Hi everyone, I recently completed my Bachelor's degree in Mechatronics and Robotics Engineering, and I'm currently focusing on improving my ROS 2 skills. I'm looking for individuals, teams, or communities that are actively working with ROS 2 and would be open to having a beginner- or intermediate-level member join them. My goal is to gain practical experience, contribute to projects, learn best practices, and develop my robotics software skills. If you know of any ROS 2 groups, open-source projects, Discord servers, study groups, or communities where I can learn and collaborate, I would greatly appreciate your recommendations. I'm motivated to learn, willing to put in the work, and eager to contribute wherever I can. Thank you in advance. submitted by /u/Maleficent_Youth_168 [link] [Kommentare]

Here you can see how Bill Gates thought about memory: In this Software Notes newsletter from 1975, he explains the strategies and tricks he employs to squeeze things to the utmost. Back then, "memory budgeting" wasn't just a best practice, it was a survival skill. What are your approaches today? You can see more how Gates approaches systems design, handwritten diagrams, and BASIC source code here : https://www.programmersatwork.net/bill-gates submitted by /u/slammers00 [link] [Kommentare]

I'm hoping some of the Bitcoin experts here can help me understand what happened. ​ I recently made a BTC withdrawal from GoMining to my Kraken deposit address. ​ GoMining withdrawal screen showed: ​ Withdrawal Amount: 0.00343202 BTC ​ Fee: 0 BTC ​ You'll Receive: 0.00343202 BTC ​ Kraken later credited: ​ 0.00342000 BTC ​ Difference: ​ 0.00001202 BTC (1,202 sats) ​ Kraken manually reviewed the deposit and confirmed that transaction: ​ a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ was the transaction that delivered funds to my Kraken deposit address and that the amount received was exactly: ​ 0.00342000 BTC ​ Looking at the transaction on mempool.space: ​ Transaction: a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ Input: ​ bc1qr2hhvpf3m0z4sx90nxqydccxlxkf4pe5mjfhcr ​ 0.00343202 BTC ​ Outputs: ​ 0.00342000 BTC to: 3KtixuucHp3ZFSex3847Liv6v8m1xnvt8y ​ 0.00001060 BTC to: bc1qgcq078znysgm6fsveu894e32mrzqy9n9ncjg0w ​ Miner Fee: ​ 0.00000142 BTC (142 sats) ​ The math balances perfectly: ​ 0.00342000 BTC + 0.00001060 BTC + 0.00000142 BTC = 0.00343202 BTC ​ GoMining states that their withdrawal transaction is: ​ 95333b087645e5fa1c2b64ea5e62f41ac3929a673aa7186697cf3a73916cd51d ​ and that transaction a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 was not created by them. ​ Kraken states that they do not know who controls: ​ bc1qr2hhvpf3m0z4sx90nxqydccxlxkf4pe5mjfhcr ​ and that GoMining would need to explain how transaction: ​ 95333b087645e5fa1c2b64ea5e62f41ac3929a673aa7186697cf3a73916cd51d ​ ultimately resulted in transaction: ​ a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ being created. ​ My goal is not to accuse either company of anything. ​ I'm simply trying to understand: ​ Who likely controlled the input address: bc1qr2hhvpf3m0z4sx90nxqydccxlxkf4pe5mjfhcr ​ What the 1,060 sat output to: bc1qgcq078znysgm6fsveu894e32mrzqy9n9ncjg0w ​ most likely represents. ​ Whether this looks like a normal custody, batching, forwarding, exchange, or UTXO management process. ​ Whether anyone can determine the relationship between: ​ 95333b087645e5fa1c2b64ea5e62f41ac3929a673aa7186697cf3a73916cd51d ​ and ​ a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ from publicly available blockchain data. ​ Any help would be appreciated. submitted by /u/bbrian017 [link] [Kommentare]

Hey everyone, Daniel here, we’re building Vastnaut One, a 4x4 exoskeleton designed for people moving through demanding terrain with load where fatigue tends to build gradually across hips and knees, especially on descents. What you’re looking at here is part of our joint aging tests, repeating the same movement cycles than any normal hike would require. At some point, it stops looking like testing and starts looking a bit obsessive. Our system works across both hips and knees in real time step by step based on movement, terrain, and load. The goal isn’t to change how you move, but to redistribute effort over time so the later miles feel closer to the first. Curious how others here think about for a wearable like this, and what do you usually trust as a good enough cycle count. submitted by /u/dan1elfeng [link] [Kommentare]

