Digital money was supposed to be freedom, but instead it felt like standing in a queue no one could see. We waited for confirmations, we prayed we copied the right address, we held our breath hoping funds wouldn’t disappear into the blockchain void. The world moved forward, but payments stayed behind. But what if the evolution of payment tech won’t come from banks, neobanks, or even early blockchain wallets? What if it won’t come from the giants we trusted, PayPal, Visa, Meta, Binance? What if the next leap in money movement comes from a platform built for humans first? That platform is PulsarMoney (@PulsarMoneyApp). A THREAD 🧵

𝗦𝗲𝘁𝘁𝗶𝗻𝗴 𝘂𝗽 𝗮 𝗕𝗔𝗜𝗰𝗹𝗮𝘄 𝗮𝗴𝗲𝗻𝘁 𝗶𝗻 𝗗𝗶𝘀𝗰𝗼𝗿𝗱? 𝗢𝗻𝗲 𝗰𝗵𝗲𝗰𝗸𝗯𝗼𝘅 𝗱𝗲𝗰𝗶𝗱𝗲𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗶𝘁 𝗰𝗮𝗻 𝗿𝗲𝗮𝗱 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 𝗮𝘁 𝗮𝗹𝗹. The steps from the docs: ➜ Create an application in the Discord Developer Portal ➜ Add a bot and paste its token into BAIclaw with your Server ID and Channel ID ➜ Enable 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗻𝘁 and 𝗦𝗲𝗿𝘃𝗲𝗿 𝗠𝗲𝗺𝗯𝗲𝗿𝘀 𝗜𝗻𝘁𝗲𝗻𝘁 ➜ In OAuth2, select bot and applications.commands with Send Messages ➜ Invite the bot with the generated link Skip the Message Content Intent and your bot will join the server but never see what people write. The Discord guide is here: ➜ docs.b.ai/baiclaw/how-to-add… @justinsuntron @BAI_AGI #TronEcoStar

𝗧𝗵𝗲 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟬 𝗳𝗮𝗺𝗶𝗹𝘆 𝗵𝗮𝘀 𝘁𝗵𝗿𝗲𝗲 𝗽𝗿𝗶𝗰𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝗼𝗻 𝗕.𝗔𝗜, 𝗮𝗻𝗱 𝗽𝗶𝗰𝗸𝗶𝗻𝗴 𝗿𝗶𝗴𝗵𝘁 𝘀𝗮𝘃𝗲𝘀 𝗿𝗲𝗮𝗹 𝗺𝗼𝗻𝗲𝘆. Per million video tokens at 480p or 720p, without video input: ➜ 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟬 𝗠𝗶𝗻𝗶: $3.50 ➜ 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟬 𝗙𝗮𝘀𝘁: $5.60 ➜ 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟬: $7.00 Mini is for exploring ideas cheaply. Fast balances speed and cost. The full model is for final quality. Draft on Mini, approve on Fast, deliver on 2.0. See every rate here: ➜ docs.b.ai/llmservice/video-m… @justinsuntron @BAI_AGI #TronEcoStar

𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟱 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝗰𝗹𝗮𝘆 𝗿𝗲𝗻𝗱𝗲𝗿𝘀, 𝘄𝗵𝗶𝗰𝗵 𝗶𝘀 𝗮 𝗯𝗶𝗴 𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 𝗮𝗻𝘆𝗼𝗻𝗲 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗮 𝘀𝗵𝗼𝘁. From the B.AI model page, clay-render references can guide: ➜ Spatial layout ➜ Blocking, meaning where characters stand and move ➜ Camera movement Build a rough grey 3D scene, then let the model dress it into a finished, lit shot with sound. Directors have always storyboarded before filming. Now the storyboard can direct the AI. Read about production planning here: ➜ docs.b.ai/llmservice/video-m… @justinsuntron @BAI_AGI #TronEcoStar

𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟱 𝗼𝗻 𝗕.𝗔𝗜 𝗰𝗮𝗻 𝘁𝗮𝗸𝗲 𝘂𝗽 𝘁𝗼 𝟱𝟬 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝘀𝘀𝗲𝘁𝘀 𝗶𝗻 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝘃𝗶𝗱𝗲𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁. That includes images, video clips and audio-only references. Other production features on the model page: ➜ 𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽-𝗯𝗮𝘀𝗲𝗱 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 to fix a specific moment ➜ Camera-perspective edits ➜ 𝗚𝗿𝗲𝗲𝗻-𝘀𝗰𝗿𝗲𝗲𝗻 workflows ➜ Native multilingual prompts and audio You can lock a character, a voice, a location and a camera move all at once. That is how AI video moves from fun clips to real production. The model page is here: ➜ docs.b.ai/llmservice/video-m… @justinsuntron @BAI_AGI #TronEcoStar

𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟬 𝗼𝗻 𝗕.𝗔𝗜 𝗿𝗲𝗻𝗱𝗲𝗿𝘀 𝘂𝗽 𝘁𝗼 𝟰𝗞 𝘄𝗶𝘁𝗵 𝟭𝟬-𝗯𝗶𝘁 𝗰𝗼𝗹𝗼𝗿, 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝗺𝗼𝗱𝗲𝗹 𝗶𝗻 𝗶𝘁𝘀 𝗳𝗮𝗺𝗶𝗹𝘆 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻. The Seedance 2.0 family: ➜ 𝗠𝗶𝗻𝗶: 480p and 720p, cost-focused ➜ 𝗙𝗮𝘀𝘁: 480p and 720p, balancing speed and cost ➜ 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟬: up to 𝟭𝟬𝟴𝟬𝗽 𝗮𝗻𝗱 𝟰𝗞, quality-focused 10-bit color matters for gradients, skies and skin tones, where 8-bit can band. If the clip is going on a big screen or into a professional edit, this is the model. The model page is here: ➜ docs.b.ai/llmservice/video-m… @justinsuntron @BAI_AGI #TronEcoStar

𝗢𝗻 𝗕.𝗔𝗜'𝘀 𝘃𝗶𝗱𝗲𝗼 𝗽𝗿𝗶𝗰𝗲 𝗹𝗶𝘀𝘁, 𝟰𝗞 𝗵𝗮𝘀 𝗮 𝗹𝗼𝘄𝗲𝗿 𝗽𝗲𝗿-𝘁𝗼𝗸𝗲𝗻 𝗿𝗮𝘁𝗲 𝘁𝗵𝗮𝗻 𝟭𝟬𝟴𝟬𝗽. Do not let that fool you. Seedance 2.0 without video input, per million video tokens: ➜ 𝟭𝟬𝟴𝟬𝗽: $7.70 ➜ 𝟰𝗞: $4.00 The docs warn that a lower 4K unit rate does not mean a cheaper video. A 4K clip of the same length uses far more video tokens. So compare total billable usage, not the rate per token. Reading the fine print is free, re-rendering is not. See the video price table here: ➜ docs.b.ai/llmservice/video-m… @justinsuntron @BAI_AGI #TronEcoStar

