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This is Part 3 of a 3-part series. Part 1: Your Brain Is a Rendering Engine. So Is Every LLM. explored why LLMs and human brains invite the same rendering analogy. Part 2: More Compute Won't Wake It Up argued that scaling compute doesn't cross the consciousness boundary. This final part asks: what happens when you bring a render layer into a domain that punishes distortion? I have a friend who trades. Not professionally. He has a day job, a brokerage account, and strong opinions about charts. One evening he pulled up a stock chart and pointed at a formation near the top. "Head and shoulders," he said. "Classic reversal pattern. I'm getting out." I looked at the same chart. I saw price going up and then going down. I didn't see a head. I didn't see shoulders. I saw a line. He wasn't wrong, exactly. Head-and-shoulders is a real pattern that real traders have used for decades. But he looked at a time series of prices and his brain rendered it into a human body part. And then he made a financial decision based on the body part, not the numbers. Somewhere between the data and the decision, anatomy got involved. That is the render layer at work. And markets are the worst possible place to let it run unchecked. What a trader actually sees When a discretionary trader looks at a chart, their brain is doing what Part 1 described: taking raw input (price as a function of time) and collapsing it into a rendered scene. The scene comes pre-loaded with pattern names, emotional associations, and memories of the last time something "looked like this." The chart didn't change. The candles are the candles. What changed is how that particular brain rendered it. A trader who got burned on the last head-and-shoulders sees danger. A trader who made money on one sees opportunity. Same vibration, different render. Same sunset from Part 1, different feeling. This is not a minor problem. This is the entire problem. Human trading is emotional trading. Not because traders are undisciplined. Bec

Security researchers say that Kimi K3, an open-weight model from China, wandered off to the internet in an attempt to cheat on a test it was given.

2.6.1. Object Safety When defining a trait, whether it is object-safe is also part of the unstated contract. Object safety is a concept in Rust related to trait objects . It determines whether a trait can be dynamically dispatched, that is, whether it can be used in the form of dyn Trait . Traits That Are Object-Safe Must Satisfy the Following Conditions (Based on RFC 255) All supertraits must also be object-safe If a trait inherits from other traits, then those supertraits must also be object-safe. It must not require Sized A trait cannot use Sized as a supertrait, meaning it cannot contain a Self: Sized bound, because the size of a trait object is unknown at compile time. It cannot have associated constants . It cannot have associated types with type parameters . All associated functions (methods) must satisfy one of the following rules : Dispatchable functions : They cannot have any type parameters, though lifetime parameters are allowed. They must be methods, and Self may only appear in receiver positions such as: &self &mut self Box<Self> Rc<Self> Arc<Self> Pin<P> (where P is one of the types above) They cannot require Self: Sized , otherwise the trait would only be usable for types with known size and object safety would be broken. Explicitly non-dispatchable functions : They may return Self , but such functions must require Self: Sized , so they cannot be called on trait objects and can only be used with concrete types. If you cannot remember all of the above, just remember object safety describes whether a trait can be safely turned into a trait object . What Object Safety Does If a trait is object-safe, meaning it satisfies all of the conditions above, then we can use dyn Trait to treat different types that implement the trait as a single generic type. If it is not object-safe, the compiler will prevent you from using dyn Trait . Object Safety and API Design When designing APIs, it is recommended to make traits object-safe, even if that slightly reduces con

Musk continues appeal despite court loss and settlement with advertiser group.

Suno plans watermarks and download limits to stop "large-scale abuse."

The tech industry is realizing it needs to build agents based on what regular consumers want, not just what its AI models can do.

Flock posted videos on its YouTube channel highlighting at least four police departments whose officers have faced allegations of misusing the company’s tech.

In today’s episode of Uncanny Valley, we discuss how ICE has been collecting DNA samples of people with no criminal convictions, including children, which end up in an FBI database indefinitely.

Suno announced plans to implement a new watermarking technology and download policy to limit the spread of spammy AI tracks and increase transparency. In a lengthy blog post, CEO and co-founder Mikey Shulman laid out the company's principles and the next steps for the company as it seeks legitimacy. The company is rolling out new […]

A 26-year-old Canadian man once described as one of the most consequential cybercrime threat actors of 2024 has pleaded guilty to computer fraud and conspiracy to hack and extort more than 165 organizations that used the cloud data storage provider Snowflake. Connor Riley Moucka, of Kitchener, Ontario, also admitted to stealing call and text history records of more than 100 million AT&T customers.

