perplexity's live web search, generates three distinct outline options with different angles and hooks, saves them to a Google Doc, and sends you a message telling you they're ready. You read the outlines, and you pick one. You then reply with the command /outline 2. The system writes the full piece in your voice, following your brand guidelines, with specific examples and concrete claims. It can be done in under two minutes of your time. The whole thing runs on n8n, with no subscriptions beyond what you already use. If content takes too long or you don't have ideas, this solves that. I built this for myself; I can do it for you. If you're tired of knowing you should be posting and still not doing it, let's talk.$

I built a content engine that runs on telegram. Two commands... /discover: sends out to 9 sources across HackerNews, Reddit communities covering AI automation, prompt engineering, vibe coding, and specialist newsletters. Pulls everything published in the last 24 hours, runs each item through an AI extraction layer that scores it against 100+ niche keywords, deduplicates, and drops the relevant ideas into a Notion database. Takes about 90 seconds. Costs fractions of a cent. /ideas: this command pulls the top scored ideas from that database, randomizes the selection so you're not seeing the same ones every time, and sends them to you in a clean numbered list. You reply with /write 3 or whatever you choose, and the system researches the topic using Perplexity's live web search, generates three distinct outline options with different angles and hooks, saves them to a Google Doc, and sends you a message telling you they're ready. You read the outlines, and you pick one. You then reply with the command /outline 2. The system writes the full piece in your voice, following your brand guidelines, with specific examples and concrete claims. It can be done in under two minutes of your time. The whole thing runs on n8n, with no subscriptions beyond what you already use. If content takes too long or you don't have ideas, this solves that. I built this for myself; I can do it for you. If you're tired of knowing you should be posting and still not doing it, let's talk.

Savvy | Ai & Automation

14,879 views • 4 months ago

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

AlphaSignal

91,979 views • 3 months ago

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

A peanut-sized Chinese model just dethroned Gemini at reading documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.

Jafar Najafov

13,630 views • 3 months ago

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,080 views • 1 month ago

THIS GUY BUILT A TOOL THAT TURNS YOUR STARTUP SCREENSHOTS INTO POLISHED WALKTHROUGH VIDEOS you drop in a screenshot of your app and it generates a full motion video walkthrough or feature announcement no after effects, no premiere, no motion designer needed, and no spending 3 hours on a 15 second clip just upload your screenshot and it animates everything into a clean product demo you can use on your landing page or social media launch every startup founder has the same problem. you build something great but your demo video looks horrible. a screen recording with a cursor clicking around randomly this solves that. the output looks CLEAN and professional if you're launching anything right now you should probably use this asap

THIS GUY BUILT A TOOL THAT TURNS YOUR STARTUP SCREENSHOTS INTO POLISHED WALKTHROUGH VIDEOS you drop in a screenshot of your app and it generates a full motion video walkthrough or feature announcement no after effects, no premiere, no motion designer needed, and no spending 3 hours on a 15 second clip just upload your screenshot and it animates everything into a clean product demo you can use on your landing page or social media launch every startup founder has the same problem. you build something great but your demo video looks horrible. a screen recording with a cursor clicking around randomly this solves that. the output looks CLEAN and professional if you're launching anything right now you should probably use this asap

Om Patel

100,910 views • 2 months ago

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.

Cortex

11,380 views • 2 months ago

TikTok Shop + Seedance 2 + Claude Code is f*cking cracked i just built a tool that scrapes the top-selling TikTok Shop products in your niche, pulls every creator video selling them, and lets you recreate any one as an on-brand video for your own product using AI. if you're a DTC brand or ecom operator and you're still paying creators $500 per video or briefing an agency every week, this replaces that entire workflow. here's how it works: > drop in your brand info and 1-3 product photos search your niche on TikTok Shop like "gut health supplements" or "skincare" or whatever you're selling > browse the top-selling products and every creator video promoting them pick a viral video that's already proven to convert > the AI analyzes the hook, structure, and pacing > one click and Seedance 2 generates a new version built around your product why test from scratch when you can just recreate what's already working? RT + reply "VIRAL" and i'll send you the full app + setup guide (must follow so i can dm)

Miko

98,366 views • 2 months ago

Fable 5 is INSANE for creating ads i built an agent that studies your competition and thousands of viral ads, then gives you a full viral blueprint for your brand to copy just feed it a product photo and a landing page link and competitors names, 3 minutes later you've got a proven creative playbook for your ads, here's how it works: - scans your brand's own website (copy, offers, structure, CTAs) - scans competitor websites (you list them, or Claude finds likely ones for you) - pulls live ad data straight from Meta Ad Library + TikTok via spytools - extracts proven hooks, scripts, and visual/creative patterns from every source - synthesizes everything into one swipe-file library - output is a ready-to-paste doc, drop it into Claude and generate new ad scripts from proven angles instantly - runs locally, one command, your own API keys, no video generation step slowing you down runs 100% local, without $300/mo on spytool subscriptions or agency fees, full control, and API cost only RT + reply w/ "AGENT" and i'll send it to you so you can use it for your brand (must follow so i can DM)

