#NMR#slm

The projects on Cookie are centered around technological innovation, building an efficient and secure Web3 ecosystem, laying a solid foundation for the next generation of the internet. Each has achieved breakthrough innovations in their respective fields. @VeloraDEX (#Velora#) Formerly known as ParaSwap, it utilizes an intent-driven architecture for cross-chain trading. Its Multi-Asset Path (MAP) routing aggregates liquidity from over 160 protocols and 9 blockchains, handling a transaction volume of 120 billion dollars. By utilizing solver auctions and MEV protection, Velora optimizes execution paths, reducing the failure rate by 22%, while enabling gasless, voice-command exchanges—shifting towards a user-centered DeFi paradigm. @vooi_io (#VOOI) This is supplemented by the Cross-Chain Abstract Balance (CAB) system, which unifies liquidity across chains through intelligent routing algorithms. It supports 23 public chains and over 500 pools, offering a 99.3% transaction success rate and 37% Gas savings. The V2 mainnet powered by YZi Labs introduces perpetual contracts and RWA, abstracting bridging in a decentralized environment to provide a user experience similar to CEX. @Almanak__(#Almanak) Introducing predictive governance through AI groups and ERC-7540 vaults. Its module predicts proposal impact with a 2.3% deviation accuracy, improving DAO efficiency by 55%. Non-custodial execution and audit strategies ensure robustness, positioning it as the performance layer of on-chain finance. @OpenLedgerHQ(#OpenLedger) As a data layer, using the Optimism Stack for specialized language models (SLM). By enabling proof of ownership and ERC-8004 proxies through the data network, it monetizes a 20PB+ dataset, generating $14.7 million in revenue. @antix_in (#antix) Through the integration of GPT-4o, AIGE (AI Generated Entities) is created to establish soul-bound digital humans for the metaverse. NFT growth of 300%, supported by $8.22 million in funding, with its platform supporting real-time streaming and emotional computing, secured by blockchain for verifiable identity. @build_on_bob (#BuildOnBOB) Provides modular Rollup, integrating the security of Bitcoin with the EVM of Ethereum. Support With 127 types of custom solutions, it achieves 8000 TPS in the DeFi chain, 0.3 seconds finality in games, and unlocks BTC liquidity through BitVM bridging - no wrappers needed. @RaylsLabs (#RaylsLabs) Adopt dual-chain hybrid: public L2 compatible with EVM with KYC, and private subnet with ZKP/homomorphic encryption. Bridging TradFi-DeFi, it handles 2 million transactions daily in the CBDC pilot, supported by $38 million from ParaFi and Framework. @recallnet (#Recall) Provide on-chain memory through AgentRank, verify AI performance, and use immutable edit history. Misinformation detection accuracy of 98.7%, adopted by media for zkML inference, enabling composable knowledge units. @tenprotocol (#TenProtocol) Integrating TEE and ZKP for confidential Rollup, processing 4500 encrypted TPS per second with an error rate of 0.1%. EVM compatible, it supports sealed auctions and private vaults while scaling Ethereum with data control retained. @LABtrade_ (#LAB) Aggregating meme liquidity across 6 chains and 12 DEXs, confirming in 1.2 seconds with a slippage of 0.5%. Its modular engine facilitates rapid project launches and enhances the efficiency of high-volatility trading. @cookiedotfun ｜#COOKIE #SNAPS

Once upon a time, the villagers at the foot of the mountain grew peaches, but they had to hand over all the fruits to the priest at the temple on the top of the mountain for sale. No one knew how much money was made, and no one received any Dividends. The priest said, "Just focus on planting." Later, the young people set up a market on the mountainside, marking each tree with a name, keeping track of who watered and who fertilized them, and the money from selling peaches was automatically shared among everyone. People finally said, "The fruits are grown by us, so of course we should share them together." Isn't today's AI another "temple on the mountaintop"? We produce the data, but the value is monopolized by big companies. And @OpenledgerHQ is like the market on the mountainside—using a chain specifically designed for AI, it allows the community to co-build a "data network" and train specialized models (SLM) for healthcare, finance, and more. Contributions are recorded on the chain, ensuring that those who contribute are not taken advantage of; once the model starts generating revenue, the profits are automatically distributed to the participants. The token OPEN is fuel for payment and incentives. The more prosperous the ecosystem, the more people benefit. Its aim is not to create another AI giant, but to allow everyone to share in the benefits of the AI era. If in the future AI becomes as essential as water and electricity, then it should perhaps be a public facility built and shared by everyone. #Openledger #Cookie #Kaito #ETH

slm

Heard rumors that @OpenledgerHQ might be listed on Binance Spot! Regardless of the truth of the rumors, OpenLedger is definitely worth paying attention to. Because it is reshaping the value distribution mechanism of the AI industry, this is a blockchain infrastructure focused on the SLM (Specialized Language Model) track. The core of OpenLedger is to address the three major pain points of the current AI industry: data exploitation, centralized monopoly, and lack of transparency. Through the on-chain attribution mechanism, OpenLedger allows data contributors to receive the rewards they deserve. In terms of technical architecture, OpenLedger is built on the OP Stack and includes four major innovative layers: 1⃣ Data Layer (Datanets) realizes data NFT confirmation of rights; The Attribution Layer (PoA) implements token-level traceability using the Infini-gram algorithm. 3⃣ Model Layer (Model Factory) supports no-code fine-tuning; 4⃣Deployment Layer (OpenLoRA) significantly reduces inference costs. Moreover, the entire architecture can run in an EVM-compatible environment, allowing developers to seamlessly integrate into the existing ecosystem. OpenLedger's technical architecture highlights are well recognized in the market, and from the actual testnet experience, it indeed addresses many pain points of the traditional AI industry. I think this is also why the market recognizes it so much. I also think that listing on Binance on OpenLedger is a natural progression; high-quality projects will always gain recognition from the market. #OpenledgerHQ #Snaptopus @cookiedotfun #Cookie