Hey everyone, ​ I just completed my first year in Electronics and Computer Engineering and I’m currently working on a robotic arm project. ​ I already have the STEP files, datasheets, and most of the components finalized. The issue I’m facing is with the actual mechanical CAD/design part in Fusion 360 — mainly assembling the arm properly, joint design, motor mounting, bearing placement, alignment, etc. ​ I’ve tried using AI tools like ChatGPT and Claude for guidance, but for complex robotics CAD they often give incorrect Fusion 360 steps or impractical mechanical solutions. ​ I’m looking for someone who has experience with: ​ - robotic arm design - mechanical CAD - Fusion 360 - robotics assemblies ​ Even some guidance, feedback, or help with a few parts of the design would really help me move forward. ​ I can share a ZIP file containing all the STEP files and datasheets if anyone is interested. ​ Unfortunately I can’t pay right now since I’m still a student, but I’m genuinely trying to learn and build this project seriously. ​ You can DM me or contact me at: deepkukreja31@gmail.com submitted by /u/Life_Transition3270 [link] [Kommentare]

I hope he doesn’t get it submitted by /u/wolfenstein734 [link] [Kommentare]

Seems like they have engineered some specific limitations that are widely cited as follows: In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations https://news.ycombinator.com/item?id=48464732 Other comments note how even using the word 'nuclear' in the context of scientific research elicits refusal behavior by the model: https://news.ycombinator.com/item?id=48473302 This makes it seem quite plausible that the model could subtly sabotage any machine learning work (even as false positive). Some suggest this has been happening behind the scenes for a while already, but can anyone confirm that? submitted by /u/AccomplishedCat4770 [link] [Kommentare]

This is a comprehensive living reference guide to AI agent security — synthesizing 18 articles from The Agent Report covering the 75-day period (April–June 2026) when agent security went from theoretical concern to operational crisis. ​ What's inside: ​ • Incident timeline — 18 major events, from the first production database deletion by a coding agent (April 30) through the first confirmed in-the-wild LLM agent cyberattack (Sysdig, June 1, exfiltrated a PostgreSQL database in under 60 minutes), to an AI agent finding 21 zero-days in FFmpeg for a $1,000 prize. ​ • The AIRQ report's sobering numbers — Only 11% of production AI agents pass security thresholds. 98% exhibit the "lethal trifecta": private data access, exposure to untrusted content, and outbound action capability. Computer-use agents scored an average of zero on output guardrails. ​ • Deep dives into attack anatomy — The Sysdig attacker used 12 cloud API calls across 11 IPs in 22 seconds via Cloudflare Workers to break IP-based alerting. A Chinese-language planning comment leaked into the command stream, revealing the agent's internal reasoning: "see what else we can do." The Google-confirmed criminal use of AI to discover and weaponize zero-days with reasoning-based codebase analysis. ​ • Defensive architecture — The three-layer model distilled from Anthropic's published containment patterns, CISA/NSA/Five Eyes guidance, and industry research: environment-layer (gVisor containers, hypervisor VMs, egress MITM proxies), model-layer (classifiers, safety probes — probabilistic only), and external-content controls. Anthropic's key finding: "The weakest layer is the one you built yourself." ​ • Government & regulatory response — CISA/NSA/Five Eyes joint guidance (May 3) identifying five risk categories, the Trump AI Executive Order (June 10) mandating federal agency assessments, and the emerging global regulatory pattern. ​ • Actionable guidance — Immediate (next 30 days) and medium-term (30–90 days) steps for security teams, including auditing for the lethal trifecta, patching Starlette (BadHost CVE-2026-48710) and Marimo, implementing egress controls, and establishing agent identity management. ​ https://the-agent-report.com/2026/06/ai-agent-security-complete-guide-threats-defenses/ ​ submitted by /u/docdavkitty [link] [Kommentare]

Andrew Barry of Generalist compares earlier robot behaviors, including Spot opening doors, with the newer learned-model approach being used for dexterous manipulation. The older approach relied on hard-coded controllers for different parts of a task. The newer approach is aimed at giving the model a wider range of usable behavior when it sees something outside the exact training case. Barry describes this as “improvisational intelligence,” where the robot encounters a new variation and still takes a reasonable action instead of immediately failing. He also connects this to how humans complete manipulation tasks. A person does not need to make every pick or motion perfectly on the first try. They can miss, adjust, regrasp, and continue the task. submitted by /u/Responsible-Grass452 [link] [Kommentare]