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"My token usage is probably down 30x compared to before.” Tim Davis, Co-Founder & President Modular on why he builds AI tooling locally first: “Token usage is incredibly expensive. If you just throw your whole codebase into a model, you’re constantly uploading everything and burning hundreds of thousands or...

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Forward Future

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65,173 views • 7 months ago •via X (Twitter)

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STOP PAYING FOR TOKENS ! START GETTING PAID INSTEAD Introducing Straitly, the first AI service that PAYS YOU for using tokens Unlike > Openrouter charges 5.5% service fee > Vercel charges 2.9% payment fee > Anthropic gives you low rate limits > OpenAI only lets you use GPT models Straitly: > PAYS YOU 5% CASHBACK on all closed-source models > 10% CASHBACK on open-source models > High rate limits > 177 AI models > 99.8% reliability > ZDR This means that you can save up to 25% on your token bills compared to using services like OpenRouter or Vercel, while still getting the same reliability and model choices Since our waitlist launch 3 weeks ago we have processed ~150B tokens already, and are growing at 35% week over week We're backed by LeapYear, Sand Hill North, and other incredible angels in our mission to build the utility layer for all future token usage This is just the beginning Today Straitly is live and open for public access Start getting paid today at To celebrate, we are giving out $10k in free AI credits to 400 people Comment below what your are building and what model you use the most, then head over to Straitly and sign up We'll select the winners at the end of September !
1:01

STOP PAYING FOR TOKENS ! START GETTING PAID INSTEAD Introducing Straitly, the first AI service that PAYS YOU for using tokens Unlike > Openrouter charges 5.5% service fee > Vercel charges 2.9% payment fee > Anthropic gives you low rate limits > OpenAI only lets you use GPT models Straitly: > PAYS YOU 5% CASHBACK on all closed-source models > 10% CASHBACK on open-source models > High rate limits > 177 AI models > 99.8% reliability > ZDR This means that you can save up to 25% on your token bills compared to using services like OpenRouter or Vercel, while still getting the same reliability and model choices Since our waitlist launch 3 weeks ago we have processed ~150B tokens already, and are growing at 35% week over week We're backed by LeapYear, Sand Hill North, and other incredible angels in our mission to build the utility layer for all future token usage This is just the beginning Today Straitly is live and open for public access Start getting paid today at To celebrate, we are giving out $10k in free AI credits to 400 people Comment below what your are building and what model you use the most, then head over to Straitly and sign up We'll select the winners at the end of September !

Mujtaba

161,423 views • 20 hours ago

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.
4:10

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 views • 1 year ago

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.
1:27

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.

m0h

70,509 views • 3 months ago

Boris Cherny, the creator of Claude Code at Anthropic, just listed 9 patterns that waste 73% of your tokens. in this podcast he breaks down exactly how the model burns tokens before it even reads your prompt: - the 14% you lose to CLAUDE.md before typing a word - the 13% you pay re-reading old chat history - the 11% from hooks you forgot you installed - why most "Claude got dumber" complaints are wrong if you're hitting Max limits more than once a week, you have at least 4 of these. Probably 7. instead of another show tonight, watch this. my own breakdown based on 400+ hours of usage is below, read it after the podcast
1:23:02

Boris Cherny, the creator of Claude Code at Anthropic, just listed 9 patterns that waste 73% of your tokens. in this podcast he breaks down exactly how the model burns tokens before it even reads your prompt: - the 14% you lose to CLAUDE.md before typing a word - the 13% you pay re-reading old chat history - the 11% from hooks you forgot you installed - why most "Claude got dumber" complaints are wrong if you're hitting Max limits more than once a week, you have at least 4 of these. Probably 7. instead of another show tonight, watch this. my own breakdown based on 400+ hours of usage is below, read it after the podcast

Mnimiy

1,380,902 views • 4 months ago

PM Carney on Trump's 51st state post impacting trade negotiations: "The president is an exceptionally active user of social media. You probably can chart his usage of it. It's only gone up in recent months and we're not going to respond or react to everything that he posts."
0:25

PM Carney on Trump's 51st state post impacting trade negotiations: "The president is an exceptionally active user of social media. You probably can chart his usage of it. It's only gone up in recent months and we're not going to respond or react to everything that he posts."

