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Adobe is updating its Firefly AI assistant with new chops, and adding it to Premiere, Illustrator, InDesign and Frame.io.

Adobe's plan to stick AI assistants into all of its Creative Cloud suite is now fully underway, with new chatbots now rolling out to its biggest editing and design apps. As part of a public beta launching today, Photoshop, Premiere, Illustrator, InDesign, and Frame.io now each have a bespoke AI Assistant that can be used […]

Adobe is introducing some new capabilities for its Firefly AI assistant, alongside a "reimagined" AI studio that lets you edit and generate new designs from a single interface. The new Firefly experience launching today in private beta is designed to give you "persistent context, reusable assets, and organized workflows" across your projects, according to Adobe, […]

XGIMI's MemoMind One is a good pair of smart glasses blighted by a creepy AI that spies on you.

Ultrahuman's M2 Live uses Abbott's cheaper Lingo sensors to help keep track of your blood sugar.

Adobe has added its Firefly AI tool to some of its most important apps.

For the past few days, I've been trying to parse a PDF (scanned and text based) which has the same contents. PDF has nested tables Tables start at one page and end at another Currently I have been using (docling)[ https://docling-project.github.io ] to help me out with this text and convert it to the formats I require. I have a few limitations that I have a limit of 30s per page (2 minutes for the 4 paged pdf). And the biggest limitation is that I have to optimize it for the CPU. It has to run at a maximum of around 30s per page on the CPU . I have been trying a lot but docling is always failing at figuring out the table breaking in between two pages, and one single table and information that spills out from one page to another, is created into different tables by docling. How do I resolve this ? I am not being able to find a solution that works well within my given constraints better than docling currently. I've tried PyMuPDF, I've tried camelot as well. Camelot gave very nice results in converting to CSV, but it fails when nested tables come into the picture. I even tried to integrate camelot + docling into a hybrid pipeline but that also failed with my PDF with nested tables. Has anyone faced this problem before? Does anyone know of resources that could help me out with this problem? Any recommendations? Anything? :sob: submitted by /u/Kakarot_DB [link] [留言]

Ok so first, let me be honest. This post is partly me venting. I've been maintaining node-dependency-injection for about 9 years now. 300 stars on GitHub. Not exactly viral. And every time I look at InversifyJS or tsyringe climbing in popularity I think "yeah, but at what cost". So let me explain my problem with decorators. The coupling nobody talks about When you write this: @ Injectable () export class UserService { constructor (@ Inject ( MAILER_TOKEN ) private mailer : IMailer ) {} } Where does that @Injectable() live? In your DI framework. Which means your UserService — which is domain logic, business rules, the thing that should outlive any framework decision — now has a direct dependency on your IoC container library. Your domain knows about your infrastructure. That's the wrong direction. I know, I know. "It's just a decorator, it doesn't do anything". But it's still an import. It's still coupling. And if you ever want to swap the container, or move that service somewhere else, or just test it without spinning up the whole container — you now have to think about it. With NDI, your service is just a class: export class UserService { constructor ( private mailer : IMailer ) {} } That's it. No imports from my library. No decorators. No metadata. The service doesn't know it's being injected. The wiring lives completely outside — in a YAML file or in a bootstrap file. Your domain stays clean. "But Symfony does decorators and it's fine" Symfony doesn't use decorators in services actually. The DI config is external — YAML, XML, PHP config files. Your service is just a PHP class. That's literally what inspired NDI from the beginning. What NDI actually does Quick example. You have two payment providers and you want to inject the right one based on context: services : payment.stripe : class : ' payments/StripePayment' keyed : group : payment key : stripe default : true payment.paypal : class : ' payments/PaypalPayment' keyed : group : payment key : paypal checkout.ser

Originally published at kunalganglani.com — read it there for inline code, hero image, and live links. Generative AI vs agentic AI vs AI agents. Three terms, used interchangeably by people who should know better, burning engineering budgets across the industry in 2026. Generative AI refers to models that produce new content — text, images, code — from a prompt. AI agents are software systems that wrap those models with planning, memory, and tool use to pursue goals autonomously. Agentic AI is the broader paradigm: orchestrated systems of agents, workflows, and decision-making that operate with minimal human oversight. Getting these distinctions wrong doesn't just lose you a Twitter argument. It determines whether your production system costs $500/month or $50,000. Every quarter, someone on a leadership team says "we need to go agentic." What they usually mean is one of three completely different things. And the architecture you pick for each one has wildly different implications for cost, latency, reliability, and maintenance burden. I've watched teams burn entire quarters building autonomous agent systems when a well-tuned prompt engineering pipeline would have shipped in a week. That's not a hypothetical. I watched it happen twice in 2025. This post cuts through the buzzword soup. I'll define all three paradigms with concrete technical distinctions, show you how they map to real production architectures, and give you a decision framework for picking the right one. What Is Generative AI? The Engine, Not the Vehicle Generative AI is the foundation layer. It's a large language model (or image model, or audio model) that takes an input and produces new output. GPT-4, Claude, Gemini, Llama — these are all generative AI. You send a prompt, you get a completion. That's it. The critical thing to understand: generative AI is stateless by default . Each API call is independent. The model doesn't remember what you asked five minutes ago. It doesn't plan a sequence of steps.

pgrac is an open attempt to rebuild Oracle RAC's core machinery (shared-everything storage, multiple active nodes all writing one database, a cluster-wide change number) on top of PostgreSQL 16. Build log #1 laid out the four problems that fight back. This one is about the problem that turns a node failure into silent data corruption, and the first, deliberately modest, layer pgrac ships against it. The failure mode In a shared-nothing cluster an evicted node is mostly harmless: it owns its own disks, so the cluster routes around it. In a shared-everything cluster the same event is dangerous, because every node writes the same storage. Picture the classic split: node 2 misses heartbeats, the cluster declares it dead and remasters its work elsewhere, but node 2 is not actually dead. It is frozen on a long GC pause, or its interconnect NIC flaked, and it is about to wake up and finish the write it started. Now two nodes believe they own the same blocks, and shared storage will accept both writes. That is not a crash. It is corruption you find three days later. Oracle RAC's answer is I/O fencing: before remastering a dead node's resources, you make certain it can no longer touch the storage, with STONITH, SCSI-3 persistent reservations, or a hardware watchdog. The node is fenced at a layer below its own software, because the whole point is that you cannot trust the dead node's software to behave. That hardware layer is real work, and it is not what pgrac built first. What it built first is the layer above it: an in-process cooperative write-fence, now default-ON. The rest of this is precise about what that does and does not buy you, because "we have fencing" is the kind of claim that is worth less than nothing if it is overstated. A fence needs an authority everyone can agree on You cannot fence on local opinion, because the whole problem is that the dead node disagrees about being dead. Authority has to live on durable, shared, quorum-backed storage. pgrac writes a sm

The company's latest recall of 3,871 vehicles follows incidents of its autonomous cars prioritizing other hazards or failing to recognize closed construction zones altogether.