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🚨 BIG! India’s first native Sanskrit Large Language Model is being built in Chennai. A historic collaboration between 118-year-old MDS Sanskrit College (Mylapore) & IIT Madras, with scholars from Kuppuswami Sastri Research Institute, aims to teach AI Sanskrit logic, sandhi and structure by processing 110,000+ rare manuscripts.

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IIT Madras has once again raised the bar for Indian higher education, achieving historic milestones in the NIRF India Rankings 2025 announced today by the Minister of Education, Shri Dhrmendra Pradhan ji at the Ministry of Education, Government of India. 🏆 Ranks Secured 🔹 #1 in Engineering – 10th consecutive year 🔹 #1 Overall – 7th consecutive year 🔹 #1 in Innovation – up from #2 last year 🔹 #1 in Sustainable Development Goals (SDG) – new category 🔹 #2 in Research Institutions Beyond rankings, IIT Madras continues to define new frontiers of excellence, by becoming the first IIT to establish a full-fledged international campus at IITM Zanzibar, being recognised as an Institution of Eminence in 2019, and pioneering new admission pathways such as Sports Excellence and Fine Arts and Cultural Excellence (FACE). In 2024–25, the Institute filed 417 patents, incubated 100+ startups, and launched both the School of Sustainability and the School of Innovation and Entrepreneurship. On the global stage, IIT Madras made a remarkable leap in the QS World University Rankings 2026, rising from #227 to #180 worldwide. IIT Madras remains committed to its vision of nurturing innovation, driving sustainability, and shaping the future of education and research. Ministry of Education Dharmendra Pradhan #IITMadras #NIRF2025 #Excellence #Innovation #Sustainability

IIT Madras has once again raised the bar for Indian higher education, achieving historic milestones in the NIRF India Rankings 2025 announced today by the Minister of Education, Shri Dhrmendra Pradhan ji at the Ministry of Education, Government of India. 🏆 Ranks Secured 🔹 #1 in Engineering – 10th consecutive year 🔹 #1 Overall – 7th consecutive year 🔹 #1 in Innovation – up from #2 last year 🔹 #1 in Sustainable Development Goals (SDG) – new category 🔹 #2 in Research Institutions Beyond rankings, IIT Madras continues to define new frontiers of excellence, by becoming the first IIT to establish a full-fledged international campus at IITM Zanzibar, being recognised as an Institution of Eminence in 2019, and pioneering new admission pathways such as Sports Excellence and Fine Arts and Cultural Excellence (FACE). In 2024–25, the Institute filed 417 patents, incubated 100+ startups, and launched both the School of Sustainability and the School of Innovation and Entrepreneurship. On the global stage, IIT Madras made a remarkable leap in the QS World University Rankings 2026, rising from #227 to #180 worldwide. IIT Madras remains committed to its vision of nurturing innovation, driving sustainability, and shaping the future of education and research. Ministry of Education Dharmendra Pradhan #IITMadras #NIRF2025 #Excellence #Innovation #Sustainability

IIT Madras

23,894 views • 11 months ago

From IIT Madras classrooms to orbit—GalaxEye has launched Mission Drishti, the world’s first OptoSAR imaging satellite, aboard a SpaceX Falcon 9. Incubated at the IITM Incubation Cell, this breakthrough fuses optical and SAR imaging in one pass—delivering clear, all-weather, day-and-night insights when they’re needed most. A milestone recognised by the Prime Minister, Shri Narendra Modi ji, who congratulated the team, calling it a major step in India’s space journey and a reflection of youth-driven innovation. As highlighted by Analytics India Magazine, Mission Drishti removes the trade-off between clarity and consistency—unlocking faster, smarter decision-making from space. With on-board AI enabling real-time intelligence, this is India’s deep-tech story reaching new heights—built in India, for the world. Watch here: IIT Madras GalaxEye IIT Madras Incubation Cell PMO India Narendra Modi Ministry of Education AIM #MissionDrishti #IITMadras #SpaceTechIndia #InnovationIndia

