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SQLite Internals: Win32 Malloc; PostgreSQL 19 LZ4 Compression, Spock 6 Beta Today's Highlights This week's database news highlights a critical discussion on SQLite's internal memory management for Windows, a major performance-boosting compression change planned for PostgreSQL 19, and the beta release of Spock 6 for multi-master PostgreSQL replication. Discussion on Dropping SQLITE_WIN32_MALLOC Support (SQLite Forum) Source: https://sqlite.org/forum/info/e280641d16b70a0980c5409280cfda598b2141a41cbd63be86e943ea334f346f This forum discussion centers on the potential removal of SQLITE_WIN32_MALLOC support in future SQLite versions. SQLITE_WIN32_MALLOC is a compile-time option that allows SQLite to use Windows-specific memory allocation functions (like HeapAlloc , VirtualAlloc ) rather than its default internal memory allocator or the standard C malloc / free . The primary motivation for considering its removal appears to be reducing code complexity and maintenance burden, as this specific memory allocation strategy is less commonly used by the broader SQLite developer community. Developers who embed SQLite into Windows applications and have configured custom memory management (e.g., for specific performance tuning, memory tracking, or integration with application-level memory pools) often rely on this option. Its deprecation would necessitate a review of their build processes and potentially a migration to a different memory allocation strategy, such as SQLite's default options or their own custom sqlite3_config() hooks. The conversation delves into the trade-offs between backward compatibility, library size, and ongoing maintainability, highlighting the intricacies of supporting a highly configurable, embedded database system across diverse environments. Comment: This discussion is crucial for anyone embedding SQLite on Windows with specific memory management requirements. It's a clear signal to start evaluating alternative memory allocation strategies for future-proof

I have been working on building a private, secure network accessible from anywhere. The goal was to connect my mobile phone and my local development laptop using a WireGuard VPN , hosting the central gateway on a free-tier Google Cloud Platform (GCP) e2-micro instance. I wanted to access my self-hosted services, specifically my Docker-hosted Open WebUI , running on my local home Wi-Fi connected laptop, directly from my phone using mobile data. It sounded straightforward. But if you read my other from scratch journeys, you might have already guessed, it was not. The Setup My architectural plan was a simple hub-and-spoke topology: The Hub: GCP VM ( 10.66.66.1 ) with IPv4 forwarding enabled. Spoke 1 (My Phone): 10.66.66.2 Spoke 2 (My Laptop): 10.66.66.3 I wrote my server configurations, enabled IP forwarding ( net.ipv4.ip_forward=1 ), wrote the iptables rules to allow forwarding between peers, and started the interfaces. Then came the moment of truth. I tried to bring up the tunnel. Absolute silence. No packet moving from anywhere. Hurdle 1: The Classic Cloud NAT Trap (Internal vs. Public IP) Before I could even worry about routing packets between my phone and laptop, I couldn't even get them to handshake with the GCP server. Like many of us do when working inside a VM, I had run ip addr on the GCP instance to grab its IP address for my client configurations. I set up the WireGuard peers to point to this IP. Nothing connected. The Culprit: GCP (and AWS) operates on a 1:1 NAT mapping. The virtual network interface inside your VM only sees and binds to a private, internal cloud IP (e.g., 10.128.0.x ). The public IP assigned to your instance lives outside the VM at the VPC gateway level. By putting the internal IP into my client configs, my phone and laptop were trying to connect to a private address that didn't exist on their local networks. The Fix: I had to swap the internal IP in the client configurations with the GCP Ephemeral/Static External IP . Once the handshake

Most people, when a prompt stops working, write more . They add clarifications, repeat instructions in different words, hedge against edge cases they haven't encountered yet. The prompt doubles in length. The output gets worse. This is the opposite of what you should do. A long prompt is not a precise prompt. It is an ambiguous prompt that happens to have a lot of words in it. Every sentence that does not tightly constrain the output is a sentence that dilutes the sentences that do. Why Long Prompts Underperform When a language model processes your prompt, it attends to all tokens simultaneously — but not equally. Attention is probabilistic. Instructions that are buried in filler, repeated in slightly different forms, or surrounded by low-information prose get proportionally less weight. The model's ability to track which constraint takes precedence over which degrades as the signal-to-noise ratio of the prompt drops. In quantitative trading, the signal-to-noise ratio (SNR) is the single most important property of any strategy signal — a strategy that works in backtesting but fails live is almost always a noise problem, not a signal problem. The same principle applies directly to prompts. Every redundant qualifier, every throat-clearing sentence, every hedge phrase is noise riding on top of your actual instruction signal. The model's attention mechanism cannot distinguish intent from filler. It weighs them together, which means your real constraints compete for attention against your own verbal padding. A concrete way to see this: take a 600-word prompt and a 120-word prompt that contains the same core logic. The 120-word version, if well-constructed, will frequently outperform the 600-word one. Not because brevity is a virtue in itself, but because removing the surrounding noise forces the remaining tokens to do all the work — and they accumulate proportionally more attention weight. This is not speculative. It is the same mechanism behind why prompt drift happens

