Pratik Sen

🔎 Hiring Panel Lens [Series 19] Why Strong PMs Sound Junior In AI Interviews After multiple AI-focused interviews, one pattern is very clear: Strong PMs are getting downgraded… not because of capability — but framing. When asked about AI work, most candidates start with: ➡️the model ➡️the feature ➡️the implementation Hiring panels are listening for something else: 👉 What decision the system is responsible for 👉 What happens when that decision is wrong 👉 How you designed for that failure Example: Weak answer: “We built an LLM to automate support.” Stronger answer: “We needed to decide which support queries could be resolved autonomously without increasing escalation risk.” Same system. Very different signal . AI products are not model problems. They are decision systems under risk. Quick self-check: When you describe your AI work, do you start with: A) The model B) The feature C) The decision the system makes Comment DECISION. I’ll share how strong candidates structure this in interviews. #productmanagement #aiproductmanager #productleadership #artificialintelligence #careergrowth #techcareers #hiringpanellens

16

3 Comments

Projecr Titel: Strassen’s Algorithm for matrix multiplication Contributor: Rajeev Singh Sisodiya, ORCID: 0009-0003-6585-4744 Strassen’s algorithm is a divide-and-conquer algorithm for matrix multiplication that reduces the computational complexity compared to the standard (naïve) method. Instead of performing 8 multiplications when dividing matrices into blocks, Strassen reduces it to 7 multiplications by increasing the number of additions/subtractions. Strassen’s algorithm is much deeper than “just a faster multiplication trick.” It is fundamentally about tensor rank reduction, which connects it directly to fast linear algebra and even quantum algorithms. The standard algorithm corresponds to tensor rank = 8, because it uses 8 scalar multiplications. After Strassen, research focused on minimizing the tensor rank of matrix multiplication. Key milestones: 1.Strassen (1969) 2.Coppersmith–Winograd (1987) 3.Alman–Vassilevska Williams (2021) The relationship between HHL, block-encoding, and QSVT (Quantum Singular Value Transformation) is essentially the evolution of quantum linear-algebra algorithms from a specialized construction to a general operator-transformation framework. You can think of it historically and structurally: HHL (2009) → first quantum linear-system solver Block-encoding (2015–2019) → unified operator representation QSVT (2018–2019) → universal matrix-function compiler https://lnkd.in/gHqfydKT

1

🚨 NeurIPS 2025 Spotlight Paper Alert 🚨 ➡️Paper Title: STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation 🌟Few pointers from the paper 🎯Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy, and is crucial in domains such as robotics or healthcare where direct interaction with the environment is costly or unsafe. 🎯Existing OPE methods are ineffective for high-dimensional, long-horizon problems, due to exponential blow-ups in variance from importance weighting or compounding errors from learned dynamics models. 🎯To address these challenges, authors of this paper proposed “STITCH-OPE”, a model-based generative framework that leverages denoising diffusion for long-horizon OPE in high-dimensional state and action spaces. 🎯Starting with a diffusion model pre-trained on the behavior data, STITCH-OPE generates synthetic trajectories from the target policy by guiding the denoising process using the score function of the target policy. 🎯STITCH-OPE proposes two technical innovations that make it advantageous for OPE:  (1) prevents over-regularization by subtracting the score of the behavior policy during guidance, and  (2) generates long-horizon trajectories by stitching partial trajectories together end-to-end. 🎯They provide a theoretical guarantee that under mild assumptions, these modifications result in an exponential reduction in variance versus long-horizon trajectory diffusion. 🎯Experiments on the D4RL and OpenAI Gym benchmarks show substantial improvement in mean squared error, correlation, and regret metrics compared to state-of-the-art OPE methods. 🏢Organization: University of Toronto, University of Toronto Robotics Institute, Toyota Research Institute, Vector Institute 🧙Paper Authors: Hossein Goli, Michael Gimelfarb, Nathan Samuel de Lara, Haruki Nishimura, Masha Itkina, Florian Shkurti 📝 Read the Full Paper here: https://lnkd.in/gz2-DSQn 🗂️ Project Page: https://lnkd.in/gV5vdcF4 🧑‍💻 Code: https://lnkd.in/g9AvhmpE 🎥 Be sure to watch the attached Technical Summary Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #NeurIPS2025

11

Last few days have been running ForgeCAD CAD-as-code system as a benchmark on most major AI models with same prompt to build an AC system. The results were somewhat surprising. While many new models compete on existing benchmarks and show results matching or beating the strongest models, here the results were less saturated. Only a few models continuously performed good. Opus 4.5, Opus 4.6, GPT-5.2-Codex, GPT-5.3-Codex, and surprisingly Gemini-3-Flash (for not hard models or low complexity changes). Most other models I tried had difficulties with spatial reasoning. With GPT-5.3-Codex I took it further, asking for harder and harder models, so far the capabilities are very strong. I shared the results of all tested models in the GitHub repo: https://lnkd.in/dm5SmCYT

17

The most ironic part of all this is, the subject they deem only theoretical often define our real world. AI functioning on matrices and theorems? That's not something which you can build or realise. It's a concept, a method, a thought process. And that very thought process now impacts the whole world. Pure Science, Mechanical Engineering, Aeronautics, Food Technology, Philosophy, Psychology, all these supposed niche fields require huge brain power, intellectual capability and creativity. The applied fields and concepts are nothing without their theoretical part. We must remember that.

6

Indian? Want free GPU compute? Notebooks, Models, Datasets and more..... Govt of India offers this for citizens: https://lnkd.in/ecAnNGWd PS: Not sure if this is available for NRIs, PIOs etc. Check it out.

6