From IIT Madras classrooms to orbit—GalaxEye has launched Mission Drishti, the world’s first OptoSAR imaging satellite, aboard a SpaceX Falcon 9. Incubated at the IITM Incubation Cell, this breakthrough fuses optical and SAR imaging in one pass—delivering clear, all-weather, day-and-night insights when they’re needed most. A milestone recognised by the Prime Minister, Shri Narendra Modi ji, who congratulated the team, calling it a major step in India’s space journey and a reflection of youth-driven innovation. As highlighted by Analytics India Magazine, Mission Drishti removes the trade-off between clarity and consistency—unlocking faster, smarter decision-making from space. With on-board AI enabling real-time intelligence, this is India’s deep-tech story reaching new heights—built in India, for the world. Watch here: IIT Madras GalaxEye IIT Madras Incubation Cell PMO India Narendra Modi Ministry of Education AIM #MissionDrishti #IITMadras #SpaceTechIndia #InnovationIndia

IIT Madras

23,517 views • 2 months ago

How IIT Madras is Changing Global Security The End of Hacking? IIT Madras is spearheading India's push into quantum-secure communications through the IITM-C-DOT Samgnya Technologies Foundation, launched as the National Hub for Quantum Communication under the National Quantum Mission. This initiative aims to make communications "unhackable" by shifting from math-based encryption to physics-based security, countering future quantum computer threats. The hub, inaugurated in December 2025 at IIT Madras Research Park, partners with C-DOT and is funded by the Department of Science and Technology (DST). It focuses on developing indigenous quantum hardware, QKD networks, quantum repeaters, and satellite-based systems to protect critical infrastructure like AI data centers and defense networks. Traditional encryption relies on complex math that quantum computers will crack in years. QKD uses quantum physics: any eavesdropping alters the quantum state, alerting the system instantly and burning the key. IIT Madras claims an edge in secure transmission over computing leaders like the US and China. The hub builds real-world testbeds, subsidizes hardware for startups, trains experts, and links globally via IITM Global outposts. It positions India to lead in quantum-secure networks for sovereign AI and government use within five years. IIT Madras also runs CyStar, a cybersecurity center advancing quantum security, AI model protection, and IoT defenses since 2024. This aligns with national goals for unhackable comms amid rising cyber threats. Credit : AIM Network.

How IIT Madras is Changing Global Security The End of Hacking? IIT Madras is spearheading India's push into quantum-secure communications through the IITM-C-DOT Samgnya Technologies Foundation, launched as the National Hub for Quantum Communication under the National Quantum Mission. This initiative aims to make communications "unhackable" by shifting from math-based encryption to physics-based security, countering future quantum computer threats. The hub, inaugurated in December 2025 at IIT Madras Research Park, partners with C-DOT and is funded by the Department of Science and Technology (DST). It focuses on developing indigenous quantum hardware, QKD networks, quantum repeaters, and satellite-based systems to protect critical infrastructure like AI data centers and defense networks. Traditional encryption relies on complex math that quantum computers will crack in years. QKD uses quantum physics: any eavesdropping alters the quantum state, alerting the system instantly and burning the key. IIT Madras claims an edge in secure transmission over computing leaders like the US and China. The hub builds real-world testbeds, subsidizes hardware for startups, trains experts, and links globally via IITM Global outposts. It positions India to lead in quantum-secure networks for sovereign AI and government use within five years. IIT Madras also runs CyStar, a cybersecurity center advancing quantum security, AI model protection, and IoT defenses since 2024. This aligns with national goals for unhackable comms amid rising cyber threats. Credit : AIM Network.