Scott Robertson

156,110 views • 3 months ago

BREAKING: Zach Dell (Zach Dell) CEO of Base Power says " Chinese models are going to overtake domestic models in usage." "I spend a lot of my time thinking about energy, the country, & our ability to consume, generate, store, sell electricity. And I spend a lot of time thinking about China & their ability to produce & consume electricity. One thing that has been interesting to me is Chinese tokens & the rate at which they're being generated & the cost. My hot take is that Chinese models are going to overtake domestic models in terms of usage. If you look at platforms like OpenRouter & other online platforms where you can track model usage, I think the Chinese are gonna continue investing incredibly heavily in energy production. They're going to capture the Watt flop or the Watt token spread by turning a lot of that energy into compute. And that's gonna drive massive amount of usage in Chinese models, which just means we have to go faster. The US energy infrastructure industry has to go faster. We have to build power faster. We have to develop compute infrastructure faster. We must accelerate." "We're here at Hill & Valley, which has me feeling awfully patriotic." Base Power at Hill & Valley Forum 2026 (The Hill & Valley Forum)
1:22

BREAKING: Zach Dell (Zach Dell) CEO of Base Power says " Chinese models are going to overtake domestic models in usage." "I spend a lot of my time thinking about energy, the country, & our ability to consume, generate, store, sell electricity. And I spend a lot of time thinking about China & their ability to produce & consume electricity. One thing that has been interesting to me is Chinese tokens & the rate at which they're being generated & the cost. My hot take is that Chinese models are going to overtake domestic models in terms of usage. If you look at platforms like OpenRouter & other online platforms where you can track model usage, I think the Chinese are gonna continue investing incredibly heavily in energy production. They're going to capture the Watt flop or the Watt token spread by turning a lot of that energy into compute. And that's gonna drive massive amount of usage in Chinese models, which just means we have to go faster. The US energy infrastructure industry has to go faster. We have to build power faster. We have to develop compute infrastructure faster. We must accelerate." "We're here at Hill & Valley, which has me feeling awfully patriotic." Base Power at Hill & Valley Forum 2026 (The Hill & Valley Forum)

Molly O’Shea

41,529 views • 5 months ago

Day 5 of why you need to use Jupiter Mobile: TRUSTED TOKEN DATA & INSIGHTS See trusted, verified token data + a clean feed of relevant content in Jupiter Mobile Token Pro’s ‘About’ section – powered by VRFD, Jupiter’s verified data layer for token information and insights. Onchain finance has never had a reliable source of truth for token information. Most wallets surface raw links or leave you to dig through socials and explorers. Instead, you get: - Simple summaries explaining what the token is and what’s currently happening - A clean feed of verified community insights, not random noise - Verified token metadata with direct links to official websites and socials If a token is covered by VRFD, you see community-validated information. If it isn’t, the full project X feed is surfaced automatically, so you still have full context. What used to take digging across multiple pages now lives directly on the token page. Just Use Jupiter (Mobile).
0:59

Day 5 of why you need to use Jupiter Mobile: TRUSTED TOKEN DATA & INSIGHTS See trusted, verified token data + a clean feed of relevant content in Jupiter Mobile Token Pro’s ‘About’ section – powered by VRFD, Jupiter’s verified data layer for token information and insights. Onchain finance has never had a reliable source of truth for token information. Most wallets surface raw links or leave you to dig through socials and explorers. Instead, you get: - Simple summaries explaining what the token is and what’s currently happening - A clean feed of verified community insights, not random noise - Verified token metadata with direct links to official websites and socials If a token is covered by VRFD, you see community-validated information. If it isn’t, the full project X feed is surfaced automatically, so you still have full context. What used to take digging across multiple pages now lives directly on the token page. Just Use Jupiter (Mobile).

Jupiter

239,841 views • 8 months ago

Dylan on why access to the most intelligent AI models will determine who wins in every industry: "What really matters is access to the most intelligent tokens and leveraging them at the highest-value tasks. A lot of folks will want tokens, but the shitty SaaS startup in SF who is using Claude is not necessarily creating a ton of value, and therefore they're going to get priced out of tokens. Anthropic only released Mythos to certain companies for cybersecurity. You also don't want people to distill your model so you release them to a fewer and fewer customers. Those customers are now wrestling over the tokens. Token usage and therefore the additional value generated on top of those tokens aggregates among fewer and fewer companies. I don't have Mythos but you know who has Mythos, top banks. There are people like Ken Griffin of Citadel who can just sign a deal with OpenAI or Antrhopic for access to your models. I'll buy the first $10 billion worth of tokens each year. So whenever you release the model, I'll spend the first 10 billion tokens and then everyone else can get the model after that. Now, he's going to crush everyone in the markets. I can envision because I have an enterprise Anthropic contract and Anthropic people like me, they're willing to give us slightly earlier access or higher rate limits, and then my competitor doesn't have that, and I'm able to f*cking crush them."
2:22

