Gemini CLI *currently* has 3 modes... 🟣- Build (default)... 🟡- Accepting edits (shift+tab) 🔴- Yolo mode (ctrl+y) Build mode keeps you in full control, accepting edits auto approves file edits/writes, yolo mode accepts edits and shell commands. Which do you use most often?show more

Hermes Agent has a new Blank Slate setup mode.... The default Quick/Full setup modes work great for most, but if you would rather build your agent from the ground up you can now start with just a provider, model, file operations, and terminal, then manually add in anything else.show more

A drone that flies, drives, and switches modes in... 0.1 seconds: [Build it yourself: CAD + parts ⬇️] No extra actuators, no deformation, just clever mechanics and full control. DUAWLFIN is a ground-aerial robot with unified actuation: flying like a quadcopter, rolling like a car, and transitioning seamlessly between modes. ✅ Climbs 30° slopes ✅ Hits 2 m/s on wheels with just 15W ✅ Only 3% added energy in flight mode ✅ Mode switch in 0.1s ✅ Fully open-source and 3D-printable Perfect for urban logistics, indoor nav, or just rethinking what drones can be. Paper: Website: Build it yourself: CAD + parts list in the paper 📍 BOOKMARK FOR LATER This is how you merge air and ground without compromise. —- Weekly robotics and AI insights. Subscribe free:show more

subagents are just recursive agents where you can apply... different prompts + models depending on the task. since they’re just a primitive, Cursor cli can actually spawn subagents by calling cursor-agent in headless mode via shell commands. that’s what makes the cli so nice. you can extend it, experiment, and have a lot of fun exploring orchestration patterns. here’s one way to do it w. dynamic model selection: 1. create a subagents.mdc rule 2. drop in: ``` --- alwaysApply: true --- ALWAYS spawn subagents by running `cursor-agent -p [task] --output-format=text --force --model [model]` in the terminal. Each subagent should return a summary of the changes it made. Subagents should be used for ALL tasks You can adopt a fan-out pattern where you spawn subagents to perform parallel isolated tasks, and then fan-in the results. Use the following models: - `--model gpt-5` for reasoning, researching, and planning - `--model sonnet-4` for implementation ``` 3. start cursor cli and try it out you can also adjust the rule to be more explicit when it should use subagents, when not to, which models when etc.show more

Building The On-Chain Cooperative 🟡 Welcome to the dawn... of a new era in the crypto space, where the buzzword "community" is not just a hollow echo but a vibrant force that propels us towards a brighter future. Let's delve into the heart of MODE, the Onchain Cooperative that seeks to redefine the landscape of web3. What does MODE stand for? MODE stands for building an on-chain cooperative focused on sustainable growth and collective prosperity. At its core, MODE is guided by the principles of cooperation, shared incentives, and community-driven development. The goal is to shift from the "fat protocol" mentality where most value accrues to the blockchain/protocol itself, towards an ecosystem where builders, users, and applications can thrive together. What’s MODE's vision and mission in the web3 space? MODE's vision is to return to web3's founding promise - a future that is better for all, not just the individual. A world with aligned incentives that drive growth for everyone involved. A place with opportunities for all, not just the few. The mission is to pioneer the on-chain cooperative - where contributors are rewarded fairly based on the value they provide. Features like Sequencer Fee Sharing distribute a portion of fees to smart contract developers, incentivizing participation. The aim is to encourage collaboration instead of confrontation. Together, the MODE community can deliver new models for cooperation and shared prosperity in web3. Mode Network will solve many problems today in Web3: • Lack of incentives for developers: Developers creating decentralized apps (dApps) currently have few direct economic incentives to create and maintain their projects. Mode provides them with a steady source of income through fee-sharing. • Lack of collaboration: There are few incentives for blockchain projects to compete less and collaborate more for the benefit of the entire ecosystem. Mode's model encourages collaboration by aligning participants economically. • Excessive value accrual at the protocol layer: Mode aims for a more balanced model where the protocol's success is fueled by the success of application developers/builders and the wider community. Growth is a two-way street – "as we grow, you grow". The MODE Pledge 💛 The promise of crypto and blockchain is a brighter future. One that is better for all not just the individual. Where nothing is more important than community. We've strayed from this path. Entering a world of player vs player. Where value is extracted rather than shared. The game is zero sum rather than positive sum. And incentives are aligned with domination, rather than cooperation. Mode is the dawn of a new age. and a return to the promise of what can be. A world with aligned incentives that drive growth for builders, users and projects. A place with opportunities for all, rather than the few. Where we say goodbye to the 'fat protocol', and hello to the onchain cooperative. Join us on our mission to grow together. If this vision for a community-powered web3 ecosystem resonates - where creators are rewarded for their contributions - you can join the MODE on-chain cooperative! Visit Join the discord community Follow Mode 🟡 Together, we can transform web3 into a positive-sum game that unlocks new possibilities for all. Where your growth fuels the growth of others. Let's build the on-chain cooperative!show more

