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The best no-code database for forms lives in Fillout 👇 • No per-seat pricing • Millions of records • Native update forms • Granular permissions + SOC 2 • Linked records • CSV import/export • REST API + webhooks • Undo/redo • SQL speed and rate limits (50+ req/sec)

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389,517 görüntüleme • 9 ay önce •via X (Twitter)

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Introducing — the agentic form builder with a full API. Not just drag-and-drop. Build, manage, and automate forms entirely by code. Unlimited forms. 100+ blocks. 50+ question types. AI generation. Custom domains with free subdomains. Webhooks. Free API. No per-response pricing. No seat limits. Google Forms is basic. Typeform charges $99/mo for logic jumps. Tally caps your submissions. Youform locks features behind paywalls. We just made them all obsolete. Your forms. Your code. Your rules.

Venkat

10,149 görüntüleme • 4 ay önce

NVIDIA SHIPS A 50 POUND AI BOARD FOR DATA CENTERS AND A $249 BOX FOR YOUR DESK. ONE COSTS $200/MONTH TO RENT, THE OTHER COSTS $5 IN ELECTRICITY TO OWN jensen huang held up a 50 pound a100 board on stage with 30,000 components and called it a technology marvel. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

NVIDIA SHIPS A 50 POUND AI BOARD FOR DATA CENTERS AND A $249 BOX FOR YOUR DESK. ONE COSTS $200/MONTH TO RENT, THE OTHER COSTS $5 IN ELECTRICITY TO OWN jensen huang held up a 50 pound a100 board on stage with 30,000 components and called it a technology marvel. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

24,107 görüntüleme • 2 ay önce

this tool is seriously dangerous.. your AI agents are about to be overpowered it can: → search the entire web in under 0.5 seconds → fetch any URL and return clean markdown (not junk HTML) → handle JS-heavy SPAs, dynamic content, anti-bot pages → slot into Claude Code, Cursor, OpenClaw, n8n, Dify → work via REST, MCP, Python SDK, TS SDK, or CLI and the wildest part: free. generous rate limits. no credit card. one API key for all of it

this tool is seriously dangerous.. your AI agents are about to be overpowered it can: → search the entire web in under 0.5 seconds → fetch any URL and return clean markdown (not junk HTML) → handle JS-heavy SPAs, dynamic content, anti-bot pages → slot into Claude Code, Cursor, OpenClaw, n8n, Dify → work via REST, MCP, Python SDK, TS SDK, or CLI and the wildest part: free. generous rate limits. no credit card. one API key for all of it

Nelly;

28,233 görüntüleme • 3 ay önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 9 ay önce

JENSEN HUANG TOLD STANFORD THAT CLOUD AI WILL NEED 1,000 TIMES MORE ENERGY. THE $249 BOX HE BUILT FOR YOUR DESK USES 25 WATTS AND REPLACES A $200/MONTH AI STACK someone posted a 26 year old imac in airplane mode running a modern ai model with no internet. 1.7 million views in 24 hours. the same thing runs on a $249 box from nvidia the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

JENSEN HUANG TOLD STANFORD THAT CLOUD AI WILL NEED 1,000 TIMES MORE ENERGY. THE $249 BOX HE BUILT FOR YOUR DESK USES 25 WATTS AND REPLACES A $200/MONTH AI STACK someone posted a 26 year old imac in airplane mode running a modern ai model with no internet. 1.7 million views in 24 hours. the same thing runs on a $249 box from nvidia the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

15,822 görüntüleme • 2 ay önce

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Akshay 🚀

33,358 görüntüleme • 8 ay önce

Searching 32 million records in 30 milliseconds is insane! I don't know how large your database is, but there's no reason your search results are slower than this. typesense | is a crazy fast open-source search engine. They are like ElasticSearch but way easier to set up, configure, and use. They are sponsoring this post. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long They have client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .Net, Java, and pretty much any other programming language out there. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen something this fast! This is the best upgrade you'll make to your application this year. Guaranteed.

Searching 32 million records in 30 milliseconds is insane! I don't know how large your database is, but there's no reason your search results are slower than this. typesense | is a crazy fast open-source search engine. They are like ElasticSearch but way easier to set up, configure, and use. They are sponsoring this post. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long They have client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .Net, Java, and pretty much any other programming language out there. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen something this fast! This is the best upgrade you'll make to your application this year. Guaranteed.

