FCC to let ISPs stop listing all passthrough fees, give single "up to" price.
I sometimes need to tune the inertial property of the robot by changing the density or mass of each parts. Doing it in CAD and have it re-export to URDF takes a bit long and too tedious. So this online editor lets you (and me) quickly make changes, and have the inertia tensor of the links be recomputed immediately. You can then copy-paste the updated URDF. This is basically entirely made by claude (with some of my help :)) (And yes, it is placed under my startup's domain as a potential lead magnet. but I think it could be useful for some people nonetheless. EDIT (forgot to post the link) Welcome to try: https://urdf.aperobotics.io/ submitted by /u/FearlessPrice7187 [link] [Kommentare]
I've been working on this for the past few weeks. The idea is simple , before emotions take over, you write down your exit plan and investment thesis, then keep everything in one place. I also added market data for the top 100 COINS It's still an early version, so I'm mostly looking for honest feedback. What feels useful? What would you change , You're free to just ignore You can check it in frst Comment Thanks! submitted by /u/Big_Shoulder_9595 [link] [Kommentare]
1/ Most IoT security is built on a wrong foundation: patch after deployment, static certs, and heavy cloud dependency. You're always one step behind the attack. VoidChain flips the model. It builds native, decentralized immunity directly into the kernel. Here is how it works 👇
‘Inverse’ design approach means the carbon-fibre panels are manufactured more cheaply and efficiently than standard aluminum options.
OSnews
Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmarks attempt to remedy this but heavily constrain an agent's design decision space, making it impossible to faithfully measure how their decisions shape future extensions. We introduce SlopCodeBench, a benchmark of 36 problems and 196 checkpoints where agents repeatedly extend their own solutions. Unlike prior iterative benchmarks, our evolving specifications demand architectural decisions but leave internal structure to the agent. We measure two forms of degradation: structural erosion (concentrated complexity) and verbosity (redundant code). Evaluating 15 coding agents across open and closed models, we find that no agent fully solves any problem end-to-end, and the best agent passes 14.8% of checkpoints. Quality degrades across checkpoints, with structural erosion rising in 77% of trajectories and verbosity in 75.5%. Compared to 473 open-source Python repositories, agent code is 2.3x more verbose and 2.0x more eroded, and the human repositories degrade less often and by smaller margins across their git histories. Explicit quality guidance reduces initial verbosity and erosion by up to a third, without affecting degradation rates. SlopCodeBench provides the first measurement of code degradation under iterative extension, revealing that agents pass checkpoints while producing code that erodes and bloats with each turn.
There is no there, there
Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while. Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use. Which closed-source models would you recommend for generating high-quality attacks? Which open-source models have worked well for you? Have you noticed any models that consistently generate more realistic or challenging attacks than others? I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect... I also have another question. Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch. I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories. Thanks in advance! Any advice or insights would be greatly appreciated. submitted by /u/Background-Song2007 [link] [Kommentare]