Cybercriminals, part of a gang known as Silent Ransom Group, have sent people pretending to be IT support employees to law firms' offices, where the criminals have stolen data using USB drives or remote access tools.

After spending the last few weeks reading through the reasoning literature, I noticed a trend that seems worth discussing. For the past 2–3 years, a large fraction of progress in LLM reasoning came from making models generate more intermediate thoughts. Chain-of-Thought prompting (Wei et al., 2022) pushed PaLM 540B from roughly 18% to 58% on GSM8K. Self-Consistency added another 17.9 percentage points by exploring multiple reasoning paths before committing to an answer. Tree-of-Thoughts later showed that GPT-4's success rate on Game of 24 could jump from 4% to 74% when reasoning was reformulated as search rather than a single chain. DeepSeek-R1 and OpenAI's o1 pushed the idea even further by allocating substantial test-time compute to reasoning itself. Taken together, these results seemed to point in the same direction: giving models additional reasoning trajectories, search paths, or thinking steps often improved outcomes. Recent work increasingly asks whether those traces are actually necessary. Quiet-STaR doesnt treat reasoning traces primarily as explanations for humans. Instead, it trains models to generate internal rationales that improve future token prediction. COCONUT goes a step further and asks a more radical question: why force reasoning to be represented as language at all? Rather than generating reasoning tokens, it feeds continuous hidden states back into the model and performs reasoning directly in latent space. Fast Quiet-STaR then shows that some of the benefits of explicit reasoning can be retained even after removing thought-token generation during inference. This feels like a meaningful shift in research direction. For a while, the field seemed focused on making reasoning more visible. Recent work increasingly explores whether visibility is actually necessary. One way to interpret this is that Chain-of-Thought was never the reasoning process itself. It was a computational scaffold. Transformers perform a fixed amount of computation per generated

GitHub热门项目 | LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar | Stars: 15,979 | 108 stars today | 语言: Python

Some time ago, I embarked on the journey to radically simplify building realtime apps. I wanted: No backend/socket.io setup, and no logins A simpler client to handle race conditions Ultimately I came up with: A public/free relay server that anyone can use A thin WebSocket client that talks directly to that service (or any other WS server) The Dilemma: I always assumed it should be angled at the rapid prototyping crowd, since it's literally a service you can use in a single line from your browser DevTools, but the client itself is pretty f*cking amazing... for ~466 bytes, you can do things like this: connect('wss://socket.massive.com/crypto') .on('*', e => console.log(e['0'])) // listen .send({ action: 'auth', params: 'MY-API-KEY' }) // login .send({ action: "subscribe", params: "XQ.*" }) // subscribe If you notice, that's usually a race-condition nightmare that involves callbacks/promises, etc. The tiny client sorts all that out under the hood (and much more). While of course I use the underlying service to power apps, I find myself using the client itself just to check any existing WS service, because it's 100x easier to use than native WS code. The Question In the NPM library specifically, which should I focus on? The hosted integration, or strictly as a more user-friendly WebSocket client (with an aside mention of the integrated service)? It's all 100% free, so it's not like this is a product question - it's more of a "am I sleeping on something that could help more of the community?" question. submitted by /u/kevin_whitley [link] [留言]

Your trip starts impacting the planet before you even leave home. Here are a few pointers for keeping your footprint small.

Hotels and other service providers pitch themselves as eco-friendly when they’re not. Here’s how to call their bluff.

The actual eco-friendliness of ecotourism varies considerably. One research station in the Peruvian Amazon is out to prove it can bring visitors to the area without disrupting the environment.

From San Francisco to Stockholm, a new generation of electric ferries is entering passenger service, marking a tipping point for green maritime technology.

As the conflict in Iran disrupts the world’s oil supply, airlines are looking for jet fuel alternatives. The answer: energy from used cooking oil and french fry grease.