MAX

141,767 views • 19 days ago

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

Akshay 🚀

126,122 views • 8 months ago

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 views • 4 months ago

🚨BREAKING: Google just merged Gemini and NotebookLM into one unified workspace and it changes everything about how you use AI for deep work. It's called Notebooks in Gemini and it's the personal knowledge base that power users have been begging for. You create a notebook for a project, drop in your files, PDFs, and documents, give Gemini custom instructions, and every chat you have stays organized in one place. No more hunting through old conversations. No more re-uploading the same files every session. The wildest part is the sync. Anything you add in Gemini automatically appears in NotebookLM. Anything you add in NotebookLM automatically appears in Gemini. One source of truth. Two powerful apps. Zero friction switching between them. So you can start a research notebook in Gemini, ask it questions all week, then flip to NotebookLM to generate a Cinematic Video Overview from the same material. Next morning, open Gemini and ask it to write a full report on exactly what you just watched. That workflow used to take three apps and a lot of copy-pasting. Now it's one notebook. Rolling out this week to Google AI Ultra, Pro, and Plus subscribers on web. Mobile and free users coming soon. What do you think?

🚨BREAKING: Google just merged Gemini and NotebookLM into one unified workspace and it changes everything about how you use AI for deep work. It's called Notebooks in Gemini and it's the personal knowledge base that power users have been begging for. You create a notebook for a project, drop in your files, PDFs, and documents, give Gemini custom instructions, and every chat you have stays organized in one place. No more hunting through old conversations. No more re-uploading the same files every session. The wildest part is the sync. Anything you add in Gemini automatically appears in NotebookLM. Anything you add in NotebookLM automatically appears in Gemini. One source of truth. Two powerful apps. Zero friction switching between them. So you can start a research notebook in Gemini, ask it questions all week, then flip to NotebookLM to generate a Cinematic Video Overview from the same material. Next morning, open Gemini and ask it to write a full report on exactly what you just watched. That workflow used to take three apps and a lot of copy-pasting. Now it's one notebook. Rolling out this week to Google AI Ultra, Pro, and Plus subscribers on web. Mobile and free users coming soon. What do you think?

Mayank Vora

136,639 views • 3 months ago

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 views • 1 year ago

#รักปากแข็งEP1 #SWSHGL #รักปากแข็ง #เฟเมษ์ #FayMay #fay_riezz #maywyda #MBKxFayMaySWSHGL [𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗻 𝗦𝗾𝘂𝗮𝗱 - 𝗣𝗿𝗲𝗺𝗶𝗲𝗿𝗲 𝗙𝗮𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁] ｡˚♡ From day one, we've been cheering you on from every corner of the globe. We are the fans who have been with you from day one, through every dream, every challenge, and every triumph. We believed in you then, and we believe in you now more than ever. This journey you've embarked on is your own, but you are not alone. To celebrate this new chapter, we've put together a money bouquet to wish you all the success in the world.. We've also poured our hearts into a special book for you, one that is truly a labor of love. This project is special because of the collective love it represents from your fans all over the world. It’s filled with letters and art, with many of us staying up late and literally pouring our blood, sweat, and tears into it. Every penny, every late night, every single drawing and letter—it’s all for you. This wouldn't have been possible without the generous donations from so many fans around the world. So, a huge thank you to everyone who contributed!

#รักปากแข็งEP1 #SWSHGL #รักปากแข็ง #เฟเมษ์ #FayMay #fay_riezz #maywyda #MBKxFayMaySWSHGL [𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗻 𝗦𝗾𝘂𝗮𝗱 - 𝗣𝗿𝗲𝗺𝗶𝗲𝗿𝗲 𝗙𝗮𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁] ｡˚♡ From day one, we've been cheering you on from every corner of the globe. We are the fans who have been with you from day one, through every dream, every challenge, and every triumph. We believed in you then, and we believe in you now more than ever. This journey you've embarked on is your own, but you are not alone. To celebrate this new chapter, we've put together a money bouquet to wish you all the success in the world.. We've also poured our hearts into a special book for you, one that is truly a labor of love. This project is special because of the collective love it represents from your fans all over the world. It’s filled with letters and art, with many of us staying up late and literally pouring our blood, sweat, and tears into it. Every penny, every late night, every single drawing and letter—it’s all for you. This wouldn't have been possible without the generous donations from so many fans around the world. So, a huge thank you to everyone who contributed!

FAYMAY Interfan Squad

23,514 views • 11 months ago