Poorva Rane

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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Compressing LLMs but worried about accuracy? 🎯 EoRA uses eigenspace low-rank approximation to compensate for errors—no retraining needed. A promising direction for scalable, task-adaptive LLMs. New paper from #NVIDIAResearch.

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Compressing LLMs but worried about accuracy? 🎯 EoRA uses eigenspace low-rank approximation to compensate for errors—no retraining needed. A promising direction for scalable, task-adaptive LLMs. New paper from #NVIDIAResearch.

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Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech. To be skilled at building AI systems, I recommend that you: - Take AI courses - Practice building AI systems - (Optionally) read research papers Let me share why each of these is important. I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly! For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important! Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day! At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too! [Truncated for length. Full text: https://lnkd.in/gwv8nsgN ]

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$1B+ bet on the next frontier of AI: Physical & Spatial Intelligence Today, Yann LeCun’s new startup, AMI - Advanced Machine Intelligence, announced a massive $1.03 billion funding round to challenge the dominance of Large Language Models (LLMs). Current AI is "trapped" in a digital box of text. To reach human-level autonomy, AI needs to understand the physical world. It needs to know that if you push an object, it falls; it needs to navigate a room without a map; it needs to plan complex tasks in real-time. From Digital to Physical: AI is moving into robotics, smart glasses (like Ray-Ban Meta), and industrial automation. From Prediction to Planning: Moving away from probabilistic guessing toward goal-oriented reasoning. The "Ami" (Friend) Approach: Building AI that is controllable, safe, and grounded in reality. The next frontier isn't just "generative"—it’s spatial and physical. We are moving from AI that talks to us, to AI that works alongside us in the physical world. Congrats to the AMI team on this milestone. The era of "World Models" is here. https://lnkd.in/gE99YfGJ #AI #Robotics #MachineLearning #YannLeCun #AMI #Innovation #SpatialIntelligence #TechNews

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black-forest-labs/FLUX.2-klein-9B is crazy fun! Under FLUX Non-Commercial License, it bring: - Real-time AI agents with visual reasoning. - Instant, iterative design workflows. - Low-latency production pipelines on consumer-grade hardware.  - Sub-second speed - Text-to-Image + Native Editing in one pipeline. - Fits in ~20GB VRAM (RTX 4090 ready). The 4-step distilled version is a game-changer for anyone looking to deploy high-quality vision models without the massive VRAM overhead. #GenerativeAI #MachineLearning #FLUX2 #OpenSource #AIEngineering

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Claude just released a memory import tool: https://lnkd.in/eM8c7Gf5 You paste a prompt into ChatGPT (or whatever you're using), it spits out your context — preferences, projects, how you work — and you drop it into Claude's settings. That's it. Claude picks it up from there. The switching cost has always been the real barrier. Not the AI itself, just the fact that you've spent months building context somewhere else. This is Anthropic admitting that out loud and actually doing something about it. Quiet release. Worth knowing about.

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Most AI memory systems fail because they treat schema as an afterthought. I have seen dozens of RAG implementations break down in production - not because of embedding quality or retrieval accuracy, but because teams skip the foundational work of defining clear data schemas. Without structured memory models, your AI system becomes a black box that degrades over time. We recently worked with a fintech client whose LLM was hallucinating financial data. The root cause? Their memory system had no schema validation. Documents, user interactions, and context were stored as unstructured blobs. When we implemented strict schema definitions for their knowledge base, accuracy improved by 40% overnight. Start with schema design before you build your RAG pipeline. Define your data structures, validation rules, and memory hierarchies upfront. Your future self will thank you when you are debugging at 2 AM. What is your biggest challenge with AI memory reliability in production? #AIInfrastructure #RAG #MachineLearning #DataEngineering

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Is the computational ceiling about to break? The "SAT Problem" is the invisible bottleneck of our era. From EDA chip design at Nvidia and Intel to the formal verification of AWS cloud security and the scaling of DeepMind’s next-gen AI—we are all running into the same wall: Exponential Complexity. Current solvers (CDCL/Local Search) are masterpieces of engineering, but they still "stall" when faced with high-treewidth, structured monoliths. At Digital Dynamics AI Inc., we have developed a new framework: tEPIC (Topological Exact Partitioning & Integrated Certification). We have moved past heuristic-based guessing. Our solver, SOSAT, utilizes two novel, peer-reviewed-grade theorems to navigate the logic landscape deterministically: 1️⃣ The Charging Inequality (GO-360-CH): A combinatorial guard that forces any SAT instance to reveal its internal "triage" state. Every sub-component is mathematically boxed into one of three exits: a global fracture (Separator), a governing bottleneck (Core), or a high-density collision (Cycle). 2️⃣ Semantic Splice-or-Pivot (GO-361): A proof-complexity compiler that serializes local Unit Propagation traces into globally verifiable DRAT/RUP certificates. This allows us to "splice" overlapping micro-proofs into a unified solution path in strictly polynomial time. The result? A structural engine that doesn't just "try" to solve a problem—it exhausts the instance's topology until a refutation or a witness is found. No metaphors. No "magic." Just discrete graph accounting. I am now looking to present the SOSAT/tEPIC architecture to the Automated Reasoning and AI Safety teams at Microsoft Research, Google DeepMind, and Amazon Web Services. We are ready to demonstrate performance on industrial-scale "unsolvable" monoliths via a secure, black-box audit. If you are a Principal Engineer or Researcher in Formal Methods or AI Scaling, let’s talk about moving the world past the exponential wall. #SAT #DeepTech #ComputerScience #AI #FormalMethods #DigitalDynamicsAI #PvsNP #Innovation

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Comparing models.. Finding that a small 1B #llm hallucinates less than a 4B for semantic to structural matching. Small llms for targeted tasks for the win. The new Meta llama3.2 1B is better than expected and cheap on token cost.

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