Wednesday / AI 101 series: The future of compute: What are alternatives to GPU/TPU/CPU?

Friday / The comeback of the AI Unicorn Series!

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Highlight-and-edit for mid-sized tweaks.

Claude Code / Codex for larger tasks and ephemeral utilities.

GPT-5 Pro as the ultimate debugger and researcher.

Karpathy from Aug 28; Karpathy from Aug 27; Karpathy from Aug 24; Karpathy from Aug 16 (this one is not in the editorial but interesting to read as well); Karpathy from Aug 9 and from Aug 2

A new – listening – car?
Fraunhofer’s “Hearing Car” project gives autonomous vehicles something oddly human: a sense of hearing. Unlike lidar or radar, this AI-powered acoustic tech picks up sirens, slippery roads, and even the sound of kids playing – before a camera sees a thing. But: it filters out human speech in real time. What do you think? Is acoustic sensing the next big leap in driver assistance?

BMW’s Cars Learn to Drive Themselves – with a Qualcomm Accent
Qualcomm and BMW have teamed up to launch Snapdragon Ride Pilot, a jointly built automated driving system making its debut in the BMW iX3. The system blends AI-powered perception, data-fueled decision-making, and a cloud-fed feedback loop that keeps learning on the road.

Hitachi Bets Big on the American Grid
In a rare show of industrial scale and geopolitical timing, Hitachi Energy is pumping $1B into U.S. manufacturing – including $457M for a colossal transformer plant in Virginia. The goal? Power America's AI boom and grid resilience, all while creating 800+ jobs. It's energy policy meets reindustrialization – with just a dash of election-year pageantry.

Amazon's Got an Eye for It
Amazon unveils Lens Live, a turbocharged version of its visual search tool now armed with real-time product scanning and integrated AI insights via Rufus. Just point your camera, and a swipeable carousel does the rest – comparison, carting, wish-listing, all in one shot. It's shopping by sight, sped up by machine learning.

Anthropic Hits Stratospheric
Anthropic just closed a jaw-dropping $13B Series F at a $183B valuation, putting Claude squarely in the enterprise AI throne room. With revenues leaping from $1B to $5B in under a year and Claude Code raking in $500M on its own, this is no hype cycle – it's a full-scale business blitz. ICONIQ leads the charge, betting big on Anthropic’s safety-first, scale-fast playbook.

OpenAI Acqui-hires Its Experimentation Engine
OpenAI just snapped up Statsig and named its founder, Vijaye Raji, as CTO of Applications. Known for A/B testing wizardry, Statsig already powered OpenAI’s rapid iteration behind the scenes. Now it’s in-house. With Raji overseeing ChatGPT and Codex engineering, the move signals a shift toward product polish – with data-driven rigor baked in. Agile AI, meet your new chief tinkerer.

gpt5 is smarter than you are by Will Schenk

Are we truly on the verge of the humanoid robot revolution? by US Berkeley News

Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence by Stanford

Apertus: a fully open, transparent, multilingual language model with public weights, data recipes, checkpoints, and documentation for sovereign, transparent deployment →read the paper

EmbeddingGemma from google – deliver an open embedding model optimized for on-device retrieval, clustering, and search, supporting multilingual pipelines and flexible embedding sizes. ->read the paper

LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model – transform a multimodal critic into a unified critic-policy through reinforcement learning, enabling strong generation and self-critique. ->read the paper

Kwai Keye-VL 1.5 Technical Report – advance video understanding in an MLLM with a slow-fast encoding strategy, long-context training, and reasoning-focused post-training. ->read the paper

Robix: A Unified Model for Robot Interaction, Reasoning and Planning – unify high-level robot planning and dialogue in a single vision-language policy that plans, executes, and interacts in long-horizon tasks. ->read the paper

Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices – specialize compact speech recognizers by language to improve accuracy on edge hardware with efficient training mixtures. ->read the paper

