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Google Places API meets shadcn ui. ◆ Highlight matching text in suggestions ◆ Simple loading state ◆ Fetch best-matching places and by ID ◆ Works with any country code Inspired by Stripe checkout.

Maximilian Kaske 🏓

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80,294 Aufrufe • vor 1 Jahr •via X (Twitter)

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🔑 No credit card required! With the new Maps Demo Key, you can get a working API key with just your Google account and start building in seconds ➡️ We’ve removed the barrier to entry, giving developers direct access to build with select Google Maps Platform products — including Maps JS API, Places UI Kit, and Weather API. Whether you're testing new features or validating technical feasibility, prototype with confidence in a controlled sandbox with automatic usage guardrails. ➡️ Seamlessly works with AI agents to turn prompts into working geospatial prototypes. ➡️ Maps Demo Key is the easiest way to test AI-generated geospatial code without hitting setup blockers. ➡️ Easily transition to a full account to unlock our generous monthly free usage tier, additional APIs, and technical support — all while keeping your project moving. Try it at the link above.

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Introducing Replit ModelFarm, the fastest and safest way to build your next Generative AI app. Available for free on Hacker and Pro plans till October 15th. It requires zero setup, zero configuration, and zero API keys. With Replit ModelFarm, you can build a working Gen AI app in as little as 3 lines of code. Get started by installing the Replit AI library in any Python, JavaScript, or TypeScript Repl. The library implements an API for text completion, chat completion, and text embeddings. It supports streaming so your users can see model responses in real-time rather than waiting on a single output. All Hacker and Pro builders will have free access to a selection of Gen AI models offered by Google Cloud Vertex AI through Replit ModelFarm. All models are accessible from the development environment and any deployed app.

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Replit ⠕

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I just built a skill that lets Claude Code watch & analyze ANY video 🤯 Drop in any video file — UGC ads, competitor Meta ads, organic TikToks, screen recordings — and Claude hands you back a full creative teardown. All inside Claude Code. Perfect for media buyers and creative strategists who reverse-engineer competitor ads every week — and lose half a day doing it by hand. If your creative process starts with studying what's already working, you're scrubbing through competitor ads frame by frame, pausing to write down every hook, screenshotting the on-screen text, and by the tenth video you can't remember what made the first one land... This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: → Native video understanding via Gemini (not just transcripts) → Structured analysis — hook, angle, audience, pain point, CTA → Verbatim on-screen text and dialogue with timestamps → Hook variations generated directly from competitor ads → About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this, and I'm giving away the skill for free. Want the skill? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

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Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

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ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

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TRANSFER TUESDAY: Miami Hurricanes Football had one of the best portal processes of any team in the country, with UH transfer WR Sam Brown (Sam Brown jr.) being one of the best additions any team made to their WR room this offseason. Brown was a standout at Houston, and by most services was the top-rated WR in the transfer portal. A well-built (nearly 6’2, 195) perimeter receiver, Brown is a highly impressive athlete, boasting a 4.4 PAI (per Tracking Football), led by a 24’01 long jump (96th percentile), and posting a 4.45 forty and a 38 vertical in high school. He joins a now loaded, explosive offense for QB Cam Ward (Cameron Ward) and creates one of the best WR trios in college football with fellow WRs Jacolby George (Jacolby George) and Xavier Restrepo (Xavier Restrepo) Last year, he forced 20 missed tackles after catch, second-best among Big 12 WRs in 2023. And he stood out against top competition: 46% of his total receiving production came against in-state, P4 competition. Brown has high grades from NFL scouts coming into the season, and will be a part of maybe the most balanced, talent-laded skill position group in the country. He’ll be a part of what could be a special season for the 2024 Miami Hurricanes offense. 🔥🔥🔥 #ShrineBowlWhosNext

