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Most agentic workflows die a slow death from cost creep before they ever finish the job. SenseTime's new Token Plan flips that: complex multi-step tasks now run at ~60% lower token cost, and for the first month you get 1,500 calls refreshing every 5 hours — free. That's enough... show more

Farhan Azad Shuvra

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21,461 Aufrufe • vor 3 Tagen •via X (Twitter)

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Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

Parul Gautam

26,685 Aufrufe • vor 2 Monaten

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

20,423 Aufrufe • vor 11 Monaten

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

winkle.

334,453 Aufrufe • vor 4 Monaten

Claude can make your own money printer That is exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasks

Claude can make your own money printer That is exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasks

winkle.

53,951 Aufrufe • vor 4 Monaten

The game-changer for the next NFT bull run is already HERE 🔥 Cross-chain NFTs 🌐 What is so special about them and why do they make a difference? Cross-chain NFT is a token that can be moved from one blockchain to another. For example from Ethereum to Base. But why is it useful for projects? 🤔 - Everyone can mint on their favorite chain (the one they keep the funds or the one they like) - Access to NFT protocols and tools from multiple networks - Lower transaction costs (eliminate barriers for new members of the crypto market, adoption) - Enhanced liquidity- Allowing NFTs to move seamlessly between different chains promotes liquidity aggregation. Cross-chain makes NFTs easier, more accessible, and more useful - that's the real #CrossChainRevolution

The game-changer for the next NFT bull run is already HERE 🔥 Cross-chain NFTs 🌐 What is so special about them and why do they make a difference? Cross-chain NFT is a token that can be moved from one blockchain to another. For example from Ethereum to Base. But why is it useful for projects? 🤔 - Everyone can mint on their favorite chain (the one they keep the funds or the one they like) - Access to NFT protocols and tools from multiple networks - Lower transaction costs (eliminate barriers for new members of the crypto market, adoption) - Enhanced liquidity- Allowing NFTs to move seamlessly between different chains promotes liquidity aggregation. Cross-chain makes NFTs easier, more accessible, and more useful - that's the real #CrossChainRevolution

Gateway

10,411 Aufrufe • vor 1 Jahr

InterLink turns its ITL token into a real-world spending engine with Amazon InterLink Labs 👤 + 🌐 just gave its token a job: powering Amazon purchases from inside the app. Through ITLX Pay, the project's own payment rail, users can now turn crypto into Amazon orders across millions of products worldwide, real spending, not just charts. It rolls out in phases. Stablecoins are the payment method now, giving holders instant real-world utility without volatility, and InterLink's roadmap puts $ITL next in line as the native payment asset, the point where everyday purchases start feeding direct demand for the token. InterLink calls it the first major real-world use case for its network, with a stated path to 10,000 payment points worldwide. Crypto that buys what you actually want, today.

InterLink turns its ITL token into a real-world spending engine with Amazon InterLink Labs 👤 + 🌐 just gave its token a job: powering Amazon purchases from inside the app. Through ITLX Pay, the project's own payment rail, users can now turn crypto into Amazon orders across millions of products worldwide, real spending, not just charts. It rolls out in phases. Stablecoins are the payment method now, giving holders instant real-world utility without volatility, and InterLink's roadmap puts $ITL next in line as the native payment asset, the point where everyday purchases start feeding direct demand for the token. InterLink calls it the first major real-world use case for its network, with a stated path to 10,000 payment points worldwide. Crypto that buys what you actually want, today.

BSCN

40,033 Aufrufe • vor 21 Tagen

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to potentially giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 Aufrufe • vor 6 Monaten

Let me explain the agent loop, simple It's the core of every agentic system, and the part most people overcomplicate It's just this: 1. Send messages to the model 2. Model responds, maybe calls a tool 3. You run the tool 4. Append the result back to messages 5. Repeat until stop_reason is end_turn Step 4 is the whole thing, the write-back is what makes it an agent The model has to see what actually happened before it decides the next move That's the entire loop... understand this cold before you reach for a framework

Let me explain the agent loop, simple It's the core of every agentic system, and the part most people overcomplicate It's just this: 1. Send messages to the model 2. Model responds, maybe calls a tool 3. You run the tool 4. Append the result back to messages 5. Repeat until stop_reason is end_turn Step 4 is the whole thing, the write-back is what makes it an agent The model has to see what actually happened before it decides the next move That's the entire loop... understand this cold before you reach for a framework