BREAKING: Claude + Arcads can now run your entire... ecom brand tiktok like a $500/hour social media manager. I reverse-engineered how top social media brands use AI to build million-follower accounts. Here’s the crazy part: This system produces 550+ cinematic, product-ready ads per day from a single prompt. Here’s the full pipeline: → AI generates a realistic UGC persona — face, voice, personality → Arcads clones a natural voiceover in seconds → CapCut auto-edits: captions, pacing, hooks — done → our phone farm method pushes every finished video straight to TikTok Shop → Cruva Social 1 identifies which hooks are already winning in your niche before you film anything The result: 500+ videos a month, per brand, at a fraction of what one UGC creator used to cost. Most brands are still paying $300–500 per video. Testing 10 hooks takes $5,000 and three weeks. With this system, you test 100 hooks in the same timeframe. The ones that win get scaled. Automatically. AI is the new creative director. TikTok doesn’t reward the best video. It rewards the brand that shows up the most — with content that converts. Static agencies are dead. Creator dependency is a liability… and it’s soooo 2025. No more waiting on creators. No more $500 videos that flop after 200 views. The brands that automate content at scale will be the biggest winners of 2026. If you want the full breakdown: Like & comment “SYSTEM” I’ll send you the complete workflow, every prompt, and a step-by-step walkthrough. Free. (Follow first so I can DM.)show more

Impeccable 3.7 brings linting to design. Until now it... was a skill you asked for help. Now it's a design-system-aware feedback loop that runs while your agent builds, catching slop and design drift before they land. 🪝 Design hooks for Claude, Codex, and Cursor They run after every UI edit and quietly nudge your agent to fix slop and drift. The output isn't another wall of lint: it separates new findings from already-seen ones, flags clean scans, and asks the agent to use judgment. Fix real issues, leave intentional demos alone, save exceptions to config instead of littering your source. 🎨 Slop detection is now project-aware Reads your actual design system from DESIGN.md, your typography, palette, radius scale, and tokens, and flags drift from your system, not just generic AI slop: • this font isn't in your design system • this color is outside your documented palette • this radius doesn't match your rounded scale The same engine powers both the hooks and the CLI, and it's where we're investing next. 🖥️ Live Mode, ready for real projects Svelte/SvelteKit now preview variants as temporary framework components with live params, then accept cleanly back into your source component. Manual text edits got evidence / apply / discard routes, insertions preserve their anchors, and mapped lists and JSX slots clean up far more reliably. ⚡ Leaner core, sharper detector Rule-level evals across 3 providers and 4 niches cut guidance with no measurable lift and dropped examples that taught models bad patterns. The detector now skips hidden and screen-reader-only elements, understands OKLCH alpha and Sass-like inputs, and tightened checks for repeated kickers, oversized H1s, clipped overflow, and cramped padding. 🛠️ CLI caught up impeccable detect loads DESIGN.md by default, motion findings name the exact token or cubic-bezier instead of just "bounce," and impeccable ignores gives real CRUD for exceptions. Hooks and CLI share the same ignores. No split-brain config. Plus a much-improved interactive installer with hooks setup built in. Upgrade: npx impeccable install npm i -g impeccableshow more

