Netflix to Acquire Warner Bros

Netflix announced its acquisition of Warner Bros. in an ~$82.7B deal, consolidating massive content libraries including HBO, the DC Universe, and Harry Potter. This move creates an immense, proprietary multimodal dataset of scripts, video, and audio. The consolidation of this iconic IP provides a powerful, legally-unencumbered corpus for training foundation models, positioning Netflix with a significant data advantage for developing generative AI in entertainment.

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Gemini 3 Pro: the frontier of vision AI

Google's Gemini 3 Pro is a new multimodal model that sets SOTA performance in document, spatial, screen, and video understanding. It excels at complex document processing through advanced OCR and a "derendering" capability to convert visuals into structured code, and introduces spatial understanding with pixel-precise pointing for robotics and AR applications. The model's capabilities extend to robust screen understanding for UI automation and advanced video reasoning, including high frame rate analysis and cause-and-effect tracing. For developers, it offers a new media_resolution parameter to control the trade-off between visual fidelity and cost.

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YouTube caught making AI-edits to videos and adding misleading AI summaries

YouTube is testing an AI video enhancement model on select Shorts without creator consent, applying subtle retouching for clarity and noise reduction. After creators noticed the alterations, YouTube acknowledged a "small experiment" using machine learning, similar to smartphone processing. The non-consensual modification of content has raised concerns about media authenticity and the precedent for platforms altering user-generated content without disclosure.

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Wall Street races to protect itself from AI bubble

Wall Street is financing a multi-trillion dollar AI infrastructure build-out for hyperscalers while simultaneously hedging its massive exposure due to fears of a bubble. Banks are aggressively using financial instruments like credit default swaps and significant risk transfers to offload underwriting risk. This activity has driven the cost of insuring tech debt to historic highs, reflecting market anxiety over whether these huge capital expenditures will translate to long-term profitability.

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Why we can’t quit Excel

The article traces Excel's four-decade market dominance, attributing its success to technical superiority over predecessors like Lotus 1-2-3 and Microsoft's aggressive bundling strategy. While cloud competitors like Google Sheets failed to unseat it, the latest challenge comes from AI and LLMs. The piece argues Excel's position remains secure, as current LLMs struggle with the reliability and calculation central to spreadsheets. Microsoft's strategy is to augment Excel with its Copilot AI, positioning AI as an enhancement layer rather than a replacement for the core application.

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Reversing AI Model Collapse by Simulating Bounded Rationality

To combat model collapse from overly smooth synthetic data, this paper proposes simulating the cognitive processes that generate human text rather than imitating surface properties. The introduced PMCSF framework uses a Cognitive State Decoder (CSD) and a Cognitive Text Encoder (CTE) to reverse-engineer text into cognitive vectors and then re-materialize it with human-like imperfections. This approach yields synthetic data that is statistically closer to human text and demonstrates significant functional gain in financial stress tests, offering a potential solution to the AI data-collapse crisis.

ArXiv

hls4ml: A Flexible, OSS Platform for ML Acceleration on Reconfigurable Hardware

hls4ml is an open-source platform that translates ML models from common deep learning frameworks into HLS code for FPGAs and ASICs. It is designed to accelerate ML inference in applications where low latency, resource usage, and power consumption are critical. The tool is flexible, supporting multiple frameworks and targeting HLS compilers from several vendors.

ArXiv

When does a physical system compute? (2013)

This paper proposes a formal framework to determine if a physical system is performing computation, addressing the ambiguity around non-standard computing systems. It defines physical computing as the use of a physical system to predict the outcome of an abstract evolution. The framework establishes specific conditions for computation and introduces the concept of a 'computational entity' to identify computation even in systems without a human user.

ArXiv

Evaluating TCP BBRv2 on the Dropbox edge network

This paper details Dropbox's experimental comparison of the BBRv2 TCP congestion control algorithm against BBRv1 and the standard CUBIC on their Edge Network. BBRv2, which challenges loss-based approaches by modeling bandwidth and RTT, introduces improvements to network modeling and fairness. The results show BBRv2 is a definite improvement over both BBRv1 and CUBIC, with experimental data matching its theoretical design principles.

ArXiv

The Anatomy of a Triton Attention Kernel

This work demonstrates portable, efficient cross-platform LLM inference by developing a paged attention kernel using the Triton language. The kernel achieves state-of-the-art performance on both NVIDIA and AMD GPUs without low-level hand-tuning. Through algorithmic improvements, auto-tuning, and server integration, the authors boosted a generic Triton kernel's performance from 19.7% to 105.9% of the state-of-the-art, highlighting how domain-specific languages can enable portability across GPU vendors.

ArXiv

Awful AI is a curated list to track current scary usages of AI

"Awful AI" is a curated repository that documents concerning real-world applications and misuses of AI to raise awareness. It provides a categorized list of examples covering issues like systemic bias in models, disinformation, mass surveillance, and autonomous weapons. The project also tracks contestational research and technologies developed to mitigate these negative impacts.

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Show HN: Sloppylint – A linter for AI-generated Python code

Sloppylint is a Python linter that detects anti-patterns and common mistakes found in code generated by LLMs. It identifies critical issues like mutable default arguments, placeholder functions, and hallucinated package imports. A key feature is its ability to detect code patterns leaked from other languages, such as JavaScript or Java, that LLMs often mistakenly include in Python output.

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AI-Assisted Binary Reverse Engineering with Ghidra

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Advent of Code 2025: The AI Edition – By Peter Norvig

Peter Norvig's "pytudes" is a GitHub repository of Python programs for honing programming skills through puzzles and algorithms. For those interested in AI, it features several notebooks exploring LLM capabilities, such as testing their theory of mind on logic puzzles and comparing their problem-solving versus code-generation skills. The collection also includes analyses of systems like AlphaCode, classic NLP examples, and an implementation of a character-level n-gram language model.

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Show HN: Soffio – a Rust blog/CMS with static pages and an admin UI

Soffio is a publishing platform built in Rust, utilizing a tech stack of Axum, Askama, and SQLx. It is architected with a clean domain/application/infra layering model, serving a statically rendered public site and a separate admin console. The platform is designed for automation and integration, offering a comprehensive headless API with bearer token authentication, scopes, and a companion CLI.

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