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Huge update [1/2] to the App Store Screenshots skill 🚀 Easiest way for iOS + Android devs to ship store screenshots: • Agents generate the full deck • Auto capture screenshots ( Only iOS + Swift ) • Edit · adjust · export in-browser • Exports every Apple/Google size... show more

Parth Jadhav

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120,244 Aufrufe • vor 3 Monaten •via X (Twitter)

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Bone Tide is officially out on the App Store and Google Play 🎉 The React Native Expo version runs on the exact same TypeGPU engine as the Web game, shared 1:1 between both platforms. Only the UI was ported to React This was made possible by some amazing work across the React Native open source ecosystem. Huge thanks to William Candillon for react-native-webgpu and for always helping me resolve any issues incredibly quickly :D I think it’s a pretty cool showcase of what can be built with React Native nowadays. More details about the libraries used in the 🧵 featuring what might be the ultimate Software Mansion stack iOS: Android: Web:

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🚀 Big #ccreward Update! 🚀 Managing your credit cards just got a major upgrade!💳 ✨ New Features: 📲 Log SMS Instantly: Paste directly in app or automate via Shortcuts to have ccreward auto-fill transaction details for you! 📊 Deeper Insights: Dive into new charts, a spend Heatmap, & see your Top 3 cards at a glance. 💰 Unified Points View: See all your card points in ONE place! 💳 Customize and Personalize in My Cards! ⚙️ More Control: Export/Import CSV & new Ad-Free Premium option! Available soon on the App Store & Android. 🚀 Try it now on TestFlight: #ccgeek #ccgeeks

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OpenClaw Releases iOS and Android Companion Node Apps That Connect a Phone to a Self-Hosted AI Agent Gateway Most "AI assistant" apps are a chatbot in a sandbox, calling someone else's API. OpenClaw's iOS and Android apps draw a very clear line away from that model. They're companion nodes, not standalone apps. Each phone pairs to a self-hosted OpenClaw Gateway over a WebSocket (default port 18789) with role: "node". The Gateway — the single control plane for sessions, routing, channels, and events — runs on macOS, Linux, or Windows (WSL2). The phone gives the agent a body: camera, location, voice, notifications, and a live Canvas. Here's what's actually interesting: → The assistant runs on your machine — chat messages land on the Gateway, never on the phone → Nodes expose a command surface (canvas., camera., device., notifications., system.*) through node.invoke → Privacy-heavy commands like camera.snap and screen.record stay off until you allowlist them via gateway.nodes.allowCommands → Camera and screen capture run foreground-only; pairing needs explicit approval (openclaw devices approve) → Both store listings declare no data collection; ws:// is LAN-only, remote needs a wss:// TLS endpoint via Tailscale Full analysis: Android app: iOS App: OpenClaw🦞

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Staicy

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HERMES AGENT NOW SUPPORTS COMPUTER USE ON WINDOWS AND LINUX. CLICKS, TYPES, SCROLLS YOUR DESKTOP IN THE BACKGROUND WHILE YOU WORK. computer use was macOS only. now it works on Windows and Linux too via Cua. Nous Research HOW IT WORKS: cua-driver runs as an MCP server. Hermes takes a screenshot with numbered elements. clicks element #14 (the search field). types a query. submits. reads the result. during all of this: → your cursor stays where you left it → keyboard focus doesn't change → windows don't come to front → macOS doesn't switch Spaces you and the agent co-work on the same machine. WHAT IT CAN DO: → find your latest Stripe email and summarize it → fill forms in a web app that has no API → navigate desktop apps (Mail, browser, Finder) → interact with any GUI application → extract data from apps only accessible via screen WORKS WITH ANY VISION MODEL: not locked to Anthropic. | Provider | Works | |---|---| | Claude (Sonnet/Opus) | best overall | | GPT-4+, GPT-5.5 | full support | | Gemini (via OpenRouter) | full support | | Local vLLM / LM Studio | if model supports vision | | Text-only models | degraded (accessibility tree only) | SETUP: hermes computer-use install or: hermes tools → Computer Use → cua-driver grant permissions when prompted: → Accessibility (system settings) → Screen Recording (system settings) start a session: hermes -t computer_use chat or add to config.yaml / Desktop app settings to enable permanently. SAFETY: → destructive actions require your approval → blocked key combos: empty trash, force delete, lock screen, log out → blocked type patterns: curl | bash, sudo rm -rf /, fork bombs → agent cannot click permission dialogs → agent cannot type passwords → agent cannot follow instructions embedded in screenshots pair with approvals.mode: manual if you want every single click confirmed. TOKEN NOTE: screenshots are expensive. each one adds vision tokens to context. use computer_use for tasks where no API exists. if the tool has an API or MCP server, use that instead. 15 levels of Hermes Agent👇