my team didn't want me to give this away... for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it overshow more

your AI agent can watch any video now -... paste a URL and it sees every frame, hears every word, all for free 🤯 bradautomates/claude-video gives Claude the ability to watch YouTube, Loom, TikTok, local files - anything yt-dlp supports what people actually use it for: → analyze a competitor launch - what hook, what visuals, what structure → debug from a screen recording - Claude reads the exact frame where it breaks → summarize a 49-min talk in 30 seconds with frame-accurate timestamps → strip the hype from product videos - "what's actually new, skip the pitch" the mechanism: yt-dlp pulls free captions first (zero cost). ffmpeg extracts frames at scene-aware intervals - not uniform sampling, so you don't waste tokens on 12 identical frames of the same slide. Claude reads every frame as an image with timestamp markers. Groq Whisper only kicks in when a video has no caption track how to set up (3 min): > claude code: /plugin marketplace add bradautomates/claude-video then /plugin install watch@claude-video > or npx skills add bradautomates/claude-video -g for codex, cursor, gemini cli > dependencies auto-install on macOS via brew two caveats: free captions cover most but not all videos. past 10 min use --start/--end for focused sections or the token-burner mode for full coverage your buddy still watches every tutorial at 2x speed taking manual notes. you paste a URL and your agent extracts the substance in seconds for $0show more

Another WTF moment. A developer just open-sourced a coding... agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming.show more

AgentLinter is here! Is your agent sharp & secure?... I built AgentLinter, a linter for and agent config files. Here's why. Whether you're vibe-coding or agent-coding, your AI's output quality comes down to one thing: how well you wrote your But managing these files properly? Way harder than it looks. 🎯 The Silent Failure Problem Vague instructions like "write good code" let the agent interpret however it wants. Output gets inconsistent, but nothing throws an error. The failure is silent. Anthropic's own docs say write "Use 2-space indentation" not "Format code properly." But as the file grows, spotting these with your eyes alone is nearly impossible. 🔐 The Security Problem People hard-code API keys and tokens directly into or and commit them, way more often than you'd think. AgentLinter stats show 1 in 5 workspaces has exposed credentials. .gitignore doesn't catch secrets buried inside markdown files. 💥 The Consistency Problem Multiple config files = contradictions. says "be a friendly assistant," says "concise, direct tone." The agent gets confused. references files that don't exist. Past 5 files, these conflicts triple. So I thought: is code. Code has ESLint. Why doesn't this have a linter? 🔍 What AgentLinter Does It diagnoses your agent config across 8 categories: 1) Structure: file organization 2) Clarity: instruction specificity 3) Completeness: missing definitions 4) Security: exposed secrets 5) Consistency: cross-file contradictions 6) Memory: session handoff 7) Runtime Config: gateway/auth settings 8) Skill Safety: dangerous shell commands & injection patterns Each scored 0–100 with concrete fix suggestions. Write "be helpful" and it tells you to specify response length, tone, and format. Find an API key? Instant CRITICAL alert to rotate. 🔒 Privacy-First & 100% Local Everything runs on your machine. Files never leave. Only the results are shared, and you can turn that off in settings. This matters — these files can contain system prompts, security rules, and personal context. Fully open source, MIT license, 100% free. 🛠️ Multi-Tool Support Works with Claude Code, Cursor, Windsurf, and Clawdbot. Detects for project mode, or clawdbot.json for agent mode and adjusts diagnostics automatically. 🚀 Get Started with one line npx agentlinter Node.js 18+, no config needed. Run it, check your score, fix what needs fixing. Happy vibe-coding & happy agent life! 🤙 Website: Github:show more

