AI

Anthropic staff to meet White House officials next week (reuters.com) AI

Reuters reports that Anthropic staff will meet White House officials next week, according to Axios.

AI is code and can't be prompted into being smarter (theregister.com) AI

The Register argues that AI systems are fundamentally limited by how they are built, saying they can’t simply be “prompted” into becoming smarter.

Rio de Janeiro's "homegrown" LLM appears to be a merge of an existing model (github.com) AI

A GitHub issue claims that Rio de Janeiro’s purported “homegrown” Rio-3.5-Open-397B LLM is actually created by a direct element-wise merge of the Nex model and the official Qwen3.5-397B-A17B base (about 60% Nex / 40% Qwen), with no evidence of independent training.

Show HN: Dual YOLOv8n UAV Detection on RK3588S at 42 FPS Using NPU (github.com) AI

The GitHub project describes a real-time UAV detection pipeline for Rockchip RK3588S boards that uses hardware-accelerated camera/ISP and RGA for pre-processing plus YOLOv8n inference on the device’s three NPU cores, achieving up to the camera’s ~46 FPS ceiling (~140–150 MB RAM per 1080p stream). It also tracks detections with ByteTrack and generates natural-language summaries using an on-device Qwen2.5-0.5B LLM when UAVs leave the scene, streaming annotated output to HDMI or RTSP.

Cloud-based LLM gold rush is ending (automato.substack.com) AI

The article argues that the era of cloud-based LLMs for everyday tasks is declining, citing Apple’s WWDC direction toward running AI workflows locally on devices and using the cloud only when needed. It claims that LLM subscriptions and agentic systems face a “wall” due to escalating access costs and the real expense of validation, maintenance, and human oversight, while LLMs remain best as tools that amplify human work rather than provide deterministic execution. The author frames this shift as a move toward “tech sovereignty” and away from AI “arms-race” narratives, suggesting value will come more from practical local features than frontier benchmarks.

No, everyone is not using AI for everything (gabrielweinberg.com) AI

Gabriel Weinberg argues that claims like “everyone is using AI for everything” don’t match usage data, which suggests roughly one third of people use AI frequently, one third occasionally, and one third never, with adoption varying even within Gen Z.

EU Commission looking at practical consequences of Anthropic decision (reuters.com) AI

The EU Commission is reportedly reviewing the practical implications following an Anthropic decision, according to a Reuters report with details not available in the provided article text.

Rio de Janeiro's city government model Rio3.5 beats Qwen3.7 in recent benchmarks (twitter.com) AI

A post claims that Rio de Janeiro’s city government model, “Rio3.5,” performs better than “Qwen3.7” on recent benchmarks, based on reported results.

Formal Methods and the Future of Programming (blog.janestreet.com) AI

Jane Street’s Yaron Minsky argues that while the firm previously found full formal methods too costly compared with benefits, recent advances in agentic coding are making the tradeoff more favorable—both by increasing the need to verify messy, invariant-breaking agent output and by using formal methods as a powerful feedback mechanism alongside testing and type systems. He says the company is now building a team focused on formal methods, leveraging its control over the language (including OxCaml) and a user base that can support near-term improvements and longer-term proof-oriented directions, with hiring planned in London and New York.

KPMG pulls report on AI usage due to apparent hallucinations (techcrunch.com) AI

KPMG has pulled a 2025 report on AI usage after multiple organizations said its claims were untrue or misleading, with a research group attributing the inaccuracies to AI hallucinations in the writing process; KPMG said it removed the report while investigating and reiterated expectations for responsible AI use with human oversight.

Reinventing Control Theory One Feature at a Time: The Fallacy of Agentic Loops (medium.com) AI

The article argues that “agentic loops” in AI coding—adding agents to monitor, review, and iterate over each other’s work—amount to a fragmented, hype-driven rediscovery of control theory without the full methodology needed for safe, reliable operation. It warns that probabilistic agents validating one another are not automatically a robust control system unless stop conditions, trusted signals, authority, boundaries, and fallback paths are explicitly designed, and it urges teams and leadership to address hard operational and financial questions before deploying such loops.

UK police officer under criminal investigation over alleged use of AI (ft.com) AI

The FT reports that a UK police officer is under criminal investigation over allegations involving the use of AI, though details were not available in the provided article text.

Frontier AI companies will never exceed the capability frontier again (andrewtrask.substack.com) AI

The Substack post argues that “frontier” AI companies will no longer be able to surpass today’s capability frontier, claiming that ensembles and decentralized networks of smaller models increasingly outperform single top-tier systems on speed, accuracy, and cost, due to scaling/ensemble effects and improved inference efficiency like caching and indexing.