TRANSFER TUESDAY: Miami Hurricanes Football had one of the best portal processes of any team in the country, with UH transfer WR Sam Brown (Sam Brown jr.) being one of the best additions any team made to their WR room this offseason. Brown was a standout at Houston, and by most services was the top-rated WR in the transfer portal. A well-built (nearly 6’2, 195) perimeter receiver, Brown is a highly impressive athlete, boasting a 4.4 PAI (per Tracking Football), led by a 24’01 long jump (96th percentile), and posting a 4.45 forty and a 38 vertical in high school. He joins a now loaded, explosive offense for QB Cam Ward (Cameron Ward) and creates one of the best WR trios in college football with fellow WRs Jacolby George (Jacolby George) and Xavier Restrepo (Xavier Restrepo) Last year, he forced 20 missed tackles after catch, second-best among Big 12 WRs in 2023. And he stood out against top competition: 46% of his total receiving production came against in-state, P4 competition. Brown has high grades from NFL scouts coming into the season, and will be a part of maybe the most balanced, talent-laded skill position group in the country. He’ll be a part of what could be a special season for the 2024 Miami Hurricanes offense. 🔥🔥🔥 #ShrineBowlWhosNext

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London chic, one call away from a full outfit breakdown. Created with GPT Image 2 and Seedance 2 on Ima Studio #IMAStudio Prompt: Photorealistic cinematic fashion video set on an elegant London shopping street inspired by Bond Street. A stylish British woman in her late 20s walks confidently past a luxury boutique. She wears gold earrings, a burgundy double-breasted blazer, matching mini skirt, a light blue ruffled silk blouse, a structured dark red leather shoulder bag, and metallic pointed heels. Her chestnut hair is styled in a sleek low bun. Her phone rings. She glances at the screen, stops gracefully, and answers. A male voice asks, “Hello there, can you please scan your outfit?” She smiles and replies in a soft British accent, “I guess so.” The camera performs a smooth head-to-toe scan, displaying elegant text labels for each outfit item, then ends on a confident editorial pose in front of the boutique window. Bright natural sunlight, shallow depth of field, smooth gimbal movement, luxury editorial aesthetic, polished and sophisticated mood, 9:16 vertical, 15 seconds.

London chic, one call away from a full outfit breakdown. Created with GPT Image 2 and Seedance 2 on Ima Studio #IMAStudio Prompt: Photorealistic cinematic fashion video set on an elegant London shopping street inspired by Bond Street. A stylish British woman in her late 20s walks confidently past a luxury boutique. She wears gold earrings, a burgundy double-breasted blazer, matching mini skirt, a light blue ruffled silk blouse, a structured dark red leather shoulder bag, and metallic pointed heels. Her chestnut hair is styled in a sleek low bun. Her phone rings. She glances at the screen, stops gracefully, and answers. A male voice asks, “Hello there, can you please scan your outfit?” She smiles and replies in a soft British accent, “I guess so.” The camera performs a smooth head-to-toe scan, displaying elegant text labels for each outfit item, then ends on a confident editorial pose in front of the boutique window. Bright natural sunlight, shallow depth of field, smooth gimbal movement, luxury editorial aesthetic, polished and sophisticated mood, 9:16 vertical, 15 seconds.

Shami

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today in Hell Yeah Technology: an uncle of mine recently had a stroke he was at dinner with his family, making them laugh as usual. when they got home, he told his wife he was feeling “off.” by morning, he physically couldn’t get out of bed after being in a vegetative state for weeks, he’s conscious and doing a little better, but doctors say it’s unlikely he’ll ever speak or walk again thankfully, somehow, his dominant arm still works - so to make communication less frustrating for everyone involved, i built this local, iPad-first communication board for him with text-to-speech one of theee simplest, most important tools i’ve ever made being able to create something useful for a specific person’s needs, without any fluff, in a single sitting, is just unreal

today in Hell Yeah Technology: an uncle of mine recently had a stroke he was at dinner with his family, making them laugh as usual. when they got home, he told his wife he was feeling “off.” by morning, he physically couldn’t get out of bed after being in a vegetative state for weeks, he’s conscious and doing a little better, but doctors say it’s unlikely he’ll ever speak or walk again thankfully, somehow, his dominant arm still works - so to make communication less frustrating for everyone involved, i built this local, iPad-first communication board for him with text-to-speech one of theee simplest, most important tools i’ve ever made being able to create something useful for a specific person’s needs, without any fluff, in a single sitting, is just unreal