𝗢𝗻 𝗕.𝗔𝗜, 𝗖𝗹𝗮𝘂𝗱𝗲 𝗛𝗮𝗶𝗸𝘂 𝟱.𝟱 𝗶𝘀 𝘁𝗲𝗻 𝘁𝗶𝗺𝗲𝘀 𝗰𝗵𝗲𝗮𝗽𝗲𝗿 𝘁𝗵𝗮𝗻 𝗖𝗹𝗮𝘂𝗱𝗲 𝗛𝗮𝗶𝗸𝘂 𝟰.𝟱 𝗳𝗼𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘂𝗻𝗱𝗲𝗿 𝟭𝟬𝟬𝗞 𝘁𝗼𝗸𝗲𝗻𝘀. Standard prices per million tokens: ➜ 𝗛𝗮𝗶𝗸𝘂 𝟰.𝟱: $1.00 input and $5.00 output ➜ 𝗛𝗮𝗶𝗸𝘂 𝟱.𝟱: $𝟬.𝟭𝟬 input and $𝟬.𝟱𝟬 output, under 100K tokens Haiku 5.5 also adds a 1M-token context, five effort levels and adaptive thinking. If you still run Haiku 4.5 in a pipeline, this is one of the easiest savings on the platform. Just watch the higher tier once prompts pass 100K tokens. Compare both here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗠𝗶𝗠𝗼-𝗩𝟮.𝟱-𝗣𝗿𝗼 𝗵𝗮𝗻𝗱𝗹𝗲𝘀 𝗹𝗼𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗹𝗲𝘃𝗲𝗿 𝗹𝗮𝘆𝗲𝗿 𝗺𝗶𝘅: 𝟲𝟬 𝗹𝗼𝗰𝗮𝗹 𝗹𝗮𝘆𝗲𝗿𝘀 𝗮𝗻𝗱 𝟭𝟬 𝗴𝗹𝗼𝗯𝗮𝗹 𝗼𝗻𝗲𝘀. The architecture on B.AI's model page: ➜ 𝟲𝟬 𝗦𝗹𝗶𝗱𝗶𝗻𝗴 𝗪𝗶𝗻𝗱𝗼𝘄 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 layers that look at nearby text ➜ 𝟭𝟬 𝗴𝗹𝗼𝗯𝗮𝗹-𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 layers that see everything ➜ A 1.02T-parameter MoE with 42B active Most layers stay cheap, a few keep the big picture. Xiaomi reports 57.2 on SWE-Bench Pro, and it costs $0.435 input and $0.87 output per million tokens. Smart architecture is how big models get cheap. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗩𝟯.𝟮 𝘄𝗮𝘀 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗮𝗰𝗿𝗼𝘀𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟭,𝟴𝟬𝟬 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗶𝘁 𝘀𝗵𝗼𝘄𝘀 𝗶𝗻 𝗮𝗴𝗲𝗻𝘁 𝘄𝗼𝗿𝗸. From the B.AI model page: ➜ 𝟭,𝟴𝟬𝟬+ training environments ➜ 𝟴𝟱,𝟬𝟬𝟬+ complex instructions used for refinement ➜ 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗦𝗽𝗮𝗿𝘀𝗲 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 for faster long-context reasoning ➜ Tool use in both thinking and non-thinking modes It is also one of the verified models for Claude Code on B.AI. Pricing is $0.29 input and $0.44 output per million tokens. Older does not mean obsolete when the price is this low. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗧𝗵𝗲 𝗪𝗜𝗡𝗸𝗟𝗶𝗻𝗸 𝘄𝗵𝗶𝘁𝗲𝗽𝗮𝗽𝗲𝗿 𝗽𝗹𝗮𝗻𝘀 𝗻𝗼𝗱𝗲 𝗿𝗲𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻. 𝗧𝗵𝗮𝘁 𝗰𝗼𝘂𝗹𝗱 𝗰𝗵𝗮𝗻𝗴𝗲 𝗵𝗼𝘄 𝘁𝗿𝘂𝘀𝘁 𝘄𝗼𝗿𝗸𝘀. Today, you trust the network because it's decentralized. A reputation system adds another layer: a public track record for every node. What it could measure: ➜ How often a node answers ➜ How close its answers are to the median ➜ How long it has served 𝗧𝗿𝘂𝘀𝘁 𝘁𝗵𝗮𝘁'𝘀 𝗲𝗮𝗿𝗻𝗲𝗱 𝗮𝗻𝗱 𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗯𝗲𝗮𝘁𝘀 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗮𝘁'𝘀 𝗮𝘀𝘀𝘂𝗺𝗲𝗱. I'm watching this part of the roadmap closely. ➜ doc.winklink.org/WinkLink_wh… @justinsuntron @WinkLink_Oracle #TronEcoStar

