We did the math. At $185 billion a year, in eight years, Google would be spending $1.5 trillion, slightly more than OpenAI has committed to spend over the same time period. Extend that out to 10 years, as Vahdat noted, and Google would be spending $1.9 trillion.

Vahdat is clear that this is “not a promise” that Google would spend that much over the next 10 years. But the decade-long view he takes suggests the scope of Google’s bet. “The point here is that we are, at Google, investing at the highest levels,” he says.

There’s a big difference between Google’s data center ambitions and OpenAI’s: Google is a money-making machine. In the fourth quarter, Google parent Alphabet raked in $113 billion in revenue; for the full year, sales topped $400 billion for the first time in the company’s more than 25 year history. By comparison, OpenAI is spending at similar levels and only brought in about $13 billion in revenue last year — a tiny fraction of Google’s revenue, and less than half of Google’s cash reserves.

Google’s TPUs previously were only used in house for Google’s own infrastructure — to power consumer apps like Gmail and YouTube, and eventually train self-driving cars and develop and run AI models like Gemini. Now, they’re one of the industry’s go-tos: maybe not as popular as Nvidia’s top of the line Blackwells, but still useful for pretraining and operating AI models at scale. Google first started selling access to them through a cloud service in 2018, letting other companies rent out processing power. But more recently, Google has inked high profile deals, like a big contract with Anthropic, and has reportedly been in talks with Meta to use its chips. In December, Morgan Stanley estimated that TPUs could generate $13 billion for Google by 2027. “It is fair to say that the demand for cloud TPUs has been unprecedented,” Vahdat says, particularly in the last few years.

In August, Vahdat, Google Chief Scientist Jeff Dean, and 10 other researchers and execs at the company, co-published a paper aiming to contextualize AI’s power guzzling. The paper says that the median prompt for Google’s Gemini AI model uses the same amount of energy it takes to power 9 seconds of television and consumes around five drops of water, which they write is “substantially lower than many public estimates.” (One report says large data centers can consume up to 5 million gallons per day, equivalent to the water use of a town populated by up to 50,000 people.)

Coding After Coders: Summary

The New Reality of AI-Assisted Programming

• Elite software developers now rarely write code themselves — instead, they direct AI agents in plain English
• Tools like Claude Code deploy multiple agents simultaneously: one writes, one tests, one supervises
• Tasks that once took days now take under an hour

The Strange New Workflow

• Developers spend their days describing intent to AI, reviewing the AI's "plan," then letting agents execute
• When agents misbehave, developers have resorted to scolding, pleading, ALL-CAPS commands, and emotionally charged language ("embarrassing," "national security imperative") — and it seems to work
• Prompt files have become records of hard-won rules to constrain unpredictable AI behavior

Economic Stakes

• Coding was once considered near-guaranteed, high-paying employment ($200K+)
• It may be the first expensive white-collar skill AI can fully replace — unlike AI video or legal briefs, AI-generated code that passes tests is indistinguishable in value from human-written code
• Irony noted: Silicon Valley workers, who told others to "learn to code," got automated first

Developer Sentiment: Mostly Euphoric

• Most developers interviewed were energized, not demoralized — reporting 10x to 100x productivity gains
• Key insight from tech executive Anil Dash: unlike creative fields where AI removes the soulful work and leaves drudgery, in coding AI removes the drudgery and leaves the soulful parts

Historical Context: A Long Arc of Abstraction

• Each programming era simplified the one before: Assembly → high-level languages (Python) → open-source packages → now natural language intent
• AI represents the highest abstraction layer yet: developers no longer need to manage syntax, memory, or debugging minutiae
• The open question, now being asked at Anthropic itself: what is coding, fundamentally, when the code-writing is gone?

generally considered the foundational academic work on indirect prompt injection. It's been reproduced against virtually every major agentic system since.

Website: https://sites.google.com/view/invitation-is-all-you-need

The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware—maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web application, mobile application, and Google Assistant).

Our analysis focuses on a new variant of Promptware called Targeted Promptware Attacks, which leverage indirect prompt injection via common user interactions such as emails, calendar invitations, and shared documents. We demonstrate 14 attack scenarios applied against Gemini-powered assistants across five identified threat classes: Short-term Context Poisoning, Permanent Memory Poisoning, Tool Misuse, Automatic Agent Invocation, and Automatic App Invocation. These attacks highlight both digital and physical consequences, including spamming, phishing, disinformation campaigns, data exfiltration, unapproved user video streaming, and control of home automation devices

Over the course of our work, we deployed multiple layered defenses, including: enhanced user confirmations for sensitive actions; robust URL handling with sanitization and Trust Level Policies; and advanced prompt injection detection using content classifiers - Google

Nobody likes the boldr in the HASS forums:

Sad news: the pairing option in the settings menu is now gone. The firmware update took it away. I just hope that doesn’t mean my klima is too old to support it or something. If that’s the case, I have a brick for their office windows :laughing:

I'm a infrared heater manufacturer that people liked. People paired them with smart plugs to turn them on and off.

Regarding the boldr kelvin infrared heater:

It replaced a small portable radiator I bought from target years ago, that thing did a fantastic job if I'm honest. It could maintain a good temperature in the room, only reason I wanted to replace it with the kelvin is because it didn't look nice in the room. The portable heater cost a tenth of the cost of the kelvin and did a better job.

Thank you. I made the mistake of purchasing one. I agree with everything that you say. So dissapointing.

Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides:

Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.
Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.
Experiments - Track and evaluate changes to prompts, LLMs, and retrieval.
Playground- Optimize prompts, compare models, adjust parameters, and replay traced LLM calls.
Prompt Management- Manage and test prompt changes systematically using version control, tagging, and experimentation.