✨ I open sourced my first Chrome extension 🚀... SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.show more

hey if you have a 3060, or any GPU... with 8GB or more sitting in a drawer right now, that thing can run 9 billion parameters of intelligence autonomously. and you don't know it yet. 2 hours ago i posted that 9B hit a ceiling. 2,699 lines across 11 files. blank screen. said the limit for autonomous multifile coding on 9 billion parameters is real. then i audited every file. found 11 bugs. exact file, exact line, exact fix. duplicate variable declarations killing the script loader. a canvas reference never connected to the DOM. enemies with no movement logic. particle systems called on the class instead of the instance. fed that list as a single prompt to the same Qwen 3.5 9B on the same RTX 3060 through Hermes Agent. it fixed all 11. surgically. patch level edits across 4 files. no rewrites. no hallucinated changes. game boots. enemies spawn, move, collide. background renders. particles fire. and here's what nobody is talking about. this is a 9 billion parameter model running a full agentic framework. Hermes Agent with 31 tools. file operations, terminal, browser, code execution. not a single tool call failed. the agent chain never broke. most people think you need 70B+ for reliable tool use. this is 9B on 12 gigs doing it clean. the model didn't fail. my prompting strategy did. the ceiling is not the parameter count. the ceiling is how you prompt it. this is not done. bullets don't fire yet. boss fights need wiring. but the screen that was black 2 hours ago now has a full game rendering in real time. iterating right now. anyone with a GPU from the last 5 years should be paying attention to what is happening right now.show more

Met my girlfriend's parents for the first time. Her... dad asked what I do for work. I said I build trading systems. He said like Wall Street? I said no. 6 AI agents. They work while I sleep. He laughed. So robots are making you money? I did not argue. I opened my laptop. Showed him the terminal. 6 agents running. 47 mispriced markets caught in the first week alone. His face changed. That is not gambling. That is automation? Exactly. Then I showed him how it works. Built the whole thing in 6 hours. Agent 1: Monitoring Runs 24/7. Watches Polymarket for mispriced markets. Spots an anomaly. Writes to memory and pings me on Telegram instantly. Agent 2: Research Parses news, X, macro data via browser tool on a cron schedule. Every morning I have a full digest on all open positions before I check my phone. Agent 3: Trading Reads the research agent memory. Sees the market has not reacted yet. Acts. Execution tool in gateway mode with a whitelist. No full access on a live server. Agent 4: Watchdog Heartbeat every 5 minutes. Monitoring running. No errors. Positions up to date. Something breaks. Immediate Telegram message. All of this. One Gateway. One config file. Isolation via per-agent scope. The token trick: stopped dumping everything into one file. Critical rules in bootstrap. Markets, patterns, past trades in memory. Semantic search pulls it when needed. Token spend dropped 3x. From $0.40 per request to $0.13. First week running: → 47 mispriced markets caught before Polymarket adjusted → Average entry edge 8 to 12 cents per position → Watchdog fired 3 times and caught a broken RPC before it cost me anything The whole system is plain text files. Open an editor. Change one line. Agent behaves differently. No deploy. No build. Her dad went quiet. Then he asked can you teach this? Her mom asked for the setup guide. I built the entire framework. Six agents. Full deployment. Memory architecture. Telegram alerts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Claude" 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the 6-agent system this week. Start with $200. Scale on evidence.show more