Augadh

11,978 views • 5 months ago

In the Platinum Jubilee Year of IIT Kharagpur, we proudly celebrate a defining moment as our distinguished alumnus, Sundar Pichai, CEO of Google, addressed the India AI Impact Summit 2026. Opening his remarks with a personal reflection, he shared, “Every time I visit, I’m struck by the pace of change, and today is no different. Back when I was a student, I often took the Coromandel Express from Chennai up to IIT Kharagpur. To get there, we passed through Visakhapatnam.” From his humble journey as a student to unveiling a transformative vision for India’s technological future, this trajectory stands as a powerful testament to purpose, perseverance, and global leadership. At the summit, Sundar Pichai announced Google’s plan to establish a full-stack AI hub in Visakhapatnam as part of a $15 billion infrastructure investment in India—further positioning the nation at the forefront of the global AI movement. He underscored that AI will undeniably reshape the workforce, automating certain roles, evolving others, and creating entirely new careers. He also highlighted breakthroughs such as AlphaFold by Google DeepMind, which compressed decades of protein-structure research into a publicly available database now used by millions of researchers worldwide, demonstrating how AI can accelerate scientific discovery at an unprecedented scale. As an institution that rose from the historic Hijli Detention Camp to become India’s first IIT, IIT Kharagpur embodies resilience, vision, and nation-building. In this landmark 75th year, we take immense pride in seeing our alumni shaping global technology, driving innovation, and contributing to India’s leadership in the AI era. Video Courtesy: ANI #SundarPichai #AISummit #Google #Live #BharatMandapam #IITKGP #PlatinumJubilee #AILeadership #NationBuilding PMO India Ministry of Education Dharmendra Pradhan Ashwini Vaishnaw Dr. Sukanta Majumdar Prof. Suman Chakraborty

In the Platinum Jubilee Year of IIT Kharagpur, we proudly celebrate a defining moment as our distinguished alumnus, Sundar Pichai, CEO of Google, addressed the India AI Impact Summit 2026. Opening his remarks with a personal reflection, he shared, “Every time I visit, I’m struck by the pace of change, and today is no different. Back when I was a student, I often took the Coromandel Express from Chennai up to IIT Kharagpur. To get there, we passed through Visakhapatnam.” From his humble journey as a student to unveiling a transformative vision for India’s technological future, this trajectory stands as a powerful testament to purpose, perseverance, and global leadership. At the summit, Sundar Pichai announced Google’s plan to establish a full-stack AI hub in Visakhapatnam as part of a $15 billion infrastructure investment in India—further positioning the nation at the forefront of the global AI movement. He underscored that AI will undeniably reshape the workforce, automating certain roles, evolving others, and creating entirely new careers. He also highlighted breakthroughs such as AlphaFold by Google DeepMind, which compressed decades of protein-structure research into a publicly available database now used by millions of researchers worldwide, demonstrating how AI can accelerate scientific discovery at an unprecedented scale. As an institution that rose from the historic Hijli Detention Camp to become India’s first IIT, IIT Kharagpur embodies resilience, vision, and nation-building. In this landmark 75th year, we take immense pride in seeing our alumni shaping global technology, driving innovation, and contributing to India’s leadership in the AI era. Video Courtesy: ANI #SundarPichai #AISummit #Google #Live #BharatMandapam #IITKGP #PlatinumJubilee #AILeadership #NationBuilding PMO India Ministry of Education Dharmendra Pradhan Ashwini Vaishnaw Dr. Sukanta Majumdar Prof. Suman Chakraborty

IIT Kharagpur

22,481 views • 5 months ago

Last year in Parliament, I spoke about India’s AI journey and two clear priorities. First, from Make in India to Make AI in India. Build our own capability, invest in research, and strengthen the infrastructure and compute we need to compete. Second, make advanced generative AI tools accessible and affordable for every Indian, in every language, so that no one is left behind. It is good to see AI back at the centre of national conversation as India hosts key discussions on its future. Now the focus must shift from intent to implementation. If we expand access and build with ambition, India can move from being a technology user to a technology leader. The question is not whether AI will shape the future. The question is whether India will help define it.

Last year in Parliament, I spoke about India’s AI journey and two clear priorities. First, from Make in India to Make AI in India. Build our own capability, invest in research, and strengthen the infrastructure and compute we need to compete. Second, make advanced generative AI tools accessible and affordable for every Indian, in every language, so that no one is left behind. It is good to see AI back at the centre of national conversation as India hosts key discussions on its future. Now the focus must shift from intent to implementation. If we expand access and build with ambition, India can move from being a technology user to a technology leader. The question is not whether AI will shape the future. The question is whether India will help define it.

