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I'm a software engineer, not a scientist and I love it. I enjoy solving hard, distributed applied problems at scale. It's what gets me out of bed in the morning, ready go keep learning even after over twenty years of doing this professionally. However, I also love to understand how things work. What makes them tick. How I can bend them to do my bidding, even if that's not what they were originally intended to do. Some may call this a hacker's mindset. Over the last couple of years, this has also applied to the nature of LLMs and where they are heading. Recently, I started peeling back the layers of the LLM black box. Instead of the academic path, I took that of the applied practitioner: Get a solid handle on how to use the thing and then take the knowledge learned from using it and enhance it by digging into how that black box actually works. Something of late has stumped me and I'm looking for those smarter than I to help me understand something: If the definition (as much as one can nail one down) of AGI is something akin to "a hypothetical type of computer software or machine intelligence that can match or surpass human cognitive abilities across any intellectual task", how an it possibly achieve that with current LLM architecture? At its core and at a /very/ high level, it predicts a probability distribution over the next token, conditioned on the tokens that came before it. Autoregressive decoding doesn't give a model an independent mechanism to know when it's wrong and deliberation steps don't fundamentally change that. Harnesses and the "intelligence" or "agentic reasoning" built into them to provide extra context, external feedback and loops work remarkably well in terms of providing what /seems/ to be intelligence, but at its core, it's "just" layers of probabilistic systems providing a magical output. The model itself isn't learning anything new. Ordinary inference doesn't update the model's weights from experience. Autoregressive generation itself doesn't

Dress a tree in borrowed blossoms, and it looks alive. The roots still tell the truth. — The 36...

Been experimenting with a workflow on dimension.so where an agent builds out a 3D pre-vis scene first - blocking objects, character motion, and camera - then feeding that directly into Seedance 2.0 Mini as a video-to-video reference instead of prompting from scratch. Added GPT6/Asta into the agent's reasoning layer and I dont think the improvement in multistep task handling is noticeable in how consistent the scene composition turns out. Curious what people think - what went well in the pre-vis scene itself, and what could've been improved before it even got passed to Seedance? submitted by /u/KeyCod3923 [link] [留言]

GM's lineup is dropping Apple CarPlay and Android Auto in favor of its own infotainment systems. But one model is keeping the phone-mirroring capabilities.

Vulcan oven mitts, spaceship baking dishes, and an out-of-this-world communicator grater—you'll need warp speed to Klingon to this limited edition kitchen kit, ideal for perfecting the broth of Khan.

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Kel_Thuzad11 [link] [留言]

Can language models write code in one step? Yes: make language continuous, use diffusion, then distill the trajectory to one step. Paper: https://arxiv.org/abs/2609.04531 Code: https://github.com/pengzhangzhi/plaidq submitted by /u/pengzhangzhi [link] [留言]

Stuxnet! Here reproduced by me. Only researchs educations purposes.

I built an agent last year, and I was proud of it. It had a planner. It had tools. It had a...

Faker Doesn't Know Your Entities Are Related, So I Built Something That Does You've added a second entity to the schema, wired up a @ManyToOne , and gone back to your seed script to generate fifty more rows. Ninety seconds later, the app refuses to start: unique constraint violation, somewhere inside a loop you wrote three weeks ago at 11pm. You fix it. You restart. A different field breaks a different constraint. This is the exact moment every Spring Boot developer eventually meets the real limit of tools like Faker. They're brilliant at generating a name, an email, an address. They have no idea the Payment sitting in front of them needs a Counterparty to already exist. So you do what everyone does: hand-write the wiring. Create parents first. Hold onto their generated IDs. Wire them into children. Hope you didn't just violate a @NotNull somewhere in the process. It works, for a while. Then the schema changes, and the script quietly stops matching reality until the next 3am debugging session finds out the hard way. I hit this enough times that I stopped patching the script and looked at the actual problem: the information needed to seed this correctly already exists. It's sitting right there in the entity, in the annotations you already wrote. @ManyToOne , @NotNull , @Column(unique = true) , JPA already knows the shape of your data. Nothing should need to be told that twice. That became SynthForge . The core idea Instead of writing a script that generates data, you annotate the entity: @Entity @Seed ( count = 50 ) public class Counterparty { /* fields only */ } @Entity @Seed ( count = 200 ) public class Payment { @ManyToOne ( optional = false ) private Counterparty counterparty ; } Start the app in a dev profile. Both tables populate, correctly ordered, on every restart. No seed method. No calling code, anywhere. The entity is the seed script. What's actually happening underneath Entity scanning. SynthForge reads JPA-managed attributes through the jakarta.persisten

