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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...

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Farhan Azad Shuvra

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21,461 Aufrufe • vor 1 Monat •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
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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

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BRC EXCHANGE IS OFFICIALLY LIVE 🚀 First, a confession: Over the past month, our plan was to build BRC Exchange, launch it under a different project name, and sell a brand-new token. But we love Arc — and we love our Baracat mascot too much to let go. So we made the call: $BRC stays as the official token of the project. And today, we're announcing it to the world. What is BRC Exchange? The FIRST full-featured DEX on Arc — complete Uniswap v3 functionality, with additional features powered by Uniswap v4. What you can do RIGHT NOW: 🔄 Swap every token on Arc through one DEX 💧 Provide liquidity for any token and earn trading fees 🌉 Move USDC between Base ⇄ Arc via the USDC Market 💎 Stake $BRC to earn more $BRC + USDC 🛠️ Access on-chain utility tools Some features are already complete but remain locked — Arc's infrastructure isn't fully deployed yet. We'll be revealing them gradually 👀 Check it out now: 1️⃣ 2️⃣ 3️⃣ (retiring once the transition completes) ⚠️ Heads up: Over the next few days, the new domains (1 & 2) may get flagged with security warnings by browsers or Web3 wallets. This is completely normal for new domains on a new chain — Arc doesn't have full verification tooling yet (contract verification, etc.), so automated scanners misfire. We expect to resolve it within 1–2 days. What's next: Token Migration The old $BRC → new $BRC migration begins in roughly 72 hours. Until then, the old token trades normally. Holding the old token will unlock extra benefits during migration. Full migration details drop within 24 hours 🔔 📚 Docs: Once again: Building Arc. Every single day. 🦇🐀🐱
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BRC EXCHANGE IS OFFICIALLY LIVE 🚀 First, a confession: Over the past month, our plan was to build BRC Exchange, launch it under a different project name, and sell a brand-new token. But we love Arc — and we love our Baracat mascot too much to let go. So we made the call: $BRC stays as the official token of the project. And today, we're announcing it to the world. What is BRC Exchange? The FIRST full-featured DEX on Arc — complete Uniswap v3 functionality, with additional features powered by Uniswap v4. What you can do RIGHT NOW: 🔄 Swap every token on Arc through one DEX 💧 Provide liquidity for any token and earn trading fees 🌉 Move USDC between Base ⇄ Arc via the USDC Market 💎 Stake $BRC to earn more $BRC + USDC 🛠️ Access on-chain utility tools Some features are already complete but remain locked — Arc's infrastructure isn't fully deployed yet. We'll be revealing them gradually 👀 Check it out now: 1️⃣ 2️⃣ 3️⃣ (retiring once the transition completes) ⚠️ Heads up: Over the next few days, the new domains (1 & 2) may get flagged with security warnings by browsers or Web3 wallets. This is completely normal for new domains on a new chain — Arc doesn't have full verification tooling yet (contract verification, etc.), so automated scanners misfire. We expect to resolve it within 1–2 days. What's next: Token Migration The old $BRC → new $BRC migration begins in roughly 72 hours. Until then, the old token trades normally. Holding the old token will unlock extra benefits during migration. Full migration details drop within 24 hours 🔔 📚 Docs: Once again: Building Arc. Every single day. 🦇🐀🐱

Brc Exchange

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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
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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

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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

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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
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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.