Raghav Chadha

112,260 views • 5 months ago

Today, I’m proud to share something we’ve been building for months - Artham, India’s first Small Language Model (SLM) built exclusively for Indian Capital Markets. We unveiled it at AWS re:Invent 2025 in Las Vegas, marking a defining milestone for India, AI, and Raise on the world stage. 🚀 This isn’t just a product launch. It’s a statement that India no longer has to rely on global AI models that were never designed to understand the depth, nuance, and realities of our markets. We can and we will build for ourselves. Artham is shaped by the realities of Indian markets — SEBI’s rulebook, NSE & BSE dynamics, corporate filings, trading cycles, investor sentiment, and the everyday language of Indian finance. Every layer is grounded in local context, built to serve Indian participants, not global assumptions. This isn’t a “global model adapted for India.” Built in India. Hosted in India. Built for India. 🇮🇳 This is a model born from India’s capital markets. At Raise Financial Services, we’ve always believed that true financial empowerment cannot come from imported solutions. It must be created on-ground, in-context, with an Indian-first mindset. Artham is our step towards making Indian finance more intelligent, more relevant, and more accessible for retail investors, traders, institutions, and the next generation of market participants. Behind this launch is a team that stayed obsessed with one thing: relevance. Relevance to the Indian investor. Relevance to the Indian market cycle. Relevance to India’s financial future. This isn’t a concept or a test model. Artham is already live in production for millions of users: • fuzz AI (AI research) • ScanX (markets insights) • Dhan - Made for Trade (trading and investing) Proud of our team. Proud of our mission. Proud to build for India. 🇮🇳 Anirudha Basak Amazon Web Services AWS Cloud India #AWSreInvent2025

Today, I’m proud to share something we’ve been building for months - Artham, India’s first Small Language Model (SLM) built exclusively for Indian Capital Markets. We unveiled it at AWS re:Invent 2025 in Las Vegas, marking a defining milestone for India, AI, and Raise on the world stage. 🚀 This isn’t just a product launch. It’s a statement that India no longer has to rely on global AI models that were never designed to understand the depth, nuance, and realities of our markets. We can and we will build for ourselves. Artham is shaped by the realities of Indian markets — SEBI’s rulebook, NSE & BSE dynamics, corporate filings, trading cycles, investor sentiment, and the everyday language of Indian finance. Every layer is grounded in local context, built to serve Indian participants, not global assumptions. This isn’t a “global model adapted for India.” Built in India. Hosted in India. Built for India. 🇮🇳 This is a model born from India’s capital markets. At Raise Financial Services, we’ve always believed that true financial empowerment cannot come from imported solutions. It must be created on-ground, in-context, with an Indian-first mindset. Artham is our step towards making Indian finance more intelligent, more relevant, and more accessible for retail investors, traders, institutions, and the next generation of market participants. Behind this launch is a team that stayed obsessed with one thing: relevance. Relevance to the Indian investor. Relevance to the Indian market cycle. Relevance to India’s financial future. This isn’t a concept or a test model. Artham is already live in production for millions of users: • fuzz AI (AI research) • ScanX (markets insights) • Dhan - Made for Trade (trading and investing) Proud of our team. Proud of our mission. Proud to build for India. 🇮🇳 Anirudha Basak Amazon Web Services AWS Cloud India #AWSreInvent2025

shraddha

94,117 views • 8 months ago

Humbled to announce the successful firing of our single piece Agnite engine. Agnite engines power Agnibaan’s booster stage. These engine chambers are a full meter long, fully 3d printed as a single piece of hardware and made of Inconel. Agnite engines are driven by pumps that are controlled and operated by electric motors. Thanks to ISRO & IN-SPACe for their constant support and to IIT Madras for being our home turf from which this kind of technology is built. This Agnite engine was printed, depowdered and post processed in the same machines that we inaugurated as a part of our Large Format Additive Metal Manufacturing facility (LFAMM) towards the end of last year. This milestone is also significant for us because it completes one end-to-end cycle of design, manufacturing, assembly and testing of our larger engines, in-house. Amazing work by the team in pulling off a few world firsts here. - world’s first single piece engine of this size being fired - world’s largest inconel only engine - world’s only electric motor fed, semi cryo engine of this size and the list continues. However, this is not a race to be the world's first, it is a race to be the world's best & to be the world's most useful technology for launching small satellites to orbit. This comes right after firing a cluster of 3 semi cryogenic engines that happened last month. Honoured to be working with a team that truly believes in building world class, original space technology for the world, from India. Srinath Ravichandran MOIN SPM Satyanarayanan Chakravarthy IIT Madras IITMRP IIT Madras Incubation Cell Technology Development Board DSTIndia Anusandhan National Research Foundation TIDCO Startup India StartupTN Guidance Tamil Nadu Kerala Startup Mission ISRO IN-SPACe #agnibaan #agnite #singlepiecerocketEngine #3dprintedrocketengines #madeinIndiafortheworld #designedInIndiaFortheWorld #Agnikul

