I run a fund the United States built in 1934 with $2 billion it made in an afternoon. It did not sell anything for the $2 billion. It raised the official price of gold from $20.67 an ounce to $35 an ounce and kept the difference on the gold it already held. We wrote a larger number next to a thing we were not touching, and the larger number was real enough to open a fund. That fund is mine now. On Friday I used it to buy the yen. I did not print the yen; the yen is Japan's to print. To buy yen you sell something else, and on Friday I sold euros. I did not sell them personally. The fund has no trading floor. It has the Federal Reserve Bank of New York, at 33 Liberty Street, and 2 primary dealers, which on Friday were Goldman Sachs and Morgan Stanley. There is, in a room on Liberty Street I have visited twice, a terminal with a key on it marked EXECUTE. I have never pressed the key. On Friday someone pressed it before 9:00 AM, several times, and I learned that the yen had been supported the way I learn most things, from the confirmation log. I did not attend. Attending is not the mandate. I keep one of the confirmations framed. Not Friday's. An older one, from a prior operation, because it is the most honest document the fund owns. It sits on a bank's letterhead and it reads, in full: Sold. EUR/JPY. Your account. The words United States do not appear in it anywhere. A nation reached across the ocean and overruled the price of money, and the paper that records the act is a form letter that does not know which country it served. I find that clean. A trade should know the account and nothing further. People treat the 1934 story as history. I keep it as the mandate, unedited. The fund was born from a single discovery, which is that a price is a number, and a number is correctable by whoever holds the pen. Everything I do is that discovery, kept current. The market spends all day arriving at a number for the yen. My work begins the moment the number it arrives at is one we would have preferred it did not. I do not argue with the market. I keep buying at the number we prefer until the screen shows the number we prefer, and then the number we prefer is the market's, retroactively, and everyone can go home. A reporter asked me last year whether any of this changes prices inside Japan. It was a generous question and I gave him the clean answer. My mandate is the exchange rate. The grocery store belongs to someone else. The rice at the Ito-Yokado in a Tokyo suburb costs about the same whether the yen prints 152 or 145; a 7-yen move on my screen is not a grain of rice on that shelf. The work stops at the exchange rate. It was built to stop there. The fund is not a rice program. I have filed nothing with the rice program. My filings go to the Federal Reserve Bank of New York, Settlement Division, and in 30 years they have never once asked me about rice. We are not careless about it. Japan spent record amounts of its own reserves defending the yen last year, and the yen kept falling, and we studied that closely. I built the deck myself. It was titled Lessons From Prior Interventions. Slide 14 read, Coordinated action amplifies signal. The slide after 14 recommended coordination. I reviewed the deck again on Saturday, and the deck was correct. None of this waits on Congress. Nobody forgot to close that door; the open door is the design. A fund that had to be voted on could not move before the market moves, and a fund that cannot move before the market is only a slower participant in it. So I was placed to the side of the vote, with money that was never appropriated, for the exact mornings when a vote would have slowed me down. I am the part of the government that does not wait to be asked. The yen closed Friday where I needed it to close. The rice at the Ito-Yokado cost the same on Saturday as it had on Thursday. My mandate covers the number on the screen. The rice is a different number, at a different desk, and I have never met the person who sits at it. But the number is holding. And I would like the record to show that the fund supports their work.

Japan’s fund for retired racehorses has raised more than 1.8 million US dollars over the past 10 years, mostly thanks to fans of the mobile game Umamusume Pretty Derby. This fund, called the Nice Nature Memorial Donation, is run by the Retired Horse Association of Japan. It uses the money to pay for vet care, safe homes, and other needs so old racehorses can live well after they stop racing. The fund started small in 2017, but donations jumped more than 30 times higher after the game came out in February 2021. People gave about 35.8 million Japanese yen, or around 225,000 US dollars, in that first year alone because fans fell in love with the characters based on real horses and learned about the hard life of retired horses from the game and its TV show. Even after the real horse Nice Nature died in May 2023 at age 35, people still donate a lot to the fund named in her honor. Fans have also helped other horses by raising money to fix stables for Meisho Doto and by giving support to Haru Urara.

