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Google added two new models to the Gemini API today: Nano Banana 2 Lite (image generation) and Gemini Omni Flash (video generation + editing). Neither is the Gemini 3.5 Pro release people have been waiting for, so it's easy to miss. Here's what's actually in them. TL;DR Nano Banana 2 Lite: gemini-3.1-flash-lite-image = text-to-image in ~4s, $0.034/1K images Gemini Omni Flash: gemini-omni-flash-preview = video gen + conversational editing, $0.10/sec Both are built to be chained: generate an image fast, then animate it into video Neither model is positioned as a quality upgrade = both are cost/speed plays Nano Banana 2 Lite Model ID: gemini-3.1-flash-lite-image Text-to-image output in about 4 seconds $0.034 per 1K-resolution image Positioned as the direct replacement for the original Nano Banana ( gemini-2.5-flash-image ) - if you're on that model, this is a drop-in upgrade Available in Google AI Studio, Gemini API, Gemini Enterprise Agent Platform, and consumer surfaces (Search AI Mode, Gemini app, Photos, NotebookLM, Flow, Google Ads) Gemini Omni Flash Model ID: gemini-omni-flash-preview Public preview in Google AI Studio and the Gemini API Conversational editing - refine a generated video using plain-language instructions instead of re-prompting from zero Multimodal referencing - combine text, image, and video inputs to keep a scene consistent $0.10 per second of video output (same rate as Veo 3.1 Fast) Known limitations right now Generations capped at 10 seconds No audio reference uploads yet No scene extension yet Video references under 3 seconds are accepted by the API schema but not correctly processed yet Character consistency across scene changes/pans still has rough edges Google says longer durations are coming. The part worth paying attention to: chaining them Generate an image with Nano Banana 2 Lite (fast, cheap) Pass that image as a reference into Omni Flash Omni Flash animates it into a video Both models are optimized for throughput and cost, not for to

The Quest Begins (The "Why") I still remember the first time I faced a problem that asked for the sum of numbers in a sub‑array, over and over again, with updates sprinkled in between. It felt like I was stuck in a never‑ending loop of for i in range(l, r+1): total += arr[i] – O(n) per query, and with up to 10⁵ queries the solution timed out every single time. I was staring at the screen, thinking, “There has to be a smarter way to answer these range questions without scanning the whole array each time.” That moment was my dragon: a seemingly simple problem that kept biting me because I kept reaching for the brute‑force sword. I needed a data structure that could give me the answer in logarithmic time while still supporting point updates. Enter the segment tree – the tool that turned my O(n·q) nightmare into an O((n+q)·log n) victory. The Revelation (The Insight) So why does a segment tree work? Imagine you have an array and you want to know the sum of any interval [l, r] . If you could break that interval into a handful of pre‑computed chunks, you’d only need to add those chunk values together instead of touching every element. A segment tree is exactly that: a binary tree where each node stores the aggregate (sum, min, max, etc.) of a segment of the original array. The root covers the whole array [0, n‑1] . Its two children cover the left half and the right half, and this keeps splitting until the leaves represent single elements. The magic lies in two facts: Every node’s value is a function of its children. If you know the sum of the left child and the sum of the right child, the parent’s sum is just their addition. This means we can build the tree bottom‑up in O(n) time. Any interval can be represented as O(log n) disjoint nodes. When you walk down the tree to answer [l, r] , you either take a whole node (if its segment lies completely inside the query) or you recurse further. Because the tree’s height is log₂n, you’ll visit at most 2·log₂n nodes. Thus, building

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

The EU went after Google for the practice of bundling its search engine and browser with Android.

This tutorial was written by Néstor Daza . This is the second article in a series about building Claudius , my own Claude-based chatbot ( Github ). The prologue made the case for building it, and for choosing MongoDB as its foundation. Open the conversations collection in Claudius’ database and you find the usual fields of a thread header but nothing else: a userId , a title , some timestamps , and so on, but no array of messages, no messages collection sitting beside it either! The text of every conversation lives somewhere else entirely, in the LangGraph checkpointer, which I wire up later in this series. This absence is a modeling decision, and how I came up with the database schema for my chatbot is the theme of this article. If you come from a relational background, you're used to modeling the data first when designing a database. For a project like this, you would start by finding the entities and normalizing them, and the final schema would come out of the data's structure: a conversations table and a messages table with a foreign key between them, because that is what the data looks like. Document modeling runs the other way. You start from how the application reads and writes, and the shape of the document follows the access patterns. Claudius never reads conversation messages without the agent's full working state wrapped around them, and that state is persisted using the LangGraph checkpointer. A separate messages table would add nothing, since the app would always have to join it back to that state on every read. The access pattern says the messages belong with the agent state, so that is where they go, and conversations are left as the lightweight header the list view actually needs. That inversion, modeling around use rather than around the data, runs through everything below. Schema-flexible is not schemaless This is the misconception lots of people often carry, and it is worth killing on the way in. A document database does not mean no schema; it mea

OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

A top Democrat on the Senate's Intelligence Committee warned that the information accessed on a Homeland Security intelligence-sharing network may risk national security.

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste

Google tries balancing AI data center emissions with clean energy efforts.

