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According to an Economic Daily News report spotted by MacRumors, executives for Apple supplier Pegatron confirmed during an earnings call that the iPhone 18 Pro series phones will launch this fall, but the base iPhone 18 won't arrive until the first quarter of next year. The more affordable iPhone 18e and a new iPhone Air […]
Scientists have deliberately turned male mouse embryos into females for the first time. A team based in Japan used a CRISPR-based approach to remove the Y chromosome from male cells and create female clones of male mice. “No one has done this before,” says Monika Ward, a reproductive biologist at the University of Hawaii, who…
A payment decision has to be exact and repeatable. So in the product I built, the money logic is deterministic code, and the agent only touches the parts where judgement is genuinely open-ended. Every AI demo right now is an agent doing everything. Point it at the problem, let it reason end to end, marvel at the trace. It demos beautifully. Then you try to put it in front of a real workflow with real money and it falls apart, because the thing that makes a demo impressive, the model deciding freely, is exactly the thing you cannot allow when the output is a payment. I spent a while building a procure-to-pay product: a vendor invoice comes in, gets extracted, matched against a purchase order, routed through an approval workflow, and reconciled. It is the kind of thing everyone now wants to put an agent on. So I did, sort of. But the interesting decision, the one that took the longest to get right, was not where to add the agent. It was where to refuse to. The rule: a payment decision must be exact and repeatable A model is a probability distribution. Ask it the same question twice and you can get two answers. That is a feature when the task is fuzzy and a liability when the task is "does this $48,200 invoice match this purchase order". Matching, the approval engine, reconciliation: these have to be exact, auditable, and identical every run. So they are plain deterministic code. No model in the path. If a controller asks why this got approved, the answer is a code path they can read, not "the model felt it was fine". That sounds obvious written down. It is not how most people are building AI features right now. The default has become: agent first, and carve out the deterministic parts only when something breaks. I did the opposite. Deterministic by default, agent only where the trajectory is genuinely open-ended. The three places the agent actually earns its keep Once you hold that line, the places where AI belongs get very clear, because they are exactly the places a
In ledgerloop, a clean invoice under $1,000 posts with no human involved. I built the approval gate, then put a price on it. A company raised $30M last week to take the human out of agent payments. The expected take from someone with my background is a post defending the human. I agree with them, up to a thousand dollars. Two conditions, and nothing else The manager gate fires on two conditions: any exception, or a clean bill over $1,000. Below that, a clean three-way match posts straight through and no one signs. From the seeded scenarios: $730 clean goes straight through. $9,360 clean still stops, because a material bill gets a human whatever the match says. A steel bar invoiced 9% over the PO stops. Invoiced 100 units, received 80, stops. Two things exactly, and they are the load-bearing ones. Below the floor, what posts the invoice is deterministic tested code, not the model: the agent reads, investigates and proposes, it does not decide an amount. And these are seeded demo scenarios, not production traffic. The argument I actually want It is not whether humans should approve payments. It is that "a human approves payments" stops being a control the moment no one wrote down which payments. An unwritten threshold is not a policy, it is a habit, and a habit cannot be audited. The number itself is arguable and probably wrong for your business. Its existence, in code, with a reason next to it, is not. If you run AP: what is your straight-through limit, and who set it? The whole loop, an agent deriving the workflow then a real invoice routed through it, is in the ledgerloop case study . Originally published at dylan.merigaud.com .
Enterprise fintech deals don't die in the demo. They die in week six of onboarding, while someone re-types the customer's approval rules into a canvas. I spent two years inside a procurement fintech and the pattern was consistent: the product demos great, the contract gets signed, and then comes the wall. Setup that drags for weeks. Change requests every single week. Users who don't fully understand what was configured for them, so they ask instead of doing. The three things that cut onboarding time by 90% Integrating end to end with the systems the client already runs. The ERP connection wasn't a checkbox: granular sync per data type, bulk imports, master data flowing both ways. Every field the client doesn't re-enter is a support ticket that never exists. Generating a v1 of their approval workflow instead of handing them a blank canvas : business rules and best practices, applied to the real people pulled from their HRIS. The client reviews and adjusts a draft. Nobody designs from zero. Giving clients simple tools to help themselves , including a chatbot, so "how do I change this?" stopped requiring us. None of it was glamorous. All of it was product engineering aimed at time-to-first-value. A CTO building in this space told me recently that time-to-first-value, not features, is what decides procurement deals. That matches everything I saw from the inside. The workflow was never missing Watch an enterprise onboarding for any workflow product and you'll see the same ritual: a kickoff call, a shared screen, and someone rebuilding the org's approval logic box by box. Who approves above $10k. Who signs off on IT purchases. What happens when the manager is on leave. None of that information is new. It sits in the HRIS (who reports to whom, titles, departments) and in the ERP (vendors, open POs, spend history). The customer is being asked to re-enter reality the software could have read. What the next iteration looks like ledgerloop is that idea taken further. An agent
