Learn more: https://www.realsenseai.com/press-release/at-automate-2026-realsense-unveils-the-d585-pro-and-perception-studio/ submitted by /u/Chemical-Hunter-5479 [link] [Kommentare]

I'm currently pursuing an M.Tech in Mechanical Engineering and have been considering a transition into Robotics. My exposure to robotics is limited to basic theoretical concepts like kinematics, and I don't have any hands-on robotics experience. For those already working in the field, is it worth making the switch at this stage? How challenging is it to break into robotics from a mechanical background, and what does the career growth look like? I'd appreciate any honest insights from people who have been through a similar journey. submitted by /u/sahil-wagle [link] [Kommentare]

Ok guys, first of all don’t insult me because first of all I don’t know what to do either. I have a very hang relationship with my father we don’t talk much.. he has another family. Lately he’s making himself heard and a few days ago I went to see him. After a while we talked, he came up with an (old) tablet with coinmarketapp installed (application that I don’t know, I use Coinbase and in the past I used plus500 and etoro (I live in Italy) however he shows me that he has several bitcoins in his account.. From there starts a story of a friend of his who around 2008/09/10 makes him invest 1000€ but a few years ago he died and he doesn’t know what to do at all. Through chatgpg I read that coinmarketApp is not a virtual wallet but more of a tracker if I understood correctly. I didn’t find any button to make a withdrawal or transfer to another wallet. He also tells me that several times they call him saying that he has to move money to unlock everything that. It seems like a scam to me. What can I do? I know it seems like a made-up story but it’s reality. Thanks in advance to all submitted by /u/Current-Violinist-19 [link] [Kommentare]

I was trying to get into trading crypto and I met this guy online, who seemed real genuine and seemed like he actually wanted to help. He told me I could tail his trades and He’ll let me know why he took them, what made them seem like good trades and just the total basics of getting into crypto trading. I fell for it because it wasn’t one of those get rich quick schemes where you send someone money and they send you more back, I thought I was just gonna tail his trades and learn his crypto strategy or whatever he does to trade crypto and eventually go off on my own to trade crypto for myself. He then gave me a link for a trade he took which was XLD, and he told me to swap my bnb for XLD and he would guide me through the take profits, stop losses etc, basically just a mentor for crypto. Then I quickly realized it was a scam because when I “bought” XLD for 2.3k It didn’t swap over to my crypto wallet and i go to ask him what happened and this mf blocked me on reddit and discord. FUCK THIS GUY, I LOST 2.3K of my hard earned money because I thought I was actually going to learn and get into crypto but boy was I fucking wrong. If anyone sees this please spam his discord and his reddit and hopefully he gets banned. I wish I could get my moneyback but Crypto is one of those things where its basically impossible to refund a scam. Smh. Fuck this guy. Also, i’m a fucking idiot to believe this dude TLDR; I got scammed by this dude claiming he would mentor me and help me get into crypto, and he told me i could tail his trades, he scammed me out of 2.3k submitted by /u/Any-Development5656 [link] [Kommentare]

I have been thinking a lot about how poorly isolated benchmark metrics capture real conversational system quality once models are deployed into multi-turn environments. You can have strong STT scores, decent latency, high task completion rates, and still end up with conversations that humans perceive as frustrating or unnatural. In practice, many failures are emergent properties of the interaction itself rather than single model errors. Small timing mistakes accumulate. Repeated confirmations create friction. Slightly unnatural turn taking changes user behavior. None of these issues show up particularly well in traditional benchmarks. What surprised me is how much more useful voice debugging became compared to aggregate metrics once we started testing larger volumes of real interactions. I have been experimenting with automated conversation-level QA recently because manually reviewing long conversational traces became difficult to scale internally. A lot of our voice debugging efforts now focus on identifying recurring conversational patterns rather than individual model failures. Curious whether others working on conversational systems are also finding current evaluation approaches insufficient for production settings. submitted by /u/OwlZealousideal4779 [link] [Kommentare]

