开发者工具3 days ago
AI Hardware Fit — 내 GPU에서 돌아가는 AI 모델 찾기
查看 GPU 兼容的 LLM 模型及成本预算方案。
Open source
jaeseok614 · HN
Open-source calculator for "will my GPU run this LLM?"
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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?"
LLM API支出分析仪表板，按模型和环境分类，含优化建议
ATsimbalistov · HN
Show HN: Tracking GenAI cost and endpoint fragility so app teams don't have to
提交 LLM 推理优化内核，在专用硬件上进行基准测试并竞争排名。
carsenk · HN
Frontier.fast – Help push the frontier of LLM speed forward
向任何LLM发送前屏蔽敏感数据，然后在回复中恢复。
@velumprivacy · X
Ornymo通过语义缓存减少LLM查询成本和延迟。
u/ornymo_official · Reddit
how to reduce ai costs there are lots of way to reduce costs but there all complex to setup i know this cause i tried one in production so i built ornymo we cache meaning not the exact string allowing us to give same awnsers thus reducing llm costs and latency check it out at ornymo.com free for a limited time and let me know your feedback submitted by /u/ornymo_official to r/buildinpublic [link] [comments]
在欧盟托管私有 LLM 实例，固定月费无使用限制。
CodingPanda42 · HN
Virtual Private LLM, fixed fee with no usage or token limits
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
An LLM gateway for OpenAI, Anthropic, Google and Azure. Every request logged, priced to the token, and audited for waste you can actually recover.
@razdagan3 · X
向多个前沿大模型提问，获得经过同行评审的综合答案。
u/Puzzleheaded-Log-27 · Reddit
Building a multi-model AI deliberation tool taught me something about trust LLM Counsel isn't another wrapper around one model - it sends your question to a panel of frontier LLMs, has them peer-review each other anonymously, and an impartial "chairman" model returns one synthesized answer. Free to start, pay-as-you-go after, credits don't expire. What I've learned so far: people trust a synthesized answer a lot more once they can see that the models actually disagreed and how that disagree
将LLM调用路由到成本最低的合适模型，保持质量。
george_avila · Product Hunt
IQ Routing Trajectory-aware LLM routing that cuts agent cost
Codalio从PRD和技术规范生成生产级代码。
@codalioinc · X
The vibe-coding loop: "fix it" × 7, 512K tokens, still not shipped. The Codalio loop: PRD → build → deploy. Need a change? Edit the PRD. +1,900 tokens. Stop paying tokens to discover your own requirements.