教育学习1 month ago
Learn Hardware Engineering Online with GateCode
通过交互式Verilog和数字逻辑问题练习硬件工程。
@Aimenbuilds · X
- leetcode for hardware engineers! We map job postings of hardware roles 1-1 to course requirements!
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通过交互式Verilog和数字逻辑问题练习硬件工程。
@Aimenbuilds · X
- leetcode for hardware engineers! We map job postings of hardware roles 1-1 to course requirements!
实时可视化硬件在运行LLM推理时的性能指标
dev_dan_2 · HN
WatchMachineGo – A visualizer to show hardware performing LLM inference
查看 GPU 兼容的 LLM 模型及成本预算方案。
jaeseok614 · HN
Open-source calculator for "will my GPU run this LLM?"
学习手机硬件和软件工程，包括视频教程和进度跟踪功能。
@Abodiessbk · X
Check out what I just built with Lovable!
Real Android device hardware database — sensors, SoC, GPU, memory, display, and fingerprints from thousands of real devices.
@DevCheckAI · X
Building DevCheck AI — spot fake/refurbished Android phones & sketchy IPs & hardware self-test. 80,000+ real device specs at 📲
查找与您硬件兼容的AI模型并查看性能和价格估计。
cdnsteve · HN
Tokenstead, find AI models for your hardware
用AI管理音乐事业，提供数据分析、播放列表推广和新闻资料工具。
@GrYoT · X
A manager for independent musicians. instead of juggling like 5 tools for analytics, playlist pitching, content scheduling, booking etc, you just talk to it like you would a real manager and it pulls your actual data and does the busywork
在发布前检查AI生成内容中的错误和安全问题。
u/Brief_Dust8845 · Reddit
I pivoted from my initial idea after realizing I was solving the right problem at the wrong time When I started building GaaS Guard, it was an AI governance tool for companies. The idea was to help organizations defend against prompt injection and unsafe AI interactions. It was technically interesting, and I still genuinely believe I was solving a real problem. The problem was, it just wasn’t selling—to be brutally honest. Here’s how I actually ended up pivoting. I started using a b
NEURL-OS is a free 2-minute weekly calibration that transforms your weekly habits into a predictive model of your performance.
@NEURL_OS · X
Hey we’ve built NEURL-OS, a tool that turns daily habits into data and a weekly capacity score. For founders juggling constant decisions, it helps reveal what’s fueling your focus and output—and what’s quietly draining it. Try it free:
用Sakura基准测试本地编码模型，测量准确性、延迟和吞吐量。
u/Unfair_Association89 · Reddit
I built a reproducible benchmark for local coding models (Ollama, 27 tasks, live leaderboard) ran it on my 8GB card, here's what I found I kept eyeballing "vibes" to decide whether one quant of a coding model was actually better than another on my machine, so I built Sakura to get real numbers instead. What it does: - Points at any Ollama model and runs it through 27 hand-curated tasks: codegen, bugfix, SQL, refactor, systems design, protocol implementation, and terminal-agent episode
浏览并购买来自肯尼亚地区分销中心的企业硬件。
@KennedyOre2202 · X
Check out what I just built with Lovable!
Three AI instruments for turning raw material into finished work: Flow cuts long video into short-form, Listen turns meetings into notes, Study turns course material into a workspa
@SlatedStudio · X