How 2 psyop yourself to success 🍸🥂 Visualization >... + unmatchable lvls of desire (for a specific goal other than money) >> get addicted to work. [ and most importantly DREAM big, DREAM about it, think about it, work for it, and do it every single day, while your sleeping think about it, when you eat, do it, when your walking do it, when your working think about it] In lab studies I’ve compiled tons of data for behavior in hopes of using it for marketing purposes However, it also if curated shows you the triggers people react to The mindset changes that curate and sway them to do more of x (in this case work and create insane drive to achieving a goal or YOUR goals) Remember, every stimuli, every reaction curates your mind. When you come in contact with whatever it may be it sways your future behavior & current attitude/ psychological state So at all times if you want it bad enough you should try to curate your environment (special music that gets you in the mood or NO music, depends on the person bc music distracts you thus means you work less OR if you’re brain storming then it helps) Take out all negative energy, comments, or bs that distracts you and breaks your momentum because one it breaks from the smallest thing then that snowballs and has a negative effect going forward Another thing that I personally like to do is read and put myself into those shoes. For me it’s business and mindset related books and nothing compares to John D. Rockefeller’s mindset, work ethic and GRIT, his absolute desire for SUCCESS : I like his 38 letters to his son, John D the founding fathers of the rockefellers, and a study in power. And most importantly you need to just want it bad enough. Lose sleep for it, not because you want it but because you want it so bad that you’re working nonstop to get to it. This is how Rockefeller succeeded How Elon musk succeeded James Dyson Andrew Carnegie And most importantly how Peter kell succeeded All these billionaires and killers got hyper successful because of their drive and mindsets. It’s so important. If you believe something so strongly it’s revisited your mind to making it reality. Ex. Study DOI: 2003.11.018 > strongly visualizing exceedingly finger abductors boosted muscle strength by 35% Another study showed that basketball players who visualized making free throws showed significant improvement over others. And so on and so on. We as marketers have practiced this forever. We know when we tell a emotional story it changes the viewers mind, and we can lead that into a sale at a much higher conversion rate To get a brutal mindset focused on a dream you have that you truly want with your entire being is the result of the same hormones and neuro pathways after they have been curated and triggered by specific stimuli like in a VSL or ad You need to build your mind, you need to build a dream and want it, give yourself inspiration with books and ideas and positive energy. Build motivation and joy by watching others do it and building proof elements that it can really be done in your mind. Break the barriers and limits Movies are good for this, I like edits for this exact reason because yes it can lead you to doom scrolling but also it can be so so valuable in making you see that goal, want it even more and go for it. I personally really like war dogs, lord of war, limitless, and probably a lot more I’m forgetting. But you get the idea. Your success is in your hands alone, if you believe it fully and truly with a pure heart, then it will happen. Good luck🥂 P.s. * I will drop data, sauce, and more info about this and the methods & stimuli I talked about in this thread continuously so bookmark and come back to check them out.show more