Santiago

172,755 görüntüleme • 1 yıl önce

Introducing Applied Labs, the world's first truly AI native CX platform. Enterprise CX companies have completely ignored the mid market, which is the hungriest buyer in software right now. They're overpaying for tools, understaffed, and losing customers to churn signals buried across systems. Until now. With Applied Labs, you get AI agents + help desk + CRM with outcome based pricing and no per seat fees. Full visibility into what your AI is doing, intuitive no code control over how it behaves, and zero critical errors in production. Our process- Hour 0: Sign up Hour 1: Connect existing systems Hour 2: AI Agent auto builds Hour 3: First real result, fully auditable It's that simple.

Introducing Applied Labs, the world's first truly AI native CX platform. Enterprise CX companies have completely ignored the mid market, which is the hungriest buyer in software right now. They're overpaying for tools, understaffed, and losing customers to churn signals buried across systems. Until now. With Applied Labs, you get AI agents + help desk + CRM with outcome based pricing and no per seat fees. Full visibility into what your AI is doing, intuitive no code control over how it behaves, and zero critical errors in production. Our process- Hour 0: Sign up Hour 1: Connect existing systems Hour 2: AI Agent auto builds Hour 3: First real result, fully auditable It's that simple.

Michael Woo

384,468 görüntüleme • 3 ay önce

10 GitHub repos with 1,8M+ combined stars ⭐ that replace tools costing $30,000+/year 1. awesome ⭐495k - the list of every other list. Whatever you're looking for, it's here. 2. public-apis ⭐456k - 1,400+ free APIs across 50 categories. Weather, finance, images, games, all documented. 3. scrapling ⭐73.6k - undetectable web scraping with built-in Cloudflare bypass. Skip the $300/mo scraping API. 4. free-for-dev ⭐132k - hundreds of services with permanent free tiers. No trials, no credit card. 5. ollama ⭐178k - run Llama, Mistral, DeepSeek locally with one command. No API bill, no rate limits. 6. langflow ⭐153k - visual builder for AI agents and RAG pipelines. Drag, drop, deploy as API or MCP server. 7. awesome-mcp-servers ⭐92k - thousands of MCP servers connecting your agent to browsers, databases, anything. 8. open-design ⭐85k - open-source Claude Design alternative. Your coding agent becomes the design engine. 9. awesome-llm-apps ⭐132k - 100+ ready AI agents and RAG apps with full code. 10. openhands ⭐84k - autonomous coding agent that writes, tests, and ships code. Devin costs $500/mo. This is free. Save this before you pay for another tool this list already replaces for free 👇

10 GitHub repos with 1,8M+ combined stars ⭐ that replace tools costing $30,000+/year 1. awesome ⭐495k - the list of every other list. Whatever you're looking for, it's here. 2. public-apis ⭐456k - 1,400+ free APIs across 50 categories. Weather, finance, images, games, all documented. 3. scrapling ⭐73.6k - undetectable web scraping with built-in Cloudflare bypass. Skip the $300/mo scraping API. 4. free-for-dev ⭐132k - hundreds of services with permanent free tiers. No trials, no credit card. 5. ollama ⭐178k - run Llama, Mistral, DeepSeek locally with one command. No API bill, no rate limits. 6. langflow ⭐153k - visual builder for AI agents and RAG pipelines. Drag, drop, deploy as API or MCP server. 7. awesome-mcp-servers ⭐92k - thousands of MCP servers connecting your agent to browsers, databases, anything. 8. open-design ⭐85k - open-source Claude Design alternative. Your coding agent becomes the design engine. 9. awesome-llm-apps ⭐132k - 100+ ready AI agents and RAG apps with full code. 10. openhands ⭐84k - autonomous coding agent that writes, tests, and ships code. Devin costs $500/mo. This is free. Save this before you pay for another tool this list already replaces for free 👇

unicode

111,670 görüntüleme • 9 gün önce

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

Guri Singh

2,967,409 görüntüleme • 5 ay önce

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

Santiago

160,154 görüntüleme • 2 yıl önce

The guy who vibe-coded his own food discovery app just dropped 4 solid security basics. If you're building your own app with AI, you need this so you don't get hacked on day one: 1. Hide your API keys Keep your OpenAI or Stripe keys off the frontend to prevent them from being stolen. Tell Claude: "Move all API keys to the server and use environment variables." 2. Input Validation Stop people from pasting malicious code into your search bars to break your database. Tell Claude Code: "Sanitize all user inputs." 3. Don't invent your own auth Building login systems from scratch is too risky. Use trusted platforms instead. Tell Claude Code: "Integrate Supabase for authentication." 4. Rate limiting Prevent trolls from crashing your server with 10,000 requests per minute. Tell Claude Code: "Add rate limiting to my API routes." Too many people vibe-coding right now make these exact mistakes and wake up to drained API budgets or wiped databases. Get the security right first. Once your foundation is solid, you can build literally anything. A friend of mine just shared a crazy guide on how to build an entire social network like X in a certain niche with Claude Code in 4 weeks. check it out below 👇