Managing a multi-million product catalog on Magento presents unique challenges around performance, scalability, and operational efficiency. At Rave Digital, we recently undertook a Magento performance optimization project for a large-scale eCommerce merchant struggling with slow site speed, infrastructure bottlenecks, and backend instability. This use case breakdown details how we modernized their Magento architecture, optimized database performance, and scaled infrastructure to deliver a stable, high-speed shopping experience. This post is tailored for eCommerce managers, directors, and Magento merchants—especially those running Adobe Commerce or Magento Open Source platforms—who want to understand practical strategies for Magento architecture scaling and performance tuning for large catalogs. The Problem: Performance Bottlenecks in a Complex Magento Environment: Our client operated an enterprise Magento store with a multi-million product catalog. Despite Magento’s robust capabilities, the site suffered from: Slow page load times impacting user experience and SEO Scalability challenges as product volume and traffic grew Infrastructure bottlenecks causing backend instability and downtime Complex integrations and manual processes limiting operational efficiency Platform limitations in handling large catalog management and real-time inventory updates These issues collectively threatened the site’s ability to support growth and deliver a seamless customer experience. The client sought a comprehensive Magento platform modernization to address these challenges. Context: Why Magento Architecture and Infrastructure Matter Magento’s flexibility and extensibility make it ideal for enterprise eCommerce, but large catalogs require careful architecture and infrastructure planning. Key technical pain points include: Database performance under heavy read/write loads Indexing delays and cache invalidation impacting site speed Integration complexity with third-party systems and API

Forget Syntax and Lines of Code. Do This to Stand Out Cesar Aguirre Cesar Aguirre Cesar Aguirre Follow Mar 9 Forget Syntax and Lines of Code. Do This to Stand Out # coding # beginners # career # careerdevelopment 13 reactions Comments 2 comments 2 min read

Money would keep coal plants open, build the first new plants in over a decade.

Docker has a massive surface area, but your day-to-day workflow only relies on a handful of commands. here is the essential cheat sheet for running, debugging, and cleaning up. 1. Running Containers ( docker run ) The docker run command is your workhorse. By combining a few key flags, you can control exactly how your container behaves. Run in the background with a specific name: docker run -d --name my-cache redis:7-alpine -d runs the container in detached mode (background). --name gives it a recognizable name instead of a random hash. Expose ports securely (Localhost only): docker run -p 127.0.0.1:8080:80 nginx:alpine Maps port 80 inside the container to port 8080 on your host machine, restricting access strictly to your local loopback address. Inject Environment Variables: # Single variable docker run -e ENV = prod python:3.12-slim # From a file docker run --env-file .env python:3.12-slim Auto-cleanup for one-off tasks: docker run --rm python:3.12-slim python -c "print('Done!')" --rm ensures the container is automatically deleted from your system the moment it stops running. 2. Debugging and Interacting Once a container is running, you need visibility into what it's doing. Stream live logs: docker logs -f my-cache The -f (follow) flag streams the logs in real-time. Use Ctrl+C to exit. Drop into a shell of an already running container: docker exec -it my-cache sh Unlike docker run (which creates a new container), exec opens an interactive ( -it ) shell inside an existing one. 3. Container Lifecycle Knowing how to stop a container properly prevents data corruption and hanging processes. Graceful shutdown: docker stop my-cache Sends a SIGTERM signal, giving the container time to save state and shut down cleanly. Force shutdown (when frozen): docker kill my-cache Sends an immediate SIGKILL signal. Use this only when stop fails. 4. Visibility and Cleanup Docker accumulates stopped containers and unused images fast. Keep your system clean. List all containers (including

PM at a mid-size startup here. Didn’t really notice how bad it got until this week. My workflow now: • Claude for ideation • ChatGPT for rewriting specs • Cursor for implementation • Perplexity for research • Notion AI for docs • Atoms AI for larger tasks None of these tools actually replaced my work. They just redistributed it. I’m still the one dragging context between all of them.Yesterday I literally caught myself pasting the exact same requirement into 4 different tools and thinking… this can’t be how it’s supposed to work. I don’t even think any single tool is bad. It just feels like we hired 6 smart interns and completely forgot to get a manager. submitted by /u/Dangerous-Guava-9232 [link] [留言]

I build and teach this, so here's the honest mechanics, not the hype. Build one consistent AI character (custom-trained, not just prompting), run it as a social presence, monetize on platforms that allow AI. The edge isn't quality vs humans — it's near-zero content cost, no burnout, horizontal scaling. The underrated hard part: consistency is genuinely difficult, and the money is in audience relationship management, not the content. The content's the easy 20%. Broader signal: when content cost hits zero, the bottleneck becomes distribution and trust. Applies way past this niche. Happy to go deeper on any part — it's what I do daily. submitted by /u/PoleTV [link] [留言]