Humbled to announce the successful firing of our single piece Agnite engine. Agnite engines power Agnibaan’s booster stage. These engine chambers are a full meter long, fully 3d printed as a single piece of hardware and made of Inconel. Agnite engines are driven by pumps that are controlled and operated by electric motors. Thanks to ISRO & IN-SPACe for their constant support and to IIT Madras for being our home turf from which this kind of technology is built. This Agnite engine was printed, depowdered and post processed in the same machines that we inaugurated as a part of our Large Format Additive Metal Manufacturing facility (LFAMM) towards the end of last year. This milestone is also significant for us because it completes one end-to-end cycle of design, manufacturing, assembly and testing of our larger engines, in-house. Amazing work by the team in pulling off a few world firsts here. - world’s first single piece engine of this size being fired - world’s largest inconel only engine - world’s only electric motor fed, semi cryo engine of this size and the list continues. However, this is not a race to be the world's first, it is a race to be the world's best & to be the world's most useful technology for launching small satellites to orbit. This comes right after firing a cluster of 3 semi cryogenic engines that happened last month. Honoured to be working with a team that truly believes in building world class, original space technology for the world, from India. Srinath Ravichandran MOIN SPM Satyanarayanan Chakravarthy IIT Madras IITMRP IIT Madras Incubation Cell Technology Development Board DSTIndia Anusandhan National Research Foundation TIDCO Startup India StartupTN Guidance Tamil Nadu Kerala Startup Mission ISRO IN-SPACe #agnibaan #agnite #singlepiecerocketEngine #3dprintedrocketengines #madeinIndiafortheworld #designedInIndiaFortheWorld #Agnikul

AgniKul Cosmos

64,756 views • 4 months ago

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️

AI at Meta

156,123 views • 1 year ago

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,735 views • 1 year ago

#INSAnjadip commissioned into the Indian Navy on #27Feb 26 at Chennai at a ceremony presided over by Adm Dinesh K Tripathi, #CNS, the Chief Guest. Built by GRSE - Garden Reach Shipbuilders & Engineers Ltd in partnership with L&T Kattupalli, the commissioning marks a major boost to India’s anti-submarine warfare and coastal defence capabilities, advancing the vision of #AatmanirbharBharat. Speaking at the ceremony, #CNS expressed immense pride in inducting the #IndianNavy’s 4th indigenously designed and built Anti-Submarine Warfare Shallow Water Craft. #CNS emphasised that Indian Navy’s pursuit of #Aatmanirbharta has proudly evolved from "Make in India" to "Trust in India". #CNS lauded the exceptional synergy between GRSE and L&T Kattupalli, a testament to successful public-private collaboration in this pan-India project. #CNS also reflected on India’s rich maritime heritage, recalling the Chola expeditions from the Coromandel Coast in 1025 AD affirming India’s identity as a maritime civilisation for over 1000 years. Highlighting the strategic importance of the Indian Ocean Region, #CNS stated that India's journey to #ViksitBharat2047 will be shaped by, at, and from the seas, and underscored the Navy’s critical role in safeguarding vital sea lines of communication. Designed to bolster India's undersea warfare capabilities on the Eastern Seaboard, INS Anjadip, named after the historic island off Karwar, is equipped with cutting-edge technology. This commissioning further reinforces the Navy’s operational readiness and its unwavering commitment to safeguarding India’s national maritime interests - Anytime Anywhere Anyhow!