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27,677 Aufrufe • vor 3 Monaten

There’s been ANOTHER terrorist attack, less than a week after the Belfast Attack. This time, a man in Burnley sneaks up behind a 17 Year Old Girl and starts stabbing her in the neck. It’s unknown if the girl is okay. There is only one solution to this, we must bring back the death penalty for these types of criminals. There is zero deterrent. With the British People’s support, as a Member of Parliament I will campaign to do exactly that. Once again, I demand the Prime Minister immediately release the details of this attacker. Immigration is the number one threat to our country, Legal and Illegal. Millions of people are in our country who shouldn’t be, and many are coming from places where crimes like this are normalised. If you are not a net positive on our country, you must go. If you don’t speak English, you must go. If you don’t pay tax, you must go. If you’re an immigrant who claims benefits, you must go. If that means millions go, then millions must go. I don’t care. Every week there is a new atrocious terrorist attack on innocent British People, carried out by foreigners who shouldn’t be in our country. In the next election, 2029, I hope you all will stand by me. We cannot put up with this for any longer, our country is being completely destroyed. We must rip apart the establishment and hold the politicians responsible for this accountable. I will be at the forefront of saving our country in 2029, you can count on it. Enough is enough. May God Save our United Kingdom.

There’s been ANOTHER terrorist attack, less than a week after the Belfast Attack. This time, a man in Burnley sneaks up behind a 17 Year Old Girl and starts stabbing her in the neck. It’s unknown if the girl is okay. There is only one solution to this, we must bring back the death penalty for these types of criminals. There is zero deterrent. With the British People’s support, as a Member of Parliament I will campaign to do exactly that. Once again, I demand the Prime Minister immediately release the details of this attacker. Immigration is the number one threat to our country, Legal and Illegal. Millions of people are in our country who shouldn’t be, and many are coming from places where crimes like this are normalised. If you are not a net positive on our country, you must go. If you don’t speak English, you must go. If you don’t pay tax, you must go. If you’re an immigrant who claims benefits, you must go. If that means millions go, then millions must go. I don’t care. Every week there is a new atrocious terrorist attack on innocent British People, carried out by foreigners who shouldn’t be in our country. In the next election, 2029, I hope you all will stand by me. We cannot put up with this for any longer, our country is being completely destroyed. We must rip apart the establishment and hold the politicians responsible for this accountable. I will be at the forefront of saving our country in 2029, you can count on it. Enough is enough. May God Save our United Kingdom.

Alex Barnicoat

45,277 Aufrufe • vor 2 Monaten

The newest member of MVP: two-time Canadian Olympian and amateur world champion Tammara “Tamm” Thibeault MVP deepens its commitment to empowering female fighters by signing Thibeault, who makes a bold statement for equality with a historic pro debut tonight under men’s rules—four three-minute rounds—at Most Valuable Prospects 10 Originally from Shawinigan, Quebec, Canada, the 27-year-old middleweight (160lbs) southpaw, stands 6 feet tall and forged an outstanding career in the amateur ranks. A two-time Olympian, Thibeault made her Olympic debut at Tokyo 2020, where she advanced to the quarterfinals of the women’s 75kg (165lb) event, matching Canada’s best-ever Olympic result in any women’s boxing event. Thibeault, speaks French, English, and Spanish and recently completed a Bachelor’s degree in Urban Studies from Concordia University in 2023. She currently lives in Sheffield, England, where she is earning a one-year Master’s degree in urban design and urban planning while training at the renowned Steel City Gym. She is now poised to embark on the next chapter of her career, setting a new precedent in the pro ranks by competing under men’s rules, three-minute rounds, during her pro debut on the main card of MVP’s Most Valuable Prospects 10. Welcome to the MVP family Taam.

