Dmitry Kalashnikov

Google Antigravity exposes the critical flaw in autonomous AI agent architectures: trust based governance at scale. Antigravity represents a major shift in how developers work. Instead of writing code line by line, you delegate entire tasks to autonomous agents that can modify files, run tests, browse the web, and execute changes across your codebase in parallel. The problem? These agents operate on trust, not proof. One developer reported: On Day 3, an agent confidently refactored a utility function and silently deleted a critical edge case check. This is not a bug. This is the inevitable result of autonomous agents operating without cryptographic authority validation. When you have three agents working asynchronously across different files, two critical questions emerge: how do you enforce what each agent is authorized to do, and when multiple agents coordinate on shared resources, how do you maintain isolation between their operations? Policy based guardrails do not work at this scale. I have solved both problems through complementary cryptographic architectures. For individual agent authorization, my 13 layer cryptographic governance system validates AI agent authority mathematically before execution. For multi agent coordination, my YIN COLLAB architecture implements Agent Specific Compliance Tokens with per agent privacy isolation maintaining cryptographic boundaries preventing cross contamination. Every action carries immutable proof of authorization. Every decision boundary is pre validated cryptographically. 26 USPTO patents. 2,330 claims. Validated with 640x timing resistance and 500 plus concurrent agent support with sub 15ms latency. Mathematical proof, not policy promises. For developers using Antigravity, Cursor, or any autonomous agent platform: the question is whether you can prove mathematically that unauthorized operations are impossible and that multi agent coordination maintains isolation. Because as agent orchestration becomes the dominant development paradigm, the liability surface expands exponentially. One misconfigured agent with access to production systems is an organizational failure. One agent leaking sensitive data to another agent through shared context is an architectural vulnerability. Making it mathematically impossible for agents to violate boundaries is the only governance model that scales. Autonomous agents are the future of software development. Cryptographic governance is the only way to make that future safe. https://lnkd.in/ejPREk9D #GoogleAntigravity #AIGovernance #AutonomousAgents #Cybersecurity #AIAgents #DeveloperTools #CryptographicSecurity #ZeroTrust #AICompliance #SoftwareDevelopment #TechInnovation #AIEthics

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⚙️ Claude Code is creating a buzz among Seattle engineers, who describe the AI tool as a supercharged coding partner that can refactor large codebases, handle tedious boilerplate, and stitch services together at speed. The article paints a picture of software teams rethinking roles, workflows, and onboarding as coding assistants shift from autocomplete to true collaborators. For engineering leaders, the big challenge is how to harness this productivity jump while maintaining code quality, security, and human expertise in the loop. #AI #Claude #SoftwareDevelopment #EngineeringLeadership #DevTools #Productivity #AIFreakyFacts

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Microsoft Just Made 100B Parameter LLMs Run on Your Laptop. Who Governs Them? BitNet breaks the GPU barrier. 1.58 bit quantization. 100 billion parameters on a single CPU. No cloud. No API costs. Local inference at human reading speed. This is a genuine breakthrough for accessibility. But accessibility without governance is liability. Running locally solves the infrastructure problem. It does not solve the governance problem. A 100B parameter model on your laptop can still hallucinate with confidence. It can still produce biased outputs. It can still make decisions with no audit trail. It can still violate constitutional bounds you never defined. Privacy from cloud providers is not the same as privacy from the model itself. Local execution means your data stays on your device. It does not mean the model operates within governance constraints. The question is not where the model runs. The question is whether the model can prove it operated within bounds. YIN-GATEWAY solves this. YIN-GATEWAY is a cryptographic control plane that sits between any LLM and its users. Cloud model, local model, quantized model, full precision model. The deployment changes. The governance requirement does not. Layer 2 Admissibility Gates reject non-compliant queries before execution. The model never sees requests that violate policy. Layer 3 Constitutional Bounds verify every output before it reaches users. If the output would violate governance constraints, it does not proceed. Layer 14 SENTINEL Verification runs independent binary checkpoint validation. Pass or fail. No partial compliance. Layer 15 Human Override requires cryptographic signatures before high-risk outputs proceed. Not a checkbox. A mathematical proof that a human authorized this specific action. BitNet gives you a 100B parameter model on your CPU. YIN-GATEWAY gives you cryptographic proof that the model operated within constitutional bounds, regardless of where it runs. The hardware revolution is here. The governance layer was already built. https://lnkd.in/eb2EdEwR 32 USPTO applications. 3,057 claims. Any model. One governance layer. Enforced behavior. #BitNet #YINMazariArchitecture #YINGATEWAY #AIGovernance #CryptographicCompliance #Microsoft #LocalAI #LLM #PrivacyPreserving #ResponsibleAI #EdgeAI #CPUInference #OpenSource