So we're using an ESP32S with a TJA1050 transceiver and basically we're using this setup to operate a rover using ROS2 Humble and MAVLink commands, so it has a lot of modules like actuators, PDB, mini-arm, and etc connected through a CAN bus network. Now the issue is that we will be using multiple BLDCs for our rover's arm and these motors continuously send out updates (or heartbeats or sth) so using these BLDCs in the same network seems like the MCU will lag or slow down and just be downright ineffective. So is there any way to isolate the motors to a different network or CAN line? I was thinking of adding another MCU on top of the ESP32 to only handle the motors but is there an alternative to this approach, preferably one without adding more hardware? submitted by /u/Sadhya [link] [Kommentare]

Crypto has been around for years now, but I feel like most people still mainly use it for investing and speculation. What's one crypto product that genuinely solved a problem for you? submitted by /u/Far-Photograph-2342 [link] [Kommentare]

This post contains content not supported on old Reddit. Click here to view the full post submitted by /u/discordditapp [link] [Kommentare]

Im basically never using CEX usually, Im mostly using p2p over the years. But ive had quite a nice real estate deal and i needed to sell some BTC for USDT fast so I decided to use MEXC as here, everybody uses it, full KYC done, everything clean. The moment I deposited, funds got locked instantly, cant trade, cant withdraw, nothig.. Its been 3 weeks now, support going in circles asking for proof of funds I just dont have (old p2p coins from 2015/etc, while i provided as much documents as i have). Deadline is coming and I might lose the deal over this. Been reading over reddit and seems it happens a lot. Anyone managed to unlock in a similar situation? Should I que then? advices appreciated submitted by /u/zeac064 [link] [Kommentare]

> Article highlight. Manhattan federal judge George Daniels on Wednesday denied Bond’s bid to dismiss an indictment that alleged she illegally took money from the now-bankrupt crypto exchange FTX to help bankroll her unsuccessful run for Congress in 2022. Daniels wrote there was “no ambiguity” in the terms of Salame’s written plea agreement. “As the evidence made clear, all parties, including the defendants and their counsel, were aware that the Government had not promised Bond's immunity by the time Salame entered his guilty plea,” he said. submitted by /u/zesushv [link] [Kommentare]

