标签：#AR

I shaved my head 15 years ago and never looked back. This GroWell LED cap changed that.

Apparently, talking or mentioning conspiracy theories is enough for Anthropic to ban you from using Claude. submitted by /u/Bitter-Heart7039 [link] [留言]

Is this significant news? submitted by /u/sstiel [link] [留言]

Back in the 1980s a debate raged about whether it was okay to let children use calculators in elementary school. Critics warned that giving kids calculators would lead to the "destruction of student math skills." A similar debate is happening today across a range of areas, including coding, writing and even music. Will using AI lead a brain drain across these and many other areas? One of my favorite authors is Isaac Asimov. He's better known for his Foundation and Robot series of books where he contemplates whether an algorithm can successfully predict (and guide) humankind's development and the relationship between super artificial intelligence and humans. In some ways he predicted what we're experiencing today with AI: the rise of powerful, inscrutable artificial machines that are so complex humans can't understand or maintain them. In the short story, "The Last Question" he wrote: "Multivac was self-adjusting and self-correcting. It had to be, for nothing human could adjust and correct it quickly enough or even adequately enough." We're living an age that was once the stuff of science fiction. The question is: what comes next? submitted by /u/SpiritRealistic8174 [link] [留言]

submitted by /u/SpeedAssassin [link] [留言]

While the AI fundraising machine keeps breaking its own records, some founders are building in the other direction. Mirror founder Brynn Putnam just raised money for Board, a startup focused on bringing people together through in-person games and social experiences. Cyberdeck creators are going viral crafting whimsical DIY computers that literally encourage users to touch grass. Unlike the AI-free browser crowd, this doesn’t just feel like backlash, […]

been building AI agents for a while and noticing a pattern: the LLM reasoning part works. the part that breaks is everything around accounts, logins, and verification. agent gets to "sign up for this service" and then: - email verification loop breaks - OTP times out while the agent is mid-step - captcha or bot detection fires - session expires between steps the model figured out what to do. the infrastructure around it didn't cooperate. curious if this matches what others are building. where do your agents actually fail in production? is it the reasoning, or is it the plumbing? submitted by /u/kumard3 [link] [留言]

submitted by /u/ProfessorDeep8754 [link] [留言]

Supabase, an example of an open source project becoming a fast-growing company, has greatly benefited from AI tools like Claude, Codex, and other vibe-coding platforms.

Disclosure: I work on the benchmark below, so flagging that up front. We've been testing whether LLMs can critique recent science-paper summaries — catch planted flaws, overclaims, and missing evidence — and, separately, how calibrated they are about their own judgments (confidence scored with Brier, a strictly proper rule). The pattern that keeps showing up: the models best at spotting problems are also among the most confidently wrong when they miss. Critique skill and calibration look like different axes, not the same one. There's also a clear gap between raw accuracy and knowing when to abstain. It's open (Apache-2.0) if you want to poke at it: Leaderboard: https://huggingface.co/spaces/BGPT-OFFICIAL/refute-leaderboard Dataset: https://huggingface.co/datasets/BGPT-OFFICIAL/refute Curious how others think about measuring calibration vs. raw capability — is a proper scoring rule enough, or do you need explicit abstention metrics too? submitted by /u/connerpro [link] [留言]

The Logitech G512 X 98 lets you swap between mechanical and analog switches in an attempt to achieve the best of both worlds. Unfortunately, its solution isn't as well thought-out as I'd hoped.

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

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

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

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

The Air succeeds as a minimalist, reliable fitness tracker, but Google's AI Health Coach feels unnecessary.