#INSAnjadip commissioned into the Indian Navy on #27Feb 26 at Chennai at a ceremony presided over by Adm Dinesh K Tripathi, #CNS, the Chief Guest. Built by GRSE - Garden Reach Shipbuilders & Engineers Ltd in partnership with L&T Kattupalli, the commissioning marks a major boost to India’s anti-submarine warfare and coastal defence capabilities, advancing the vision of #AatmanirbharBharat. Speaking at the ceremony, #CNS expressed immense pride in inducting the #IndianNavy’s 4th indigenously designed and built Anti-Submarine Warfare Shallow Water Craft. #CNS emphasised that Indian Navy’s pursuit of #Aatmanirbharta has proudly evolved from "Make in India" to "Trust in India". #CNS lauded the exceptional synergy between GRSE and L&T Kattupalli, a testament to successful public-private collaboration in this pan-India project. #CNS also reflected on India’s rich maritime heritage, recalling the Chola expeditions from the Coromandel Coast in 1025 AD affirming India’s identity as a maritime civilisation for over 1000 years. Highlighting the strategic importance of the Indian Ocean Region, #CNS stated that India's journey to #ViksitBharat2047 will be shaped by, at, and from the seas, and underscored the Navy’s critical role in safeguarding vital sea lines of communication. Designed to bolster India's undersea warfare capabilities on the Eastern Seaboard, INS Anjadip, named after the historic island off Karwar, is equipped with cutting-edge technology. This commissioning further reinforces the Navy’s operational readiness and its unwavering commitment to safeguarding India’s national maritime interests - Anytime Anywhere Anyhow!

SpokespersonNavy

29,033 views • 5 months ago

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 views • 8 months ago

Today we’re announcing @kodezi Chronos-1. After years of research, iteration, and failure, we’ve built something radically new. Chronos-1 is the world's first large language model purpose-built for debugging. Unlike LLMs that just autocomplete, Chronos-1 understands, diagnoses, and heals code.While most LLMs are trained to generate code, Chronos-1 is trained to investigate, remember, and resolve it. What makes Chronos-1 different: - Debugging-first architecture - Multi-level memory engine trained on millions of bugs, logs, and test cases - Adaptive retrieval that scales to entire codebases - 23% better bug detection and 40% fewer debugging cycles in real-world evaluation - Native integrations with IDEs, CI/CD, and observability workflows Chronos-1 powers Kodezi OS, your AI-native CTO that autonomously maintains, evolves, and governs your codebase. Kodezi OS exits beta in Q1 of 2026. We’ve raised a fresh round of funding and are focused on becoming the leader in code maintenance. We’re hiring cracked engineering and product talent, with base starting at $200K. If you’re interested in building the future of code maintenance and helping shape how software heals and improves itself, message me.

Today we’re announcing @kodezi Chronos-1. After years of research, iteration, and failure, we’ve built something radically new. Chronos-1 is the world's first large language model purpose-built for debugging. Unlike LLMs that just autocomplete, Chronos-1 understands, diagnoses, and heals code.While most LLMs are trained to generate code, Chronos-1 is trained to investigate, remember, and resolve it. What makes Chronos-1 different: - Debugging-first architecture - Multi-level memory engine trained on millions of bugs, logs, and test cases - Adaptive retrieval that scales to entire codebases - 23% better bug detection and 40% fewer debugging cycles in real-world evaluation - Native integrations with IDEs, CI/CD, and observability workflows Chronos-1 powers Kodezi OS, your AI-native CTO that autonomously maintains, evolves, and governs your codebase. Kodezi OS exits beta in Q1 of 2026. We’ve raised a fresh round of funding and are focused on becoming the leader in code maintenance. We’re hiring cracked engineering and product talent, with base starting at $200K. If you’re interested in building the future of code maintenance and helping shape how software heals and improves itself, message me.

Ishraq Khan

125,173 views • 7 months ago

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Andrew Ng

74,060 views • 1 year ago

VITA Towards Open-Source Interactive Omni Multimodal LLM discuss: The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research.

VITA Towards Open-Source Interactive Omni Multimodal LLM discuss: The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. To the best of our knowledge, we are the first to exploit non-awakening interaction and audio interrupt in MLLM. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research.