The guy who vibe-coded his own food discovery app just dropped 4 solid security basics. If you're building your own app with AI, you need this so you don't get hacked on day one: 1. Hide your API keys Keep your OpenAI or Stripe keys off the frontend to prevent them from being stolen. Tell Claude: "Move all API keys to the server and use environment variables." 2. Input Validation Stop people from pasting malicious code into your search bars to break your database. Tell Claude Code: "Sanitize all user inputs." 3. Don't invent your own auth Building login systems from scratch is too risky. Use trusted platforms instead. Tell Claude Code: "Integrate Supabase for authentication." 4. Rate limiting Prevent trolls from crashing your server with 10,000 requests per minute. Tell Claude Code: "Add rate limiting to my API routes." Too many people vibe-coding right now make these exact mistakes and wake up to drained API budgets or wiped databases. Get the security right first. Once your foundation is solid, you can build literally anything. A friend of mine just shared a crazy guide on how to build an entire social network like X in a certain niche with Claude Code in 4 weeks. check it out below 👇

Spivach

11,103 görüntüleme • 2 ay önce

I got laid off 3 weeks ago. My landlord called yesterday asking about next month's rent. Severance ran out. No interviews lined up. Then I found a trader who built a Quant Bot with Claude and pulled $221,897 profit on Polymarket. Since Apr 12, the setup averages $3,467 per day running 41 trades per hour. 62,812 predictions. 58% win rate. 64 days straight. His first trade was Mar 6. The real shift came Apr 12 when the bot switched to pure arbitrage: It finds moments when YES + NO pricing drops below 100 cents and exploits the gap before the market corrects. No prediction. Just delayed pricing and execution speed. He deposited $82.8K total. Now sitting at +267.9% ROI. His biggest wins: $5,475 became $10,651 (+94.52%) $2,109 became $5,082 (+140.9%) $1,316 became $3,916 (+197.44%) The process is repeatable: Find delayed pricing, enter early, press the same edge until the gap disappears. Most traders try to forecast outcomes. This bot just finds mispricing and acts faster than human reaction time. I rebuilt the same arbitrage framework with Claude. One prompt. Connected to Polymarket API. Let it monitor pricing gaps 24/7. The bot scans every market continuously, calculates when YES + NO fall below parity, and fires trades in milliseconds. No emotions. No hesitation. Just math exploiting inefficiency. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word [Money] 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the arbitrage bot this weekend. Start with $500. Scale on evidence.

I got laid off 3 weeks ago. My landlord called yesterday asking about next month's rent. Severance ran out. No interviews lined up. Then I found a trader who built a Quant Bot with Claude and pulled $221,897 profit on Polymarket. Since Apr 12, the setup averages $3,467 per day running 41 trades per hour. 62,812 predictions. 58% win rate. 64 days straight. His first trade was Mar 6. The real shift came Apr 12 when the bot switched to pure arbitrage: It finds moments when YES + NO pricing drops below 100 cents and exploits the gap before the market corrects. No prediction. Just delayed pricing and execution speed. He deposited $82.8K total. Now sitting at +267.9% ROI. His biggest wins: $5,475 became $10,651 (+94.52%) $2,109 became $5,082 (+140.9%) $1,316 became $3,916 (+197.44%) The process is repeatable: Find delayed pricing, enter early, press the same edge until the gap disappears. Most traders try to forecast outcomes. This bot just finds mispricing and acts faster than human reaction time. I rebuilt the same arbitrage framework with Claude. One prompt. Connected to Polymarket API. Let it monitor pricing gaps 24/7. The bot scans every market continuously, calculates when YES + NO fall below parity, and fires trades in milliseconds. No emotions. No hesitation. Just math exploiting inefficiency. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word [Money] 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the arbitrage bot this weekend. Start with $500. Scale on evidence.