In the demo, it's three boxes: request, manager, CFO. Everyone nods. Then production shows up with questions the canvas never asked. The questions the canvas never asked The approver left the company last month, and the workflow still points at them. The amount lands exactly on the threshold. Above 10k goes to finance. Is 10k above 10k? The request was approved, then someone edited one line. Does the whole chain re-run, or just the delta? Who decides that? The manager is on leave and delegated their approvals. Does the delegate's own delegation count? Until when? Approval by group: any of the five? All of them? Three out of five? In what order? A condition depends on an answer given two steps earlier. That answer just changed. I spent two years shipping and maintaining an approval workflow engine at a procurement fintech. The three boxes took a sprint. The list above took the rest. How we actually answered it We froze the workflow at init: conditions resolved once at launch, and a running request never re-derived them. Mid-flight edits simply didn't exist. Approval groups came straight from the teams in the HRIS. Vacations earned a proper feature, a replacement approver that applied even to workflows already running, because absence is the one thing you can't freeze. And the approver who had left the company? Fixed by hand, more often than I'd like to admit. Freezing at init isn't a hack. It's the honest trade-off: deterministic, auditable, and it quietly declines half the list above. A workflow builder is a programming language your users never asked to learn Every condition is syntax, every unhandled edge case is a bug they'll file. So my opinion hasn't moved: keep the engine boring, deterministic, tested code, and derive the configuration from the systems that already know the answer, editable in plain language. That is what ledgerloop does with the HRIS, and what the components in approvals-ui model directly: quorum gates, amount thresholds, and a policy lint th
Looking to get your hands on the latest Pixel devices? After weeks of leaks and rumors, Google has officially announced its next generation of Pixel phones and watches. The base Pixel 11 starts at $899, the Pro and Pro XL are $1,099 and $1,299 respectively, and the Pro Fold starts at $1,899. The watch comes […]
Twitch users can now opt out of allowing their content to be used to train Amazon's generative AI models. Opting out means that "your streams, VODs, clips, stream chats, and pictures and text on your channel" won't be used in "future training" of an Amazon AI model "whose purpose is to generate or synthesize text, […]
Immigration and Customs Enforcement (ICE) is aiming to spend up to $20 million on equipping officers and agents with specialized gloves that deliver painful electric shocks. These plans were outlined in a notice published by the Department of Homeland Security on Monday, with an unspecified quantity of the devices set to be delivered by March […]
Amazon is fully stepping back from MMOs. After saying last year that it would be halting "a significant amount" of its work on first-party AAA games, "specifically around MMOs," Amazon will be handing over live operations of Throne and Liberty and Lost Ark in the West to other companies, according to announcements on Wednesday. Throne […]
The Road to Battlefield competition is now in its second year, and its purpose is to give founders across Central Eurasia a direct route to Startup Battlefield
Google knows creators are a big audience. The occupation is growing fast, and landing some influential names could be a major turning point for the Pixel's market share. This year, Google is building features directly into its new Pixel lineup that it says can help creators record, organize, edit, and publish content faster and more […]
Google is hosting a Pixel launch event on Wednesday night where it will show off its latest lineup of devices. But you don't have to wait until then for all the news: We were able to check out the devices ahead of the event and take a look at everything Google's unveiling, including the latest […]
Google focuses more on AI and curated experiences than spec upgrades in 2026.
More than two years after launching its own device-tracking network, Google has finally followed up with its own tracker. The Pixel Tag, which goes on sale in November, looks like one of the more capable trackers too: It combines ultra wideband (UWB) with Bluetooth 6.0 Channel Sounding for two distinct types of precision tracking. The […]
If you look at a photo taken by a smartphone today, it's hard to find much that's technically wrong. Faces are bright and visible. The skies are pristine and blue. Even at night, scenes are well lit and noise-free. If the physical limitations of a tiny camera are what you're trying to solve for, you […]
SpaceXAI has introduced Grok Bot, an always-on AI agent service designed to behave like independent "AI teammates" that can do your work for you. The bots share their own cloud-based computer environment, and can sign into apps, tools, and websites you already use to complete multi-step workplace tasks, only coming back when their assigned work […]
Did a prototype Microsoft gamepad fall off the back of a truck? We can't say, but a Redditor has legit-looking photos of a prototype Xbox controller that's almost certainly the Xbox Elite Series 3. Incredibly, they claim they bought the pad for $200 on OfferUp. In May, a Brazilian regulator's leaked images revealed that Xbox […]
This follows many drone incursions into Romania and a failed attack in Germany.
For the 14th time, a Google product has hit 1 billion users. Google CEO Sundar Pichai posted on X that a billion people are using Gemini every month, and that Gemini is Google's fastest-growing product ever. A billion users is a huge milestone, but Google isn't the first AI app to hit it. OpenAI's ChatGPT […]