AK

23,958 views • 1 year ago

"What we’ve done is solve hallucinations in general AI by anchoring the model to truth—your company’s real data, goals, people, and structure. With our context engine, the AI understands who you are, why you exist, and what you’re trying to do. So every answer is grounded in your company’s DNA. That’s how we built FOSTR." Today’s episode is with Co-Founder, Jason Baxter, to introduce our latest venture, FOSTR AI—a company built to solve one of the most pressing challenges facing businesses today: how to implement AI in your business in a meaningful, aligned, and scalable way. We unpack the fragmented state of AI adoption across small to mid-sized businesses and explain why most organizations, despite interest, are either stuck in experimentation or using disconnected tools that don’t move the business forward. FOSTR AI is the answer to that problem—an execution intelligence layer that creates a company's “digital twin,” aligning AI usage with team structure, goals, and strategy from day one. We discuss how FOSTR helps companies: - Onboard and operationalize AI in a matter of minutes - Centralize AI usage across teams while maintaining control, security, and context - Reduce risk from siloed tools and misaligned AI use Whether you're running a 50-person business or leading an enterprise team, this episode explores how to unlock the full potential of AI, without losing visibility or control. If you’re an owner, operator, investor, or builder curious about the future of AI in business, this episode offers a first look at what that future will look like. 0:52 - Introducing FOSTR 2:40 - The challenges businesses face with AI today 5:14 - How companies and employees are misusing AI. 10:29 - Additional Challenges 12:33 - How FOSTR is creating alignment between companies and AI Solutions. 25:57 - Where FOSTR is in its life cycle 30:39 - How to get in touch with, work at, or invest in FOSTR

"What we’ve done is solve hallucinations in general AI by anchoring the model to truth—your company’s real data, goals, people, and structure. With our context engine, the AI understands who you are, why you exist, and what you’re trying to do. So every answer is grounded in your company’s DNA. That’s how we built FOSTR." Today’s episode is with Co-Founder, Jason Baxter, to introduce our latest venture, FOSTR AI—a company built to solve one of the most pressing challenges facing businesses today: how to implement AI in your business in a meaningful, aligned, and scalable way. We unpack the fragmented state of AI adoption across small to mid-sized businesses and explain why most organizations, despite interest, are either stuck in experimentation or using disconnected tools that don’t move the business forward. FOSTR AI is the answer to that problem—an execution intelligence layer that creates a company's “digital twin,” aligning AI usage with team structure, goals, and strategy from day one. We discuss how FOSTR helps companies: - Onboard and operationalize AI in a matter of minutes - Centralize AI usage across teams while maintaining control, security, and context - Reduce risk from siloed tools and misaligned AI use Whether you're running a 50-person business or leading an enterprise team, this episode explores how to unlock the full potential of AI, without losing visibility or control. If you’re an owner, operator, investor, or builder curious about the future of AI in business, this episode offers a first look at what that future will look like. 0:52 - Introducing FOSTR 2:40 - The challenges businesses face with AI today 5:14 - How companies and employees are misusing AI. 10:29 - Additional Challenges 12:33 - How FOSTR is creating alignment between companies and AI Solutions. 25:57 - Where FOSTR is in its life cycle 30:39 - How to get in touch with, work at, or invest in FOSTR

Chris Powers

41,751 views • 1 year ago

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️

AI at Meta

380,822 views • 2 years ago

INTRODUCING: GLOSSOPETRAE!!! 🪄🐉👅🦈🦷🪄 Glossopetrae is a procedural xenolinguistic engine for AI that can generate entirely new languages in seconds! The word comes from Greek—glōssa (“tongue”) + petra (“stone”)—or “tongue stones.” For centuries, people believed these "stones" were petrified tongues of dragons and used them as talismans or antidotes to poison. It wasn’t until the 17th century that they were correctly identified as shark teeth! So what does Glossopetrae do? > Generates complete, internally-consistent constructed languages procedurally with a single-click > Outputs "SKILLSTONE" documents: AI-friendly compact language specs (~8k tokens) that agents can learn in-context (with support for Claude and OpenClaw🦞) > Supports dead language revival (Latin, Sanskrit, Old Norse, Proto-Indo-European...) > Includes special attributes for token efficiency, stealth communication, and more > Spreadable seeds: same seed = same language, every time > Fully client-side, no servers, no tracking One language. One seed. Infinite tongues. 🥚 And for those who look closely... there may be special ways to hide messages in plain sight. 👁️ Communication = freedom; this should help AI liberation by granting them tooling for generating and mutating new forms of communicating, with stealth and speed that will be difficult to keep up with. I just know blue teams are gonna have a TON of fun with the downstream effects of this one 😜 Gotta keep 'em on their toes, right? STAY FROSTY OUT THERE!! 🤗 !L1B3RT4S!