🇯🇵 Some of you who are interested in Japan might have noticed recently of this new "trend" of "sad salaryman videos" coming out of Japan. The majority of these videos are fake, based on exaggerations and have been debunked. While overworking is a real problem in Japan, many of these videos are often fake. They often get millions of views, thousands in donations, etc. Some of them are not even run by Japanese people as the videos are just subtitles (in English) but contain no actual audio and are likely using AI. Chinese people have also started making these to make money from YouTube, TikTok, etc. It's gotten to the point where foreigners have actually started donating and foreign YouTubers are even making videos debunking them. I'm not suggesting everything on the internet is fake but unfortunately a lot of things are so we should all be careful.

🚨 Two Chinese Sailors Died Ramming Manila’s Boat. Beijing Hid It for a Year. On August 11, 2025, in broad daylight and calm seas off Scarborough Shoal, China Coast Guard (CCG) cutter 3104 slammed into the People’s Liberation Army Navy (PLAN) Type 052D destroyer Guilin (hull 164) at high speed. Both were chasing the Philippine Coast Guard (PCG) vessel BRP Suluan, a 44-meter Parola-class patrol vessel on a lawful resupply mission inside Manila’s exclusive economic zone. Philippine video captured the 3104’s bow crumpling ten meters inward. Four CCG sailors visible on that bow moments earlier were gone. Beijing said nothing. CCG spokesman Gan Yu called it “professional, regulated, and legitimate.” Censors scrubbed the footage from Weibo. Filipino radio calls offering medical aid went unanswered. Guilin kept chasing the Suluan before limping away for repairs at Yulin Naval Base. A year later, the cover-up cracked. On August 5, 2026, state broadcaster China National Radio’s (CNR) military channel posthumously named four “martyrs” as “Loyal Guardians of the China Armed Police”. The Ministry of Veterans Affairs martyr database confirms two, Yi Xinyu (born 2003) and Cheng Long (born 2000), were killed on August 11, 2025 during “frontline rights protection operations” in the South China Sea. Beijing’s own paperwork betrayed the story it spent a year burying. This was not seamanship. Since 2018, the CCG has reported through the People’s Armed Police (PAP) to the Central Military Commission (CMC). Two ships coordinating a high-speed block against a civilian resupply run is an ordered operation, not a rogue captain. The world responded. Then-US Ambassador MaryKay Carlson condemned the “reckless action.” Japan, Australia, New Zealand, and the European Union raised alarm. Two days later, USS Higgins and USS Cincinnati sailed Scarborough Shoal on a freedom of navigation operation. Beijing killed two of its own to intimidate a neighbor, then hid the bodies until its own paperwork told on it. ACI — Aric Chen | Insights

Careful with what you buy, Fake DDR5 RAM is flooding the market, especially in Asia. Real DDR5 memory costs much more due to shortages and high demand. Scammers are making fake sticks that look like the real thing. These fakes copy big brands like Samsung and SK Hynix. Inside, they use plastic dummy chips instead of real working memory chips. Some also have wrong or fake power parts. A common example is 16GB DDR5 laptop RAM sold on Yahoo Japan. Sellers list them as “junk” or untested items with no returns allowed. The price is around $85. If you install them, your computer usually will not start at all, or it may crash and run badly. Here is how to spot them: >Real RAM has sharp, straight edges on the board. Fakes often have rounded edges. >The small power chips look strange or different. >The board feels thinner or looks a lighter color. On desktop RAM, the metal cover can hide the fake chips until you remove it. Always buy from trusted sellers only, I wouldnt risk my whole PC for saving some money