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most token launches still follow the same pattern. dump a large percentage at tge. hope the narrative holds long enough for the next unlock. watch momentum fade before any real utility has time to compound. Ethra Ship is structured differently. total supply fixed at 1b. tge reference around $0.05. only about 12% circulating on day one, moving toward roughly 50% after 24 months. the part that stands out is the flywheel. wallet score increases the longer you hold and stake, unlocking better rewards and weight. treasury activity and optional rwa yield feed buybacks. a portion of those buys get burned. the design is deliberately paced. instead of forcing everything into the first few weeks, it leaves room for the loops to actually work. i have seen too many unlock schedules kill projects before the product side could catch up. quiet choices like lower day-one circulation and longer emission windows usually matter more than the loud launch numbers. still early. watching how the real operational side and the token loops move together over a longer horizon.
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most token launches still follow the same pattern. dump a large percentage at tge. hope the narrative holds long enough for the next unlock. watch momentum fade before any real utility has time to compound. Ethra Ship is structured differently. total supply fixed at 1b. tge reference around $0.05. only about 12% circulating on day one, moving toward roughly 50% after 24 months. the part that stands out is the flywheel. wallet score increases the longer you hold and stake, unlocking better rewards and weight. treasury activity and optional rwa yield feed buybacks. a portion of those buys get burned. the design is deliberately paced. instead of forcing everything into the first few weeks, it leaves room for the loops to actually work. i have seen too many unlock schedules kill projects before the product side could catch up. quiet choices like lower day-one circulation and longer emission windows usually matter more than the loud launch numbers. still early. watching how the real operational side and the token loops move together over a longer horizon.

Trathoa

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1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.
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1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.

AiMind

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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.
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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

41,038 Aufrufe • vor 2 Monaten

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.
0:11

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

elvis

32,623 Aufrufe • vor 1 Monat

Monitor and control your AI spend on every provider on Our early users save 40% on average. Every week, the price-intelligence-latency frontier shifts, and we expect this trend to continue. Tradeoffs between latency, reasoning, cost, service tier, open source and closed source models are shifting constantly. Router sends every request to the model that's actually best for the task and helps you control what tokens you buy. We benchmark it against real work: ~40% lower cost for the same outputs. Today we're opening it to everyone. Two lines of code or just change your base URL. No Ramp account needed. Free through 2026, first $26 on us. Get an API key today at
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Monitor and control your AI spend on every provider on Our early users save 40% on average. Every week, the price-intelligence-latency frontier shifts, and we expect this trend to continue. Tradeoffs between latency, reasoning, cost, service tier, open source and closed source models are shifting constantly. Router sends every request to the model that's actually best for the task and helps you control what tokens you buy. We benchmark it against real work: ~40% lower cost for the same outputs. Today we're opening it to everyone. Two lines of code or just change your base URL. No Ramp account needed. Free through 2026, first $26 on us. Get an API key today at

Veeral Patel

1,406,765 Aufrufe • vor 11 Tagen

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
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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 2 Monaten

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.
0:13

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 7 Monaten

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)
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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

46,449 Aufrufe • vor 6 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?
0:15

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 2 Monaten

DEEPSEEK V4 FLASH AND QWEN3.6 ARE FREE ON AMD'S OWN CLOUD AT $10 OF USAGE A DAY • AMD Token Factory > DeepSeek V4 Flash, Qwen3.6 35B, MiniCPM5 and MiniCPM-V46 at zero cost: > $10 every day, not once -- the counter resets daily. > Everything outside that list runs on Firework AI credits. • SuperGrok, seven days > Grok's paid tier free for a week: > A card is required and it renews on its own, so set the reminder the moment you start. > Availability still depends on your region. AMD built this the way NVIDIA built NIM -> the chip vendors are handing out inference now, because what they are really buying is which silicon you learn on. One of these resets every morning. The other one you only get once ↓
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DEEPSEEK V4 FLASH AND QWEN3.6 ARE FREE ON AMD'S OWN CLOUD AT $10 OF USAGE A DAY • AMD Token Factory > DeepSeek V4 Flash, Qwen3.6 35B, MiniCPM5 and MiniCPM-V46 at zero cost: > $10 every day, not once -- the counter resets daily. > Everything outside that list runs on Firework AI credits. • SuperGrok, seven days > Grok's paid tier free for a week: > A card is required and it renews on its own, so set the reminder the moment you start. > Availability still depends on your region. AMD built this the way NVIDIA built NIM -> the chip vendors are handing out inference now, because what they are really buying is which silicon you learn on. One of these resets every morning. The other one you only get once ↓

slash1s

73,760 Aufrufe • vor 19 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
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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

𝗗𝗥𝗘𝗔𝗗 𝗕𝗢𝗡𝗚𝗢

36,640 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.
0:14

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 6 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 🎁
0:18

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

91,675 Aufrufe • vor 1 Jahr

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 →
0:10

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

124,860 Aufrufe • vor 2 Monaten