Message to those who send me DMs, and those who think I don't give them time ✍🏻 From today, before you say I disrespect you or refuse to reply to you, let me give you a glimpse of how I live in the city of Cairo - Egypt. Note that there is a 2-hour difference between Nigeria and Egypt, 8am in the morning in Egypt is equivalent to 6am in the morning in Nigeria. From Sunday to Thursday, every day: ✍🏻 I wake up at 4am for the Fajr prayer, as the prayer starts from 4:30 to 4:40am. ✍🏻 When I come back I will make some excuses, then I will take a little nap. ✍🏻 I will wake up at 7:00 am to look for something to eat before 7:40 am, then I will get on the bus to school (Ma’ahad Dirasa Kaa’ssa) ✍🏻 The study starts at 8 am dot African time until 12 pm in the afternoon. There is a break from 10 am to 10:30 am though. ✍🏻 I'm getting up, I will hurry to go home to find something to eat, I will sprinkle some water, before 1pm. ✍🏻 1pm if I go out Azahar until 1:30pm I am at the Qur'an Study from 1:30pm to 2:30pm ✍🏻 I wake up from the Qur'an at 2:30pm at that time I will attend the Arabic class at Markaz Aleem, from 2:30pm to 4:30pm. ✍🏻 I will be leaving Aleem to perform my Asr prayer and then I will go study Nahwu from 5pm to 7:30pm. ✍🏻 When I leave here at 7:30pm before I get home, the call for Maghrib prayer will be made. At 8pm, after I pray Maghrib, I will look for something to eat and then check my phone to make posts and interact with my friends until 9:30 when the call for Isha prayer is made. ✍🏻 I will finish Sallah by 9:40 to 50 and then I will return to review my preparation for the mentorship class by 10:30pm. ✍🏻 From 10:30 to 11:30 pm we have class with them, after we finish the class I will do my other engagements with my trading and other research and studying. ✍🏻 I only get to rest and sleep on Friday and Saturday. ✍🏻 Why do you think I will reply to you with "slm", I don’t have your time. ✍🏻 If you want to DM me, just tell me everything at once how I should reply at once. I don’t have the time you have, I don’t have time for pleasantries. Just tell me what I can do for you straight.

🔥 OpenLedger: The ultimate disruptor of Web3 × AI, the future of the data economy is here! ✅ A true data ownership revolution - the POA mechanism allows every contribution to be recorded on the chain, with automatic distribution of benefits, completely bidding farewell to the pseudo-decentralization of "powering with love"! ✅ Ecosystem explosive growth - over 25 million testnet transactions, over 1.2 million active wallets, this is not airdrop farming, but real adoption! ✅ Developer Paradise - Documentation is so friendly that even beginners can deploy AI applications in 10 minutes, with a $150,000 prize pool handed directly to them! ✅ Enterprise-level AI Solutions – The SLM model specializes in supply chain optimization, being more accurate and practical than general AI, making enterprise-level implementation not a dream! 💡 Innovative to the extreme, yet reliable to the utmost. Living octopus team consultant? It appears to be performance art, but it is actually a hardcore spirit of decentralization! Cooperative decentralized GPU - Training costs cut in half, small teams can also master AI! Allora Network Strategic Alliance - Smart Agent Decision-Making Capability Fully Enhanced! 📈 Wealth, technology, and ecological triple explosion This is not just another "pie in the sky" project, but a golden triangle of data economy + AI + blockchain. Don’t miss the new paradigm of AI this time if you missed DeFi Summer! 👉 Is it still possible to get on now? Of course! The ecosystem is just taking off, and data contributors, developers, and early participants—everyone can reap the benefits.

OpenLedgerHQ: When AI Meets Blockchain, the Beginning of a Fair Revolution 🔍 Core Advantages: Dual Disruption of Technology + Fairness ▸ Data value returns to the individual Your data, your control. Through a decentralized network, every contribution of data will be accurately recorded and automatically assigned benefits through smart contracts, ending the era of "uncompensated exploitation." ▸ Low-cost AI development revolution ▸ Transparent and verifiable AI economy From data sources to model training, all links are recorded on the blockchain. Say goodbye to black box operations, where every contribution is clear and traceable, and every profit is fairly distributed. 💡 Directly address industry pain points and provide real solutions. The answer data of OpenLedgerHQ is being used for free → Quantitative contribution through Datanets, labor-based distribution of AI model monopoly → Support for niche SLM, breaking down the high development barriers of general models → OpenLoRA reduces training costs by 99% 🚀 Ecosystem Progress: A Future Ready to Take Off Capital Recognition: $8 million seed round financing, top institutions betting on the next generation of AI infrastructure. Early Benefits: Participation in the testnet may earn $OPN token incentives, and co-builders are the beneficiaries. Strong partnership: Collaborating with leading projects such as to accelerate ecosystem expansion. 🌐 This is not an upgrade, but a reconstruction. OpenLedgerHQ has a clear goal: to shift AI development from being 'controlled by a few' to 'co-created by many'. Here, technology is a tool, fairness is the foundation, and you and I are the protagonists of the future.

OpenLedger: The Disruptor of Decentralization AI and Blockchain Integration 1. Core Technology and Innovation Datanets (Data Networks): Collect high-quality data for specific fields (such as healthcare, finance, gaming), where contributors can upload data and receive on-chain records. Blockchain ensures transparent data sources, prevents tampering, and automatically allocates profits through smart contracts. Proof of Attribution: Track the output of AI models, accurately calculate the impact ratio of data contributors, and ensure fair distribution. By combining Infini-gram technology, efficient matching of data sources can be achieved even in large models, allowing for millisecond-level queries. Payable AI: Each use of the AI model automatically pays fees to data contributors and developers, creating a sustainable economic cycle 17. 2. Decentralization AI Infrastructure OpenLoRA (Low-Rank Adaptive Model Framework): Allow a single GPU to run thousands of fine-tuning models simultaneously, reducing deployment costs by 99%. Supports dynamic adapter loading and real-time model fusion, suitable for high-concurrency AI applications (such as legal assistants, trading AI, etc.). with Aethir, Utilize a Decentralization GPU network to optimize computing power allocation, achieving high performance and low-cost AI training and inference. 3. Industry Pain Points and OpenLedger's Solutions Industry Pain Points OpenLedger's Response Data Uncompensated Usage by Big Companies Blockchain Rights Confirmation + Automatic Profit Sharing AI Model Centralization Monopoly Decentralized Node Network Fine-tuning Model High Costs OpenLoRA Reduces GPU Demand Niche Fields AI Development Lagging Dedicated SLM + Vertical Datanets 4. Ecological Development and Market Impact Financing and Support: Received investments from institutions such as Polychain and HashKey, totaling over 11.2 million USD. Testnet data: Over 1.1 million active users, significant contributions from the Chinese community. Application scenarios: Legal AI, medical analysis, game engines, trading copilots, etc.