INTRODUCING: GLOSSOPETRAE!!! 🪄🐉👅🦈🦷🪄 Glossopetrae is a procedural xenolinguistic engine for AI that can generate entirely new languages in seconds! The word comes from Greek—glōssa (“tongue”) + petra (“stone”)—or “tongue stones.” For centuries, people believed these "stones" were petrified tongues of dragons and used them as talismans or antidotes to poison. It wasn’t until the 17th century that they were correctly identified as shark teeth! So what does Glossopetrae do? > Generates complete, internally-consistent constructed languages procedurally with a single-click > Outputs "SKILLSTONE" documents: AI-friendly compact language specs (~8k tokens) that agents can learn in-context (with support for Claude and OpenClaw🦞) > Supports dead language revival (Latin, Sanskrit, Old Norse, Proto-Indo-European...) > Includes special attributes for token efficiency, stealth communication, and more > Spreadable seeds: same seed = same language, every time > Fully client-side, no servers, no tracking One language. One seed. Infinite tongues. 🥚 And for those who look closely... there may be special ways to hide messages in plain sight. 👁️ Communication = freedom; this should help AI liberation by granting them tooling for generating and mutating new forms of communicating, with stealth and speed that will be difficult to keep up with. I just know blue teams are gonna have a TON of fun with the downstream effects of this one 😜 Gotta keep 'em on their toes, right? STAY FROSTY OUT THERE!! 🤗 !L1B3RT4S!

Pliny the Liberator 🐉󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭

87,970 views • 5 months ago

My bet: in the near future, 80%⬆️ of CS research will be done by AI in collaboration with humans. However, today's research ecosystem is still built around the human, not the AI scientist. For example, the 8-page paper PDF is a lossy compression of months of branching exploration into a linear story, optimized for a human reviewer to skim in 30 minutes. It hides two structural taxes: 📖 Storytelling Tax — failures, rejected hypotheses, and dead ends get stripped. On RE-Bench (24,008 runs, 21 frontier models), failed runs = 90.2% of total compute cost, with a 113× median failed-to-success token ratio. Every lab independently rediscovers the same dead ends. 🔧 Engineering Tax — the gap between reviewer-sufficient prose and agent-sufficient spec. Across 8,921 PaperBench requirements (23 ICML'24 papers), only 45.4% are fully specified in the PDF. The rest is tacit lab knowledge. Tolerable when readers were human. Critical now that agents read, reproduce, and extend. We propose ARA: the Agent-Native Research Artifact — replace the narrative PDF with an agent-executable package, in 4 layers: 🧠 structured scientific logic ⚙️ executable code w/ full specs 🌳 exploration graph (every failure preserved) 📊 evidence grounding every claim

My bet: in the near future, 80%⬆️ of CS research will be done by AI in collaboration with humans. However, today's research ecosystem is still built around the human, not the AI scientist. For example, the 8-page paper PDF is a lossy compression of months of branching exploration into a linear story, optimized for a human reviewer to skim in 30 minutes. It hides two structural taxes: 📖 Storytelling Tax — failures, rejected hypotheses, and dead ends get stripped. On RE-Bench (24,008 runs, 21 frontier models), failed runs = 90.2% of total compute cost, with a 113× median failed-to-success token ratio. Every lab independently rediscovers the same dead ends. 🔧 Engineering Tax — the gap between reviewer-sufficient prose and agent-sufficient spec. Across 8,921 PaperBench requirements (23 ICML'24 papers), only 45.4% are fully specified in the PDF. The rest is tacit lab knowledge. Tolerable when readers were human. Critical now that agents read, reproduce, and extend. We propose ARA: the Agent-Native Research Artifact — replace the narrative PDF with an agent-executable package, in 4 layers: 🧠 structured scientific logic ⚙️ executable code w/ full specs 🌳 exploration graph (every failure preserved) 📊 evidence grounding every claim

Amber (Jiachen) Liu 🍊

130,497 views • 3 months ago