The newest member of MVP: two-time Canadian Olympian and amateur world champion Tammara “Tamm” Thibeault MVP deepens its commitment to empowering female fighters by signing Thibeault, who makes a bold statement for equality with a historic pro debut tonight under men’s rules—four three-minute rounds—at Most Valuable Prospects 10 Originally from Shawinigan, Quebec, Canada, the 27-year-old middleweight (160lbs) southpaw, stands 6 feet tall and forged an outstanding career in the amateur ranks. A two-time Olympian, Thibeault made her Olympic debut at Tokyo 2020, where she advanced to the quarterfinals of the women’s 75kg (165lb) event, matching Canada’s best-ever Olympic result in any women’s boxing event. Thibeault, speaks French, English, and Spanish and recently completed a Bachelor’s degree in Urban Studies from Concordia University in 2023. She currently lives in Sheffield, England, where she is earning a one-year Master’s degree in urban design and urban planning while training at the renowned Steel City Gym. She is now poised to embark on the next chapter of her career, setting a new precedent in the pro ranks by competing under men’s rules, three-minute rounds, during her pro debut on the main card of MVP’s Most Valuable Prospects 10. Welcome to the MVP family Taam.

MVP - Most Valuable Promotions

49,214 Aufrufe • vor 1 Jahr

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Akshay 🚀

215,532 Aufrufe • vor 6 Monaten

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 Aufrufe • vor 7 Monaten

Over the past 6 months, we've rearchitected Cline's agentic loop into a standalone "cline core" gRPC service that runs independently of any editor. This enabled us to decouple from VS code and build - JetBrains (released in GA this week) - CLI built in Go (releasing soon) - Secret project that may be announced soon The CLI is our newest product and will ship without a TUI. Our focus is to release a true primitive. something close to the metal that pipes cleanly into RL environments, CI/CD systems, scripts, and automation workflows. Any "presentation layer" mentioned above can connect to the same running cline core - maintaining full feature parity (e.g. checkpoints, settings, api configurations), context, and conversation state. There's an SQLite-based instance and file/directory lock registry that prevents port conflicts and coordinates graceful shutdowns between paired processes - so you can start up thousands of cline instances in parallel with no conflicts or dangling processes. What's remarkable is that this cline core architecture opens Cline to any interface imaginable: mobile apps, web dashboards, custom tools - all powered by the same intelligent core that understands your codebase.

Over the past 6 months, we've rearchitected Cline's agentic loop into a standalone "cline core" gRPC service that runs independently of any editor. This enabled us to decouple from VS code and build - JetBrains (released in GA this week) - CLI built in Go (releasing soon) - Secret project that may be announced soon The CLI is our newest product and will ship without a TUI. Our focus is to release a true primitive. something close to the metal that pipes cleanly into RL environments, CI/CD systems, scripts, and automation workflows. Any "presentation layer" mentioned above can connect to the same running cline core - maintaining full feature parity (e.g. checkpoints, settings, api configurations), context, and conversation state. There's an SQLite-based instance and file/directory lock registry that prevents port conflicts and coordinates graceful shutdowns between paired processes - so you can start up thousands of cline instances in parallel with no conflicts or dangling processes. What's remarkable is that this cline core architecture opens Cline to any interface imaginable: mobile apps, web dashboards, custom tools - all powered by the same intelligent core that understands your codebase.

pash

59,960 Aufrufe • vor 10 Monaten

THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

Bonsai 🌳

110,437 Aufrufe • vor 2 Monaten

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

30,070 Aufrufe • vor 4 Monaten

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 Aufrufe • vor 7 Monaten