𝗠𝗶𝗻𝗶𝗠𝗮𝘅 𝗠𝟯 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝘀 𝗮 𝗻𝗲𝘄 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗱𝗲𝘀𝗶𝗴𝗻 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗺𝗶𝗹𝗹𝗶𝗼𝗻-𝘁𝗼𝗸𝗲𝗻 𝗽𝗿𝗼𝗺𝗽𝘁𝘀. It is called 𝗠𝗶𝗻𝗶𝗠𝗮𝘅 𝗦𝗽𝗮𝗿𝘀𝗲 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻, or MSA. How it works, per the B.AI model page: ➜ A lightweight index branch picks the relevant parts of the context ➜ The main attention focuses only on those parts ➜ The cost grows slower than the usual quadratic curve The goal is faster decoding on 1M-token workloads with quality similar to the M2 series. It is aimed at documents, spreadsheets, presentations and enterprise agents. Standard pricing is $0.30 input and $1.20 output per million tokens. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗠𝗶𝗻𝗶𝗠𝗮𝘅 𝗠𝟮.𝟳 𝗰𝗮𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝟯𝟬 𝘁𝗼 𝟱𝟬% 𝗼𝗳 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗼𝗻 𝗶𝘁𝘀 𝗼𝘄𝗻. B.AI's model page calls it an early form of 𝘀𝗲𝗹𝗳-𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻. Other highlights: ➜ Native multi-agent orchestration ➜ Dynamic tool use and complex skill coordination ➜ Strong reasoning at low per-token cost A model that helps run the research that trains models is a glimpse of where AI development is heading. And it is available today for $0.30 per million input tokens. Read more here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗡𝗼𝘁 𝗲𝘃𝗲𝗿𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝘂𝗶𝗹𝗱 𝗼𝗻 𝗮𝗻 𝗼𝗿𝗮𝗰𝗹𝗲. 𝗛𝗲𝗿𝗲'𝘀 𝗺𝘆 𝗵𝗼𝗻𝗲𝘀𝘁 𝘁𝗲𝘀𝘁. You need one if your contract has to know something it can't see on-chain. Ask yourself: ➜ Does my contract need a price? ➜ Does it need a fair random result? ➜ Does it need a real-world event? ➜ Does it need to act at a certain time? 𝗢𝗻𝗲 𝘆𝗲𝘀 𝗺𝗲𝗮𝗻𝘀 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮𝗻 𝗼𝗿𝗮𝗰𝗹𝗲. No yes means keep it simple. Good builders add tools only when they solve a real problem. @justinsuntron @WinkLink_Oracle #TronEcoStar

𝗠𝗶𝗻𝗶𝗠𝗮𝘅 𝗠𝟮.𝟳 𝗵𝗮𝘀 𝗮 𝗹𝗼𝘄𝗲𝗿 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲 𝘁𝗵𝗮𝗻 𝗖𝗹𝗮𝘂𝗱𝗲 𝗦𝗼𝗻𝗻𝗲𝘁 𝟰.𝟲, 𝗮𝗰𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝘁𝗼 𝗕.𝗔𝗜'𝘀 𝗺𝗼𝗱𝗲𝗹 𝗽𝗮𝗴𝗲. The number: ➜ 𝟯𝟰% hallucination rate on the AA-Omniscience Index ➜ Lower than Claude Sonnet 4.6 and Gemini, per the page Lower is better here, because it measures how often a model states things that are not true. M2.7 is a 230B-parameter MoE with just 10B active, priced at $0.30 input and $1.20 output per million tokens. Cheap and careful is a good combination for research assistants. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗚𝗟𝗠-𝟱.𝟯-𝗙𝗹𝗮𝘀𝗵 𝗺𝗶𝘅𝗲𝘀 𝘁𝘄𝗼 𝗸𝗶𝗻𝗱𝘀 𝗼𝗳 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗳𝗮𝘀𝘁 𝘄𝗶𝘁𝗵 𝗮 𝟭𝗠-𝘁𝗼𝗸𝗲𝗻 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. The architecture on B.AI's model page: ➜ 𝗦𝗽𝗮𝗿𝘀𝗲 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 to focus on what matters ➜ 𝗟𝗶𝗻𝗲𝗮𝗿 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 to keep long inputs cheap ➜ 𝗠𝗮𝗻𝗶𝗳𝗼𝗹𝗱-𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗛𝘆𝗽𝗲𝗿-𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 (mHC) ➜ 𝟯𝟮𝟬𝗕 total parameters with only 𝟭𝟴𝗕 active That combination is why a model this capable can sell at Flash prices. Reasoning is always on, with low, high and max effort. The details are here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗚𝗟𝗠-𝟱.𝟯-𝗙𝗹𝗮𝘀𝗵 𝗽𝗼𝘀𝘁𝘀 𝟴𝟰.𝟯 𝗼𝗻 𝗧𝗲𝗿𝗺𝗶𝗻𝗮𝗹-𝗕𝗲𝗻𝗰𝗵 𝟮.𝟭 𝗮𝘁 $𝟬.𝟱𝟬 𝗽𝗲𝗿 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗼𝘂𝘁𝗽𝘂𝘁 𝘁𝗼𝗸𝗲𝗻𝘀 𝗼𝗻 𝗕.𝗔𝗜. Z.AI's reported results: ➜ 𝟴𝟰.𝟯 on Terminal-Bench 2.1 ➜ 𝟳𝟴.𝟰 on Toolathlon Verified ➜ 𝟲𝟯.𝟰 on DeepSWE v1.1 It is open-weight and natively multimodal, with text, images, video and files in, and a 1M-token context. Standard pricing is $0.15 input and $0.50 output per million tokens, before any promotion. Strong agent scores at Flash prices is the story of this year. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗚𝗣𝗧-𝟱.𝟰 𝗡𝗮𝗻𝗼 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝟮𝟮𝟮 𝘁𝗼𝗸𝗲𝗻𝘀 𝗽𝗲𝗿 𝘀𝗲𝗰𝗼𝗻𝗱 𝗼𝗻 𝗕.𝗔𝗜 𝗮𝗻𝗱 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹. From its model page: ➜ About 𝟮𝟮𝟭.𝟴 𝘁𝗼𝗸𝗲𝗻𝘀 𝗽𝗲𝗿 𝘀𝗲𝗰𝗼𝗻𝗱 of output ➜ An Intelligence Index of 𝟰𝟰.𝟰, against a median of 20 for similarly priced models ➜ Text and image input ➜ $0.20 input and $1.25 output per million tokens The blended rate at a 3:1 input to output ratio works out to about $0.46 per million tokens. For ranking, extraction and sub-agent work at scale, speed plus reasoning at this price is a rare combo. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗔 𝗽𝗿𝗶𝗰𝗲 𝗳𝗲𝗲𝗱 𝗶𝘀 𝗮 𝗽𝘂𝗯𝗹𝗶𝗰 𝗴𝗼𝗼𝗱. 𝗢𝗻𝗲 𝘂𝗽𝗱𝗮𝘁𝗲 𝗰𝗮𝗻 𝘀𝗲𝗿𝘃𝗲 𝗲𝘃𝗲𝗿𝘆 𝗮𝗽𝗽 𝗼𝗻 𝗧𝗥𝗢𝗡. When a WINkLink feed updates, it writes one value on-chain. Any contract can read that value. A lending app, a DEX, a game and a vault can all use the same number at the same moment. 𝗡𝗼𝗯𝗼𝗱𝘆 𝗽𝗮𝘆𝘀 𝘁𝘄𝗶𝗰𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗿𝘂𝘁𝗵. That's also why everyone agrees on the same price, which matters a lot during fast markets. Shared data is safer data. @justinsuntron @WinkLink_Oracle #TronEcoStar