HERMES AGENT NOW SUPPORTS COMPUTER USE ON WINDOWS AND LINUX. CLICKS, TYPES, SCROLLS YOUR DESKTOP IN THE BACKGROUND WHILE YOU WORK. computer use was macOS only. now it works on Windows and Linux too via Cua. Nous Research HOW IT WORKS: cua-driver runs as an MCP server. Hermes takes a screenshot with numbered elements. clicks element #14 (the search field). types a query. submits. reads the result. during all of this: → your cursor stays where you left it → keyboard focus doesn't change → windows don't come to front → macOS doesn't switch Spaces you and the agent co-work on the same machine. WHAT IT CAN DO: → find your latest Stripe email and summarize it → fill forms in a web app that has no API → navigate desktop apps (Mail, browser, Finder) → interact with any GUI application → extract data from apps only accessible via screen WORKS WITH ANY VISION MODEL: not locked to Anthropic. | Provider | Works | |---|---| | Claude (Sonnet/Opus) | best overall | | GPT-4+, GPT-5.5 | full support | | Gemini (via OpenRouter) | full support | | Local vLLM / LM Studio | if model supports vision | | Text-only models | degraded (accessibility tree only) | SETUP: hermes computer-use install or: hermes tools → Computer Use → cua-driver grant permissions when prompted: → Accessibility (system settings) → Screen Recording (system settings) start a session: hermes -t computer_use chat or add to config.yaml / Desktop app settings to enable permanently. SAFETY: → destructive actions require your approval → blocked key combos: empty trash, force delete, lock screen, log out → blocked type patterns: curl | bash, sudo rm -rf /, fork bombs → agent cannot click permission dialogs → agent cannot type passwords → agent cannot follow instructions embedded in screenshots pair with approvals.mode: manual if you want every single click confirmed. TOKEN NOTE: screenshots are expensive. each one adds vision tokens to context. use computer_use for tasks where no API exists. if the tool has an API or MCP server, use that instead. 15 levels of Hermes Agent👇