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Podonos just raised $2.4 million in pre-seed funding, and the signal is loud and clear: the voice AI industry finally has someone measuring the sound, not just making the noise. Based in Los Gatos, Podonos is building the infrastructure layer that decides whether voice AI feels real, human, and ready for primetime. The industry is racing toward a projected $47.5 billion by 2034, but growth without standards is chaos. Podonos is making sure the hype actually holds up when you press play. At the center is ✦Soohyun Bae, PhD in Computer Engineering from Georgia Institute of Technology and Y Combinator W22 alumnus. His track record stretches from engineering roles at Google Maps to leading AR mapping at Niantic, plus co-founding Bobidi and TickTock AI. He has seen what happens when technology scales without proper #guardrails. With Podonos, he's betting the future of AI isn't just about how smart the models get, but how believable and trustworthy they sound. Podonos evaluates models across naturalness, similarity, emotion, recognition accuracy, pronunciation, tone, and resilience. Add in #personaconsistency, and suddenly you're not just testing machines, you're testing their ability to pass as human. The twist? They deliver results in under twelve hours, when the legacy approach drags on for two months. In AI time, that's the difference between relevance and irrelevance. Investors caught the beat. Serac Ventures, led by Kevin Moore, took the lead, backed by NAVER D2SF, the venture arm of South Korea's NAVER, and KAIST Venture Investment Holdings. Together, they've now backed Podonos twice, with this $2.4 million round stacking on top of a $750,000 raise in March 2025. Total funding sits at $3.15 million, and every dollar is pointed toward scaling #engineering, refining #analysistools, and launching into the U.S., Europe, and Southeast Asia. This isn't theory. Podonos already serves Resemble AI, Play AI, and Sanas AI, hitting six-digit ARR by November 2024 with just four employees. Their secret weapon is reach: 150,000 evaluators spread across nine languages and thirteen locales, forming a human-in-the-loop platform that blends speed, rigor, and global coverage. That reach lets Podonos deliver evaluations at a scale and pace that no competitor has matched. The strategy is ambitious: expand into high-demand sectors like #healthcare, #finance, #gaming, and #advertising, then move beyond voice into #multimodal model evaluation, spanning video AI and large language models. The mission isn't just to check if AI works, it's to decide if AI works for people. #Startups #StartupFunding #EarlyStage #VentureCapital #PreSeed #AI #AIInfrastructure #Voice #VoiceAI #VoiceTech #Infrastructure #Technology #Innovation #TechEcosystem #StartupEcosystem #Hiring #TechHiring If software engineering peace of mind is what you crave, Vention is your zen.

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Indeed. A small number of big tech companies have grossly overinvested in LLMs and overextended themselves -- even laying off tens of thousands of critically needed human employees and enshittifying their primary products and services to reallocate investment into it -- shooting Nvidia's profits and shares to the moon -- all in an insane attempt to "be first" for something that has no forseeable compelling ROI. It's a collective delusion and hype bubble. This is markedly different from the strategic "operate in the red to build marketshare" strategy of the few survivors of the dot-com bust, because at least those survivors were able to articulate (and realize) a logical ROI after reaching a tipping point of critical mass. This is more like the 98% of dot-com startups that burned bright fueled by VC right until they burned themselves out because they never actually had any believable plan for monetizing anything they were building. The lack of ROI will come rudely crashing down on these companies over the next several quarters as they run out of tricks to cook the books and hide the losses. Shareholders will get spooked and back out, share prices will crash, and big tech companies that have bet the farm on LLMs will suddenly find themselves without the necessary employees, capital, quality products/services, or reputational sway needed to save themselves. And when they collapse, Nvidia will collapse with them. And then maybe, finally, we can get back to the business of making things of actual value to humans.

