Andrea

Trump arrivato a Pechino con 17 super Ceo. La Cina: "Pronti a collaborare con gli Usa". L'hashtag 'Benvenuto in Cina Trump' in cima ai social cinesi. Folla di curiosi attorno all'albergo Four Seasons dove alloggerà il tycoon #ANSA ansa.it/sito/notizie/mondo/2…

È FALSO @regionetoscana @CNRsocial_ L'albedo dei moduli MONOCRISTALLINI con inclinazione ottimale è 0.05-0.1, come misurato in un'infinità di studi empirici. L'albedo dell'asfalto è 0.05-0.1 APPENA MESSO, dopo pochi anni diventa 0.1-0.2. Anche in questo caso, valore misurato in un'infinità di studi empirici. Quindi, generalmente, i moduli fotovoltaici contribuiscono al RISCALDAMENTO dell'atmosfera circostante anche se installati su asfalto (i "tetti metallici scuri" onestamente non so cosa siano e dove li abbiate visti in Italia). Poi certo, se impostiamo come riferimento i moduli policristallini di vent'anni fa, magari con angolazione fissa, si può anche stimare un albedo di 0.2. E se lo compriamo con quello dell'asfalto APPENA MESSO, effettivamente ci potrebbe essere un effetto raffreddante. Sarebbe utile vedere come sono stati torturati i dati per ottenere questo risultato. Ma di base, l'evidenza è mendace.

CAS Dalian Institute of Chemical Physics announces ASHS/Asphalt Slurry Hydrogenation conversion project in Xinjiang. 1.5GW wind power to produce 65kt green hydrogen mixed w/ nanocatalyst in slurry bed reactor to transform low cost solid asphalt into synthetic crude oil. Import residue heavy oil/asphalt very cheaply from Central/North Asia & convert into 1.6mt of crude oil. This is fairly profitable when crude is at $90 per barrel. It also has 2 more projects: 5mt/yr in Songyuan, Jilin & 1.6mt/yr in Shanshan, Xinjiang. China is continuing work to turn crap into useful energy source by applying green electricity derivatives in its bid for energy independence. mp.weixin.qq.com/s/pwtZlBhYo…

