标签：#ui

Gaming handhelds are amazing. They make it so much easier to fit all kinds of games into my day. Sadly, they’re less affordable than they’ve ever been — due to an unprecedented, AI-fueled shortage of memory chips, an unforced oil crisis, rampant inflation, fallout from tariffs, and more. But that’s not going to stop you. […]

These nifty tools combine the ease of jotting notes by hand with the power of saving them digitally.

Light up and secure your driveway, backyard, or porch with a floodlight security camera.

The idea of running a local LLM (Large Language Model) has always appealed to me, especially concerning data privacy and cost control. However, when I first delved into this, I realized through my own experiences how misleading market claims like "a few GB of RAM is enough" can be. In real-world scenarios, running a 70B parameter model with 8GB of VRAM is only possible with significant optimizations, which come with certain trade-offs. In this post, I will share my experiences, the problems I encountered, and the solutions I found, from hardware selection to optimization techniques for local LLMs. My goal is to offer a concrete, practical, and "good enough" perspective to anyone interested in this field. As we begin, we must remember that VRAM is the most critical part of this equation. VRAM: The Heart of Local LLMs and Capacity Limits At the core of running an LLM locally is keeping the model's weights in the GPU's VRAM. As the model size grows, the amount of VRAM it needs naturally increases. For example, a 7 billion parameter (7B) model in 16-bit float (FP16) format requires about 14GB of VRAM, while a 70B parameter model can demand up to 140GB. These values are far beyond the hardware owned by an average user. While working on AI-powered operations for my side product and a production planning model for a client project, I had the opportunity to experiment with models of different sizes. I clearly saw that there can sometimes be differences between theoretical VRAM requirements on paper and practical usage, especially as the context window grows. A 7B model, with a common quantization like Q4_K_M, can generally run with around 5-6GB of VRAM. However, for a 13B model, this value jumps to 8-10GB, and for a 70B model, it can soar to 40-50GB. This also varies depending on parameters like context window and batch size. 💡 VRAM Monitoring Tips You can monitor the real-time status of your GPU and VRAM with the nvidia-smi command. Using watch -n 1 nvidia-smi to update VR

I played the ultimate game of strategy on a variety of smart chess boards to find the best for online and in-person matches.

These WIRED-tested ebook readers let you take your library anywhere.

If you're someone who needs to (or likes to) take their work on the go, a portable monitor will make a huge difference. These are my favorite that I tested.

Set Up Your Own ChatGPT: Ollama + Open WebUI for Data That Never Leaves Home As artificial intelligence models rapidly integrate into our lives, privacy concerns are growing in parallel. Especially for companies or individuals working with sensitive data, sending information to cloud-based services can pose a serious risk. At this point, setting up your own local Large Language Model (LLM) infrastructure offers a great solution. In this guide, I will explain step-by-step how to set up your own chat interface using tools like Ollama and Open WebUI, ensuring your data never leaves your system. This approach allows you to both reduce costs and maximize your data security. This setup is particularly important for those like me, with a background in enterprise software development, who believe that data flows should always follow the most secure path. In the past, working on a production ERP, transferring supply chain data to external systems without anonymization could lead to serious security vulnerabilities. This is where local LLM solutions come into play. Why You Should Set Up Your Own Local LLM While cloud-based LLM services are incredibly convenient, they come with some fundamental drawbacks. Most importantly, every piece of data you input is potentially sent to the service provider's servers. This can be unacceptable, especially when dealing with financial data, patient information, trade secrets, or sensitive code in your personal projects. By setting up your own local LLM, you eliminate these risks. In recent months, while working on my side project, a financial calculator, I felt the need to use an LLM for complex financial analyses. However, the details of these analyses could not be leaked externally. This situation led me to search for a solution where I could keep my data under my own control. Ollama and Open WebUI emerged as the most practical and powerful duo in my search. ℹ️ Data Privacy and Control A local LLM solution gives you full control over where

Andrew Yang’s 2020 presidential campaign was based on a warning that automation and AI would hollow out the labor market and concentrate wealth in the hands of a few. At the time, ideas like Universal Basic Income felt fringe. Now Dario Amodei, Sam Altman, and Bernie Sanders are all saying versions of the same thing. An entrepreneur at heart, […]

Unravel the tangled world of cords and find the ones you need to charge your gadgets and transfer data.

I’ve tried every single Kindle. Here’s how Amazon’s ebook readers stack up.