Interesting paper: “ Cybersecurity Mission Creep .” Abstract: Cybersecurity is experiencing mission creep. Policymakers are casting more and more problems as issues of cybersecurity. So reframed, wildly different policy issues, from misinformation, to child social media safety laws, to antitrust regulations, to alleged journalist misconduct, to anti-sex trafficking statutes become what this Article calls “cybersecuritized.” Before this reframing, these issues present as important but not existential. But once cybersecuritization positions the issues as threats intensified by their technological nature, they gain access to the politics and law of urgency and exceptionalism and invite troubling governance responses...

Apple chose Google Cloud to run Private Cloud Compute outside its own data centers for the first time, using NVIDIA Blackwell GPUs, Intel TDX, and Google's Titan chip. Apple maintains an independent append-only hardware ledger and dual-vendor attestation roots. AWS and Azure are not part of the collaboration. By Steef-Jan Wiggers

When I checked Google Search Console after a month, only 2 of my 8 sites were indexed. The other 6 had zero pages in Google's eyes. No penalty, no error banner. Just silence. The bug My build script generated the sitemap by mapping over page objects. Somewhere a URL field was an object, not a string. So the sitemap shipped lines like: <url><loc> https://example.com/[object Object] </loc></url> Google fetched the sitemap, saw garbage URLs, and quietly skipped the whole file. No crawl, no index. How I caught it GSC > Sitemaps > it said "Success" but "Discovered pages: 0". That mismatch is the tell. I opened the raw sitemap.xml in the browser and searched for [object . There it was. Root cause: url: page.url where page.url was itself { path, params } , not a string. The fix // before loc : page . url // -> [object Object] // after loc : `https://livephotokit.com ${ page . path } ` Redeployed, resubmitted the sitemap, and requested indexing on the core pages. Pages started landing in the index within a couple of days. Takeaway A "Success" status on your sitemap does not mean Google read your URLs. Always open the raw XML and eyeball it. One bad [object Object] can silently sink an entire site. I'm building LivePhotoKit and a handful of other small tools solo with AI. Sharing the real bugs as I hit them.

📚 Series: Databricks on AWS (Part 0, prologue) The Target Architecture ← you are here Building a Databricks AI Platform on AWS RBAC with Function-Role Groups Compute Governance: Pools, Policies, Clusters The BOOTSTRAP_TIMEOUT Mystery Fixing It with AWS PrivateLink How We Structure the Terraform Before the build story, here's the destination. This is the target-state data architecture we designed the whole platform toward — the three principles that shaped every later decision, and the Unity Catalog governance model that keeps production data safe from human hands. The rest of this series is a build log: workspaces, RBAC, compute, the networking rabbit hole, the Terraform layout. But every one of those decisions was made in service of a target picture we drew first . This post is that picture — the "to-be" architecture, not the scaffolding we happened to have up on any given week. It's built on three things Databricks basically hands you if you lean into them: the Lakehouse (one store, ACID tables, no separate warehouse to sync), the Medallion architecture (raw → cleaned → integrated → business, each layer a promotion), and Unity Catalog as the single governance plane across all of it. The interesting part isn't reciting those three buzzwords — it's the specific way we wire them so that prod, dev, and analyst sandboxes never step on each other. Three principles, and everything follows Almost every concrete rule later in this series is a consequence of one of these three. 1. Nobody touches production by hand. Create, update, delete in prod data happens only through an automated, code-reviewed pipeline running as a service principal. Human accounts don't get write on prod — not analysts, not engineers, not admins. The blast radius of a bad afternoon is capped at whatever a person can do with read-only. This one principle is why the whole "promote" flow later exists. 2. Never copy production to look at it. If an analyst wants to explore the gold layer, they read it in p

Meta says usernames improve privacy, but critics question whether its safeguards can prevent impersonation.

Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.

The government has removed restrictions on Anthropic’s Fable 5 and Mythos 5 AI models—but there were strings attached.

Venice AI is already profitable, with annualized run-rate revenues of over $70 million, CEO Erik Voorhees said.

Smart speakers have spent the past few years searching for a compelling second act. Beyond music, timers, and controlling your lights, they've struggled to justify taking up space on the kitchen counter. AI promised to change that. Amazon debuted its new hardware powered by a revamped Alexa last fall, and now it's finally Google's turn. […]

Papa Johns is spying on people’s buying activities to predict when they are low on food: The pizza chain recently tapped NBCUniversal, Instacart and the dentsu-owned media agency Carat for help reaching consumers when they’re low on groceries—and thus more likely to be swayed by a mouth-watering ad. The idea is to reach hungry consumers by “knowing what is in their fridge without being too creepy,” said Carrie Drinkwater, chief investment officer at Carat. To achieve that goal, NBCU and Instacart created a custom audience of shoppers who regularly purchase grocery staples on Instacart, such as eggs, milk, meat and produce. Based on that data, Papa Johns can determine which days of the week certain consumers are likely to run out of groceries and serve them an ad on NBCU streaming content accordingly. The brand served custom creatives to consumers based on their food preferences—such as whether they buy meat regularly—with QR codes and calls to action such as, “Light on groceries?” or “Empty fridge?”...

Adjust your options for things like account recovery, ad personalization, and which parts of your Google profile are shared publicly.