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 3show more

I've been working in silence for quite a while... now. Tbh, I don't really even know where to start, so cue the rambling and ranting. Regardless of which side of the fence you sit on, no one can argue the past few years haven't been politically and economically wild. For crypto as a whole it feels like a never ending game of tug of war. A lot of X content has become toxic, so I just largely am not interacting these days. But I read, I read a lot of it. I think we like to forget history a bit in this community. $PLS launched off the highs, and the SEC swooped in right after. Very few people want to admit it, but it shook confidence immediately. I mean no other crypto project has survived such a thing at the time. But #PLS $PLSX and $HEX did. However, winning doesn't unshake that confidence. And RH during and after that event took social precautions to protect himself and his creations. Thing is, the guy isn't stupid. Someone once asked me if I thought certain aspects of the launch we rushed because he knew it was coming? And honestly, maybe. I'd attribute at least a non-zero probability to it. And If that were the case, im glad it was rushed. That case may have gone differently otherwise. Do I still think #PulseChain, #HEX, etc... all have futures? Yes. RH has had the opportunity to just straight up bounce from all of this. Why hasn't he? You could point to exhibit A, B, C, D, etc... of how he's likely got the funds to do that and we all could relatively do nothing about it. So why is he still around? I think it's pretty simple. The usual answer, he wants to win. It's in his twitter handle for Christs sakes. I'll go a step further and say he likely also wants us to win by extension, arguably not as much as he wins, but I mean that's pretty locked in at the moment 🤣 That's not to say he hasn't long been encumbered. And in that state, at lot has gone on without him. Much of which is / was bad. $pDAI guys... I pointed out from day one how building all this around a protocol in a dangerous state was a risky move. And I was right about that.... on multiple occasions... But does that matter now? I suppose not as much. In its current state, it's seemingly no longer exploitable. No different than a meme token now. (presumably, not like I have deep dove on any further risks since ESM). So I guess just whale risk mainly now? Now a lot of people here are in the anti-pdai camp. Me too for what it's worth. But I don't care as much about it's negative anymore in its current state. A lot of people are still in the #pDAI camp strongly. We view this as tribalism, but it's important to note that makes all of us in the #PulseChain camp universally. So these day I find myself relatively pDAI neutral. If you guys want to send it to $1 do it. Only whales can stop you, they run out eventually. (insert super strong this is NOT financial advice). Hell you can maybe even use Sigma to help? Or maybe it wont help, idk. Depends on how people use the software. Conversely, when looking at chain state overall... Why is there nearly $50M in stables sitting on the sidelines. Why not just bridge it out if you want out of what you think is a dead chain. Surely leaving it there exposes you to bridge risk? Why all these yield movements, why the $HEX dusts.... Something is happening. People are seemingly waiting to see what that something is. Or I am reading into things, NFA as always. This whole post is just ramblings of someone trying to do the best they can and certainly not any kind of advice. When I look at other ecosystems, I see a level of polish we don't have. I see tooling we don't have, I see a fostered developer environment we don't have. So I've just been building it, painstakingly.... Because someone has to if we want to be taken seriously. And what I've been building has allowed me to get Sigma to where it is. Sigma is so close... Really just in UI mode, performance optimization, going through nice to haves. I don't believe in launching in a non-finished immutable state. So yeah, I take my time. As with everything. But my point with all of this, and the "why" #Sigma question.... It's unifying, anyone can participate. Which tribe you're in doesn't matter. And if you don't like it, don't use it. It's just software you can use or not use. As it should be. The years of tooling work to deliver this has been a lot of work for one guy in silence. In that time AI has appeared. My take, every dev should be using it. Given the right direction and context. It will make you better. If you blindly trust it, it will make you worse. GPT 5.4 audits smart contracts better than most auditing services. Especially if you give it the context of what you are trying to do. Anyways I digress, testnet is soon. Soon more meaning a feeling of near completion not always reality. That how software is. I do think Sigma stands to unify the chain in a common goal, and shift liquidity into more meaningful places, but ultimately it up to the people the decide to use the software or not use it. And after these frameworks I've built will be applied to what I am tentatively calling the universal hex UI. More or less something aggregative of every derivative I can reasonably support. With data and analytics we since lost. So not just $HEX, $HDRN, and $ICSA, but others as well. However, that depends on some aspect of $Sigma to exist first, so sigma first, chain unity first. And last but not least, take care of yourselves and strive to do cool things. If we aren't doing cool things then what's the point? Hope you think my UI looks good, I spent a while on it. /rantshow more

This morning, The Princess of Wales was in London... to attend The Future Workforce Summit bringing together the UK’s most influential business leaders to drive further action and investment in the early years🥰 She was greeted by Sir Gareth Southgate among the other leaders present and she delivered an address to her audience of Business leaders and the remark centered around "Love". I loved these sections of her speech greatly redacted by journalist Antonello Guerrera: "My passion and the work of The Centre for Early Childhood stems from one essential truth; that the love we feel in our earliest years fundamentally shapes who we become and how we thrive as adults."👌🏽 "Love is the first and most essential bond. But it is also the invisible thread, woven with time, attention and tenderness, through consistent, nurturing relationships which creates the grounded and meaningful environments around a child. It is this texture, the weave of love, which forms a child’s emotional world and becomes the foundation, the very fabric of resilience and belonging."❤️ "The home should be the space where love, safety and rhythm enable a child to thrive. A loving home ultimately teaches us how to love and how to care, but every environment has the potential to shape our hearts." " At The Centre for Early Childhood, we believe that we must do all we can to create the conditions for love to flourish. That is how we invest in our future. Every child deserves respect and safety, and everyone who cares deserve recognition and appreciation."🔥 "I believe in restoring the dignity to the quiet, often invisible work of caring, of loving well, as we look to build a happier, healthier society. You are here because you care, so thank you."❤️ And that is how it is done: As Royal, you are given an immense platform to do so much, and I love that Catherine understands how to use that platform efficiently and to create real change around her, not for self promotion. After the last Business summit where The Princess called on Leaders to provide more Family time on their workforce based on her early childhood research, Deloitte decided to rewrite their Policy of Parental leave for both Male and female, from the minimum 2 weeks parental leave to extend it to 26 weeks; That is 6 months full months of Paid leave for their workforce of almost 500k professionals around the world🤩 I cannot wait to see what actions comes from this new summit👌🏽 #PrincessofWales 📹Emily Fergusonshow more