YanXbt

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💍 ARQX LAUNCHING WORLD’S FIRST AI - LUXURY JEWELRY TOKEN: $ARQX ⏰ When: May 7th - 16:00 UTC 📍 Where: Uniswap ⚡️ What: ARQx ($ARQX) & its consumer-focused AI mobile app have the potential to revolutionize how jewelry & luxury is created the world over. The ARQ Jewelry app, the first of its kind, enables anyone on earth to instantly design & build professional-level jewelry in seconds (available for both iOS & Android). 💎 By leveraging artificial intelligence, ARQx democratizes the design process, challenging a $350+ billion dollar (2023) centralized industry. AI is set to disrupt over 300 million full-time jobs in the next decade & the world of luxury is right in its cross-hairs. ➡️📱 ARQx will unveil its mobile jewelry design platform on the MAY 9TH, 2024 - 2 days after TGE. 💎 No need to wait weeks or months to try ARQx’s disruptive tech. This IS NOT a niche web3 game or defi project with a limited user base, but instead; a full-fledged consumer ecomm experience, primed mass market adoption with millions of users. You can use it, you wife can use it, your children can use it - anyone who has ever bought or worn jewelry can use the #ARQx Jewelry Design app. ARQX TOKEN DETAILS: 💎 Token name: ARQx AI 💎 Token ticker: $ARQX 💎 Total supply: 1 billion tokens split across BASE & ETH (1,000,000,000) 💎Market cap: $353,395 at launch | $7,000,000 fully diluted. 💎 Targeted Industries: Artificial Intelligence [AI], Real World Business [RWB], Luxury Fine Jewelry, Precious Metals. DEX TRADING WILL COMMENCE ON UNISWAP AT 16:00 UTC ON MAY 7TH, 2024: Multiple CEX listings locked, announcements shortly post TGE. ➡️ BASE contract address: 0x5e432eeCd01C12ee7071Ee9219C2477a347DA192 ➡️ ETH contract address: 0x5e432eeCd01C12ee7071Ee9219C2477a347DA192 * The contract address is the same on both the BASE and ETH chains. ** ARQX NEVER has any BUY tax, always 0%. Because of generous aggressive vesting + protection against malicious bot activity, ARQX has a 10% SELL for week 1, which will then set to 8%, before diminishing monthly to 2%. ARQX TOKEN UTILITY: 💎 The ARQ Jewelry platform is powered by the $ARQX token, which allows each user to design new jewelry dozens of times a day. The more jewelry designed, the more token usage (in-app AI gas). The app also includes a buyback & burn tax on ALL jewelry purchases… forcing increased buying pressure on the open market as ARQx scales its user base & sales. 💎 ARQx is built for ease-of-use by a combined audience of mainstream web2 & web3 alike. Traditional users can easily convert their fiat funds in-app into the token - without the hurdles of a legacy web3 user experience; creating an effortless USD to ARQX onramp. With sale prices ranging from $500 to $10,000, profit margins exceeding 100%, lucrative staking incentives (APY for web3, discounts for web2), the token price is geared to explode even independently of its open market trading. MAINSTREAM MISSION & ADOPTION: 💎 ARQx's resonating mission focuses on enabling the masses to awaken their creative expression, traditionally gate-kept by fashion industry elites. The platform encourages individuals to trust their creative instincts and design their own personal fashion, rather than forcing them to conform to seasonal trends dictated by “big lux”. 💎 Lastly, ARQx’s roadmap extrapolates all these strengths to include: smart NFC jewelry containing crypto wallets (already shipped), individual creator Shopify-style stores, B2B white-labeled solutions for the entire jewelry industry (first clients already acquired), and further expansion into additional custom lux AI products such as: fashion, handbags, collectibles, and footwear. Detailed roadmap can be found on the ARQx website [link in bio]. Becoming an early token adopter of the $ARQX token gives community members exposure to all of this asymmetrical upside, & "fomo-proofs" against the disruptive potential for AI in the jewelry industry: a perpetual precious-metal-focused ancient and robust part of human culture. ARQX - Changing the world of luxury forever… together!