Dylan on why access to the most intelligent AI models will determine who wins in every industry: "What really matters is access to the most intelligent tokens and leveraging them at the highest-value tasks. A lot of folks will want tokens, but the shitty SaaS startup in SF who is using Claude is not necessarily creating a ton of value, and therefore they're going to get priced out of tokens. Anthropic only released Mythos to certain companies for cybersecurity. You also don't want people to distill your model so you release them to a fewer and fewer customers. Those customers are now wrestling over the tokens. Token usage and therefore the additional value generated on top of those tokens aggregates among fewer and fewer companies. I don't have Mythos but you know who has Mythos, top banks. There are people like Ken Griffin of Citadel who can just sign a deal with OpenAI or Antrhopic for access to your models. I'll buy the first $10 billion worth of tokens each year. So whenever you release the model, I'll spend the first 10 billion tokens and then everyone else can get the model after that. Now, he's going to crush everyone in the markets. I can envision because I have an enterprise Anthropic contract and Anthropic people like me, they're willing to give us slightly earlier access or higher rate limits, and then my competitor doesn't have that, and I'm able to f*cking crush them."

Patrick OShaughnessy

36,100 views • 4 months ago

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.
2:09

OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

Karl Mehta

17,254 views • 1 month ago

YC Managing Partner Harj Taggar says many young founders are still too conservative with token spend and advises them to not skimp on token budget. "Token spend is more like rent. You don't say, 'I'm not moving to San Francisco to build my tech startup because the rent's expensive.' Yes, it's expensive, but it's the place to be. You don't conserve on that. You actually want to spend money on that. There's other things you save money on." "Many of the founders, especially if they're younger, just don't have the budgets to use the latest models and be cost insensitive and token-max. They're just used to conserving and trying to do more with less." "Internally, we wonder if there's some training to push the founders to spend more on tokens because maybe they're thinking about tokens as something you should conserve spend on."
1:42

YC Managing Partner Harj Taggar says many young founders are still too conservative with token spend and advises them to not skimp on token budget. "Token spend is more like rent. You don't say, 'I'm not moving to San Francisco to build my tech startup because the rent's expensive.' Yes, it's expensive, but it's the place to be. You don't conserve on that. You actually want to spend money on that. There's other things you save money on." "Many of the founders, especially if they're younger, just don't have the budgets to use the latest models and be cost insensitive and token-max. They're just used to conserving and trying to do more with less." "Internally, we wonder if there's some training to push the founders to spend more on tokens because maybe they're thinking about tokens as something you should conserve spend on."

TBPN

66,654 views • 2 months ago

OpenAI's chairman just responded to the Kimi K3 panic 👇 The timeline calls K3 a frontier killer. Bret Taylor's answer on CNBC: every CEO he talks to asks the same 2 questions. 1️⃣ Am I getting value from my token spend? 2️⃣ What's my moat in the age of AI? His counter on cheap Chinese models: frontier models finish the same task in far fewer tokens. Cheap per token means little if you burn 5x more of them The new battleground is tokens per task, and the price war just forced OpenAI to argue efficiency instead of capability
8:11

OpenAI's chairman just responded to the Kimi K3 panic 👇 The timeline calls K3 a frontier killer. Bret Taylor's answer on CNBC: every CEO he talks to asks the same 2 questions. 1️⃣ Am I getting value from my token spend? 2️⃣ What's my moat in the age of AI? His counter on cheap Chinese models: frontier models finish the same task in far fewer tokens. Cheap per token means little if you burn 5x more of them The new battleground is tokens per task, and the price war just forced OpenAI to argue efficiency instead of capability

Ruben

76,682 views • 1 month ago

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."
2:06

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

110,354 views • 2 months ago

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."
1:34

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

TBPN

16,294 views • 6 months ago

“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. castform bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇
2:23