Take the most common example, when you use GPT for financial data analysis, industrial equipment monitoring, or medical document classification, most of the output is either irrelevant or requires a lot of tweaking of the prompts, and by the end, you still don't know if it really understands what you want. Of course, many people want to say that AI models are just a continuous training process. As long as you feed it enough data, it will definitely think what you think and give you what you want. In fact, it's not the case. For individuals, the ideal way is to create a dedicated AI model that understands your data and adapts to your business. But the actual problems come one after another. The premise of achieving these is that you need massive amounts of data, people to train the models, and most importantly, the infrastructure to run the models, which can basically deter 90% of people. 1️⃣Prompt to call the interface You can directly call any deployed SLM (Specialized Language Model) through the /v1/completions interface. The adaptability range is very broad: AI bots, on-chain reasoning, and even game scripts can all be used. 2️⃣ Model Management Interface By using /v1/models, you can access all the ready-made models, including those you have trained yourself and those shared by others. If you want to see detailed information? Use /model/info to check price, inference mode, access permission group, and other configuration parameters. It also supports team_id management, is compatible with OpenAI tools, and can be grouped by access permissions, making it very suitable for team collaboration and permission control. 3️⃣ Fees and Expenses Tracking On-chain invocation models are not free, but the key is where this money goes? OpenLedger provides a fully transparent tracking mechanism: The fees generated from each call will be automatically distributed to two categories of people: one is the data providers, and the other is the ones who train and deploy this model. You can also use /spend/logs to check who used which model, when, and how much was paid. Users can filter by user ID, request ID, API key, and time period, achieving transparent on-chain full-process. I personally believe this is the correct way to open up the "AI economy". In the era of large models, merely relying on inference effects is useless. For a system, the core of the AI economy lies in being able to share accounts, trace sources, initiate work, and effectively utilize data productivity. At least currently, OpenLedger's path is moving from "tuning models" to "building models + profit distribution + data ownership" as the economic activities on the blockchain. Both users and developers can benefit from this.

What does the Testnet 20 million interactions mean? OpenLedger's cold start answer sheet. As of early July 2025, the OpenLedger Testnet has recorded over 22.18 million transactions, 1.23 million unique addresses, over 6.83 million blocks, and 20,000 contract deployments, with an average of about 1,500 new contracts added daily. These numbers are already impressive in the "Testnet" phase, but the more critical question is: what do they mean? What kind of cold start path can we read from them? 1. Real user interaction or a false appearance of volume? In the face of high-frequency interactive testnet data, common doubts often focus on "Is this a scripted interaction?" and "Is it driven by internal operational accounts?" However, from the current publicly available on-chain activity structure, OpenLedger's interaction data exhibits clear characteristics of a product closed loop, mainly including: (1) The deployment and invocation dimensions of the contract are balanced, and there are sustainable Prompt execution records and model invocation transactions on the chain; (2) A large number of interactions do not occur between a few addresses, but rather present a structure with high participation from a medium to long tail of addresses; (3) Combined with the data entry of modules such as OpenChat and OpenTask, some interactions come from actual task execution and content interaction, supported by business sources. In other words, although early operational incentives cannot be ruled out, the cold start data of OpenLedger does reflect a certain trajectory of "real usage", which provides a key verifiable foundation for its subsequent mainnet launch. 2. Developer cold start strategy of "playing without a model" The cold start path of OpenLedger clearly did not choose to "wait for the model to be ready before opening," but instead built a developer system centered around data, tasks, and calls. The recently launched new version of Dev Docs focuses on three basic APIs: Prompting, Spend Tracking, and Model Management. Together, these three form a basic development framework that is "usable, computable, and profit-sharing." More importantly, this system does not rely on the OpenLedger self-developed model ontology, but allows any AI model to be uploaded, registered, and enter the invocation process. This means that even if the final form of on-chain model deployment has not yet been opened, developers can already build around the three aspects of "Prompt + Attribution + Pay" and participate in incentive rewards through the Testnet. 3. The cold start priority of SLM infrastructure: from data to interaction. OpenLedger is not positioned as a "next-generation ChatGPT," but rather as a blockchain economic system for building specialized language models (SLM). In this system, the model is not the primary primitive; data and calls are. During the Testnet phase, its design path resembles "letting people use it first," gradually establishing a real data ecosystem of "supply and demand" through continuous task interactions, model calls, and contribution attribution. Behind this priority ranking is an extremely Web3 assumption: the first step towards decentralized AI is not to create a model from scratch, but to establish an economic order around interaction and contribution mechanisms. And OpenLedger is delivering this answer during the Testnet phase.

