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63 projects
Ecossistema de infraestrutura de automação: 376 templates com JSONs de n8n de IDs sanitizados, scripts de provisionamento e checkout Web3 em BNB Smart Chain.
@fxfire4345 · X
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
为小型制造商快速生成 Tier-1 级别的质量文档。
@TheCuneginGroup · X
Vibe coding ships a thousand prototypes a week. Most die before a supplier sees them. Specvault's $19-59 kits are for the survivors.
SpecKit 根据项目描述自动生成完整的技术规格、架构和开发路线图。
@satulipa_ · X
My workflow: 1. Open Speckit → answer 5 questions 2. Get blueprint (PRD, DB, API, roadmap) 3. Take it to Cursor/Claude Code 4. Vibe code with actual direction Built with Next.js, Vercel, try it on
为物理系统生成可定制的安全文档和风险分析。
@mokka_abhi · X
Building the safety evidence layer for physical AI so robots, cars, and drones can actually ship.
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 📲
400kA铝电解槽柔性负荷调节中试系统三维数字孪生操作台
@idingxs · X
给客户做了一个3D工厂模拟， 收多少钱合适尼？ 总体消耗了plus得一周token的量。 工具主要是：@Blender & @threejs
Size your way to private local AI. Directional sizing for one resident text model: KV memory, combined prefill/decode serving, and runtime sessions. Compare eligible on-prem, sover
juxhinr · GitHub
bindwidth Evidence-aware on-prem LLM inference sizing and TCO calculator
SpecParse 将工程PDF表格自动转换为格式化的Excel工作簿
@SpecParse · X
Convert Complex PDF Tables into Styled Excel Workbooks
估算llama.cpp模型推理所需的显存大小
hypfer · HN
According to this shitty vibecoded thing "I" built https://hypfer.github.io/will-it-fit-llama-cpp/ (and I guess according to math too), FP16 K/V would give me something like 90k context at the same model quant, which doesn't really fit my usage. But maybe someone else has experience to share there