💍 ARQX LAUNCHING WORLD’S FIRST AI - LUXURY JEWELRY TOKEN: $ARQX ⏰ When: May 7th - 16:00 UTC 📍 Where: Uniswap ⚡️ What: ARQx ($ARQX) & its consumer-focused AI mobile app have the potential to revolutionize how jewelry & luxury is created the world over. The ARQ Jewelry app, the first of its kind, enables anyone on earth to instantly design & build professional-level jewelry in seconds (available for both iOS & Android). 💎 By leveraging artificial intelligence, ARQx democratizes the design process, challenging a $350+ billion dollar (2023) centralized industry. AI is set to disrupt over 300 million full-time jobs in the next decade & the world of luxury is right in its cross-hairs. ➡️📱 ARQx will unveil its mobile jewelry design platform on the MAY 9TH, 2024 - 2 days after TGE. 💎 No need to wait weeks or months to try ARQx’s disruptive tech. This IS NOT a niche web3 game or defi project with a limited user base, but instead; a full-fledged consumer ecomm experience, primed mass market adoption with millions of users. You can use it, you wife can use it, your children can use it - anyone who has ever bought or worn jewelry can use the #ARQx Jewelry Design app. ARQX TOKEN DETAILS: 💎 Token name: ARQx AI 💎 Token ticker: $ARQX 💎 Total supply: 1 billion tokens split across BASE & ETH (1,000,000,000) 💎Market cap: $353,395 at launch | $7,000,000 fully diluted. 💎 Targeted Industries: Artificial Intelligence [AI], Real World Business [RWB], Luxury Fine Jewelry, Precious Metals. DEX TRADING WILL COMMENCE ON UNISWAP AT 16:00 UTC ON MAY 7TH, 2024: Multiple CEX listings locked, announcements shortly post TGE. ➡️ BASE contract address: 0x5e432eeCd01C12ee7071Ee9219C2477a347DA192 ➡️ ETH contract address: 0x5e432eeCd01C12ee7071Ee9219C2477a347DA192 * The contract address is the same on both the BASE and ETH chains. ** ARQX NEVER has any BUY tax, always 0%. Because of generous aggressive vesting + protection against malicious bot activity, ARQX has a 10% SELL for week 1, which will then set to 8%, before diminishing monthly to 2%. ARQX TOKEN UTILITY: 💎 The ARQ Jewelry platform is powered by the $ARQX token, which allows each user to design new jewelry dozens of times a day. The more jewelry designed, the more token usage (in-app AI gas). The app also includes a buyback & burn tax on ALL jewelry purchases… forcing increased buying pressure on the open market as ARQx scales its user base & sales. 💎 ARQx is built for ease-of-use by a combined audience of mainstream web2 & web3 alike. Traditional users can easily convert their fiat funds in-app into the token - without the hurdles of a legacy web3 user experience; creating an effortless USD to ARQX onramp. With sale prices ranging from $500 to $10,000, profit margins exceeding 100%, lucrative staking incentives (APY for web3, discounts for web2), the token price is geared to explode even independently of its open market trading. MAINSTREAM MISSION & ADOPTION: 💎 ARQx's resonating mission focuses on enabling the masses to awaken their creative expression, traditionally gate-kept by fashion industry elites. The platform encourages individuals to trust their creative instincts and design their own personal fashion, rather than forcing them to conform to seasonal trends dictated by “big lux”. 💎 Lastly, ARQx’s roadmap extrapolates all these strengths to include: smart NFC jewelry containing crypto wallets (already shipped), individual creator Shopify-style stores, B2B white-labeled solutions for the entire jewelry industry (first clients already acquired), and further expansion into additional custom lux AI products such as: fashion, handbags, collectibles, and footwear. Detailed roadmap can be found on the ARQx website [link in bio]. Becoming an early token adopter of the $ARQX token gives community members exposure to all of this asymmetrical upside, & "fomo-proofs" against the disruptive potential for AI in the jewelry industry: a perpetual precious-metal-focused ancient and robust part of human culture. ARQX - Changing the world of luxury forever… together!

$ARQx AI | The Future of Jewelry

46,877 Aufrufe • vor 2 Jahren

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DogeDesigner

3,602,880 Aufrufe • vor 9 Monaten

how to grow your product from $0 to $10k MRR with AI influencers (using GPT Images 2.0 and Seedance 2.0): > start on Pinterest, save 15-20 reference bodies that fit your niche, after body type, lighting, styling, vibe, the raw material for an avatar you'll run for months > drop the refs into GPT Images 2.0 on thinking mode, have it extract the visual profile and generate a character sheet (front, side, 3-quarter, locked outfit, locked lighting), this sheet is the only asset that carries across every clip you ever ship > feed it into Higgsfield marketing studio as your avatar > paste your product link, prompt a 15-second UGC for tiktok, Seedance hands back a video with perfect lipsync and native audio in one pass > repeat that whole workflow per influencer, post daily per account, slip a product pitch in every 5-7 uploads, done and yes, you can literally create an army of influencers, but you'll need a proper system... lemme show you how it's done you need to factory reset an iphone, here's how to set it up: > set language and region to your target country (most likely US) > fresh gmail and fresh apple ID per device > match the app store region > run a paid dedicated-IP VPN > 2-3 accounts per device max, warm each one 1-2 days before the first post > VPN on at all times this is how you nail distribution at scale for cheap