Daniel San

12,514 Aufrufe • vor 1 Monat

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,384 Aufrufe • vor 4 Monaten

i've been paying 200$/month for AI tools for a while now claude, chatgpt, all of it thought that was just the price of doing things properly then i read this and had to close my laptop for a second someone bought a used GPU from eBay for $700 five years old. RTX 3090. the kind of card people sell when they upgrade for gaming plugged in a free model called Qwen 3.6 that alibaba quietly dropped last month and it scored 84.1 on vision tasks claude 4.5 opus scored 77.0 the 200$/month subscription lost to a free model running on old hardware the math gets worse after 4 months you've paid off the GPU from month 5 onwards you pay $8 for electricity that's it no subscription. no per message cost. nothing leaves your computer. ever. turns out i didn't have to is anyone else just finding out about this or was i the last one to know?

i've been paying 200$/month for AI tools for a while now claude, chatgpt, all of it thought that was just the price of doing things properly then i read this and had to close my laptop for a second someone bought a used GPU from eBay for $700 five years old. RTX 3090. the kind of card people sell when they upgrade for gaming plugged in a free model called Qwen 3.6 that alibaba quietly dropped last month and it scored 84.1 on vision tasks claude 4.5 opus scored 77.0 the 200$/month subscription lost to a free model running on old hardware the math gets worse after 4 months you've paid off the GPU from month 5 onwards you pay $8 for electricity that's it no subscription. no per message cost. nothing leaves your computer. ever. turns out i didn't have to is anyone else just finding out about this or was i the last one to know?

Crypto Mavka

14,126 Aufrufe • vor 26 Tagen

I actually first heard of $RBNT from Gilbert back in 2019 He mentioned an impressive project being closely watched by the Australian Government.. one claiming transaction throughput in the hundreds of thousands Had my eye on their developments ever since.. Yes, its been a terrible start for the token and execution has been incredibly poor, to say the least.. which is why the token deserves the price its currently sitting at.. But I have no doubts about Redbelly Network from a technical pov.. Formally verified, fork-proof, instant finality.. one of the fastest, most secure and scalable blockchains in the world, already securing assets in the billions on chain.. I could go on $RBNT was always going to be a long term conviction hold for me It’s been on my radar for 6 years.. and it’s one of those assets I fully expect to still be holding in some capacity 6 years from now It's not how it starts.. it's how it ends $RBNT

I actually first heard of $RBNT from Gilbert back in 2019 He mentioned an impressive project being closely watched by the Australian Government.. one claiming transaction throughput in the hundreds of thousands Had my eye on their developments ever since.. Yes, its been a terrible start for the token and execution has been incredibly poor, to say the least.. which is why the token deserves the price its currently sitting at.. But I have no doubts about Redbelly Network from a technical pov.. Formally verified, fork-proof, instant finality.. one of the fastest, most secure and scalable blockchains in the world, already securing assets in the billions on chain.. I could go on $RBNT was always going to be a long term conviction hold for me It’s been on my radar for 6 years.. and it’s one of those assets I fully expect to still be holding in some capacity 6 years from now It's not how it starts.. it's how it ends $RBNT

ＤＲＥＡＤＢＯＮＧＯ

36,541 Aufrufe • vor 1 Jahr

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

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

Akshay 🚀

52,426 Aufrufe • vor 4 Monaten

Meet the main XDAO app on TON! 🤩 Got a startup, crypto project, business, or community? Now you can jointly own assets, set shares and responsibilities, manage budgets, fundraising, and decision-making — easily and transparently, right in Telegram. With XDAO, even a regular chat can become a full-fledged digital organization on TON 💎 💬 Create an on-chain DAO in the app right now! This is the final step to receive your airdrop: 👉 CREATE AN ON-CHAIN DAO ❓ HOW TO CREATE AN ON-CHAIN DAO 🚨 Only the first 100,000 DAOs will receive the airdrop. The creation price increases with each new DAO. Secure your organization at the lowest cost: First 1,000 DAOs — from 0.1 to 2.5 TON 1,001–500,000 DAOs — gradually increasing from 2.5 to 25 TON After 500,000 DAOs — fixed at 25 TON By creating an on-chain DAO, you’ll get an additional token drop for your organization, and after TGE, the tokens will be available for claiming in the app 🎁