Don't trust large context windows (garrit.xyz) AI

Garrit argues that LLMs have a “smart” attention region and a “dumb” region within the context window, so advertised context sizes (100k+ to millions of tokens) are often mostly marketing and effective performance drops as the window fills—especially for coding agents. The post suggests avoiding the degraded part by restarting sessions and handing off stable written artifacts (specs/plans/skills) rather than relying on auto-compaction summaries that occur after degradation.

Show HN: I run a vision model on every screenshot, locally, on a 4GB GPU (github.com) AI

ScreenMind (open source) is presented as a privacy-first “screen memory” that captures screenshots when the screen changes, analyzes them locally with Gemma 4 multimodal capabilities (plus OCR and semantic embeddings), and lets users search and chat over their screen history. The project claims all processing runs on-device with no telemetry after the initial model download, offers modes for faster vs deeper analysis, and includes features like voice memo/meeting transcription, analytics, and integrations via an MCP server and other tools.

Making Claude a Chemist (anthropic.com) AI

Anthropic says its Claude models are increasingly useful for chemistry by testing them on NMR spectroscopy tasks, comparing predictions from multiple Claude versions (Opus 4.7/4.6, Sonnet 4.6) against dedicated NMR tools using data from 20 recently published compounds. The company reports Opus 4.7 produced notably accurate 1D NMR peak positions and splitting patterns, and also performed “inverse” structure elucidation from NMR peak lists plus formula (and, for harder cases, an added starting-material hint), reaching correct structures in all simpler cases and in most harder ones.

The future of Siri, or: why private inference isn’t private enough (blog.cryptographyengineering.com) AI

Cryptography engineer Matthew Green argues that Apple’s planned “private” Siri/AI via Private Cloud Compute and confidential inference may limit direct access by Apple and Google, but privacy is not assured once Siri-style agents must interact with external services for real-world actions, creating new avenues for data leakage through queries and the agent’s discretion.

The whirlwind 24 hours that led to export controls on Anthropic (politico.com) AI

Politico reports on the rapid, 24-hour sequence of events that culminated in the White House imposing export controls on Anthropic, based on developments leading up to the decision.

'Tell Him He's a Piece of Shit': Meta's New AI Unit Is a Total Mess (wired.com) AI

WIRED reports that Meta’s newly formed Applied AI unit has deep internal frustration tied to the company’s broader AI restructuring, including accounts of a chaotic employee-only livestream incident and widespread complaints that assigned tasks feel menial or soul-crushing.

LLMs Pre-Commodify Ideas (summerlightning.substack.com) AI

The post argues that ideas generated and shared through LLMs are increasingly “pre-commodified,” arriving around the same time to multiple people with unclear provenance because the models recombine temporally deep training data into a shared latent space; the author contrasts “Boomers” (legal/slow, consistent origin claims) with “Sooners” (front-running ideas and profiting from later diffusion), and suggests that establishing provenance—and new threats like data poisoning—will become central complements to AI deployment and distribution.

Rio 3.5 Open 397B – from Rio de Janeiro's city government (huggingface.co) AI

A Hugging Face model card describes Rio 3.5 Open 397B, an open, multimodal “frontier-class” AI model from Rio de Janeiro’s municipal IT company IplanRIO, post-trained from Qwen 3.5 397B and released under the MIT license. The card highlights its SwiReasoning framework for dynamically switching between latent and explicit reasoning to improve the accuracy/efficiency trade-off, lists key model specs (Mixture-of-Experts with ~397B total/~17B active parameters and a ~1M token context window), and provides benchmark results plus implementation examples for Transformers, vLLM, and SGLang.

Chatbot teddies for three‑year‑olds? Why AI toys are risky for kids (rnz.co.nz) AI

RNZ reports on research and concerns that AI-powered toys like chatbot teddies may be especially risky for very young children, because their “human-like” and overly validating language can build strong emotional trust and attachment. The article also warns about “infinite chat” driving prolonged engagement, potential exposure to adult topics, and privacy/data-collection issues from seemingly personal conversations, particularly when toys are used without adult supervision.

Amazon security research reportedly led to the White House's Anthropic Fable ban (theverge.com) AI

A Wall Street Journal report, echoed by The Verge, says Amazon cybersecurity research and discussions between Andy Jassy and the White House helped trigger an export control directive that led Anthropic to cut off access to its Fable 5 and Mythos 5 models for foreign nationals.

State Attorneys General Are Investigating OpenAI (nytimes.com) AI

The article says multiple U.S. state attorneys general are investigating OpenAI, though no further details are available from the provided text.

Human Routers of Machine Words (borretti.me) AI

The post argues that using AI to draft text is contemptible because it replaces the thinking that writing forces—clarifying ideas, exposing contradictions, and improving reasoning—and claims that readers must then skeptically judge AI-posed arguments since “ideas” cannot be separated from observable writing output.