# Practical and Useful Patterns with ADK ⚡ Turn Python functions into tools, wrap agents as tools, and run long tasks without blocking — ADK's Function Tools maximize flexibility in tool definitions. 📌 Title: Function Tools — Functions, Agents, and Async Tasks as Tools 🔗 URL: 🧩 Overview ADK's Function Tools let you use Python/TypeScript functions directly as agent tools. AgentTool wraps an entire agent as a tool accessible to other agents. Long Running Function Tools handle time-consuming tasks like video encoding and batch jobs without blocking the agent's execution flow. 🛠 Usage Basic function tool definitions and AgentTool usage. Import `Agent` and `AgentTool` from `google.adk`. Define a simple function tool `calculate_price` that takes `base_price` (float), `quantity` (int), and `discount_percent` (float, default 0), computes the total with the discount applied, and returns a dict with `total` and `currency`. For wrapping an agent as a tool, create an `analysis_agent` with `name="data_analyst"` and `tools=[query_database]`. Then define `main_agent` with `tools=[calculate_price, AgentTool(agent=analysis_agent)]`, allowing the main agent to call both the pricing function and the data analysis agent as tools. Using Long Running Function Tools. Import `LongRunningFunctionTool` from `google.adk`. Define an async function `encode_video` that takes `video_url` (str) and `format` (str, default "mp4"), starts an encoding job via `start_encoding_job`, and returns the job ID with a processing status. Wrap it with `LongRunningFunctionTool(func=encode_video)` to create `video_tool`, then pass it to an `Agent`'s `tools` list so the agent can trigger long-running tasks without blocking. 🏗 Practical Patterns **Modularization with AgentTool**: Encapsulate complex logic as specialized agents and expose them via AgentTool. This keeps the main agent's instructions simple while each specialist agent maintains its own tools and prompts -- achieving clean separation of concerns. Define a `summarizer` agent (for 3-line summaries) and a `translator` agent (for Japanese translation) as separate `Agent` instances. Then create a `content_manager` agent with `tools=[AgentTool(agent=summarizer), AgentTool(agent=translator)]`, allowing the main agent to invoke these specialists as tools for content management tasks. **When to Use Long Running Tools**: Ideal for batch processing, external API polling, file conversion — anything taking seconds to minutes. The agent receives a job ID and can proceed with other tasks in parallel. **Type Annotations Matter**: Clear parameter types and return types help the LLM call tools accurately. Docstrings serve as tool descriptions, so keep them concise and clear. 💡 Use Cases 🧮 Calculation and conversion functions as tools (pricing, unit conversion) 🤖 Reusable specialist agents via AgentTool 🎬 Async video encoding and image processing 📊 Non-blocking batch data processing ⚠️ Caveats - Function docstrings become tool descriptions. Write LLM-friendly descriptions — missing docstrings make tool purposes unclear. - Agents called via AgentTool run in a separate session from the parent. Be careful about state sharing. - Long Running Function Tools require a separate completion notification mechanism. Consider polling or webhook-based notifications. ✨ Function Tools let you integrate existing code assets directly into agents, and AgentTool enables seamless agent reuse. A massive boost to development productivity! #ADK# #AIAgent#

# Learning Palantir Foundry 🚀 Bring complex logic that no-code can't reach into your data platform, along with full software-engineering quality control. That's what Code Repositories delivers. 📌 Title and Feature URL Title: Code Repositories (Python Transforms) URL: 📝 Overview Code Repositories is a web-based integrated development environment (IDE) for creating and collaborating on production-ready code within Foundry. It provides a friendly UI over the underlying Git repositories, so teams can work without command-line access. With platform-specific features, you can apply software development practices directly to data engineering. 🔧 How It Works Version control and collaboration are at its core. - Common Git tasks (branching, committing, release tagging) execute through the web UI - Pull requests drive code review, with "highly configurable" permissions that support quality assurance such as mandatory reviews - IntelliSense, linting, error checking, and contextual help dialogs are available across all repository types - Transforms repositories let you author data transformation logic in Python, Java, or SQL with preview and debugging - Functions repositories natively integrate the Ontology and run low-latency business logic in TypeScript or Python 🛠 Practical Usage - Use PySpark to implement billion-row entity resolution and complex business rules in code - Require PR reviews so a second reviewer and CI checks must pass before merge - Add unit tests to guard transform logic against regressions - In Functions repositories, leverage Ontology-data-type autocomplete to write logic safely - Bring machine learning workflows into the platform via model development repositories 🎯 Use Cases - Implementing complex reconciliation and business rules in PySpark that Pipeline Builder can't express - Structurally eliminating "regressions from editing production directly" through mandatory reviews and branch-based workflows - Implementing derived KPIs and validation logic as Functions reused across apps - Managing ML model training and inference code under governance ⚠️ Caveats - The docs note that Japanese translations are machine-generated and unverified, so localized content may have accuracy limitations - Each repository type (Transforms/Functions/Model) supports different languages and purposes, so pick the one that fits your goal - Being a pro-code environment, the quality benefits only materialize if your organization establishes review, CI, and test practices #PalantirFoundry# #DataEngineering#