I have a paper accepted at a non-archival ICML workshop this year, and I am trying to decide whether it is worth registering and attending. By coincidence, I will already be in Seoul around that time, but I would have to pay the workshop registration fee (~$400) out of my own pocket. I would only be registering for the workshop day since I have other commitments during the rest of the conference. I am thinking of applying to PhD programs this fall (I applied this year too, but didn't get in), and the workshop speakers and panellists look genuinely great. Not sure what the real benefits are here or whether I should go for it. For context, I am also attending ACL 2026 this year, but that trip is fortunately sponsored, so this would be a separate personal expense. I would also appreciate guidance on how non-archival workshops work in general. Since the paper is non-archival and not formally published (at least to my understanding), is registration still expected or required for accepted papers? Do authors typically attend and present in person, or is it common to skip attendance and conference registration? Has anyone been in a similar situation? I want to understand the benefits of this. Any advice would be greatly appreciated because I honestly have no idea how to evaluate this. submitted by /u/YOYOBOYOO [link] [留言]

imagine you are working in a large codebase. you need to fetch different kinds of data and transforming them, grouping them or sorting them. lets take a closer look at Sorting and Heaps in golang Specifically. we already familiar with heaps and its important interface. the Heap.Interface. a very performant and impressive implementation of heaps and its sorting functionality. type Interface interface { sort . Interface Push ( x any ) // add x as element Len() Pop () any // remove and return element Len() - 1. } as a programming language, it couldnt be done better than what it is today. but most of the time we might not need to implement all the interface items. dont get me wrong the functionalities should exist but mostly all that matters for us is that how the sorting will be done. ZenQL's Implementation In the latest version take advantage of sorting and heaps functionality. in a fast and agile way! result := From ( personList ) . Where ( func ( person Person ) bool { return person . Active == true }) . CollectSorted ( func ( person Person , person2 Person ) bool { return person . Identifier < person2 . Identifier }, true ) In the code snippet above we perform a sort on our collections using the thor engine very easily. we just express our desire about how the sorting needs to be done and wether its ascending or descending. and other functionalities are implemented as below: type Sortable [ T any ] struct { Items [] T less func ( a , b T ) bool desc bool } func ( h Sortable [ T ]) Len () int { return len ( h . Items ) } func ( h Sortable [ T ]) Swap ( i , j int ) { h . Items [ i ], h . Items [ j ] = h . Items [ j ], h . Items [ i ] } func ( h * Sortable [ T ]) Push ( x any ) { h . Items = append ( h . Items , x . ( T )) } func ( h * Sortable [ T ]) Pop () any { old := h . Items n := len ( old ) item := old [ n - 1 ] h . Items = old [ : n - 1 ] return item } be faster and more agile with the Golang ZenQL. Click To Visit ZenQLRepository

The Problem With Choosing a Local Model Everyone has an opinion on which local LLM is best. "Use Llama — it's the most popular." "Mistral 7B has the best quality." "Phi-3 Mini is small and efficient." None of these claims come with numbers. Specifically: your numbers, on your hardware, for your workload. I built a benchmarking system to change that. Three models, 30 prompts, full latency distribution, memory profiling per inference call, and a JSON validation layer to measure structured output reliability. Here's what I found — and why the results matter for anyone deploying local models in production. The Setup Three models tested: llama3.2:3b — 3B parameters, Q4_K_M quantization, 2 GB download phi3:mini — 3.8B parameters, Q4_K_M, 2.3 GB download mistral:7b — 7B parameters, Q4_K_M, 4.1 GB download Hardware: CPU only, no GPU acceleration. This is the worst-case baseline — the scenario that exposes real latency and memory numbers. 30 test prompts across 5 categories: Short factual (10): "What is the capital of France?" Reasoning (8): "Explain why the sky appears blue." Code generation (5): "Write a Python function to reverse a string." Structured output (5): "List 3 frameworks in JSON format with name and use_case." Multi-step (2): Complex chained reasoning tasks. Architecture POST /query → Pydantic validation → Ollama HTTP API → JSON Validator → QueryResponse POST /benchmark → Load test_prompts.json → For each prompt: psutil memory before → Ollama → psutil memory after → NumPy: P50/P95/P99 latency, avg TPS, peak/avg memory → BenchmarkResult JSON The benchmark runs prompts sequentially, not in parallel. Parallel would contaminate the per-prompt memory measurements. Results Llama 3.2 3B (Q4_K_M) avg_tokens_per_second : 42.3 p50_latency_ms : 1203 p95_latency_ms : 3847 p99_latency_ms : 5120 peak_memory_mb : 6953 avg_memory_mb : 6842 total_test_duration_s : 87.4 Interpretation: P50 at 1.2 seconds is excellent. P95 at 3.8 seconds misses a 3-second SLA — the outliers are m