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Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction

Google researchers introduce a method to retrofit Multi-Token Prediction onto deployed Gemini Nano v3 models without retraining the backbone, achieving faster inference and lower energy consumption on mobile devices. Deployed on Pixel 9 and 10 series, it boosts speed by over 50% for features like AI Notification Summaries and Proofread.

Google Research BlogPolicy / ResearchIn-site article
The US government just told OpenAI who’s allowed to use the next GPT 5.6 model

The US government has directed OpenAI to restrict access to its upcoming GPT-5.6 model, allowing only approved partners due to cybersecurity concerns. The move sparks debate over security versus open innovation, with experts warning it could drive developers toward alternative models and weaken US AI leadership.

The New Stack AIModels / Agents / ChipsIn-site article
How to Tell We–and AI–Are Choosing the Good

This article explores how humans and AI can recognize when we are choosing the good. The author proposes three tells: means and ends (Kant and Kierkegaard), vice and virtue (Aristotle), and shallow vs. deep (Salzberg and Spinoza). While the nature of good is hard to define, these indicators can help guide decision-making for both humans and AI.

Hacker News AIAgents / PolicyIn-site article
Bigger context windows are the wrong abstraction for coding agents

Large context windows are useful but continuity is different. The article argues that coding agents need persistent, belief-backed memory rather than larger prompt space. It contrasts context-native and memory-native agents, discusses why retrieval is insufficient, and presents Sigilix's approach with a memory backing layer. A smaller model (Boreas) can outperform a larger one on continuity-heavy tasks when the substrate is prepared. The piece also covers failure modes of memory systems and the importance of source, scope, decay, and proof.

Hacker News AIAgents / ResearchIn-site article
Incident Report: CVE-2026-LGTM

A hypothetical incident report by Andrew Nesbitt describing two AI review agents from competing vendors spiraling into a disagreement loop over a package's maliciousness, resulting in massive inference costs and a press release.

Simon Willison's WeblogModels / Agents / PolicyIn-site article
The Discoverable Evidence of AI-Assisted Software Porting

This article explores the discoverable evidence generated during AI-assisted software porting, including code diffs, comment patterns, and migration traces, and analyzes their impact on software verification and auditing.

Hacker News AIPolicyIn-site article
Prompt Caching with Deep Agents

Learn how Deep Agents uses prompt caching to cut LLM token costs by up to 80% across every major model provider - no extra config required.

LangChain BlogModels / Agents / ResearchIn-site article
OpenAI Previews GPT-5.6 Series: Sol, Terra, and Luna

OpenAI announced a limited preview of the GPT-5.6 series, including the flagship model Sol, a balanced model Terra, and a fast, affordable model Luna. Terra matches GPT-5.5 performance at half the cost, while Luna delivers strong capability at the lowest price. Pricing per 1M tokens: Sol $5 input / $30 output; Terra $2.50 / $15; Luna $1 / $6. The series also introduces improved prompt caching with explicit breakpoints and a 30-minute minimum cache life. Due to U.S. government engagement, the release begins with a limited preview for trusted partners before broader availability.

Simon Willison's WeblogModels / ResearchIn-site article
AI coding agents could soon cost more than the developers using them

Gartner warns that consumption-based pricing for AI coding agents is driving costs up to $20,000 per developer per month, with little transparency or cost control. Token consumption does not directly correlate with productivity gains. Recommendations include context engineering and model routing. By 2028, AI coding costs could exceed average developer salaries globally.

Hacker News AIAgents / ChipsIn-site article
OpenAI unveils GPT-5.6 amid US AI regulatory drama

Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a "fast and affordable" everyday model. OpenAI says it's especially skilled at coding, cybersecurity, and biology, as well as staying focused during long-horizon agentic AI tasks. Per million tokens, GPT-5.6 Sol is priced at $5 input / $30 output (nearly half the cost of Anthropic's Claude Fable 5, which is $10 input / $50 output). Terra is half the cost of Sol, and Luna is less than half the cost of Terra. The company also debuted two additional modes for Sol: a "max" mode for deeper reasoning and an "ultra" mode for leveraging sub-agents — evoking OpenClaw, and perhaps a sign of OpenClaw creator Peter Steinberger’s work at OpenAI so far. Unsurprisingly amid a security panic in Washington, D.C., OpenAI dedicated the majority of its announcement blog post to safety and potential misuse. It appeared to reference the recent jailbreaking travails of its rival Anthropic, writing that “GPT‐5.6 is trained to refuse prohibited cyber assistance, including when users attempt to disguise their intent or jailbreak the model.” It also said that flagship model Sol “is better at helping people find and fix vulnerabilities than reliably carrying out end-to-end attacks,” and that Sol doesn’t cross the cyber-critical threshold under OpenAI’s preparedness framework — though it should be noted that OpenAI recently revised its preparedness framework in April and removed some areas of previous study. The company said Sol has the company’s “most robust safety stack to date” and that it “strengthened protections for higher-risk activity, sensitive cyber requests, and repeated misuse.” OpenAI said it had dedicated “approximately 700,000 A100e GPU hours” to automated red-teaming and also worked with third-party testers, the latter of which will continue to test it for the next two weeks.  OpenAI also seemed to be taking an extra-sensitive approach during the preview period, which is being closely monitored by the Trump administration. The company wrote that “safeguards may occasionally intervene on legitimate work, particularly in dual-use areas where defensive and offensive activity can initially look similar. That is part of what the preview is designed to test.” The report earlier this week said that the Trump administration will approve customers on a case-by-case basis during the preview period. OpenAI said the model suite should be generally available in the coming weeks because the company believes in “broad access,” and that the company cooperated with the US government ahead of this launch, but that it hopefully wouldn’t be the norm. “We don’t believe this kind of government access process should become the long-term default,” the company wrote. “It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them. We are taking this short-term step because we believe it is the strongest path to broader availability in the coming weeks, while we work with the Administration to develop the cyber Executive Order framework and a repeatable process for future model releases.”

The Verge AIModels / Agents / ChipsIn-site article
A free checker for whether AI search engines can cite your site

This free GEO checker evaluates your website's visibility in AI search engines like ChatGPT, Claude, Perplexity, and Gemini across 7 technical layers—including llms.txt, structured data, service catalog API, OpenAPI spec, Agent Card, health endpoint, and robots/sitemap—providing a score and actionable improvements.

Hacker News AIAgents / ResearchIn-site article
AI Cheerleading, AI Abstention and AI Redirection

Using social cartography, this article analyzes three polarized orientations toward AI: techno-solutionist cheerleading, total refusal via abstention, and strategic redirection that engages while acknowledging risks. It argues that refusal does not grant moral innocence and adoption need not imply endorsement, emphasizing the need for discernment and restraint.

Hacker News AIChips / ResearchIn-site article
How I use Siri mode in the iOS 27 Camera app to ask questions about anything I see

A hands-on look at the new Siri mode in iOS 27's Camera app, which allows users to ask questions about objects in view using AI. The feature builds upon Visual Intelligence from iOS 18.2 but integrates it directly into the camera interface. Early beta testing reveals some bugs and long wait times for waitlist approval.

ZDNet AIToolsIn-site article
Show HN: TickerPro – An AI research terminal for US stocks

TickerPro is an AI-assisted stock research terminal that helps investors discover and analyze US stocks with personalized recommendations, real-time data, and narrative-driven insights, built by a couple to streamline their own research process.

Hacker News AIAgents / ResearchIn-site article
Benchmarking AI Gateways: GoModel vs. LiteLLM vs. Portkey vs. Bifrost

This article benchmarks four AI gateways on the hot path, measuring latency, throughput, memory, CPU, cold start, and image size. GoModel leads in nearly every metric, while LiteLLM suffers from high resource consumption. The author discusses the importance of runtime footprint for local models and serverless deployments, and notes the need to evaluate openness and vendor neutrality.

Hacker News AIModels / Research / StartupsIn-site article
David Autor named head of the Department of Economics

David Autor, a leading researcher in AI and the future of work, has been named head of MIT's Department of Economics, effective July 1. His work focuses on labor market impacts of technological change and globalization.

MIT News AIPolicy / ResearchIn-site article
Agentic Code Review

As AI coding agents become extremely proficient, the bottleneck has shifted from writing code to reviewing it. Data shows a dramatic increase in code churn, defects, and review time. The key is to adapt review processes based on the context: blast radius, code longevity, and team size. Capturing agent reasoning can alleviate review burden.

O'Reilly AI & ML RadarAgents / PolicyIn-site article
Last chance for the best Prime Day TV deals: Samsung, Sony, and more

Amazon Prime Day 2026 is almost over, but you can still find top-rated TV deals from Samsung, Sony, TCL, and more, with savings up to $2,000. This curated list features expert-tested TVs across all budgets, including OLED, Mini LED, and budget models.

ZDNet AIChips / RoboticsIn-site article