Improving Large Vision and Language Models by Learning from a Panel of Peers – orchestrate peer models to generate, review, and refine outputs so a target LVLM learns from collective judgments with minimal human labeling. ->read the paper

🌟 Gated Associative Memory: A Parallel O(N) Architecture for Efficient Sequence Modeling (by Rishiraj Acharya) – replace attention with fused local convolution and global associative retrieval to achieve linear scaling →read the paper

🌟 Fantastic Pretraining Optimizers and Where to Find Them (by Stanford) – compare optimizers under rigorous tuning and budgets, revealing modest end-to-end speedups and scale-dependent gains for matrix preconditioners →read the paper

🌟 Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic – extract a reasoning delta between GRPO and SFT models and apply it additively to boost downstream reasoning →read the paper

Delta Activations: A Representation for Finetuned Large Language Models – embed models by activation shifts relative to a base to cluster domains, aid selection, and enable additive composition →read the paper

UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning – scale a native GUI agent with data flywheels, stabilized multi-turn RL, and hybrid OS environments to boost cross-benchmark performance →read the paper

VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use – unify asynchronous rollouts and modular tool APIs across domains to train multi-turn, tool-augmented agents →read the paper

Towards a Unified View of Large Language Model Post-Training – unify SFT and RL under a common gradient estimator and introduce hybrid post-training that selects signals dynamically →read the paper

SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning – stabilize multi-turn tool use by filtering void-turn trajectories to prevent gradient explosions →read the paper

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR – reformulate verifiable-reward RL as supervised learning to implicitly couple actor and critic for stable training →read the paper

🌟 Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training – curate data via process-outcome consistency filters to improve final accuracy and step-quality jointly →read the paper

DCPO: Dynamic Clipping Policy Optimization – adapt clipping bounds token-wise and smooth advantages over time to keep gradients informative and effective →read the paper

Jointly Reinforcing Diversity and Quality in Language Model Generations – optimize for semantic diversity alongside quality so exploration improves both novelty and task performance →read the paper

Improving Large Vision and Language Models by Learning from a Panel of Peers – orchestrate peer LVLMs to generate, review, and refine outputs iteratively, improving benchmarks without heavy human labels →read the paper

Open Data Synthesis For Deep Research – formalize deep research as hierarchical constraint satisfaction and generate large-scale, verifiable multi-step questions to train and test evidence-synthesizing LLMs →read the paper

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth – construct a multilingual benchmark of rhetorically paradoxical yet meaningful text to probe pragmatic, emotional, and moral inference gaps →read the paper

🌟 LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations – provide an entity-annotated corpus, retrieval tools, and checkpointed models to trace how facts enter and evolve inside LMs →read the paper

🌟 The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang (by Cambridge, Oxford, UIUC) – introduce a constructed language with grammar and dictionary to test explicit rule-based reasoning distinct from pattern matching →read the paper

SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs – generate SAT-grounded reasoning tasks with controllable difficulty and validated answers for evaluation and RL training →read the paper

Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs – show that evaluation heuristics inflate prompt sensitivity and demonstrate reduced variance with LLM-as-a-judge protocols →read the paper

DynaGuard: A Dynamic Guardrail Model With User-Defined Policies – enforce customizable policies with fast detection or chain-of-thought justification across domains beyond static taxonomies →read the paper

🌟 AMBEDKAR: A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation – apply constitution-guided speculative decoding at inference to reduce culturally specific harms without retraining →read the paper

False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize – demonstrate that probes overfit instruction patterns and triggers, urging redesigned evaluations and models →read the paper

MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement – align subjects to target regions and disentangle features to preserve identities in multi-subject synthesis →read the paper

🌟 Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing – invert autoregressive sampling noise to reconstruct and edit images with prompt-aligned, structure-preserving changes →read the paper

🌟 Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots (by ByteDance) – model depth-camera noise to denoise metric depth, enabling policies trained in sim to transfer directly to real manipulation →read the paper