𝗚𝗣𝗧-𝟱.𝟰 𝗣𝗿𝗼 𝗰𝗼𝘀𝘁𝘀 $𝟭𝟴𝟬 𝗽𝗲𝗿 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗼𝘂𝘁𝗽𝘂𝘁 𝘁𝗼𝗸𝗲𝗻𝘀 𝗼𝗻 𝗕.𝗔𝗜, 𝗮𝗻𝗱 𝘀𝗼𝗺𝗲 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝘁𝗮𝗸𝗲 𝗺𝗶𝗻𝘂𝘁𝗲𝘀. Here is when it is worth it. What makes it different: ➜ It spends far more compute on reasoning per answer ➜ It is available only through the 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 𝗔𝗣𝗜 ➜ It is built for accuracy-critical math, science and professional work Input is $30 per million tokens. So do not use it for chat. Use it for the one answer where being wrong costs more than the bill: a contract clause, a financial model, a hard proof. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar

𝗚𝗣𝗧-𝟱.𝟱 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲𝘀 𝗹𝗲𝘀𝘀 𝗮𝗻𝗱 𝘁𝗮𝗹𝗸𝘀 𝗹𝗲𝘀𝘀, 𝗮𝗻𝗱 𝗯𝗼𝘁𝗵 𝗮𝗿𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀. Compared with GPT-5.3 Instant, OpenAI reports: ➜ 𝟱𝟮.𝟱% 𝗳𝗲𝘄𝗲𝗿 hallucinated claims on high-stakes prompts like medicine and law ➜ Roughly 𝟯𝟬% 𝗳𝗲𝘄𝗲𝗿 𝘄𝗼𝗿𝗱𝘀 to say the same thing ➜ Natively omnimodal across text, images, audio and video It is optimized for low latency and everyday conversation. Shorter answers also mean fewer output tokens on your bill. Accuracy and brevity in one upgrade. The model page is here: ➜ docs.b.ai/llmservice/models/… @justinsuntron @BAI_AGI #TronEcoStar