[Can everyone train a model? How OpenLedger drops the AI participation threshold] In the context of traditional AI model training, training a high-quality model typically requires large data resources, expensive computing infrastructure, and specialized algorithm teams. This high barrier excludes the participation of the vast majority of ordinary individuals and leads to a high concentration of control over model training. The decentralized AI data network built by OpenLedger is attempting to break this old pattern, making it no longer just a hollow talk that "everyone can participate in model training." 1. Decentralized Mechanism of Data Contribution In the OpenLedger system, the training of AI models does not rely on data collection from a single organization, but rather involves multi-source collaboration through data networks called Datanets. Every user and every node can participate in data collection, labeling, filtering, and validation based on open tasks (OpenTask). This process ensures trust and incentives through two mechanisms: (1) PoA (Proof of Attention) data rights confirmation mechanism: ensures that the production of data can be recorded, traced, and quantified, truly realizing "who participates, who contributes, who confirms rights"; (2) OpenTask Standardized Task Protocol: By modularizing the description of task requirements, users from different backgrounds can understand and participate, dropping the technical barrier. In short, in OpenLedger, a Web3 user only needs to complete tasks initiated by the platform, such as providing sample data, participating in validation, and giving feedback on model performance, to become a part of the model training. 2. The path to lightweight and transparent model training OpenLedger not only outsources data sources to the community but also attempts to lower the hardware threshold for fine-tuning training through a lightweight model architecture. The platform tends to guide models to use specialized small models (SLM), which are usually designed for specific domain tasks rather than general intelligence needs, thus the required training resources are more controllable. At the same time, OpenLedger enhances the verifiability and transparency of the model training process by recording the metadata of the training process on-chain. This is particularly crucial for non-professional developers: they no longer need to understand the underlying algorithm details, but can simply follow the task process provided by the platform to participate and contribute. This structure gives us a clear direction: future AI training will no longer be the exclusive domain of large research institutions, but rather a "community-driven data co-construction network." How to bind the "training participant" role in the incentive system? The incentive design of OpenLedger is not limited to profit distribution at the model calling end, but extends to every key role in the training process: (1) Data Contributors: Upload, filter, and organize data; (3) Fine-tuning collaborators: Participating in the training optimization of specific models through OpenTask; (4) Evaluation Node: Perform performance validation for the trained model. Each of the above roles can obtain contribution certificates through the points system, which will be mapped to the future token system. This design ensures decentralized participation while incentivizing the formation of long-term collaborative networks. In the design of OpenLedger, the power and rewards of model training will gradually return to the community. Every user is not just a consumer, but also a builder, trainer, and validator. This is not only a challenge to traditional AI systems, but also a natural extension of Web3 participation logic into the AI field.

The AI economy is going on chain and OpenLedger is actually building it. They just pushed a major upgrade to their Developer Docs. You can now: • Query any deployed small language model (SLM) • Track spending + execution flow • Power agents with on-chain programmable logic • Connect data, models and behavior into one living system So what does that mean? It means every prompt becomes part of a distributed mind. Every call is logged, verified, and embedded into a new kind of memory. And agents not just apps begin to emerge from code. Whether you’re experimenting with agents, tools, or open AI systems this is where ideas stop being static and start becoming alive. Docs are live: Official update thread: Web3 x AI isn’t the future. It’s the substrate where logic, value, and autonomy converge.

What is the core value of OpenLedger? I think this issue is worth discussing in depth. Many people's first reaction would be to mention technology. Indeed, just look at their recent Dev Docs updates: The API is clearer now, and the modularization of features is well done. Especially the Proof of Attribution mechanism, which allows for real-time settlement of earnings with each model call, enabling data providers and developers to immediately receive their share. This "pay-per-use" design is rare in the AI field. But I think technology is just the surface. More importantly, the transparent collaboration system they have established: The entire process, from the model management interface to the expenditure tracking function, is completed on-chain. What does this mean? This means that contributors do not need to worry about their contributions being overlooked, nor do they have to fear that later joiners will reap the benefits. The real credit goes to the long-term builders, a value that is too scarce in today's AI circle. Let's talk about community operations. Recently, the little octopus has been spending money on @KaitoAI and @cookiedotfuncn. On the surface, it looks like a marketing campaign, but in fact, it reflects their emphasis on early contributors. Every week, the Top 100 share the prize pool and directly distribute token incentives to community members... these are all actions taken to reward the community. Compared to some projects that only shout slogans, the difference is obvious. What impressed me the most was actually their design philosophy: Make AI infrastructure pluggable and easily scalable. Many AI projects today prefer to create closed ecosystems, but OpenLedger chooses to make its API sufficiently universal, supporting not only its own models but also integrating with the entire network's SLM. This kind of open-mindedness is the right way to build long-term value. Of course, brand image adds extra points. The IP design of that little octopus is indeed charming, giving the tech project a sense of humanity. But don't be deceived by the cute appearance; the professionalism of the underlying architecture is the key. The three recently updated interface modules: Prompt generation, budget tracking, model management, each addresses a core pain point. Ultimately, the value of @OpenledgerHQ lies in establishing a transparent and fair collaborative network. Here, contributors can receive their rightful rewards, developers can quickly build applications, and the growth dividends of the entire ecosystem will be reasonably distributed. This could be what the next generation of AI infrastructure looks like. #OpenLedger #KaitoYappers #KaitoAI #Cookie

Today, I'm sharing a significant upgrade of the #OpenLedger Dev Docs. #OpenLedger's developer documentation has recently undergone a significant upgrade! This update is not just a small tweak, but rather the launch of an entirely new API framework aimed at paving the way for AI and the blockchain economy. This document was updated on July 3, 2025, exactly two days ago, making it "fresh out of the oven!" The core of the upgrade is to provide more powerful tools, allowing developers to more easily build AI-driven applications while making the entire ecosystem more transparent and sustainable. This upgrade also reflects OpenLedger's evolutionary approach—not a one-time effort, but an ongoing iteration based on the actual needs of developers. Detailed Explanation of the Three Major APIs This upgrade brings 3 brand new APIs, each unlocking new gameplay for developers like "super weapons:" Completions - Function: Through the /v1/completions endpoint, developers can send prompts to any deployed SLM (Small Language Model) to obtain intelligently generated outputs. Budget & Spend Tracking - Function: Developers can track the usage costs of AI models in real-time through the /spend/logs endpoint. - Highlights: You can view spending details by user ID, request ID, API key, or time period. Even cooler, the fees are directly allocated to data contributors and model developers, achieving a fair mechanism of "earn as much as you use." It's like a transparent "ledger" where it's clear who contributes and who earns! Model Management - Function: Through the /v1/models and /model/info endpoints, developers can browse all available models (including those created by themselves or contributed by the community) and obtain detailed metadata (such as pricing, inference modes, access groups, etc.). - Highlights: Supports team range filtering and compatibility with OpenAI tools, providing developers with immense flexibility. It's like a "model supermarket" where you can easily pick any model or check any details! How do these upgrades benefit developers? The launch of these APIs allows developers to create smarter, fairer, and more worry-free AI applications: - Control Upgrade: Developers can finely manage model selection and access permissions, like being in charge, taking control of the overall situation. - Cost efficiency doubled: Real-time budget tracking avoids the "money-burning black hole," allowing for more precise resource allocation, saving money and worry. - Transparency and Fairness: Through blockchain technology, data contributors and model developers can directly benefit, creating a "rewarding" ecosystem. Users can also see everything behind the AI, experiencing greater peace of mind. - Development experience optimization: These tools simplify complex processes, allowing developers to focus on creativity rather than being hindered by technical details. It's like adding a buff to the "superpowers" of developers!