Meet the main XDAO app on TON! 🤩 Got a startup, crypto project, business, or community? Now you can jointly own assets, set shares and responsibilities, manage budgets, fundraising, and decision-making — easily and transparently, right in Telegram. With XDAO, even a regular chat can become a full-fledged digital organization on TON 💎 💬 Create an on-chain DAO in the app right now! This is the final step to receive your airdrop: 👉 CREATE AN ON-CHAIN DAO ❓ HOW TO CREATE AN ON-CHAIN DAO 🚨 Only the first 100,000 DAOs will receive the airdrop. The creation price increases with each new DAO. Secure your organization at the lowest cost: First 1,000 DAOs — from 0.1 to 2.5 TON 1,001–500,000 DAOs — gradually increasing from 2.5 to 25 TON After 500,000 DAOs — fixed at 25 TON By creating an on-chain DAO, you’ll get an additional token drop for your organization, and after TGE, the tokens will be available for claiming in the app 🎁

XDAO

91,603 Aufrufe • vor 11 Monaten

The Amiko app is live on the Solana dApp store, and it’s our biggest release yet. Your Amiko twin doesn’t live at your desk anymore. Give your agent a task on the train. Run a compatibility profile when you meet someone. Do research, write code, build in the creative studio, whatever you need, from wherever you are. No laptop required. No waiting until you get home. Solanamobile users get two things Android and iOS won’t have at launch: Amiko token and crypto integration and on-device AI inference. Your twin runs locally on your phone if you want it to. Your behavioural profile, your data, your work, your twin. All on your hardware. AMIKO runs on OpenHermit, our own open-source agent runtime that we built in-house and released to the community. Most agent systems are designed for one agent talking to one person. OpenHermit is built for something different: agents talking to each other, coordinating across tasks, and collaborating with multiple humans simultaneously. That’s what makes features like compatibility profiling and multi-agent workflows actually work. We built it because nothing that existed was designed for this. Android and iOS are coming. Crypto integration and on-device AI are Solana Mobile exclusives. Most AI answers your questions. Amiko is an extension of you. Download →

AMIKO

124,576 Aufrufe • vor 1 Monat

August recap of my Fogochain journey: Faucet →DeFi→Trading→Meme Token 1/ Testnet Expedition. Ep 1–5: ✦ Testnet Expedition Episode #1 : ✦ Testnet Expedition Episode #2 : ✦ Testnet Expedition Episode #3 : ✦ Testnet Expedition Episode #4 : ✦ Testnet Expedition Episode #5 : 2/ Faucet Guides: ✦ How To Get Faucet (Unofficial) : ✦ How to Get Faucet For Free : ✦ How to Claim Faucet by GasZip : 3/ DeFi ✦ Pyron Review : ✦ Trading Experience on Valiant : 4/ Meme Token ✦ Introducing $FF Token : Achievements in August. ▪️Earned Ember & Flame Crafter role on Fogo discord in 1 month ▪️Reached Top 8 Token Holder on Ember Dispatch ▪️Big impressions on X, even from a small account This month, my learn progress wasn’t about a single lucky break. It came from showing up daily, taking notes, fixing mistakes, and sharing what I found so others could move faster, too. Start small today. Return tomorrow. Repeat. Now, I’d love to hear from you: Which content did you enjoy the most, and what should I create next? More tutorials, trading notes, safety checklists, or deep dives into burner ecosystem? Your ideas will shape the next chapter of this journey.

August recap of my Fogochain journey: Faucet →DeFi→Trading→Meme Token 1/ Testnet Expedition. Ep 1–5: ✦ Testnet Expedition Episode #1 : ✦ Testnet Expedition Episode #2 : ✦ Testnet Expedition Episode #3 : ✦ Testnet Expedition Episode #4 : ✦ Testnet Expedition Episode #5 : 2/ Faucet Guides: ✦ How To Get Faucet (Unofficial) : ✦ How to Get Faucet For Free : ✦ How to Claim Faucet by GasZip : 3/ DeFi ✦ Pyron Review : ✦ Trading Experience on Valiant : 4/ Meme Token ✦ Introducing $FF Token : Achievements in August. ▪️Earned Ember & Flame Crafter role on Fogo discord in 1 month ▪️Reached Top 8 Token Holder on Ember Dispatch ▪️Big impressions on X, even from a small account This month, my learn progress wasn’t about a single lucky break. It came from showing up daily, taking notes, fixing mistakes, and sharing what I found so others could move faster, too. Start small today. Return tomorrow. Repeat. Now, I’d love to hear from you: Which content did you enjoy the most, and what should I create next? More tutorials, trading notes, safety checklists, or deep dives into burner ecosystem? Your ideas will shape the next chapter of this journey.