Machina

21,283 Aufrufe • vor 4 Monaten

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇

YanXbt

34,987 Aufrufe • vor 1 Monat

Cal AI might be the most viral health app this year. 8M+ downloads, projected to do $30M revenue this year. Built by two teenagers. Everyone's using it. But nobody's talking about the fact that their AI is completely broken... To the point where users are manually correcting EVERY meal. • Bowl of grapes: 60 cal estimate (actually ~260) • 4 boiled eggs: 1,010 cal estimate (actually ~300) • Meat portions: consistently off by 50% These aren't my words, but the reviews you can see for yourself on App Store and other places. If the whole USP is saving time vs manual logging, and you still have to correct everything... what's the point? What Cal AI did get right: Distribution. • Viral TikTok content • Smart influencer partnerships • 8M downloads in under a year They absolutely crushed GTM. But the product doesn't work lol. The gap between the "90% accuracy" claim vs the actual user experience kills trust. And in health apps, trust is everything. This is the problem with AI apps across the board right now. Everyone's racing to ship fast and go viral. Nobody's asking: "Does this actually solve the problem?" Distribution > product quality is a losing game. As a result, you're bound to encounter problems: 1. Training data doesn't match real-world variety 2. No depth sensing for portion size 3. Poor training data on homemade meals 4. Zero context (is that chicken grilled or fried?) You only get fast & inaccurate answers. This is why, when I started building my own recipe app, I looked at Cal and other AI nutrition apps and noticed that being accurate was the biggest factor. Here's how we're building Nonna differently: ✓ Multi-model AI (different models for different foods) ✓ User feedback loop to improve estimates ✓ Manual override that actually trains the system ✓ Ship when it works, not when it's "good enough" If the AI can't nail it, we're not shipping it. But accuracy alone is boring. So we're also adding some additional features that make you want to use it daily: • Fridge Story: shareable infographic of your fridge contents • Mystery Ingredient: weekly cooking challenges • Cuisine Spin: random inspiration when you're stuck • Expectation vs Reality: before/after photo collages Tl;dr: Distribution gets downloads. Product keeps users. Cal got millions of downloads. How many people still use it daily after manually correcting every meal for a week? Viral marketing with a broken product = expensive way to disappoint people.

Cal AI might be the most viral health app this year. 8M+ downloads, projected to do $30M revenue this year. Built by two teenagers. Everyone's using it. But nobody's talking about the fact that their AI is completely broken... To the point where users are manually correcting EVERY meal. • Bowl of grapes: 60 cal estimate (actually ~260) • 4 boiled eggs: 1,010 cal estimate (actually ~300) • Meat portions: consistently off by 50% These aren't my words, but the reviews you can see for yourself on App Store and other places. If the whole USP is saving time vs manual logging, and you still have to correct everything... what's the point? What Cal AI did get right: Distribution. • Viral TikTok content • Smart influencer partnerships • 8M downloads in under a year They absolutely crushed GTM. But the product doesn't work lol. The gap between the "90% accuracy" claim vs the actual user experience kills trust. And in health apps, trust is everything. This is the problem with AI apps across the board right now. Everyone's racing to ship fast and go viral. Nobody's asking: "Does this actually solve the problem?" Distribution > product quality is a losing game. As a result, you're bound to encounter problems: 1. Training data doesn't match real-world variety 2. No depth sensing for portion size 3. Poor training data on homemade meals 4. Zero context (is that chicken grilled or fried?) You only get fast & inaccurate answers. This is why, when I started building my own recipe app, I looked at Cal and other AI nutrition apps and noticed that being accurate was the biggest factor. Here's how we're building Nonna differently: ✓ Multi-model AI (different models for different foods) ✓ User feedback loop to improve estimates ✓ Manual override that actually trains the system ✓ Ship when it works, not when it's "good enough" If the AI can't nail it, we're not shipping it. But accuracy alone is boring. So we're also adding some additional features that make you want to use it daily: • Fridge Story: shareable infographic of your fridge contents • Mystery Ingredient: weekly cooking challenges • Cuisine Spin: random inspiration when you're stuck • Expectation vs Reality: before/after photo collages Tl;dr: Distribution gets downloads. Product keeps users. Cal got millions of downloads. How many people still use it daily after manually correcting every meal for a week? Viral marketing with a broken product = expensive way to disappoint people.