During World War II, the Anglo-Soviet invasion of Iran forced the abdication of Reza Shah and succession of Pahlavi. During his reign, the British-owned oil industry was nationalized by the prime minister Mohammad Mosaddegh, who had support from Iran's national parliament to do so; however, Mosaddegh was overthrown in the 1953 Iranian coup d'état, which was carried out by the Iranian military under the aegis of the United Kingdom and the United States. Subsequently, the Iranian government centralized power under the Shah and brought foreign oil companies back into the country's industry through the Consortium Agreement of 1954.[3]

By the 1970s, the Shah was seen as a master statesman and used his growing power to pass the 1973 Sale and Purchase Agreement. The reforms culminated in decades of sustained economic growth that would make Iran one of the fastest-growing economies among both the developed world and the developing world. During his 37-year-long rule, Iran spent billions of dollars' worth on industry, education, health, and the military. Between 1950 and 1979, real GDP per capita nearly tripled from about $2700 to about $7700 (2011 international dollars).[4] By 1977, the Shah's focus on defence spending to end foreign powers' intervention in the country had culminated in the Iranian military standing as the world's fifth-strongest armed force.

Explanations for the overthrow of Mohammad Reza include his status as a dictator put in place by a non-Muslim Western power, the United States,[321][full citation needed][322][full citation needed] whose foreign culture was seen as influencing that of Iran. Additional contributing factors included reports of oppression, brutality,[323][full citation needed][324] corruption, and extravagance.

International policies pursued by the Shah in order to increase national income by remarkable increases in the price of oil through his leading role in the Organization of the Petroleum Exporting Countries (OPEC) have been stressed as a major cause for a shift of Western interests and priorities, and for a reduction of their support for him reflected in a critical position of Western politicians and media, especially of the administration of US President Jimmy Carter regarding the question of human rights in Iran, and in strengthened economic ties between the United States of America and Saudi Arabia in the 1970s.

International scrutiny of Iran’s nuclear and missile programs intensified in 2018 after the United States withdrew from the Iran nuclear deal—known as the Joint Comprehensive Plan of Action (JCPOA)—and again in late 2024, following direct military strikes between Iran and Israel, as well as the reelection of Donald Trump. In Trump’s second term, Washington resumed talks with Tehran for the first time since pulling out of the JCPOA. However, in June 2025, after the UN nuclear watchdog declared Iran in violation of its nuclear nonproliferation agreements, the United States bombed Iran’s major nuclear facilities. Despite ongoing negotiations in early 2026, the United States and Israel launched a large-scale offensive against Iran in February with a stated aim of destroying its nuclear and missile capabilities. Although there has reportedly been some damage to one Iranian nuclear site, there is no confirmed evidence of major damage to the country’s overall nuclear facilities. Joint U.S.-Israeli strikes also killed Iranian Supreme Leader Ayatollah Ali Khamenei. Iran has retaliated by targeting Israel and U.S. military sites across the region, as well as several other Gulf countries, adding to concerns about nuclear proliferation in the Middle East.

Does Iran have a nuclear weapon? Iran does not yet have a nuclear weapon, but it has a long history of engaging in secret nuclear weapons research in violation of its international commitments. Western analysts say the country has the knowledge and infrastructure to produce a nuclear weapon in fairly short order should its leaders decide to do so. The United States, Israel, and other Middle Eastern partners regard Iran as a primary threat to their interests in the region, and view its potential acquisition of nuclear weapons as a game-changing scenario to be steadfastly prevented—by force if necessary.

Revelations in the early 2000s about the country’s secret nuclear sites and research raised alarms in world capitals about Iran’s clandestine pursuit of a nuclear weapon. Iran’s nuclear program has since been the subject of intense international debate and diplomacy, which culminated in the 2015 JCPOA. The United States unilaterally withdrew from that agreement in 2018. Since then, international monitors say that Iran has greatly expanded its nuclear activities, again heightening concerns about its “breaking out” to develop a nuclear weapon.

At least 1,245 civilians have been killed, including 194 children, by the US-Israeli war on Iran, according to the US-based Human Rights Activists in Iran group.

In Lebanon, at least 486 people have been killed by Israeli bombing, while 11 have been killed in Israel. Seven US troops have been confirmed dead and 140 injured, eight severely.

The Lebanese Red Cross condemned an Israeli strike on one of its ambulances in the Tyre district of south Lebanon on Monday night, which injured two emergency workers.

Pattern: small CLI + JSON pipes + approvals Build tiny commands that speak JSON, then chain them into a single Lobster call. (Example command names below — swap in your own.)

Lobster can run YAML/JSON workflow files with name, args, steps, env, condition, and approval fields. In OpenClaw tool calls, set pipeline to the file path.

A chrome extension that automates your browser

prose.md language reference

prose.md

Why not just play in English? English is already an agent framework—we're structuring it, not replacing it. Plain English doesn't distinguish sequential from parallel, doesn't specify retry counts, doesn't scope variables. OpenProse uses English exactly where ambiguity is a feature (inside ...), and structure everywhere else. The fourth wall syntax lets you lean on AI judgment precisely when you want to.

How is this a VM? LLMs are simulators—when given a detailed system description, they don't just describe it, they simulate it. The prose.md spec describes a VM with enough fidelity that reading it induces simulation. But simulation with sufficient fidelity is implementation: each session spawns a real subagent, outputs are real artifacts, state persists in conversation history or files. The simulation is the execution.

The Agent Skills format was originally developed by Anthropic, released as an open standard, and has been adopted by a growing number of agent products. The standard is open to contributions from the broader ecosystem.

The Agent Skills format was originally developed by Anthropic, released as an open standard, and has been adopted by a growing number of agent products. The standard is open to contributions from the broader ecosystem.

An MCP Gateway for LLM‘s that applies policies to the actions and stores credential separately from the LLM