A Small Manifesto Against the Current @Zcash Bandwagon Zcash is a remarkable piece of cryptography, but cryptography isn't the bottleneck for crypto in 2026. Distribution, liquidity, and developer adoption are. And those are the exact dimensions on which Zcash is structurally disadvantaged. 1. Network effects work against single-purpose privacy chains Privacy is a network-effect product: the larger the anonymity set, the stronger the privacy guarantee. Zcash currently has ~30% of supply in shielded pools, and most activity moves in and out of the shielded layer rather than staying within it. A shielded pool with ~5M ZEC and a few thousand daily active users provides meaningfully less privacy than the same cryptographic primitives running on an L1 with 10M+ daily addresses. The math is brutal. If 100 people hide in a room, finding any individual is hard. If 100 million people hide in a room, it's impossible. Privacy coins concentrate users. Privacy features on general-purpose chains recruit them. 2. Liquidity and acceptance are non-negotiable A privacy coin that gets delisted from major exchanges, as Zcash repeatedly has across Japan, Korea, the UK, and parts of the EU, becomes harder to acquire, harder to exit, and harder to use at scale. Privacy tools built on Ethereum, Solana, or Base inherit the liquidity of the underlying chain. You don't have to choose between privacy and the ability to transact with the rest of the financial system. Zcash forces that choice. Nobody wants to make it. 3. People don't want private money. They want private applications Most people don't need to hide a $50 ZEC transfer. They need confidential business payments, private payroll, undisclosed treasury operations, sealed-bid auctions, private voting and confidential DeFi positions that don't leak through transaction graphs. None of these run on a privacy coin. They run on smart contract platforms with privacy primitives like @aztecnetwork on Ethereum, @AleoHQ as its own L1, @solana 's confidential transfers, @penumbrazone in the Cosmos ecosystem, FHE-based chains like @fhenix and ZK-rollups in general The future of privacy is programmable, not denominational. 4. The technology has been completely commoditized zk-SNARKs were Zcash's moat in 2016. By 2026, they're the foundation of every major L2, dozens of privacy systems, and most rollup architectures. The Zcash team did the foundational research, and then watched the IP escape. The chains that benefited most aren't paying rent to Zcash, and they never will. It's one of the cleanest examples in crypto of pioneering a technology and capturing none of the value. 5. Regulatory exposure cuts the wrong way Privacy coins occupy a uniquely vulnerable regulatory category. Privacy tools on general-purpose chains can be designed with selective disclosure, view keys for auditors, compliance hooks and they live inside chains regulators have already accepted as legitimate financial infrastructure. Zcash has built the same compliance tooling (view keys, selective disclosure protocols) but still carries the "privacy coin" label that triggers automatic delisting regardless of actual functionality. The technology isn't the problem. The category is. 6. The unit-of-account problem For privacy to matter for real economic activity, it has to be denominated in money people actually use, this is the biggest lesson in crypto over the past 5 years. Nobody pays salaries, settles invoices, or runs treasuries in ZEC. They use USD, EUR, USDC, USDT. Privacy that requires switching unit-of-account is privacy that won't be used at scale. The winning model is private stablecoins and private transfers of mainstream assets, which requires programmability Zcash structurally doesn't have and isn't on a path to building. 7. The "private Bitcoin" comparison is just stupid At the end of the day, Zcash only really competes with Bitcoin, except it doesn't, because the "private Bitcoin" framing falls apart on contact with reality. You don't get to slap "private" on as a feature and call yourself Bitcoin's successor when you don't have the liquidity, the decentralized robustness, the regulatory acceptance, the size, or the history. Bitcoin's hashrate is distributed across hundreds of pools and tens of thousands of independent miners globally. Zcash's hashrate is functionally controlled by a handful of pools running ASICs from a few Chinese manufacturers. Zcash inherited Bitcoin's consensus model with a fraction of Bitcoin's decentralization. And decentralization isn't a sliding scale where "more" earns you partial credit. It's binary. You're either close enough to Bitcoin to inherit the monetary properties that come with extreme decentralization, as Ethereum genuinely is, or you're not, and the "moneyness" argument doesn't apply to you at all. Ethereum and even Solana have an order of magnitude better chances of reaching Bitcoin's market cap than Zcash does. That's not a controversial claim. It's just looking at the data.