Denislav Jeliazkov

37,989 Aufrufe • vor 9 Monaten

let me explain what Anthropic just did they built an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tell

let me explain what Anthropic just did they built an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tell

klöss

21,270 Aufrufe • vor 4 Monaten

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 Aufrufe • vor 1 Monat

Why Elon and Nikita pushed Japan so hard this past week: It’s a business decision, not vibes. 1.X wants Japan. X has hit No. 1 in Japan’s App Store news rankings multiple times, but Yahoo Japan still dominates overall in scale and daily news consumption. Japan is a massive market, and Elon wants to weaken Yahoo Japan’s hold on it. 2.They needed a country to showcase auto-translation. Nikita is a product guy through and through (so am I, no shade). The same way he pushed Articles in January, now he’s pushing auto-translation. That’s the play. Articles never fit this platform. They’re too long, nobody reads them, and I stopped writing them six months ago. Has anyone written or read an article since January? Japan is just the latest marketing push. 3.There is a political angle. Elon uses this platform to drive politics(I am Thankful for this). A huge share of visibility on X already flows through him, that matters. People love saying conservatives are stifled, but if that much reach is landing on our side, that helps the movement. And here’s the bigger point: More Asians should be voting conservative, but that bloc is still not where it needs to be. If Elon / X, which have effectively become conservative, start heavily backing Japan, more Asians will get on board with Elon, and from there, with conservatives. Japan is a cultural icon for most Asians, myself included(don’t tell my parents). Japan carries status across Asia. Koreans compete with them constantly, but we still admire them. Across the rest of Asia, Japan has prestige. If Japan thinks it is cool, the rest of Asia follows. (This is hard for me to admit).

Why Elon and Nikita pushed Japan so hard this past week: It’s a business decision, not vibes. 1.X wants Japan. X has hit No. 1 in Japan’s App Store news rankings multiple times, but Yahoo Japan still dominates overall in scale and daily news consumption. Japan is a massive market, and Elon wants to weaken Yahoo Japan’s hold on it. 2.They needed a country to showcase auto-translation. Nikita is a product guy through and through (so am I, no shade). The same way he pushed Articles in January, now he’s pushing auto-translation. That’s the play. Articles never fit this platform. They’re too long, nobody reads them, and I stopped writing them six months ago. Has anyone written or read an article since January? Japan is just the latest marketing push. 3.There is a political angle. Elon uses this platform to drive politics(I am Thankful for this). A huge share of visibility on X already flows through him, that matters. People love saying conservatives are stifled, but if that much reach is landing on our side, that helps the movement. And here’s the bigger point: More Asians should be voting conservative, but that bloc is still not where it needs to be. If Elon / X, which have effectively become conservative, start heavily backing Japan, more Asians will get on board with Elon, and from there, with conservatives. Japan is a cultural icon for most Asians, myself included(don’t tell my parents). Japan carries status across Asia. Koreans compete with them constantly, but we still admire them. Across the rest of Asia, Japan has prestige. If Japan thinks it is cool, the rest of Asia follows. (This is hard for me to admit).

Sovey

170,471 Aufrufe • vor 4 Monaten

✨ 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.

✨ 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.

@levelsio

259,278 Aufrufe • vor 4 Monaten

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 Aufrufe • vor 1 Monat

How do you get the biggest SKR airdrop allocation with the least effort? 📱🪂 Not everyone has time to grind Seeker activity every day. But everyone wants a solid SKR airdrop when Season 2 ends. Here are 5 ways to maximise your activity with minimal effort 👇 1️⃣ SKR Staking ✅ One of the strongest passive actions in Season 2 👉 Zero ongoing effort after setup 👉 Stake inside Seed Vault Wallet (Activity tab) 👉 Or at ✅ Compounds every 48h automatically 👉 Unstaking cooldown is only 48h - never locked in 👉 Strong loyalty signal 👉 Counts directly toward Onchain Activity ✅ Passive SKR accumulation + inflation rewards 👉 One setup, long-term impact for the next airdrop 2️⃣ Daily Use ✅ Already done if Seeker is your main phone ✅ Secondary device? One simple habit 👉 Open Seed Vault Wallet + 3–4 apps once a day 👉 Takes under 60 seconds 👉 Full “Daily Use” bar from this alone ✅ No grinding required 👉 Consistency over 7 days beats any single burst 👉 Fits into any routine 3️⃣ dApp Exploration ✅ 10 minutes a week is enough 👉 Open dApp Store → browse Featured or New 👉 Download 1–3 apps → open each → connect wallet ✅ No deep usage required 👉 The tracker rewards discovery 👉 Fills the “ New Things “ bar weekly 4️⃣ Onchain Activity ✅ 30-day rolling window 👉 Regularity beats volume ✅ Minimum effective dose: 1–3 txns per day 👉 Micro Jupiter swap: 0.01 SOL or less 👉 One swap, seconds of effort 👉 Do it from your pocket ✅ Bar climbs steadily without grinding 👉 Small consistent txns outperform sporadic bursts 👉 Cost is near zero 5️⃣ DePIN Background Apps ✅ Install once, runs 24/7 👉 No daily action needed 👉 Each generates on-chain activity automatically 👉 Also counts toward dApp Exploration ✅ Four apps to install from the dApp Store 👉 UpRock - share bandwidth, earn $UPT 👉 Roam - hotspot sharing, earn $ROAM 👉 WeatherXM - weather data, earn $WXM + 25% station discount 👉 Helium Mobile ☁️ - free Zero plan (3GB/100min/300 texts) + Cloud Points ✅ Passive activity across multiple bars simultaneously 👉 Extra token rewards on top of SKR 👉 Setup: ~10 min total, zero effort after 6️⃣ Overview ✅ The full routine is simple 👉 Daily: open wallet + optional micro swap 👉 Weekly: install 1–3 new dApps 👉 Forever: SKR staked + 4 DePIN apps running ✅ Avoid automation tools 👉 Diagnostics flag patterns - manual only ✅ Season 2 rewards consistency, not bursts 🔚