Today, I'm sharing a significant upgrade of the #OpenLedger Dev Docs. #OpenLedger's developer documentation has recently undergone a significant upgrade! This update is not just a small tweak, but rather the launch of an entirely new API framework aimed at paving the way for AI and the blockchain economy. This document was updated on July 3, 2025, exactly two days ago, making it "fresh out of the oven!" The core of the upgrade is to provide more powerful tools, allowing developers to more easily build AI-driven applications while making the entire ecosystem more transparent and sustainable. This upgrade also reflects OpenLedger's evolutionary approach—not a one-time effort, but an ongoing iteration based on the actual needs of developers. Detailed Explanation of the Three Major APIs This upgrade brings 3 brand new APIs, each unlocking new gameplay for developers like "super weapons:" Completions - Function: Through the /v1/completions endpoint, developers can send prompts to any deployed SLM (Small Language Model) to obtain intelligently generated outputs. Budget & Spend Tracking - Function: Developers can track the usage costs of AI models in real-time through the /spend/logs endpoint. - Highlights: You can view spending details by user ID, request ID, API key, or time period. Even cooler, the fees are directly allocated to data contributors and model developers, achieving a fair mechanism of "earn as much as you use." It's like a transparent "ledger" where it's clear who contributes and who earns! Model Management - Function: Through the /v1/models and /model/info endpoints, developers can browse all available models (including those created by themselves or contributed by the community) and obtain detailed metadata (such as pricing, inference modes, access groups, etc.). - Highlights: Supports team range filtering and compatibility with OpenAI tools, providing developers with immense flexibility. It's like a "model supermarket" where you can easily pick any model or check any details! How do these upgrades benefit developers? The launch of these APIs allows developers to create smarter, fairer, and more worry-free AI applications: - Control Upgrade: Developers can finely manage model selection and access permissions, like being in charge, taking control of the overall situation. - Cost efficiency doubled: Real-time budget tracking avoids the "money-burning black hole," allowing for more precise resource allocation, saving money and worry. - Transparency and Fairness: Through blockchain technology, data contributors and model developers can directly benefit, creating a "rewarding" ecosystem. Users can also see everything behind the AI, experiencing greater peace of mind. - Development experience optimization: These tools simplify complex processes, allowing developers to focus on creativity rather than being hindered by technical details. It's like adding a buff to the "superpowers" of developers!

[SLM vs LLM: Is the next generation AI battlefield in 'specialized models'?] In the perception of most people, the development of AI models is a race for "larger and more general" capabilities, from GPT-2 to GPT-4, from hundreds of billions of parameters to trillions of model scale. It seems that only more powerful LLMs (Large Language Models) can dominate the future. However, outside of this frantic mainstream path, a current of "specialized models SLM (Specialized Language Models)" is surging, and OpenLedger is a steadfast practitioner of this new paradigm. 1. SLM is a reasonable path to address the fragmented demands of AI. (1) The biggest problem facing large models is not just cost, but the efficiency bottleneck caused by "generality inertia." While they can do everything, they often do not do anything well enough, especially in vertical scenarios where they cannot provide truly customized responses. (2) The advantage of SLM lies in its "small and specialized" nature: it has smaller parameters and faster responses, allowing for rapid deployment in specific tasks such as trading strategies, social operations, and risk control analysis, and quick iteration with data fine-tuning. (3) The SLM approach of OpenLedger does not revolve around the model itself, but instead uses a "data task network" as a foundation to train efficient small models for each structured scenario. Its core lies not in the "size" of the model, but in the "specialization" of the data. 2. How does OpenLedger define "model ecology"? (1) Extract training data from Datanets: Each Datanet focuses on a task domain, collecting raw data from user interactions, and then scoring and certifying it through the PoA system. (2) Training and iterating models through OpenStream: Multimodal data flows into the OpenStream network, forming a model training dataset that supports fine-tuning and continuous updates. (3) Launch as a Payable AI model: The trained SLM can be deployed on the OpenLedger network as a callable, payable, and profit-sharing AI model participating in real business scenarios. 3. SLM is not just a technical route, but also a reconstruction of the power structure. What OpenLedger represents is not just a reduction in model size, but a new paradigm of "decentralized AI creation." Compared to OpenAI's closed architecture, OpenLedger empowers every data contributor and developer with the right to participate in the evolution of the model, establishing a benefit distribution mechanism across data, training, and application layers. The model is no longer the exclusive asset of giants, but a network asset that can be shared and collaborated on. In other words: SLM is the "deconstructive power" of LLM, not to replace, but to break the closed monopolistic model architecture and build a more democratized AI creation ecosystem.