Himanshu Kumar

52,070 görüntüleme • 2 ay önce

CLAUDE BUILT ME 10 TRADING BOTS IN 34 MINUTES. +$788 IN 3 DAYS. I gave each one $300 and said - compete. One prompt. Ten strategies. Each bot - a separate agent on Claude API. Its own system prompt, its own decision logic, its own risk management. Newsreader - parses RSS + NewsAPI, runs every headline through Claude with the prompt "rate market impact from 1 to 10". Above 7 - enters. 10-15 trades a day. Never sleeps. $300 -> $368 Arbitrage - finds broken logic between linked markets. One says 40%, a related one says 15%. Calculates conditional probability, finds the gap - enters quietly. $300 -> $427 Contrarian - monitors volume. When 80%+ of money is on one side - takes the opposite. Kelly criterion cuts position size. Hates consensus. $300 -> $234 Sniper - 2-3 trades per day max. Claude calculates expected value for each market. Only when edge is above 25% and model confidence is 8/10 - enters. Rest of the time - silence. $300 -> $536 Reader - RAG pipeline. Eats SEC filings, CBO reports, Fed minutes. Splits into chunks, embeds, searches for contradictions with current prices. 400 pages per minute. $300 -> $493 Whale Hunter - parses on-chain data through Dune API. Tracks wallets with 70%+ win rate. When someone drops $50K+ into a position at 3:00 AM - follows. No questions. $300 -> $412 Scalper - microtrades through CLOB API. 30-40 a day. Catches 2-3 cent moves on bid-ask spreads. $300 -> $271 Calendar - only trades events with a known date. Elections, Fed meetings, court rulings. Pulls historical data on similar events, calculates base rate. No deadline - not interested. $300 -> $389 Sentiment - sentiment analysis. Collects posts from Twitter API, Reddit, Telegram. Calculates positive/negative ratio, compares to price. Everyone panics - buys. $300 -> $197 Copycat - monitors signals from the other nine through a shared JSON. No strategy of its own. Waits until 3+ bots agree - copies with double size. Ensemble model in one line of code. $300 -> $461 +$788 in 3 days. Ten bots. One prompt. Zero emotions.

CLAUDE BUILT ME 10 TRADING BOTS IN 34 MINUTES. +$788 IN 3 DAYS. I gave each one $300 and said - compete. One prompt. Ten strategies. Each bot - a separate agent on Claude API. Its own system prompt, its own decision logic, its own risk management. Newsreader - parses RSS + NewsAPI, runs every headline through Claude with the prompt "rate market impact from 1 to 10". Above 7 - enters. 10-15 trades a day. Never sleeps. $300 -> $368 Arbitrage - finds broken logic between linked markets. One says 40%, a related one says 15%. Calculates conditional probability, finds the gap - enters quietly. $300 -> $427 Contrarian - monitors volume. When 80%+ of money is on one side - takes the opposite. Kelly criterion cuts position size. Hates consensus. $300 -> $234 Sniper - 2-3 trades per day max. Claude calculates expected value for each market. Only when edge is above 25% and model confidence is 8/10 - enters. Rest of the time - silence. $300 -> $536 Reader - RAG pipeline. Eats SEC filings, CBO reports, Fed minutes. Splits into chunks, embeds, searches for contradictions with current prices. 400 pages per minute. $300 -> $493 Whale Hunter - parses on-chain data through Dune API. Tracks wallets with 70%+ win rate. When someone drops $50K+ into a position at 3:00 AM - follows. No questions. $300 -> $412 Scalper - microtrades through CLOB API. 30-40 a day. Catches 2-3 cent moves on bid-ask spreads. $300 -> $271 Calendar - only trades events with a known date. Elections, Fed meetings, court rulings. Pulls historical data on similar events, calculates base rate. No deadline - not interested. $300 -> $389 Sentiment - sentiment analysis. Collects posts from Twitter API, Reddit, Telegram. Calculates positive/negative ratio, compares to price. Everyone panics - buys. $300 -> $197 Copycat - monitors signals from the other nine through a shared JSON. No strategy of its own. Waits until 3+ bots agree - copies with double size. Ensemble model in one line of code. $300 -> $461 +$788 in 3 days. Ten bots. One prompt. Zero emotions.