Get ready for more AI characters in Fortnite. Developer Epic Games is going to let Fortnite creators publish experiences featuring characters with AI-powered voices starting on July 30th, and ahead of that launch, it's created 36 characters with "consistent voices and personas" that creators can use as NPCs. The characters include Fortnite staples like Agent […]

From data preparation and tokenizer selection to pretraining, LoRA, RLHF, evaluation, and production monitoring, this guide covers the major stages involved in training an AI model. Training an artificial intelligence model is not simply a matter of loading a dataset onto a GPU and running a few commands. A successful model requires a measurable objective, legally usable and carefully cleaned data, an architecture suited to the problem, controlled optimization, independent evaluation, and continuous monitoring after deployment. In large language model development, a mistake in any one of these stages can waste millions of training examples and a significant amount of compute. This guide explains the model development process primarily through the training of large language models. However, fundamental concepts such as dataset splitting, loss functions, overfitting, and evaluation also apply to computer vision, speech, and predictive models. The goal is not to provide a single fixed recipe. Instead, it is to explain which training approach is appropriate for which problem and to clarify the cost difference between training a model from scratch and adapting an existing model. In Brief: How Is an AI Model Trained? First, the target task and success criteria are defined. Data is collected, reviewed for licensing and privacy, cleaned, and divided into training, validation, and test sets. The model generates predictions from the input data. The difference between the prediction and the correct target is measured using a loss function. Backpropagation calculates how each parameter contributed to the error, and an optimization algorithm updates the parameters. This process is repeated under controlled conditions until the model achieves acceptable results in independent tests and safety evaluations. What Does Training a Model Actually Mean? A neural network initially contains a large number of numerical parameters. During training, the model generates a prediction for a giv

Originally published at daniel-yang.com . I've been using passkeys for a while now, and at some point I noticed an extension in the WebAuthn spec that almost nobody talks about: PRF. It lets a website ask your authenticator to evaluate a pseudo-random function during login. Deterministic output, 32 bytes, keyed to that specific credential, never leaves your browser. That's an encryption key. Sitting inside the same ceremony everyone already uses for login. So I built pknotes to see how far the idea goes: an end-to-end encrypted notes app with no master password anywhere. Your passkey unlocks your notes in the literal, cryptographic sense. This post is the architecture writeup. There's a live demo if you'd rather poke it first (notes wiped daily). One ceremony, two jobs A normal passkey login proves who you are and nothing else. With the PRF extension, the same ceremony does double duty: The server verifies the WebAuthn assertion. That's login. The client reads the PRF output from the same response and derives a key from it. That's decryption. The server never sees the PRF bytes. They're returned to client-side JavaScript only, after user verification (Face ID, Touch ID, PIN), and only for the requesting origin. Requesting it looks like this: const credential = await navigator . credentials . get ({ publicKey : { challenge , userVerification : ' required ' , extensions : { prf : { eval : { first : new TextEncoder (). encode ( ' pknotes/prf-eval/v1 ' ) } }, }, }, }); const prfOutput = credential . getClientExtensionResults (). prf . results . first ; // 32 bytes, deterministic for this credential + this input, never sent anywhere The key hierarchy Raw PRF output shouldn't encrypt data directly, and you also want to be able to add and remove devices without re-encrypting everything. So there's a small hierarchy: Passkey PRF output │ HKDF-SHA256 ▼ KEK (key-encryption key, exists only in browser memory) │ unwraps ▼ Master key (random AES-256, generated once at signup) │

Coca Cola said dairy production at its Fairlife unit will "remain suspended" in the United States following a hack.

The soon-to-be-retired electric SUV was born from a collaboration with GM.

Foot traffic to leafy green chains is falling, data shows. Still, a few brave souls who spoke to WIRED were determined to get their fix. “I honestly didn’t even think about” the risk of explosive diarrhea, one says.

T-Mobile to restore free lines lost during plan migration, but price hikes remain.

Samsung’s Frame is different from your average 4K TV. Its biggest selling point involves what it does when you aren’t actively using it. It can display art, turning your living room into a gallery. The Frame has bezels that make it look like — you guessed it — framed art, and its matte finish can […]

Google says these changes could endanger user privacy and security.

As wildfire smoke threatens air quality and safety for millions, here’s your playbook to keep your family safe from the damaging effects of smoke.

Netflix says roughly 300 titles on its platform used generative AI, most of which occurred in post-production. The streaming service revealed the news in its second-quarter earnings report released on Thursday, saying it's "increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost." It also provided some examples of […]

Elon Musk's xAI files first lawsuit against Grok user accused of making child sex images.

Hyundai aims to deploy 25,000 Atlas robots starting with US factories in 2028.

Ryan Beiermeister, who demonstrated cool analysis in the Founders Fund YouTube series "Mafia," has joined the firm as a partner.

👋👋👋👋 Looking back on your week -- what was something you're proud of? All wins count -- big or small...

If only someone could have predicted it.

CD sales are apparently going up, reportedly thanks to fans realizing they're an affordable way to support their favorite artists. According to a new report from research firm Luminate, 16.3 million CDs were sold in the first half of 2026 in the US, a 16 percent increase year-over-year. The growth in CD sales was driven […]