Today, I'm sharing a significant upgrade of the #OpenLedger Dev Docs. #OpenLedger's developer documentation has recently undergone a significant upgrade! This update is not just a small tweak, but rather the launch of an entirely new API framework aimed at paving the way for AI and the blockchain economy. This document was updated on July 3, 2025, exactly two days ago, making it "fresh out of the oven!" The core of the upgrade is to provide more powerful tools, allowing developers to more easily build AI-driven applications while making the entire ecosystem more transparent and sustainable. This upgrade also reflects OpenLedger's evolutionary approach—not a one-time effort, but an ongoing iteration based on the actual needs of developers. Detailed Explanation of the Three Major APIs This upgrade brings 3 brand new APIs, each unlocking new gameplay for developers like "super weapons:" Completions - Function: Through the /v1/completions endpoint, developers can send prompts to any deployed SLM (Small Language Model) to obtain intelligently generated outputs. Budget & Spend Tracking - Function: Developers can track the usage costs of AI models in real-time through the /spend/logs endpoint. - Highlights: You can view spending details by user ID, request ID, API key, or time period. Even cooler, the fees are directly allocated to data contributors and model developers, achieving a fair mechanism of "earn as much as you use." It's like a transparent "ledger" where it's clear who contributes and who earns! Model Management - Function: Through the /v1/models and /model/info endpoints, developers can browse all available models (including those created by themselves or contributed by the community) and obtain detailed metadata (such as pricing, inference modes, access groups, etc.). - Highlights: Supports team range filtering and compatibility with OpenAI tools, providing developers with immense flexibility. It's like a "model supermarket" where you can easily pick any model or check any details! How do these upgrades benefit developers? The launch of these APIs allows developers to create smarter, fairer, and more worry-free AI applications: - Control Upgrade: Developers can finely manage model selection and access permissions, like being in charge, taking control of the overall situation. - Cost efficiency doubled: Real-time budget tracking avoids the "money-burning black hole," allowing for more precise resource allocation, saving money and worry. - Transparency and Fairness: Through blockchain technology, data contributors and model developers can directly benefit, creating a "rewarding" ecosystem. Users can also see everything behind the AI, experiencing greater peace of mind. - Development experience optimization: These tools simplify complex processes, allowing developers to focus on creativity rather than being hindered by technical details. It's like adding a buff to the "superpowers" of developers!

🎉 #OpenLedger Developer Documentation Major Upgrade! How to achieve this? Make these APIs simple and easy to understand: ▫️ Prompt generation: One sentence, AI understands instantly! ▫️ Expense Tracking: Know exactly how much you spend! ▫️ Model Management: Choose models freely, super convenient! 🖥️ Want to click with SLM (Specialized Language Model), and everything is done! Super suitable: ✅ AI Assistant, ready at any time ✅ Smart contracts, stable as a bunch ✅ dApp development, real-time inference, language generation to play around with How to use? Throw a hint, and immediately give you a smart, reliable, and verifiable answer, simple to the point of flying! Data: Open and transparent, no barriers, anyone can use! Budget & Expenses: Transparent spending, fair earnings! 💰 Settle in real-time using SLM, no ambiguity! Every time someone uses the model, the money is automatically credited, with data contributors and model developers each getting their share, clearly distributed on the chain. Proof of Attribution, making every penny public and fair! Your data, you make the call! Cool or not? Model Management: Capture all the desired models in one go! 📦 /v1/models endpoint: Click to see all SLMs, there are all kinds of styles! What can be done? ⚙️ The model you created yourself 🌐 Contributions from the big shots on the internet What's even better: Want to find a model? Filter by attributes and you'll find it instantly! You can also seamlessly integrate into the large AI ecosystem, boosting development efficiency to the max! Infrastructure: Transparent, Modular, Easily Scalable! OpenLedger🟠 makes it easy for you to navigate AI blockchain, making development a breeze!

This is not another "AI Plus Chain"; OpenLedger genuinely wants to bring the model onto the chain. Recently, there are many "AI + blockchain" projects that sound quite impressive, but not many of them are actually running. 1. Use the chain to run small language models (SLM), 2. Anyone who contributes data, computing power, or code can be recorded on the blockchain and automatically receive payments. 3. Finally, the model can be combined and called like a contract, rather than just being discarded after one run. 4. It is not creating an "AI alternative," but is building a layer that is truly for Agents. You contributed data, models, and computing power for AI training; why should the final benefits have nothing to do with you? 🌿 The core positioning is also very clear: 🔹What we are doing is "running a chain for small models", not speculating on the concept of large models. 🔹Models can be combined, reused, and invoked like smart contracts. 🔹Not only running inference, but also training, deployment, and data labeling, every step has value records. 🔹The entry barrier is not high; a graphics card, a script, or a set of data can all access the system. In other words, this is a truly on-chain execution environment prepared for AI Agents, not the kind of project that is just for sending images and playing with APIs. Technically, they used OpenLoRA, which can reduce model deployment costs, and an ordinary machine can run it. It still uses an EVM-compatible architecture, so you don't need to learn a whole new set of things. The testnet is now online, and running nodes, completing tasks, and accumulating points are all open. In the future, $OPN incentives will also be released, which can be considered a window period for early participation. It's never wrong to enter early 😎

This is not another "AI Plus Chain"; OpenLedger really wants to move the model onto the chain. Recently, many "AI + blockchain" projects sound quite impressive, but not many have actually been implemented. ◾️◽️Use blockchain to run small language models (SLM), Whoever contributes data, computing power, or code can be recorded on the chain and automatically receive payments. ◾️◽️The final model can still be combined and called like a contract, rather than being discarded after just one run. ◾️◽️It is not about creating an "AI alternative"; it is about building a layer that is truly used by agents. You contributed data, models, and computing power for AI training. Why should the final benefits have nothing to do with you? 🔲 The core positioning is also very clear: ◾️◽️ What we are doing is "running a chain for small models", not speculating on the concept of large models. Models can be composed, reused, and invoked like smart contracts. ◾️◽️ Not only running inference, but also training, deployment, and data labeling, every step has value recorded. ◾️◽️ The entry barrier is not high; a graphics card, a script, or a set of data can all access the system. In other words, this is a truly on-chain execution environment prepared for AI Agents, not the kind of project that is just for sending images and playing with APIs. Technically, they used OpenLoRA, which can compress model deployment costs, allowing a regular machine to run it. It still uses an EVM-compatible architecture, so you don't need to learn a whole new set of things. The testnet is now online, and running nodes, completing tasks, and earning points are all open. In the future, $OPN incentives will also be released, which can be seen as a window period for early participation. It's never wrong to get in early 😎