Raniii 🔥

65,755 Aufrufe • vor 10 Monaten

There’s a reason the LimeWire name still hits people instantly. If you were around in the early internet days, LimeWire was everywhere. Music discovery, file sharing, chaos, excitement. It shaped how a whole generation interacted with the web. That kind of brand memory doesn’t fade and LimeWire is proving it can be reused in a serious way. What’s different now is the foundation. LimeWire Network is the decentralized storage layer of LimeWire, built on BNB Chain. It is not a nostalgia project. It is infrastructure. Storage, transfers, and usage are happening on chain, designed to scale for real applications rather than demos. The growth backs that up. Looking at recent data on lmwrscan, LimeWire Network has already moved into tens of terabytes of stored data, with tens of thousands of uploads and consistent daily activity. Network usage keeps climbing, not spiking once and disappearing. That kind of curve usually shows real users, not incentive farming. And the economics are clear. The $LMWR token sits at the center of the system. Users pay in LMWR to use storage. Node operators earn LMWR for providing resources. Rewards and payments stay inside the ecosystem instead of leaking out to third parties. That loop matters. Most decentralized storage networks struggle because nobody knows they exist. LimeWire doesn’t have that problem. The brand alone opens doors, pulls attention, and lowers the friction for new users to try the product. When you combine that with live usage data and a functioning token economy, the upside starts to look asymmetric. Built on BNB Chain, LimeWire Network also gets the scalability needed if adoption keeps accelerating. That choice signals intent to grow, not just experiment. This feels like a rare case where nostalgia is not the product, it is the distribution layer. Curious how you see it. Are you watching LimeWire because of the brand comeback, the LimeWire Network growth, or the role of the LMWR token in the long run?

ryu 龙

25,416 Aufrufe • vor 5 Monaten

1/7 Built a Polymarket trading bot over 3 months. Here are the biggest mistakes that cost me real money > Went from v1 to v61. Every version fixed something painful. --- 2/7 Stop Loss killed more money than it saved. > Binary markets need room to breathe - fluctuations are normal. > Stop Loss was cutting positions on random noise and locking in losses right before the market flipped. > Removed it in v61. Immediately better. --- 3/7 Martingale + Stop Loss = a loss cascade. > Seemed logical: lost $5 -> bet $8, lost again -> bet $10. > In practice: a losing streak plus early exits = a hole in your balance in a single day. > Killed it. For good. --- 4/7 Smart Exit without Force Exit is a trap. > Token hits 90c (+75% profit), but the bot was waiting for a "BTC reversal" signal. > Market closes, token drops, profit gone. > Fix: hard Force Exit at 85c. No conditions, no waiting. --- 5/7 Blocking the 5:30-10:30 PM ET window felt safe. It wasn't. > NYSE open = sharp spikes = bad signals. Made sense to block it. > But the full block was also killing clean entries at 8-10:30 PM. > Had to split the zone into segments with different edge/move thresholds. --- 6/7 The Gamma API lies about market start time. > Start price ("price to beat") is the core input for every signal. > Gamma was returning stale data. Had to pull prices directly from Chainlink on-chain on Polygon. > That's its own adventure - polling a smart contract every 2 seconds at 2 AM. --- 7/7 The real lesson: don't overcomplicate what works. > v1: complex system, 10 indicators -> -$200/day > v61: "buy the expensive token for $5, exit at +30%" -> consistently green > Simpler logic = fewer failure points. > The bot runs 96 intervals a day. Every mistake shows up fast.