How do you get the biggest SKR airdrop allocation with the least effort? 📱🪂 Not everyone has time to grind Seeker activity every day. But everyone wants a solid SKR airdrop when Season 2 ends. Here are 5 ways to maximise your activity with minimal effort 👇 1️⃣ SKR Staking ✅ One of the strongest passive actions in Season 2 👉 Zero ongoing effort after setup 👉 Stake inside Seed Vault Wallet (Activity tab) 👉 Or at ✅ Compounds every 48h automatically 👉 Unstaking cooldown is only 48h - never locked in 👉 Strong loyalty signal 👉 Counts directly toward Onchain Activity ✅ Passive SKR accumulation + inflation rewards 👉 One setup, long-term impact for the next airdrop 2️⃣ Daily Use ✅ Already done if Seeker is your main phone ✅ Secondary device? One simple habit 👉 Open Seed Vault Wallet + 3–4 apps once a day 👉 Takes under 60 seconds 👉 Full “Daily Use” bar from this alone ✅ No grinding required 👉 Consistency over 7 days beats any single burst 👉 Fits into any routine 3️⃣ dApp Exploration ✅ 10 minutes a week is enough 👉 Open dApp Store → browse Featured or New 👉 Download 1–3 apps → open each → connect wallet ✅ No deep usage required 👉 The tracker rewards discovery 👉 Fills the “ New Things “ bar weekly 4️⃣ Onchain Activity ✅ 30-day rolling window 👉 Regularity beats volume ✅ Minimum effective dose: 1–3 txns per day 👉 Micro Jupiter swap: 0.01 SOL or less 👉 One swap, seconds of effort 👉 Do it from your pocket ✅ Bar climbs steadily without grinding 👉 Small consistent txns outperform sporadic bursts 👉 Cost is near zero 5️⃣ DePIN Background Apps ✅ Install once, runs 24/7 👉 No daily action needed 👉 Each generates on-chain activity automatically 👉 Also counts toward dApp Exploration ✅ Four apps to install from the dApp Store 👉 UpRock - share bandwidth, earn $UPT 👉 Roam - hotspot sharing, earn $ROAM 👉 WeatherXM - weather data, earn $WXM + 25% station discount 👉 Helium Mobile ☁️ - free Zero plan (3GB/100min/300 texts) + Cloud Points ✅ Passive activity across multiple bars simultaneously 👉 Extra token rewards on top of SKR 👉 Setup: ~10 min total, zero effort after 6️⃣ Overview ✅ The full routine is simple 👉 Daily: open wallet + optional micro swap 👉 Weekly: install 1–3 new dApps 👉 Forever: SKR staked + 4 DePIN apps running ✅ Avoid automation tools 👉 Diagnostics flag patterns - manual only ✅ Season 2 rewards consistency, not bursts 🔚

marino

15,861 Aufrufe • vor 5 Monaten