The hikers story happened September 1st. Three guys from Roseville used Gemini to plan a Mount Shasta summit. The AI told them to bring far less food and water than they needed. They summited at 7pm, four hours after the recommended turnaround time, descended in the dark, one of them hurt his knee, and they spent the night stranded in a canyon until rangers found them the next morning. Google says they can't replicate the bad answers Gemini gave. Maybe the prompts were vague. Maybe the AI was overconfident. Doesn't really matter which. What matters is that three people trusted a model's output as expert advice in a context where being wrong had serious consequences. Two days later OpenAI launched GPT-6 Astra. 99.9% on ARC-AGI-3, 97.6% on FrontierMath Tier 4, 100% on ExploitBench. OpenAI is calling this the start of the AGI era. Independent benchmarks from Artificial Analysis are more cautious and show Anthropic's Fable 5.1 still ahead on the broader intelligence index. But here's what I can't stop thinking about. The hikers story and the capability story are not separate things. Every time a model gets more capable, more people trust it in higher stakes situations. That gap between what the model can do and what the person using it understands about its limits doesn't close automatically when capability improves. If anything it gets harder to manage because the outputs get more convincing. I work with organizations on AI adoption and the single most common thing I see is not people being too skeptical of AI. It's people not knowing when to stop trusting it. What's your take? Does more capability make the trust calibration problem better or worse? submitted by /u/Dapper-Tale-4021 [link] [留言]

Originally published on WordPress on September 27, 2025. When AI systems fail in production, the failure is often blamed on data quality, model drift, or algorithmic limitations. In practice, many of the most damaging failures originate much earlier and much lower in the stack. They come from weak security foundations that allow systems to behave in unintended ways. Reliable AI is not just about accuracy or performance. It is about whether the surrounding infrastructure enforces discipline around access, data handling, and execution paths. Infrastructure as the First Line of AI Security Every AI system depends on infrastructure that controls how compute, storage, and networking are consumed. If that infrastructure is loosely governed, the AI system inherits that weakness. A common example is a shared compute environment where multiple teams run experiments. If isolation is poorly enforced, one workload can access artifacts, logs, or intermediate data from another. The model may be mathematically sound, but the environment allows behavior that violates assumptions about separation and control. From a reliability standpoint, this creates hidden coupling. An AI job might fail or behave inconsistently because another process consumed shared resources or modified shared state. From a security standpoint, the same weakness allows unauthorized access to sensitive datasets or trained models. Strong infrastructure boundaries do not just protect against attackers. They protect teams from each other and from accidental misuse. Access Control Across the AI Lifecycle AI systems have long lifecycles that include data ingestion, preprocessing, training, evaluation, deployment, and monitoring. Each stage introduces different access needs. Problems arise when a single identity or role is allowed to operate across too many of these stages. For example, an engineer might have permission to both modify training data and deploy models. That convenience can quietly undermine trust in the

OAuth can prove that a request may reach a resource. It does not, by itself, tell an operator the full story of the actor holding the token. That distinction matters once software can plan, call tools, retry, and act across several systems. The question is no longer only, "Is this request authenticated?" It is also: Which agent is acting? Under whose authority? For what purpose? Against which target? What evidence will remain after the action? If your system cannot answer those questions without reading the agent's prompt, it does not yet have an operational identity model. It has a credential. A token is permission, not the whole identity OAuth remains essential infrastructure for agents. The current Model Context Protocol authorization specification builds on OAuth 2.1, Protected Resource Metadata, Client ID Metadata Documents, audience binding, and least-privilege scopes. It also hardens issuer validation, defines step-up authorization, and forbids token passthrough. Those controls answer important questions: Is this token intended for this resource? Which permissions did the user approve? Has the credential expired? Does the resource server accept its audience? But a token is still one artifact inside a larger system. It can carry identity claims, but it does not automatically give that identity a lifecycle, an owner, a purpose, or a useful audit trail. An operational identity is the continuity around the token. It says this is the same agent before, during, and after a credential is issued, and that its authority can be understood and withdrawn. Borrowing a human identity breaks the record The fastest way to get an agent moving is often to lend it a human credential. Copy an API key into the environment. Reuse a browser session. Give it an access token created for an employee. Now the log says a person acted when an agent did. The credential may carry every permission the person has, even though the task needed two. Revoking the agent means revoking the human.