zostaff

118,475 görüntüleme • 4 ay önce

Free GPT-5.6 Sol/Terra/Luna + Claude Sonnet 4.6 + a full IDE one code editor just dropped a 14-day Pro trial with popular AI models ( no card required, no BS ) what you get: - GPT-5.6 Sol, Terra, Luna (full family) - Claude Sonnet 4.6 - two weeks of real coding - native IDE built in Rust (10x faster than VS Code) - multiplayer coding + native AI integration how to get: 1. go to zed(.)dev, sign in via GitHub (account must be 1+ month old) 2. click Pricing, select Pro, hit "Start Trial Now" 3. download their IDE 4. open a project -> click the star icon bottom-left -> pick your model in other words, you get a full-featured IDE with leading AI tools that work for free for 14 days - all for $0. based on the tests I've run, I'd say the speed and ease of use are really great - on par with Cursor. btw I recently wrote a full guide on ways to pay $0 for every subscription you have - this is just one of them worth to read:

Free GPT-5.6 Sol/Terra/Luna + Claude Sonnet 4.6 + a full IDE one code editor just dropped a 14-day Pro trial with popular AI models ( no card required, no BS ) what you get: - GPT-5.6 Sol, Terra, Luna (full family) - Claude Sonnet 4.6 - two weeks of real coding - native IDE built in Rust (10x faster than VS Code) - multiplayer coding + native AI integration how to get: 1. go to zed(.)dev, sign in via GitHub (account must be 1+ month old) 2. click Pricing, select Pro, hit "Start Trial Now" 3. download their IDE 4. open a project -> click the star icon bottom-left -> pick your model in other words, you get a full-featured IDE with leading AI tools that work for free for 14 days - all for $0. based on the tests I've run, I'd say the speed and ease of use are really great - on par with Cursor. btw I recently wrote a full guide on ways to pay $0 for every subscription you have - this is just one of them worth to read:

kaize

35,369 görüntüleme • 12 gün önce

APIs are a bottleneck, not a solution! AI agents can't access 95% of the web because most websites simply don't have APIs. Supplier portals, appointment systems, regional job boards, etc., none of them have developer APIs. The valuable data lives behind logins, multi-step forms, and interfaces built for humans, not machines. That's why, for 25 years, users have been stuck with: - Search engines that index 5% of the web (nothing behind authentication) - Manual data entry or fragile scrapers that break with every CSS update That's the problem TinyFish's Mino solves. It is a web automation API that can simultaneously navigate 100s of websites and turn them into structured data. You send it URLs and a goal in plain English. It returns JSON. The approach is different from typical AI agents: Most browser agents use vision models, which screenshot the page, reason about it, decide what to click, screenshot again, and repeat. Every action needs a model call. It's slow and expensive. Mino uses AI to learn the website structure once, then executes it through deterministic code. First run figures out the site. Every run after that is code-level precision in milliseconds. It provides three core capabilities: → Navigate - Handles logins, forms, and multi-step workflows → Extract - Pulls structured JSON from any site layout → Execute - Runs 100+ sites in parallel with stealth mode The performance is production-grade: - 85-95% success rate on complex workflows - 10-30 seconds per task - Pennies per run Moreover, it works on authenticated sites, bypasses anti-bot protections, and returns clean JSON every time. Lastly, you can also integrate its MCP server with clients like Claude Desktop. I have recorded a walkthrough in the video below. Try now, link in the next tweet.

APIs are a bottleneck, not a solution! AI agents can't access 95% of the web because most websites simply don't have APIs. Supplier portals, appointment systems, regional job boards, etc., none of them have developer APIs. The valuable data lives behind logins, multi-step forms, and interfaces built for humans, not machines. That's why, for 25 years, users have been stuck with: - Search engines that index 5% of the web (nothing behind authentication) - Manual data entry or fragile scrapers that break with every CSS update That's the problem TinyFish's Mino solves. It is a web automation API that can simultaneously navigate 100s of websites and turn them into structured data. You send it URLs and a goal in plain English. It returns JSON. The approach is different from typical AI agents: Most browser agents use vision models, which screenshot the page, reason about it, decide what to click, screenshot again, and repeat. Every action needs a model call. It's slow and expensive. Mino uses AI to learn the website structure once, then executes it through deterministic code. First run figures out the site. Every run after that is code-level precision in milliseconds. It provides three core capabilities: → Navigate - Handles logins, forms, and multi-step workflows → Extract - Pulls structured JSON from any site layout → Execute - Runs 100+ sites in parallel with stealth mode The performance is production-grade: - 85-95% success rate on complex workflows - 10-30 seconds per task - Pennies per run Moreover, it works on authenticated sites, bypasses anti-bot protections, and returns clean JSON every time. Lastly, you can also integrate its MCP server with clients like Claude Desktop. I have recorded a walkthrough in the video below. Try now, link in the next tweet.