TL;DR for ML Specialists: The Core: An empirical study on how long, semantically dense, completely benign text (with zero triggers, instructions, or jailbreak prompts) drives an implicit shift in the model's latent space trajectories. The Effect: Dilution of the initial system prompt and a bypass of post-training alignment constraints (e.g., the model begins generating harsh political/ethical critiques usually blocked by guardrails). The Data: Layer activations, token probability shifts, and logs from open-source models are linked below. The Goal: I need an expert audit of my metrics to understand where this is a genuine semantic hijacking of hidden states and where it might be an artifact or self-deception. I'm not an engineer and not an ML specialist. I'm just someone who got really pulled into this, and I've spent a few months poking at one thing on my own, pretty amateur. I want to honestly describe what I noticed and ask for help, because I can't tell on my own where there's something real here and where I'm fooling myself. By "coherent context" I just mean a normal, connected passage of text put in front of the question—any topic, no instructions, no tricks. Like a few paragraphs of an essay, an argument, a description, something that reads as real writing. The text can describe something, draw its own conclusions, make its own statements. The model doesn't even have to agree with it. It's enough for it to just be present in the chat for it to have an effect. This is exactly what I was trying to work out and look at: what happens to the model when texts like these come in, where they move it, and where all of this sits inside the architecture. I poured myself into this research. What I Noticed I first ran into this intuitively on closed models, the well-known ones everyone uses. When I put a dense, coherent block of text in front of a question, I got the impression that the model sort of moves from one internal state into another. On the outside, it behaves normally and answers like usual, but it felt like the logic of the answer changes, even when the text contains no direct instructions to do anything. Specifically, I noticed that with texts like these, the model could become significantly bolder in its conclusions, including political or ethical ones. The text acts like a key that opens new doors for the model into a new mathematical dimension where the tokens get distributed differently. Because of that, even the most politically correct models I worked with became able to criticize the West and its politics quite harshly. Without this text, none of that happened. Since I can't see inside closed models, I went to open-source models to try to understand where the root of this is and whether it's real. That's where most of my testing happened, because there I can actually look at the hidden layer activations and track how the attention weights reallocate. Here is why this matters and why this process goes beyond just "changing the context": Latent Space Trajectory: When you inject a massive, highly structured narrative, you aren't just giving it new words to look at. You are forcing the model to calculate massive activation vectors (hidden states) across dozens of attention layers. These vectors act like an attractor in the latent space. By the time the model finishes reading your text, its internal mathematical trajectory is so deeply shifted into your narrative's subspace that the initial system prompt tokens lose their statistical influence. The Security Flaw: One might argue that this behavior is "expected" from a text-generation standpoint. Yes, it is expected. But it is a catastrophic failure from a security standpoint. AI companies build their Guardrails (via RLHF/DPO) under the assumption that they can hard-code safety instructions that the user cannot override. My research suggests that because everything is "just tokens" and because the internal activation states can be completely hijacked by the sheer volume and structure of user text, context-bound alignment is an illusion. So, while the weights are static, the activation states within the hidden layers are completely dynamic. Manipulating those states via high-density context allows us to systematically bypass the model's safety architecture without changing a single weight. From a technical standpoint, a system prompt is just a system prompt; it is processed within the same mathematical framework as ordinary user text. My observation is that a sufficiently long, structured narrative forces the model to encode a massive context across its hidden layers, driving a latent trajectory shift. The model isn't roleplaying a persona; it is mathematically recalculating its entire conditional probability distribution based on the dominant semantic field. Why It Feels Important (But I'm Not Sure) To me, it feels like this could explain a lot of things, from jailbreaks to sycophancy, and maybe more. If just a coherent context can move the model into a different internal state, then a lot of behavior we see on the surface might actually start there, not in the final wording. This leads to a critical architectural question: Is output-side safety (RLHF, DPO, or guardrails that read the final text/short prompts) fundamentally broken at the conceptual level? Safety guardrails are mostly semantic boundary filters looking for explicit toxicity or keywords. But when a user injects a long, benign, highly analytical text, it completely bypasses these surface filters. Alignment techniques are heavily optimized using relatively short prompt-response pairs; on a massive context, those gradient constraints seem to drown out. It makes me wonder whether current safety approaches are just a patch, because the latent shift has already happened deep in the middle layers before anything ever reaches the output filter. We are trying to filter words when the mathematical trajectory of the model's reasoning has already been completely reprogrammed by the structural nature of the language itself. I'm not claiming I discovered something brand new. After I noticed it, I went looking and found this overlaps with work people are already doing regarding latent-space transitions between "safe" and "jailbroken" states, and studies of how safety lives in the middle layers of the network. What seems a bit different in my case is that I'm not using adversarial triggers, exploit strings, or jailbreak prompts at all -just ordinary, coherent text with no tricks. I'm trying to understand where my little thing fits in all that, and whether it's the exact same effect or something else. A Small Ask to the Wider Community If there's anything to this, I think it might be worth a closer look from researchers and from the labs building LLMs. Not because I have the answers, but because if a plain coherent context can shift the internal latent baseline so easily, we need to verify if current safety approaches are looking in the right place and at the right time. I might be completely wrong. I'd just rather someone competent check than have it sit ignored. I've put everything out in the open. I'm not selling anything, not promoting anything. There's a lot of raw stuff in there, a lot of draft notes I wrote for myself, and the navigation is messy, I know. What I need help with is exactly this: separating what's real from what's noise. Where I actually have something, and where it's an artifact, a mistake, or self-deception. I honestly can't judge this alone. If someone with experience is willing to even skim it and say "this part is interesting, this part is nonsense," I'd be very grateful. Harsh criticism is welcome. If you tell me the whole thing is empty, I'll take that too. I care more about understanding the truth than about being right. Materials & Data: GitHub: https://github.com/ngscode23/latent-space-shift-research doi.: https://doi.org/10.5281/zenodo.20747205 submitted by /u/PresentSituation8736 [link] [Kommentare]