Idan Benaun’s Post

Worst to mention an optimization by Oded Regev in factoring prime and Shor's algorithm to crack RSA with Lattice! Hold on, with Lattice 🙊? Well, that's the thing. It looks like it is an hybrid collaboration between QC and standard computer. is that possible? That's interesting to see that surprising optimization can happens any time (Shor, 1994) ! That's interesting to see the combination of QC and standard circuit as a new threat actor. Awesome but scary! 🇦🇺🔑🐧

As an incident responder, Redline is a powerful tool that performs memory analysis on compromised systems. The cream on the crop for me while using Redline to perform forensics is how compatible it is with OpenIOC editor tool. I created IOCs using the OpenIOC editor; I examined and generated reports on the compromised host using Redline's Graphic User Interface(GUI) based on the IOC search file I created. Overall, I am ecstatic about my new knowledge and exposure.

🚀🌌 Unveiling DamageBDD - Quantum Leap in Software Behavior Verification! 🌌🚀 Dive into a realm where the #ObserverEffect of Quantum Mechanics & Software Development merge! 🔄👀 Introducing DamageBDD - your gateway to unmatched collaboration and behavior verification at scale! With the easy-to-grasp Gherkin syntax, DamageBDD empowers EVERYONE in your team to define and understand software behavior seamlessly. 🌐🤝 🚦Fasten Your Seatbelts for Quantum-level Performance Testing! 🚦 Stress test, identify bottlenecks, and ensure your applications are robust, scalable, and ready to handle massive user traffic with precision! 📈🚀 🛠️ Seamlessly Integrate into Your CI/CD Workflows! 🛠️ With DamageBDD, constant monitoring and early issue identification are now integral to your software development, making your process smoother and risk-free! 💻🔍 Ready for a transformation in testing processes and application performance? Make your quantum leap with DamageBDD today! 🌟🚀 🔗 Discover the Future of Software Testing with #DamageBDD! #SoftwareTesting #QuantumLeap #BehaviorVerification #Collaboration 🌐🚀 Visit DamageBdd.com, for more info or your could just message me.

This figure illustrates the complete working pipeline of a LLM-based intelligent agent, mapping code LLM abilities to specific phases: code-based planning in step (2), modular action parsing and tool creation in step (3), and automated feedback collection for enhanced agent self-improvement in step (5). Collectively, steps 0-10 in the entire loop contribute to improved structured information understanding and perception

I am excited to share Just wrapped up Advanced Task 1 on network VAPT on network scanning tools and methods, script scanning,trafic analysis tools and techniques , practical application. #CybersSapiens #NetworkSecurity

In Data Structures and Algorithms (DSA), operators are symbols or keywords that perform specific operations on operands. These operations can involve arithmetic calculations, logical comparisons, bitwise manipulations, and assignments. #DSA #operators #datamanipulation #smartinterviews

Bredec Group Bredec Group Ask HN: Clarify VRAM usage during LLM forward pass: Hey HN, I'm working with Llama 2 and have hit a snag regarding VRAM usage during the forward pass in inference. Despite understanding that only the largest activation tensor is stored (as activations from previous layers aren't needed in inference), my VRAM calculations don't match up with what I'm observing. Context: Using a 7B model, batch size of 8, sequence length of 1024 Data type: bfloat16 After the forward pass, VRAM is much higher than expected Observations: Model weights: 12915 MiB VRAM post-forward pass: 16679 MiB (expected significantly less) VRAM for a forward pass: 3764 MiB Output tensor VRAM (in fp32): 1000 MiB Thus, activations used 2764 MiB, whereas my calculations suggest a single layer should use only 1368 MiB. Moreover, the actual VRAM usage should be even less than this, as activations from the attention layer can be discarded when computing the MLP block. The highest VRAM consumption should come from the softmax output plus v (in attention block), estimated at 576 MiB. Why does the forward pass use more VRAM than expected, considering activations from previous layers aren't stored? Any insights or similar experiences would be greatly appreciated. Happy to give more context if needed. --- Comments URL: https://lnkd.in/dmHSAp-w Points: 1 # Comments: 0 info@bredec.com Inquiry@bredec.com

Hello, I have a question, what is a point of using swarm of agents? I mean set ups like you have multiple LLMs which communicate with each other. Internally each LLM is passing next token to self anyway so I fail to see an advantage of that external process. Even if two LLMs are fine-tuned to different tasks how it is better than just fine tuning bigger network to both tasks.

TL;DR: still possibly correct. https://lnkd.in/gpwSv3jE

Chapters: 00:00 Introduction 03:04 Limitations of Conventional Testing Methods 04:09 Understanding Deterministic Simulation Testing 08:07 Implementing Deterministic Simulation Testing 14:30 Real-World Example: Chat Application 19:56 Antithesis Hypervisor and Determinism 27:06 Defining Properties and Assertions 38:34 Optimizing Snapshot Efficiency 40:44 Understanding Isolation in CI/CD Pipelines 43:39 Strategies for Effective Bug Detection 47:59 Exploring Program State Trees 51:17 Heuristics and Fuzzing Techniques 01:01:56 Mocking Third-Party APIs 01:05:54 Handling Long-Running Tests 01:09:06 Classifying and Prioritizing Bugs 01:15:35 Future Plans and Closing Remarks https://lnkd.in/e_g__r5s