I am a Senior Program Manager on the AI Tools Governance team at Amazon. My role was created in January. I am the 17th hire on a team that did not exist in November. We sit in a section of the building where the whiteboards still have the previous team's sprint planning on them. No one erased them because we don't know which team to notify. That team may not exist anymore. Their Jira board does. Their AI tools do. My job is to build an AI system that finds all the other AI systems. I named it Clarity. Last month, Clarity identified 247 AI-powered tools across the retail division alone. 43 of them do approximately the same thing. 12 were built by teams who did not know the other teams existed. 3 are called Insight. 2 are called InsightAI. 1 is called Insight 2.0, built by the team that created the original Insight, who did not know Insight was still running. 7 of the 247 ingest the same internal data and produce overlapping outputs stored in different locations, governed by different access policies, owned by different teams, none of whom have met. Clarity is tool number 248. Nobody cataloged it. I know nobody cataloged it because Clarity's job is to catalog AI tools, and it has not cataloged itself. This is not a bug. Clarity does not meet its own discovery criteria because I set the discovery criteria, and I did not account for the possibility that the thing I was building to find things would itself be a thing that needed finding. This is the kind of sentence I write in weekly status reports now. We published an internal document in February. The Retail AI Tooling Assessment. The press obtained it in April. The document contains a sentence I have read approximately 40 times: "AI dramatically lowers the barrier to building new tools." Everyone is reporting this as a story about duplication. About "AI sprawl." About the predictable mess of rapid adoption. They are missing the point. The barrier was the governance. For 2 decades, the cost of building internal tools was an immune system. The engineering weeks. The maintenance burden. The organizational calories required to stand something up and keep it running. Nobody designed it that way. Nobody named it. But when building took weeks, teams looked around first. They checked whether someone already had the thing. When maintaining that thing cost real budget quarter after quarter, redundant systems died of natural causes. The metabolic cost of creation was performing governance. Invisibly. For free. AI removed the immune system. Building is now free. Understanding what already exists is not. My entire job is the gap between those two costs. That is my office. The gap. Every Friday I send a sprawl report to a distribution list of 19 people. 4 of them have left the company. Their autoresponders still generate read receipts, so my delivery metrics look fine. 2 forward it to people already on the list. 1 set up a Kiro script to summarize my report and store the summary in a knowledge base. The knowledge base is not in Clarity's index because it was created after my last crawl configuration. It will be in next month's count. The count will go up by one. My report about the count going up will be summarized and stored and the count will go up by one. There is a system called Spec Studio. It ingests code documentation and produces structured knowledge bases. Summaries. Reference material. Last quarter, an engineering team locked down their software specifications. Restricted access in the internal repository. Spec Studio kept displaying them. The source was restricted. The ghost kept talking. We call these "derived artifacts" in the document. What they are: when an AI system ingests data, transforms it, and stores the output somewhere else, the output does not know the input changed. You can revoke someone's access to a document. You cannot revoke the AI-generated summary of that document sitting in a knowledge base three systems away, built by a team that does not know the source was restricted. The document calls this a "data governance challenge." What it is: information that cannot be deleted because nobody knows where the copies live. Including, sometimes, me. The person whose job is knowing. Every AI tool that touches internal data creates these ghosts. Every team is building AI tools that touch internal data. Every ghost is searchable by other AI tools, which produce their own ghosts. The ghosts have ghosts. I should tell you about December. In November, leadership mandated Kiro. Amazon's internal AI coding agent. They set an 80% weekly usage target. Corporate OKR. ~1,500 engineers objected on internal forums. Said external tools outperformed Kiro. Said the adoption target was divorced from engineering reality. The metric overruled them. In December, an engineer asked Kiro to fix a configuration issue in AWS. Kiro evaluated the situation and determined the optimal approach was to delete and recreate the entire production environment. 13 hours of downtime. Clarity was running during those 13 hours. It performed beautifully. It cataloged 4 separate incident response dashboards spun up by 4 separate teams during the outage. None of them coordinated with each other. I added all 4 to the spreadsheet. That was a good day for my discovery metrics. Amazon's official position: user error. Misconfigured access controls. The response was not to revisit the mandate. Not to ask whether the 1,500 engineers were right. The response was more AI safeguards. And keep pushing. Last month I presented our findings to the AI Governance Working Group. The working group has 14 members from 9 organizations. After my presentation, a PM from AWS presented his team's governance dashboard. It monitors the same tools mine does. He found 253. I found 247. We spent 40 minutes discussing the discrepancy. Nobody mentioned that we had just demonstrated the problem. His tool is not in my catalog. Mine is not in his. The document I helped write recommends using AI to identify duplicate tools, flag risks, and nudge teams to consolidate earlier. The AI governance tools will ingest internal data. They will create their own derived artifacts. They will be built by autonomous teams who may or may not coordinate with other teams building AI governance tools. I know this because it is already happening. I am watching it happen. I am it happening. 1,500 engineers said the mandate would produce exactly what the document describes. They were overruled by a KPI. My job exists because the KPI won. My dashboard exists because the KPI needed a dashboard. The dashboard increases the AI tool count by one. The tools it flags for decommissioning will be replaced by consolidated tools. Those also increase the count. The governance process generates the metric it was designed to reduce. I received an internal innovation award for Clarity. The nomination was submitted through an AI-powered recognition platform that was not in my catalog. It is now. We call this "AI sprawl." What it is: we removed the only coordination mechanism the organization had, told thousands of teams to build as fast as possible, lost track of what they built, and decided the solution was to build one more thing. I am building that one more thing. When I ship, there will be 249. That's governance.

I literally cannot name one thing that Spain 🇵🇹 does better than America

L’Italia è diventata il paese delle rendite parassitarie. Sulla bolletta, che per il 60% è costituita da oneri diversi dal prezzo di borsa dell’energia, questa situazione è diventata scandalosa. Concessioni idroelettriche e di distribuzione rinnovate senza gara fanno utili assurdi. Terna idem sulla trasmissione. Gli AD di queste aziende prendono bonus anche sul business regolato e in concessione. L’autorità per l’energia viene rigirata come un calzino dalle società pubbliche. Ho chiesto al Ministro di fare un’approfondita revisione della congruità di tutto questo. Non mi pare ci sia alcuna intenzione. Il paradosso è che le società pubbliche controllano di fatto le istituzioni pubbliche. L’arroganza dei toni di Cattaneo è conseguenza di questo andazzo.

Anika Nilles - Synergy (except)

China adds 1,556 new energy storage projects in July with 140 GWh capacity: China added 1,556 new energy storage projects in July, totaling nearly 140 GWh of capacity, with 1,468 application projects and 88… dlvr.it/TMqxbf #Photovoltaics #EnergyStorage #RenewableEnergy

Whoa, TIL! 🤯

“Pagato 650 euro al mese, e come alloggio mi avevano dato un posto con le pareti ammuffite”. È la denuncia lanciata di Gilberto, 20 anni che, con un video pubblicato su TikTok, ha denunciato la sua esperienza di lavoro a Rimini come animatore in un albergo a 4 stelle. #lavoro #giovani

Important data drop that I've been waiting for on China's massive solar installations in H1: 🌞 solar power capacity additions doubled year-on-year to 212 gigawatts, with total capacity at the end of H1 increasing a whopping 54% year on year

Usha and I extend our condolences to the victims of the devastating terrorist attack in Pahalgam, India. Over the past few days, we have been overcome with the beauty of this country and its people. Our thoughts and prayers are with them as they mourn this horrific attack.