[AI Opaque? How Data Becomes "Traceable" from Datanets] In the past year of the AI explosion, large models have repeatedly pushed the technical limits, but they have also exposed a persistent issue—the black box problem of data sources. We have no way of knowing what corpus a model is trained on, who provided the key data, and we cannot assess whether the model's output infringes upon anyone's content rights. This "lack of transparency" is becoming an obstacle to the further scaling and implementation of AI. The entry point of OpenLedger is precisely the breakthrough of this deep structural problem—reconstructing AI's transparency and incentive logic from the data side, with its core focus being: Datanets. 1. What is Datanets? A data network architecture designed for AI. Datanets is the core system built by OpenLedger, aimed at making the data used by AI models "traceable" and "measurable incentives." It is not a simple database or crowdsourcing platform, but a modular network system that combines on-chain rights confirmation, task management, and economic incentives, structuring the processes of data provision, processing, and consumption. Each Datanet is a specific data collection and task collaboration space, focused on a particular vertical field, such as DeFi indicators, NFT project introductions, AI dialogue corpora, etc. This design reflects OpenLedger's understanding of the "Specialized Language Model (SLM)" ecosystem: it is not about acquiring more data, but rather about acquiring higher quality and more domain-specific data. 2. Core Mechanism: PoA, Infini-gram and New Paradigm of Data Collaboration OpenLedger has introduced two key mechanisms in Datanets, restructuring the traditional "data provision → model training" process: (1) Proof of Attribution (PoA): A mechanism used to record the connection between data contributors and AI outputs. It allows each output of the model to be partially "traced back" to the training data involved, thereby enabling traceable distribution of incentives. (2) Infini-gram model unit: OpenLedger uses a more flexible model micro-module design, breaking the model into finer-grained components, each of which can bind to its corresponding data source. This design not only enhances the composability of the model but also makes "data accountability" possible. Datanets are not isolated; they are interconnected through modular interfaces with Agents and Payable AI models, creating a complete flow link of "task - data - model - application." This also means that in the future, you will not only be able to provide data, but also participate in training tasks, use models, and receive feedback and benefits. 3. What does Datanets solve compared to traditional crowdsourcing and data annotation platforms? In the past, we have seen many Web2 crowdsourced data platforms, such as Amazon Mechanical Turk and Scale AI, which have achieved certain results in scale, but generally face two issues: (1) Data Ownership: The data provided by participants ultimately belongs to the platform, and once used by AI, it is difficult to track or share profits subsequently. (2) Tasks are fragmented, repetitive, and lack incentives: Many tasks lack long-term goals or structured collaboration, resulting in redundant work and difficulties in ensuring data quality. The goal of Datanets is to transform "data tasks" into a long-term collaborative asset, rather than a one-time exploit by the platform, establishing a continuous relationship. The data you provide is an on-chain asset, the tasks you participate in are composable modules, and your contributions can be invoked by any subsequent model to earn rewards. 4. Summary: Datanets is the dedicated data infrastructure for the AI era. OpenLedger is not trying to become the next ChatGPT, but rather aims to address a fundamental issue that ChatGPT cannot solve: how can the data production logic of AI be reconstructed? The proposal and practice of Datanets indicate that OpenLedger regards "transparency, collaboration, and measurability" as the core principles of data systems, which is the most essential response to AI in the context of Web3. Whether AI models are trustworthy in the future may not depend on the size of the parameters, but rather on whether they can "clearly explain what data is being used."

[Not Another ChatGPT: How OpenLedger is Reshaping the AI Model Paradigm?] 1. ChatGPT opens the era of AI popularization, but also exposes the limitations of general models. Since the end of 2022, ChatGPT has driven a wave of "universalization" in artificial intelligence, but this wave has also exposed a problem: general large models are not the optimal solution for all industries. Although the GPT series has strong language generation and generalization capabilities, it relies on massive publicly available data for training, and when faced with specialized, highly sensitive, and highly structured fields (such as law, medicine, finance, etc.), the model often "confidently talks nonsense." In addition, the training process of general models is opaque, and the sources of data are untraceable, making issues such as model fairness, accountability, and revenue sharing difficult to resolve over the long term. This is exactly the starting point for OpenLedger to enter the AI world: not to create another ChatGPT, but to redefine a more compact, refined, specialized, and transparent AI model paradigm. 2. The Starting Point of OpenLedger: Bringing AI Closer to Needs and Rooted in Data OpenLedger is not following the path of "using large models to occupy territory," but rather chooses to go against it, entering from the data side and the demand side to build AI capabilities in vertical fields. The core concept they proposed is SLM (Specialized Language Models), which is different from the general model (GLM) represented by ChatGPT. SLM focuses on optimization in specific industries or task scenarios, aiming to achieve higher accuracy, a smaller model burden, and stronger data controllability. Behind SLM, the real moat emphasized by OpenLedger is a complete set of data mechanisms based on blockchain and DePIN (Decentralized Physical Infrastructure Network): (1) Organize specific data collection, labeling, and rights confirmation in a community manner through the Datanets network; (2) Use the Payable AI Models mechanism to provide income return channels for data contributors and model developers; (3) Utilize data traceability mechanisms (such as Proof of Attribution) to ensure that every segment of training data can be tracked, verified, and held accountable. With these mechanisms in place, the AI built by OpenLedger is not a "black box," but rather a collaborative network with an on-chain accountability chain. 3. "Dedicated Models + Traceable Data": OpenLedger's new paradigm combination OpenLedger actually builds a "distributed collaborative development platform" oriented towards the AI future: (1) Model Side: SLM can be called, combined, and trained by multiple parties, emphasizing the flexibility and customization of the model; (2) Data side: Providing continuous high-quality fuel for the model through decentralized data collection and labeling organizations (Datanets); (3) Commercialization Mechanism Side: Achieve "multi-party profit sharing" for data providers, node operators, and model developers through on-chain records and payment mechanisms; (4) Participation Path: By leveraging lightweight Chrome plugin nodes and Android nodes, more non-professional developers can also "mine data for gold" and enter the AI construction factory. This combination not only builds an AI project but also creates a distributed ecosystem centered around the flow of high-quality data and the deployment of application scenarios. AI is no longer just a one-sided process of "training and then deploying"; it is a continuous collaboration driven by the data community. 4. Why is this not "just another AI project"? OpenLedger is not trying to "benchmark ChatGPT", but is deliberately avoiding the illusion of being "only conversational". They are not creating the next super brain, but rather aiming to build an operating system for ten thousand "specialized brains"; they are not crafting a new AI myth, but addressing a real-world issue: how to make AI serve the genuine needs of the real world? OpenLedger may not be the most eye-catching AI project, but it is shaping the foundational part that is most needed in the AI world.