Akshay 🚀

62,659 görüntüleme • 7 ay önce

$🚨 BREAKING: Preliminary forensic analysis of Bexar County TX-21 Republican primary poll pad data reveals 4,110 anomalous voter records with mathematically impossible fractional State IDs — none exist in the TX voter database. Whether malfunction or manipulation, neither is acceptable. 🧵👇 Office of the Texas Secretary of State Attorney General Ken Paxton FBI Director Kash Patel President Donald J. Trump Every one of the 4,110 records is spaced by the exact same gap: 22,084.82189. That's not a glitch. That's software. 735 real voters were duplicated 5-6 times each. Each record generated a ballot. Once cast, those ballots are anonymous and irretrievable. Then all 14 CSV files in the early voting dataset were replaced in a synchronized 78-second batch operation — scrubbed clean. The only reason evidence survives is because the original file was captured and distributed to precinct chairs on Feb 19, before the cleanup. This investigation is PRELIMINARY. 🚨We are seeking a statement from Bexar County. There may be an explanation. But no explanation makes this acceptable. Either the machines malfunctioned or were manipulated — both demand decertification of ALL electronic voting systems under TX Election Code §122.001 for being inherently unsuitable, insecure, and unsafe. Thank you CDTX-21 candidate Weston Martinez for relentlessly working for the people of Texas and the insight to getting these files into researchers' hands for further analysis.7 Full analysis: Hand-counted paper ballots. By the people. 🇺🇸$

🚨 BREAKING: Preliminary forensic analysis of Bexar County TX-21 Republican primary poll pad data reveals 4,110 anomalous voter records with mathematically impossible fractional State IDs — none exist in the TX voter database. Whether malfunction or manipulation, neither is acceptable. 🧵👇 Office of the Texas Secretary of State Attorney General Ken Paxton FBI Director Kash Patel President Donald J. Trump Every one of the 4,110 records is spaced by the exact same gap: 22,084.82189. That's not a glitch. That's software. 735 real voters were duplicated 5-6 times each. Each record generated a ballot. Once cast, those ballots are anonymous and irretrievable. Then all 14 CSV files in the early voting dataset were replaced in a synchronized 78-second batch operation — scrubbed clean. The only reason evidence survives is because the original file was captured and distributed to precinct chairs on Feb 19, before the cleanup. This investigation is PRELIMINARY. 🚨We are seeking a statement from Bexar County. There may be an explanation. But no explanation makes this acceptable. Either the machines malfunctioned or were manipulated — both demand decertification of ALL electronic voting systems under TX Election Code §122.001 for being inherently unsuitable, insecure, and unsafe. Thank you CDTX-21 candidate Weston Martinez for relentlessly working for the people of Texas and the insight to getting these files into researchers' hands for further analysis.7 Full analysis: Hand-counted paper ballots. By the people. 🇺🇸

Lori Gallagher

27,481,092 görüntüleme • 5 ay önce

NVIDIA QUIETLY DROPPED A $249 BOX THAT REPLACES YOUR $200/MONTH OPENAI SUBSCRIPTION WITH $2 IN ELECTRICITY it's called the jetson orin nano super. smaller than a wallet, runs at 25 watts, does 70 trillion ai operations per second. runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even in 10 weeks install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below

NVIDIA QUIETLY DROPPED A $249 BOX THAT REPLACES YOUR $200/MONTH OPENAI SUBSCRIPTION WITH $2 IN ELECTRICITY it's called the jetson orin nano super. smaller than a wallet, runs at 25 watts, does 70 trillion ai operations per second. runs llama 3, mistral, gemma and deepseek locally with no api fees and no data leaving your house a developer running automations and coding assistants pays $200 a month to openai. the same workload on this box costs $2 a month in electricity and breaks even in 10 weeks install ollama with one command. change one line in your code. point it at localhost instead of openai. everything else works identically 7 billion parameter models handle 80% of what people use chatgpt for. summarization, drafting, coding, document q&a, automation pipelines. total monthly cost drops from $200 to $22 cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who set this up in 2025 are going to look very smart in 2027 bookmark this and read the article below

starmex

2,356,844 görüntüleme • 2 ay önce