1/7 Built a Polymarket trading bot over 3 months. Here are the biggest mistakes that cost me real money > Went from v1 to v61. Every version fixed something painful. --- 2/7 Stop Loss killed more money than it saved. > Binary markets need room to breathe - fluctuations are normal. > Stop Loss was cutting positions on random noise and locking in losses right before the market flipped. > Removed it in v61. Immediately better. --- 3/7 Martingale + Stop Loss = a loss cascade. > Seemed logical: lost $5 -> bet $8, lost again -> bet $10. > In practice: a losing streak plus early exits = a hole in your balance in a single day. > Killed it. For good. --- 4/7 Smart Exit without Force Exit is a trap. > Token hits 90c (+75% profit), but the bot was waiting for a "BTC reversal" signal. > Market closes, token drops, profit gone. > Fix: hard Force Exit at 85c. No conditions, no waiting. --- 5/7 Blocking the 5:30-10:30 PM ET window felt safe. It wasn't. > NYSE open = sharp spikes = bad signals. Made sense to block it. > But the full block was also killing clean entries at 8-10:30 PM. > Had to split the zone into segments with different edge/move thresholds. --- 6/7 The Gamma API lies about market start time. > Start price ("price to beat") is the core input for every signal. > Gamma was returning stale data. Had to pull prices directly from Chainlink on-chain on Polygon. > That's its own adventure - polling a smart contract every 2 seconds at 2 AM. --- 7/7 The real lesson: don't overcomplicate what works. > v1: complex system, 10 indicators -> -$200/day > v61: "buy the expensive token for $5, exit at +30%" -> consistently green > Simpler logic = fewer failure points. > The bot runs 96 intervals a day. Every mistake shows up fast.

Kotte

31,753 Aufrufe • vor 3 Monaten

youtube is paying $8,217 a month to a channel with zero humans. no face. just 6 AI tools publishing anime on autopilot twice a week and youtube has no idea the algorithm doesn't check who made the video. it checks one number: how long people keep watching that's the entire game an 8-hour lofi anime stream plays on loop. one upload turns into hundreds of hours of watchtime every month at $3-8 RPM that's $2,400-6,400 from a single file the pipeline runs itself claude writes the script. midjourney draws the frames. runway animates. elevenlabs voices it. suno writes the soundtrack. assembles and publishes humans in the process: zero from prompt to a finished 12-minute episode: 2 hours. from episode to youtube: zero one channel. $8,217 last month article below - every prompt for every step most people ask "will AI take my job". better question - why are you still trading hours for money when a pipeline trades prompts for watchtime

youtube is paying $8,217 a month to a channel with zero humans. no face. just 6 AI tools publishing anime on autopilot twice a week and youtube has no idea the algorithm doesn't check who made the video. it checks one number: how long people keep watching that's the entire game an 8-hour lofi anime stream plays on loop. one upload turns into hundreds of hours of watchtime every month at $3-8 RPM that's $2,400-6,400 from a single file the pipeline runs itself claude writes the script. midjourney draws the frames. runway animates. elevenlabs voices it. suno writes the soundtrack. assembles and publishes humans in the process: zero from prompt to a finished 12-minute episode: 2 hours. from episode to youtube: zero one channel. $8,217 last month article below - every prompt for every step most people ask "will AI take my job". better question - why are you still trading hours for money when a pipeline trades prompts for watchtime

Ventry

118,212 Aufrufe • vor 1 Monat

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

0xMarioNawfal

80,450 Aufrufe • vor 4 Monaten

A $29 PDF made her $6,300 last month. She wrote it two years ago and hasn't opened it since. No launch. No cart. No coaching calls at 6am. Just a file that sells while she sleeps. She isn't famous. She isn't a coach. She has a normal job and a normal following. All she did was write down the workout plan already living in her head — the one she'd text a friend for free and let AI turn it into something strangers pay for. Claude built it from her notes in an afternoon: the 4-week structure, the progressions, the swaps for bad knees. A PDF that looks like a $200 coach made it. She wrote it once. It's sold 217 times. Coaching sells your hours. You run out of hours. A product sells a stranger's tap — forever, while you sleep. One is a job. The other prints. You already own the raw material. The plan. The discipline. The thing you'd explain to a friend for free. You just never wrote it down. She did. That's the whole difference. The exact steps are in the thread above. Read it before the person training next to you writes theirs first.

A $29 PDF made her $6,300 last month. She wrote it two years ago and hasn't opened it since. No launch. No cart. No coaching calls at 6am. Just a file that sells while she sleeps. She isn't famous. She isn't a coach. She has a normal job and a normal following. All she did was write down the workout plan already living in her head — the one she'd text a friend for free and let AI turn it into something strangers pay for. Claude built it from her notes in an afternoon: the 4-week structure, the progressions, the swaps for bad knees. A PDF that looks like a $200 coach made it. She wrote it once. It's sold 217 times. Coaching sells your hours. You run out of hours. A product sells a stranger's tap — forever, while you sleep. One is a job. The other prints. You already own the raw material. The plan. The discipline. The thing you'd explain to a friend for free. You just never wrote it down. She did. That's the whole difference. The exact steps are in the thread above. Read it before the person training next to you writes theirs first.

Rich

97,155 Aufrufe • vor 9 Tagen