home page hero ✨ Design notes: - "Forever" hero... text dot pixel FX done in Unicorn Studio. (I will do a whole tutorial on this later. Unicorn's WebGL engine is absolutely wild and very powerful / robust) - built in Framer - I wanted to recreate the iOS unlock effect where your home screen icons cascade into place in a beautifully timed choreography. This took a lot of careful timing using Framer's "Appear" effect on the hero text and surrounding avatars because it was super important that we didn't lose the legibility of our main message ("Build Your Forever Audience") with all the animations. - If you look closely, the choreography is setup to lead your eye through the hero text first starting with "Build Your" then "Forever" and finally "Audience." - With those text layers in place + the surrounding avatars, there is a slight 1 sec pause before the remaining elements slide in below and above (How it works, CTA buttons, announcement badge, and lastly the main nav). - All told the entire loading sequence is 6 seconds - Custom particle system powers the interactive star field (the stars slowly gravitate to your pointer position, and the star field perspective changes ever so subtly as you move your mouse around on the page) - I have 3 shooting stars made of small white line layers that start out off canvas rotated at different angles that shoot across to another point off canvas at random times on a loop effect. - Given this hero scene is in space, I wanted the surrounding avatar elements to "float" in low gravity mode. For this I used Framer's loop effect that slowly oscillates the layer's y position. I then offset the delay of each element randomly to stagger the floating loop so each avatar floats independently/randomly - The final major treatment for this hero scene was the scroll animations on the avatars. I wanted to create a bit of a warp speed effect when you scroll down, as if the avatars were being pulled or sucked into a worm hole as you scroll down below this hero fold. - To accomplish this, I applied Framer's scroll transform affect set to "section in view" on each of the floating avatars, and set the "scroll to" position of the upper avatars to be much, much further away on the y-axis than the "scroll to" position of the lower avatars. (eg. -1700px on upper most avatars vs. -600px on lowest positioned avatars). This effectively causes the upper avatars to slide up off the hero canvas with much greater velocity than the lower positioned avatars. - And when you scroll back up to the hero section, the inverse happens where the lower positioned avatars "arrive back in place" from up above the hero canvas before the upper avatars come back into the scene and settle in place. - Overall I wanted this hero section to feel alive. The floating avatars, particle system with very subtle star movements, and the Caustics effect on the "Forever" text all sort of move at the pace of slow breathing - which is a great pace to create a sense of life and comfort in your scene. Conversion Results (so far) - When this new Framer site launched along with Calaxy v1.9 release on Base a couple weeks ago, we saw a surge in traffic, around 20k page views in the first few days. - Of those 20k hits, 11k visited the app install page ( - which is our main CTA - We saw around 10K new users in the first week after v1.9 launch Overall I'm very happy with the new site and early performance metrics. Lots of tweaking to do but its a good start. If you are a designer building in Framer - hit me with any questions on the above hero notes. Happy to share more specifics! 👾show more

I went a little overboard with Codex last week... and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.show more

This is my "feel the AGI" moment: I used... GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!show more