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Love this direction. Designing rewards that are dense while keeping benchmarks hard to hack is the right way to push LLM evaluation forward. I also appreciate how the paper introduces example-specific rubrics — making LLM judging aligned with actual user expectations instead of vague, artificial criteria. Clear, grounded rubrics are exactly what the evaluation ecosystem needs. Highly recommend reading. 👇

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AI San Diego: Community Outlook San Diego is well-suited for AI development for several key reasons, including a strong tech ecosystem, robust infrastructure, academic excellence, industry presence, and government support. Here's an overview of the factors that make San Diego an attractive location for AI development: 1. Strong Tech Ecosystem Tech Companies and Startups: San Diego is home to a vibrant tech community with numerous companies and startups specializing in AI, machine learning, and related fields. Notable AI-focused companies in the area include Shield AI, Brain Corp (robotics), and Reticular Systems. 2. Academic Excellence Universities and Research Institutions: San Diego boasts world-class universities and research institutions that contribute to AI research and development. The University of California, San Diego (UCSD) is a leading institution in AI research, with strong programs in computer science, engineering, and cognitive science. 3. Robust Infrastructure High-Speed Internet and Connectivity: San Diego has excellent digital infrastructure, including high-speed internet and reliable connectivity, essential for AI research and deployment. Data Centers: The presence of modern data centers ensures that companies have access to the necessary computing power and storage for AI training and data processing. 4. Industry Presence Defense and Aerospace: San Diego has a significant presence of defense and aerospace companies, such as Northrop Grumman and General Atomics. These industries are increasingly leveraging AI for applications like autonomous systems, cybersecurity, and data analysis. Life Sciences and Healthcare: The city is a hub for life sciences and healthcare industries, where AI is being applied for drug discovery, personalized medicine, and diagnostic tools. Companies like Illumina and ResMed are examples of leaders in this sector. 5. Government and Military Support Department of Defense (DoD) Projects: San Diego’s proximity to military installations and defense contractors facilitates partnerships on AI projects funded by the DoD, enhancing R&D in autonomous systems and defense applications. 6. Networking and Community Professional Organizations: Organizations like the San Diego Tech Hub and AI enthusiast groups foster a sense of community and collaboration among AI professionals and enthusiasts. 7. Quality of Life Attractive Living Conditions: San Diego’s high quality of life, including its favorable climate, scenic environment, and vibrant cultural scene, makes it an attractive place for tech professionals to live and work.

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The changing face of San Francisco - The rise of AI is helping to diversify SF workforce from domination by the software engineers to a broader range of talents as more advanced hardware companies are expanding in the city, including Amazon and Waymo. https://lnkd.in/g6A4YYw6

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Ruokai Yin, our summer intern from Yale to Microsoft Azure, worked with Xuan Zuo, @Sattwik Deb Mishra, Hokchhay T. and Preyas Shah and me. We published a paper based on his project: https://lnkd.in/eHuG3BSt Summary of paper: Finding optimal sharding is hard especially for large LLMs. A large number of devices have to be divided into TP, PP, EP dimensions. For TP, we have to decide the tensor dimension e.g. head, hidden size to shard for every op. Additionally, TP can be one-dimensional/two-dimensional etc. This makes for a large sharding space that is hard to cover exhaustively. SoTA methods use heuristics to select sharding e.g. GSPMD, Sequence Parallelism, Helix etc. There are significant time gaps, months or years, between the discovery of each of these methods. We want to fast-forward the process of discovery by 1) using a formal representation of the sharding space, and, 2) using reinforcement learning to search the sharding space.    This paper demonstrates that our method is 3.5x better than random search, and on par with hand-tuned heuristics when looking only at 1D sharding and without allowing sequence parallelism. We aim to extend the work to multi-dimensional sharding as well as sequence parallelism. Beyond that, we wish to combine the problem of sharding with fusion, tiling and collectives as well.

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Death, taxes, Microsoft layoffs. Another 305 staff look set to be cut in Washington just weeks after the company announced plans to lay off nearly 2,000 workers in its home state. Software engineering roles are once again among those worst affected.

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