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Your customers would rather message you on WhatsApp than fill in a contact form. That's fine at ten conversations a day. At a hundred, messages get missed, nobody knows which rep is on which deal, and at month-end somebody rebuilds the commission sheet by hand and gets it wrong. The usual answer is a $49–$499/month WhatsApp SaaS platform, priced per seat, with your customer data living in someone else's database. This post is the other answer: the same workflow on Google Sheets + Apps Script — and the one piece I see teams get wrong every single time, with the code to fix it. Where DIY WhatsApp automation actually breaks It isn't the messaging. Wiring a WhatsApp webhook into a sheet is a couple of hours of work, and I've written that build up separately — the webhook, the AI reply, and the lock that stops two reps chasing the same lead are all in Build a WhatsApp Sales Inbox in Google Sheets . I won't repeat it here. The part that breaks is the commission math . Someone writes =IF(revenue>10000, revenue*0.08, revenue*0.05) into a column, and three things kill it: A single sale spans two tiers — the formula charges the whole amount at one rate. The tiers change in July , and now every historical row recalculates at the new rate. A customer refunds in August on a sale from June, and nobody can unwind it without breaking the audit trail. So that's what this post builds: a tiered commission engine that survives rule changes and refunds. 1. Put the tiers in a table, never in a formula This is the whole trick. Make a Commission Rules tab, one row per rule: rule_id | rep_id | effective_from | effective_to | tier_1_cap | tier_1_pct | | | | | tier_2_cap | tier_2_pct | tier_3_pct --------+----------+----------------+--------------+------------+------------+----------- R1 | ALL | 2026-01-01 | | 10000 | 0.05 | | | | | 50000 | 0.08 | 0.10 R2 | rep_ayse | 2026-06-01 | | 10000 | 0.06 | | | | | 50000 | 0.09 | 0.12 rep_id is either a specific rep or ALL (the house default). Percenta

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

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

They just annoy me and provide very little value. The non-AI gems in between however are very much still worth reading. Has anybody built that already?

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.

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.

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.