“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. castform bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇

girish

460,844 views • 3 months ago

Claude hit the rate limit, he didn't open ChatGPT, he switched to the JARVIS he built himself. No subscription, no usage cap and no "you've reached your limit, upgrade to Pro" Just a custom JARVIS interface glowing teal on black like the inside of Iron Man's suit, sitting on his desk next to a Rubik's cube and AirPods. Runs offline. Runs locally. Runs on hardware he already owns. When Claude goes down and everyone scrambles, refreshing, switching to ChatGPT, Gemini, or Perplexity, he just flips to his own model. No rate limits, throttling, or monthly fees. $0/month, 0 usage caps, 0 dependency on any company's servers. The best AI assistant in 2026 isn't the one you subscribe to, it's the one you build yourself so nobody can ever take it away.
0:33

Claude hit the rate limit, he didn't open ChatGPT, he switched to the JARVIS he built himself. No subscription, no usage cap and no "you've reached your limit, upgrade to Pro" Just a custom JARVIS interface glowing teal on black like the inside of Iron Man's suit, sitting on his desk next to a Rubik's cube and AirPods. Runs offline. Runs locally. Runs on hardware he already owns. When Claude goes down and everyone scrambles, refreshing, switching to ChatGPT, Gemini, or Perplexity, he just flips to his own model. No rate limits, throttling, or monthly fees. $0/month, 0 usage caps, 0 dependency on any company's servers. The best AI assistant in 2026 isn't the one you subscribe to, it's the one you build yourself so nobody can ever take it away.

Defileo🔮

11,592 views • 4 months ago

So a core issues I see with a lot of crypto games is: No in game peer to peer economy. (ie. The game Devs make all the $$ off the items and you buy from them not other players) Couple of things I wanted to experiment with, in regards to on chain gaming is the peer to peer economy. So the way my team set it up, is you mine the token in game, but you sell the items peer to peer in SOL. (see video for a demo) This gives you two different ways to play to earn, you can farm items, from bosses and loot boxes, and sell them to players for solana:So11111111111111111111111111111111111111112 , then other players can buy them and use them to mine the token, and sell the token to traders who will speculate on the price. Or you can just buy the loot boxes, and use the items to farm the token. This should help with the sell pressure, because before the game flow was: Buy item (in token, From devs) --->farm a ton of token---> Sell token to recoup cost of item. New flow is : Player opens loot boxes (paid in token)/ Player Plays game (in game token sinks) ---> gets item ---Lists item--> other player buy Item (From players, In SOL) ----Farms token---> sells token. Now the equation is if the loot box-player spent more game tokens to get the item compared to what he gets for in sol, and it keeps more money in the in game economy, and the market will fluctuate more based on the price of the in game token. So players who play and are "locked in" can make money off arbing and this services the "trader" archetype of player, which I feel is the major player type in web3-games. (also the token name is just a test token so dont buy random shit on pump fun, thats just using the name)
1:06

So a core issues I see with a lot of crypto games is: No in game peer to peer economy. (ie. The game Devs make all the $$ off the items and you buy from them not other players) Couple of things I wanted to experiment with, in regards to on chain gaming is the peer to peer economy. So the way my team set it up, is you mine the token in game, but you sell the items peer to peer in SOL. (see video for a demo) This gives you two different ways to play to earn, you can farm items, from bosses and loot boxes, and sell them to players for solana:So11111111111111111111111111111111111111112 , then other players can buy them and use them to mine the token, and sell the token to traders who will speculate on the price. Or you can just buy the loot boxes, and use the items to farm the token. This should help with the sell pressure, because before the game flow was: Buy item (in token, From devs) --->farm a ton of token---> Sell token to recoup cost of item. New flow is : Player opens loot boxes (paid in token)/ Player Plays game (in game token sinks) ---> gets item ---Lists item--> other player buy Item (From players, In SOL) ----Farms token---> sells token. Now the equation is if the loot box-player spent more game tokens to get the item compared to what he gets for in sol, and it keeps more money in the in game economy, and the market will fluctuate more based on the price of the in game token. So players who play and are "locked in" can make money off arbing and this services the "trader" archetype of player, which I feel is the major player type in web3-games. (also the token name is just a test token so dont buy random shit on pump fun, thats just using the name)

Skely

15,064 views • 2 months ago

introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.
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introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.