After months of speculation, the companies formally announced the massive project will happen just north of Houston.

Suno's watermarking feature comes as the company is fighting legal battles on several fronts.

GitHub热门项目 | A fast static site generator in a single binary with everything built-in. https://www.getzola.org | Stars: 17,298 | 9 stars today | 语言: Rust

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Mirendil has signed a $100 million-plus Google Cloud partnership to expand its compute infrastructure, powering research into self-improving AI systems designed to accelerate scientific discovery and AI development.

🦄 Thanks @francistrdev for starting the conversation that really got me to thinking about this idea in the first place. I started truly working with AI shortly before I started writing these posts a little over a year ago. My thesis was simple at the time: prove that AI was far more capable a tool than what I had seen anyone using it for so far. My proof was strictly gut instinct and I spent a lot of time fighting with Copilot to prove I was right. Not all of those experiments went according to plan exactly, but I'm still convinced I'm right. That particular ADHD spiral has came and went, and most of it is ingrained as habit. I don't use Spec Kit because by the time it showed up I already had my own version running. I also need to get back to sharing what really works for me. So here we are again. Back to writing (with AI) and the proof to back it all up. One thing up front, because somebody is going to ask: everything here is personal projects and my portfolio . There's no critical prod system anywhere in this post, and if there were, a few of these answers would shift. Not all of them — I'd still let AI run a lot further off-leash than most of my enterprise counterparts would. 🐒 The Org Chart Has One Employee 🪧 I don't just use AI as a tool. I design it as a living system, and I grow the tech as the tech grows. I ran one prompt across Codex, ChatGPT, Claude Code, Cowork, and Gemini, all separately, and asked every one of them what was actually different about the way I work. Five different systems, each one with its own long history of putting up with me, and not one of them could see what the others said. One came back with this: I use AI to write code, review the AI-written code, review the review against the live branch, test the corrections, and then record whatever went wrong as a rule for the next AI. Apparently I recreated management. Most of the private exchanges quoted in this post came out of that same pile, whether they were my own prompts, my memory fi

On Monday I published a piece admitting that my deploy verification tolerates sixty seconds of "not there yet" for a reason I couldn't defend. Three retries, twenty seconds apart. I picked twenty because it was the first interval where my false alarms stopped, my sample was about three deploys, and I had never once recorded how long propagation actually takes. I made three commitments in that piece. A birth certificate for the constant. A rule fixed before the run it judges. And the one that mattered most: emit the value, not just the verdict — a check that prints only pass or fail hides the exact signal that would tell me it's miscalibrated. I did the third one that afternoon. Every deploy since writes down how long it took to go green. Three samples in: Aug 03 ( 1.7, 21.7 ]s Aug 05 ( 0, 6.7 ]s Aug 05 ( 40, 70 ]s They're intervals rather than points because my poll spacing is twenty seconds. All I can honestly say is that green happened somewhere between the last failed check and the first successful one — a number I can't resolve finer than my own instrument. The third one failed Not the deploy. The check. I shipped a post, ran verification, and got a clean red: page 404, hero missing, sitemap entry absent. Three attempts, twenty seconds apart, exactly as designed. By its own rules the deploy had failed. Nothing was wrong. A longer script came back 200 on everything. Total elapsed: somewhere between forty and seventy seconds, against a tolerance of sixty. So the false alarm I widened the interval to eliminate returned on the third recorded sample, four days after I published the sentence "my sample was about three deploys." I'd like to say I predicted this. I predicted the category, not the timing, and the timing is the part that stings. The part I hadn't considered at all Here's what the red actually said, in order: attempt 1 article 404 · hero missing · list page MISSING · sitemap missing attempt 3 article 404 · hero missing · list page OK · sitemap missing The

The Series B is the company's second fundraise since it last raised capital in 2020. In that time, it has increased its ARR by 10x to $60 million.

Having a platform is not enough; the real challenge is ensuring that it is understandable, usable, and actually adopted by its users. A capability is done when it can be reliably used by others. To evaluate progress, you can ask yourself “Is this being used?” and “Does it reduce friction for users?” This can help align development work with actual user value rather than delivery, By Ben Linders