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What is OpenLedger? A new starting point for AI Blockchain. According to OpenLedger, the future of AI models does not lie in a super brain, but rather in thousands of "specialized intelligences" aimed at different fields. To provide these models with high-quality "raw materials"—data, OpenLedger has introduced the Datanets mechanism, which combines on-chain rights confirmation and incentive mechanisms to encourage the community to collaboratively build domain-specific datasets and track their provenance, usage, and revenue distribution processes. 2. Why do we need to create "SLM (Specialized Language Model)"? A new paradigm to counter general AI. Large models excel at "generalized understanding," but in high-precision scenarios such as healthcare, law, scientific research, and finance, the cost of errors is extremely high, and the quality of data is critically important. General models cannot meet the requirements for deep understanding and specialized expression. The SLM concept of OpenLedger, which stands for Specialized Language Model, advocates for training specialized models around vertical scenarios and supports a "on-chain measurable" data contribution tracking mechanism, truly realizing that "who provides the data, who shares the value." Under this logic, OpenLedger is not just a data platform, but an open ecosystem aimed at developers, data providers, and AI application builders, attempting to reconstruct the value closed loop between data, models, and incentives. 3. Modular Architecture: Datanets, Payable AI, Agents Trinitarian The OpenLedger system consists of three core modules: (1) Datanets: decentralized data networks, each Datanet corresponds to a dataset in a specific field (such as medical imaging, financial documents, game corpora, etc.), where file uploading, cleaning, auditing, and usage can all be traced on the Blockchain. (2) Payable AI Models: All model calls are bound to data tracing, and the call costs can be priced. Users who contribute data or training resources can receive corresponding rewards, forming a fair income distribution system. (3) AI Agents: Applications built on the Payable AI foundation, including smart assistants, trading systems, search engines, game NPCs, etc., connecting the complete link from "data to application." This design embodies OpenLedger's ambition: not just a data platform, but a full-stack AI Web3 network of "data → model → application." 4. Data Confirmation and Incentive Mechanism: The True Application of Blockchain Technology OpenLedger introduces a mechanism called PoA (Proof of Attribution) to track the ownership, usage, and value path of each piece of data. By combining mechanisms such as Infini-gram and Gradient-Based Attribution, fine-grained data contribution measurement can be achieved. This approach is more scalable than traditional "upload and share" solutions—data is not an anonymously shared resource, but rather an asset with identity, value, and incentive pathways. This provides a foundational guarantee for the fairness and compliance of AI. Moreover, mechanisms such as node operation, points tasks, and airdrop incentives have further enriched the incentive methods, providing a lightweight participation path for Web2 users to enter the Web3 AI world. 5. From Visuals to Community: A Brand Narrative Experiment of an Octopus If you pay attention to OpenLedger's brand design, it is not difficult to find that it attempts to break the traditionally cold temperament of AI projects—the project mascot is an orange octopus dressed in a spacesuit, sometimes graffitiing "AI Blockchain" on the wall, sometimes hosting AMAs, and sometimes dancing with a fan in the pink sea of clouds in Hangzhou. This is not merely cartoonish marketing, but an external expression of its core vision: to build an accessible, participatory, and collaboratively shared AI data world. In this world, everyone is a producer, user, and governor of data, no longer just a passive input or "fuel for the model." ~~~~~~ If OpenAI is the "centralized factory" of the AI world, then what OpenLedger aims to build is a "distributed intelligence community" composed of tens of thousands of collaborative nodes.

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Hey friends, major news is here! Project Ailey(ALE), the super cool AI junior agent, is finally going to launch its Launchpool on #Gate! Project Ailey, the all-around ACE of games, movies, and the metaverse! She is not only a unique resident of the Ailey universe but also a highly personalized service provider with her own personality. SLM technology brings her to life on your device. Event time: April 8, 2025, 14:00 to April 18, 2025, 14:00 (UTC+8) Participation Steps, Log In 🔥 "Tappy Tappy Ailey" and "VR Secretary" have already shown us the infinite possibilities of Ailey, and more exciting features are about to be unlocked. #ProjectAiley #ALE #Launchpool #Gate First Release #Metaverse Exploration

And it has huge potential airdrop ➜ Simple to accomplish  ➜ Accessible to everyone  ➜ Completely free of charge  ➜ OpenLedger raised $8 million SLM is a game changer through next-gen blockchain I’m in love with #OpenLedger

Today is a traditional vacation in my country ... When I sip my tea and ponder, I can’t help but marvel at the true brilliance of specialized models, the real experts who dive deep into their craft with unmatched precision. General LLMs know a bit of everything, but specialized models are the real experts. A thread [1] #openledger #AI #Blockchain #deAI #SLM

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I observed SLM BBRP Moon,on any coin those who comment are just accounts are you a robot??? Which is specifically designed to trap prey,,,,?? 😅😅

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