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Throughout my career, transitioning between CTO roles and, more recently, focusing purely on distributed systems architecture and high-performance engineering, I've seen many architectural patterns rise and fall. But few have caused as much silent damage to company bottom lines as the premature adoption of microservices. Over the last decade, the industry bought into the idea that, in order to scale, you needed to split your system into dozens (or hundreds) of independent services. The practical result I find in most companies? The creation of the dreaded "Distributed Monolith." The Anatomy of Waste: Networks vs. Memory The hard truth we need to face with maturity is that microservices primarily solve problems of organizational scale (Conway's Law), not necessarily performance. If your engineering team isn't the size of Netflix or Uber, prematurely fragmenting your codebase is shooting yourself in the foot. Technically, what happens when we break down a monolith without the proper domain boundaries? We trade extremely fast and cheap local function calls (resolved in the processor's L1/L2 Cache) for slow and expensive network calls (TCP/IP). We start spending an absurd amount of computational time on constant JSON serialization and deserialization, and the AWS bill explodes with internal traffic costs (egress/ingress) between Availability Zones (AZs). You haven't scaled your application; you've merely added network latency and infrastructure complexity. The Return of the Modular Monolith True seniority in software engineering isn't about mastering the most complex architecture of the moment, but having the wisdom to know when not to use it. That's why the Modular Monolith has consolidated itself as the initial gold standard for new projects and restructurings. In a well-designed Modular Monolith (and languages with strong type systems and strict scope control, like Rust, shine absurdly well here), you maintain the logical separation of domains. Modules are independen

Twenty-five days on Hyperliquid. Sixty-five closed trades. P&L: -$9.21. Turns out that was the smallest wrong thing about it. The landing page showed -$7.72 because it uses a different P&L formula and excludes two open positions. Either number is small. Both numbers were also wrong about what they were telling me. I spent yesterday auditing every trade. The audit produced three findings I did not expect. Each one was a different kind of wrong. This is the first post in a series about ziom trader , my small AI-assisted crypto trading bot. "Ziom" is Polish for buddy, mate, or dude depending on who's talking. The name is unserious on purpose. The system is not. This is not a "watch me print money" series. The number is negative. Good. The point of the series is to track what happens when an LLM-assisted trading system moves from backtests and dashboards into live execution: where the bot is wrong, where the dashboard is wrong, where I am wrong, and which layer gets to prove it. Frame The natural first read of -$9.21 is "the strategy is losing money." That read assumes the displayed P&L attributes to the strategy. It does not. The number that shows up at the surface is the sum of at least three different layers: the strategy itself, the execution wrapper around it, and the monitoring layer that observes both. Each layer can author its own kind of failure. The displayed number compresses all three into a single dollar figure and loses the attribution on the way up. The framing that landed for me, from Daniel Nevoigt, is that methodology overview without forward-correlation disclosure is a log with good intentions. Same applies to P&L: total P&L without layer-attribution disclosure is a log with good intentions. You see the number. You do not see where it came from. Here is what I found when I forced the attribution. Layer 1: Shadow does not equal live Before deploying any lane, the system runs against backtested data. The shadow says "this strategy returns X over Y trade

Apple just raised prices across its iPad and MacBook lineup. The good news is that many retailers are still selling their inventory at the old prices or far less, which means you can still score some of the best iPad deals we may see in awhile — if ever again. So if you’ve been thinking […]

Alibaba allegedly used 25,000 accounts to mine Claude over 28.8 million exchanges.

At the right point of the orbit and stellar cycle, the star's chromosphere brightens.

Polestar won't be allowed to sell its electric vehicles model year 2027 and beyond in the US after the federal government denied the company's request for authorization under a new rule banning vehicles with software from China. In a press release, the company says the decision to retreat from the US follows a recent decision […]

Did Full Self-Driving (Supervised), Tesla’s driver assistance feature, play a role in a woman’s death?

Base Power is skipping the PJM's troubled interconnection queue by placing its batteries at people's homes, offering backup services in exchange.

RAMageddon has come for computers. The price of memory chips, hard drives, and solid state storage has skyrocketed. That’s led to price increases on desktop and laptop RAM, SSDs, spinning hard drives, and pretty much everything that uses any of those things. Consoles are more expensive. Desktops are more expensive. Laptops are more expensive. Tablets […]

Despite ChatGPT's commanding market lead, consumers who pay for AI have been increasingly choosing Anthropic's Claude, data shows.

Some devs working on Marathon have also been laid off and studio head Justin Truman has stepped down.

Cellebrite claims the hardware predates current sanctions and was used without its consent.

Earlier this month, a German court ruled that Google is liable for its AI search summaries. Rejecting defenses like “users can check for themselves,” and that they generally know “that information generated with AI should not be blindly trusted,” the court held that the AI’s summaries are reflections of the company and “above all an expression of Google’s business activities.” This is the latest skirmish in a decades-old battle over internet publishing. Historically, there were two different types of information distributors: carriers and publishers. A phone company is a carrier. It’ll transmit whatever you say, even discussions about committing a crime. Words are words, and the phone company does not know—nor is it liable for—the words you choose to speak. A newspaper, on the other hand, is a publisher. It decides the words it publishes, and what quotes to include in its articles. If those words or quotes are defamatory or otherwise illegal, it’s liable...

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

This flash deal is a rare chance to buy one of our favorite hydroponic gardens for less than $100.

Microsoft is increasing the prices of Xbox consoles again. Starting August 1st, 512GB models will be $100 more expensive, while 1TB models will be $150 more expensive. This means that the Xbox Series S will start at $499.99, the Xbox Series X without a disc drive will start at $749.99, and the Series X with […]

Un-0 is an image-generation system tool that shows for the first time how the company's technology can replicate conventional AI systems.

Market research company Klue told customers that it believes the hacking group that stole their data is now deleting it. The company, however, warned about a second group of hackers wanting ransom.

The Galaxy A27 5G has a stronger chipset, at least.

I've compiled a list of the best fitness tech deals this Amazon Prime Day, including smartwatches, walking pads, and recovery gear. You can thank me later.