Matthew Lam

123,006 views • 1 month ago

kimodo.cpp is live. NVIDIA's Kimodo, the best AI animation model right now, running natively on CPU or Vulkan. Type a sentence, get character animation, locally on your own machine. Before, you needed the whole PyTorch and CUDA stack standing up before you could generate a single motion. Now it is a native C++/GGML build that loads GGUF models and comes with a local web demo. No Python environment to fight. It gives you SMPL-X, and from there you retarget onto pretty much any skeleton you want, like Mixamo. Great piece of work Richard Palethorpe
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kimodo.cpp is live. NVIDIA's Kimodo, the best AI animation model right now, running natively on CPU or Vulkan. Type a sentence, get character animation, locally on your own machine. Before, you needed the whole PyTorch and CUDA stack standing up before you could generate a single motion. Now it is a native C++/GGML build that loads GGUF models and comes with a local web demo. No Python environment to fight. It gives you SMPL-X, and from there you retarget onto pretty much any skeleton you want, like Mixamo. Great piece of work Richard Palethorpe

Stefan 3D AI

106,718 views • 17 days ago

Have you made some #3D models with #gaussiansplatting using Luma or polycam ? Here is how you can now clean them up directly on the web: People nowadays call them "Splats". These 3D models have the advantage of being extremely light compared to polygon-based 3D models. You can use various apps to generate these assets like the one I mentioned above. Often what you need is just a video or a set of images that you can shoot directly from your phone. To my knowledge, none of those apps right now offer the ability to edit and "clean" the model you get.... UNTIL NOW! You can go to from PlayCanvas import the splats you have downloaded and start cleaning it up directly on your #browser. After that the asset is ready to be exported as a PLY file Was this useful? Follow me for more content around #XR and #AI 😉
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Have you made some #3D models with #gaussiansplatting using Luma or polycam ? Here is how you can now clean them up directly on the web: People nowadays call them "Splats". These 3D models have the advantage of being extremely light compared to polygon-based 3D models. You can use various apps to generate these assets like the one I mentioned above. Often what you need is just a video or a set of images that you can shoot directly from your phone. To my knowledge, none of those apps right now offer the ability to edit and "clean" the model you get.... UNTIL NOW! You can go to from PlayCanvas import the splats you have downloaded and start cleaning it up directly on your #browser. After that the asset is ready to be exported as a PLY file Was this useful? Follow me for more content around #XR and #AI 😉

Gabriele Romagnoli

70,987 views • 2 years ago

10 GitHub repos with ⭐ 450k+ combined stars that stop your agent from wasting tokens 1. caveman 98k⭐ - makes your AI agent talk like a caveman. Same answers, 65% fewer output tokens. 2. rtk 76k⭐ - CLI proxy that filters git/test/docker output before it hits your context. Up to 90% less bash noise. 3. mem0 63k⭐ - memory layer for AI agents. Stops your agent from re-explaining the same context every session. 4. context7 60.7k⭐ - pulls exact, up-to-date docs into context instead of your agent guessing and burning tokens on retries. 5. aider 48k⭐ - builds a map of your repo instead of loading the whole codebase into context. 6. code-review-graph 30k⭐ - graphs your codebase so your agent reads 2-3 files instead of the whole repo. Up to 376x fewer tokens per question. 7. serena 28k⭐ - gives agents symbol-level code access instead of reading full files line by line. 8. repomix 28k⭐ - compresses your entire repo into an AI-friendly format. ~70% token reduction. 9. ccusage 18k⭐ - shows you exactly where your Claude Code sessions are burning tokens. 10. GPTCache 8k⭐ - semantic cache for LLM calls. Stop paying twice for the same question. Save this before your next agent session burns through your subscription 👇
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10 GitHub repos with ⭐ 450k+ combined stars that stop your agent from wasting tokens 1. caveman 98k⭐ - makes your AI agent talk like a caveman. Same answers, 65% fewer output tokens. 2. rtk 76k⭐ - CLI proxy that filters git/test/docker output before it hits your context. Up to 90% less bash noise. 3. mem0 63k⭐ - memory layer for AI agents. Stops your agent from re-explaining the same context every session. 4. context7 60.7k⭐ - pulls exact, up-to-date docs into context instead of your agent guessing and burning tokens on retries. 5. aider 48k⭐ - builds a map of your repo instead of loading the whole codebase into context. 6. code-review-graph 30k⭐ - graphs your codebase so your agent reads 2-3 files instead of the whole repo. Up to 376x fewer tokens per question. 7. serena 28k⭐ - gives agents symbol-level code access instead of reading full files line by line. 8. repomix 28k⭐ - compresses your entire repo into an AI-friendly format. ~70% token reduction. 9. ccusage 18k⭐ - shows you exactly where your Claude Code sessions are burning tokens. 10. GPTCache 8k⭐ - semantic cache for LLM calls. Stop paying twice for the same question. Save this before your next agent session burns through your subscription 👇

unicode

66,891 views • 27 days ago