I spent a weekend wiring Google's Gemini and Veo APIs into a single app just to feel where the edges of multimodal AI actually are. It turned into a small studio I now use daily, and along the way I learned more about these models from plumbing them than from any paper. Here's the honest technical debrief. Three pipelines, three completely different problems I wanted one prompt box that could do video, image editing, and document Q&A. Naively I assumed they'd share most of the stack. They don't. 1. Image-to-video: the enemy is time, not pixels Generating one good frame is solved. Video is about temporal coherence — frame 13 must agree with frame 12 or you get flicker and identity drift. Modern video models treat the clip as one object in space and time (latent diffusion over a width x height x time volume, with spatiotemporal attention) rather than 120 independent images. Conditioning on a reference image as the first frame is what makes image-to-video feel controlled: you've handed the model a strong anchor and asked it to extrapolate motion, not invent a world. The surprise: native audio sync (Veo 3.1 generating clip + soundtrack jointly) does more for perceived realism than another notch of resolution. A door slam landing on the exact frame the door shuts is uncanny in a good way. 2. Instruction-based image editing: preservation is the hard part Generating is unconstrained; editing must change one thing and preserve everything else. Condition the diffusion model on both the instruction and the source image's latents, cross-attend the instruction to steer only the referenced region, and bias hard toward preserving unedited latents. Push that preservation too soft and the subject's face quietly morphs across edits — the classic 'character consistency' failure that makes or breaks storytelling use-cases. 3. PDF chat: it's retrieval, not a long context The naive 'paste the whole PDF' approach dies on long files (models get lost in the middle ) and costs you the full

What device do we use almost all the time? Our mobile phone, almost certainly. If we wanted to...

I have been an audio reviewer for 20 years. My time as a freelance headphones panelist at Wirecutter, and the multitude of reviews I’ve written for sites like Reviewed, Digital Trends, IGN, and now The Verge, have given me the chance to listen to hundreds of earbuds. Some were truly excellent, and some were pretty […]

Kit out your Apple tablet with our favorite stands, cases, keyboards, and styli.

Laptops for college should be portable, offer long battery life, and remain reasonably affordable. Based on testing hundreds of laptops, these are my top picks.

I independently shipped my first open-source repo this week. The tool I built was a cli which accesses quickbooks online data. While Claude Code did speed up the build, it still took considerable effort shaping the entire user experience for the cli around the pre-existing public APIs! Major learnings during the entire process. Would also love additional feedback from open-source developers here.I'm currently looking for feedback from experienced open-source developers: Are there any improvements you'd suggest around project structure, documentation, testing, or contributor onboarding or the tool functionality? https://github.com/intuit/intuit-cli-for-quickbooks #

Netflix's response: "Absurd."

These bird feeders come with cameras and connected apps to let you see and learn about the birds in your neighborhood.

This article was originally published on aicoderscope.com TL;DR : Gemini 3.5 Flash went GA on May 19, 2026 and costs 50% less than Claude Sonnet 4.6 on input tokens ($1.50 vs $3.00/M). It generates code at ~284 tokens per second — roughly 4.7× faster than Sonnet 4.6. Cursor already lists it natively; Cline needs one extra config step. The trap: Flash's default thinking level is "medium," which is slower and pricier than "low," the setting Google specifically tuned for coding and tool-use loops. Gemini 3.5 Flash Claude Sonnet 4.6 DeepSeek V4-Flash Best for Fast agent loops, context-heavy analysis Complex refactors, instruction fidelity Cost-capped high-volume tasks Input / Output per 1M tokens $1.50 / $9.00 $3.00 / $15.00 $0.14 / $0.28 Context window 1M tokens 200K tokens 1M tokens Terminal-Bench 2.1 76.2% — — Output speed ~284 t/s ~60 t/s — Max output per request 65,536 tokens 64K tokens 64K tokens The catch Output at $9/M erodes savings on code-gen 15× pricier output than Flash No vision, MIT-licensed Honest take : Use Gemini 3.5 Flash with Cline for multi-step agent tasks where round-trip latency compounds and context windows run large. Stay on Claude Sonnet 4.6 when you need a hard refactor to land perfectly on the first try — Sonnet's 79.6% SWE-bench Verified score still leads Flash's on correctness benchmarks. The cost math that does and doesn't work Gemini 3.5 Flash charges $1.50 per million input tokens and $9.00 per million output tokens. Against Claude Sonnet 4.6 at $3.00/$15.00, the input side is a genuine 2× saving. The output side is almost the same story: $9 vs $15 is 40% cheaper per generated token. Run the numbers on a typical Cline coding session: 8 tool calls, reading 12 files (roughly 20,000 context tokens), generating 500 lines of code output (~7,000 output tokens). Sonnet 4.6: (20K × $3 + 7K × $15) / 1,000,000 = $0.165/session Gemini 3.5 Flash: (20K × $1.50 + 7K × $9) / 1,000,000 = $0.093/session That's 44% cheaper per session. At 50 sessions a m