Apple Introduces MacBook Pro with All‑New M5 Pro and M5 Max
Apple's new MacBook Pro features the M5 Pro and M5 Max chips, delivering up to 4x AI performance compared to previous models, enabling advanced workflows for developers and creatives alike.
Show HN: I built a sub-500ms latency voice agent from scratch
Nick Tikhonov achieved a remarkable sub-500ms latency voice agent by building a custom orchestration layer, outperforming established platforms like Vapi by 2× in response time, with an end-to-end latency of approximately 400ms.
Inside the M4 Apple Neural Engine, Part 1: Reverse Engineering
Reverse engineering the M4 Apple Neural Engine (ANE) revealed direct access to its hardware, bypassing CoreML, and enabling model training on a chip designed solely for inference. This collaboration between human intuition and AI reasoning allowed for a comprehensive understanding of the ANE's architecture and capabilities.
Intel's make-or-break 18A process node debuts for data center with 288-core Xeon
Intel's Xeon 6+ processors, featuring 288 cores and built on the 18A process node, target telecom and cloud applications, integrating advanced technologies like AMX and QAT for enhanced performance.
Launch HN: OctaPulse (YC W26) – Robotics and computer vision for fish farming
OctaPulse is revolutionizing seafood production with automated fish inspection, addressing inefficiencies in a $350B global aquaculture industry that lacks data visibility and automation.
TorchLean: Formalizing Neural Networks in Lean
TorchLean introduces a PyTorch-style verified API in Lean 4, enabling both eager and compiled modes that translate to an op-tagged SSA/DAG computation-graph IR for enhanced performance and verification.
When AI Writes the Software, Who Verifies It?
AI is rapidly generating software, with companies like Google and Microsoft reporting that 25-30% of their new code is AI-generated, and predictions suggest this could rise to 95% by 2030.
[R] TorchLean: Formalizing Neural Networks in Lean
TorchLean formalizes neural networks in the Lean 4 theorem prover, bridging the gap between execution and verification by treating models as first-class mathematical objects with unified semantics.
[R] Are neurons the wrong primitive for modeling decision systems?
A recent ICLR paper introduces Behavior Learning, advocating for learnable constrained optimization blocks to replace traditional neural layers, framing decision-making as "utility + constraints → optimal decision."Read more here.
Intent-Based Commits
Ghost transforms the git workflow by allowing users to commit prompts instead of code, creating a history that captures both intent and output, making it easier to understand the rationale behind changes.
[R] AdamWClip: AdamW with adaptive gradient clipping
AdamWClip is an innovative optimizer that incorporates adaptive gradient clipping, eliminating the need for manual threshold settings while maintaining low memory usage and minimal computational overhead.
Show HN: Open-Source Article 12 Logging Infrastructure for the EU AI Act
The EU AI Act mandates automatic event recording and six-month retention for high-risk AI systems, necessitating a robust logging infrastructure to ensure compliance and accountability in AI decision-making.
[R] Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification
Vision-language models (VLMs) in radiology report generation often produce logically inconsistent outputs, leading to unsupported diagnostic claims and overlooked conclusions, which our neurosymbolic verification framework aims to rectify.
[D] The engineering overhead of Verifiable ML: Why GKR + Hyrax for on-device ZK-ML?
The GKR + Hyrax proof system offers a promising solution for on-device ZK-ML, balancing prover efficiency with mobile hardware limitations, yet poses significant implementation challenges on consumer-grade GPUs.