Developer Tools3 weeks ago
Axon — the quality & FinOps layer for your AI agents
Grades AI agents' real conversations with an LLM judge, providing A–F scorecards and FinOps analysis.
@tech_maju · X
The full gallery
Tech stack
29 projects
Grades AI agents' real conversations with an LLM judge, providing A–F scorecards and FinOps analysis.
@tech_maju · X
Compare LLM API pricing across 450+ routes and calculate real monthly costs with caching and batch pricing factors.
u/Greywolff06 · Reddit
I built LLMPrice — a free calculator for comparing LLM API costs across 450+ pricing routes I kept running into the same problem when comparing LLM APIs: the headline token price doesn't always tell you what your actual workload will cost. Caching, batch pricing, reasoning tokens, retries, different endpoints, and OpenRouter routes can change the result quite a bit. So I built LLMPrice.com. You enter your workload once — requests, input/output tokens, caching, retries, etc. — and it com
Analytics dashboard for LLM API spending by model and environment with optimization suggestions.
ATsimbalistov · HN
Show HN: Tracking GenAI cost and endpoint fragility so app teams don't have to
Semantic caching reduces LLM token costs and latency for AI queries.
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]
Convert PDF, DOCX, XLSX, CSV, JSON, XML, HTML, Images to clean Markdown optimized for AI Agents, RAG, and Vector DBs. 100% Privacy-First, In-Browser Conversion.
@13SahajChawla · X
For professionals to redact their client's sensitive informations before giving AI to process it & while converting any kind of document to a structured MD file. Better quality outputs, 100% privacy with on-browser local processing, and fully free!
API providing token-level citations for LLM output grounded in attention analysis.
apoorvumang · HN
TokenPath – token-level citations for LLM output, read from attention
Version, test, and deploy LLM prompts from a dashboard without code changes.
@why_deepanshux · X
I Just launched my first SaaS. Late night coding session, white board and my my markers knows what we built. Now it's world's turn. Please checkout Link below.
Compare LLM API pricing and calculate your monthly costs instantly.
u/ahmedk2002 · Reddit
I built a real-time LLM API pricing comparator — because I was tired of not knowing the actual cost difference between models I use LLMs daily at work and kept running into the same frustration: provider pricing pages give you raw numbers per million tokens, but no way to understand what that actually means for your specific use case. Is GPT-4o really that much more expensive than Claude Sonnet for 10k requests per day? What about DeepSeek vs Gemini Flash for high-volume summarization? I
Test small language models (8M-13M parameters) in your browser that work offline.
u/Live_Confusion_3003 · Reddit
I trained an LLM that runs on an ESP32 and directly in the browser Link to try it out yourself is: topk.sh The models download their weights directly in the browser so it works offline. Keep in mind they are very small and inaccurate. (8M and 13M parameters) However, I am building 500M and 1B+ parameter local models for agent based coding and other purposes. I will be shipping hardware designed for these tasks which connect directly to you computer or other device.
Agent-native TypeScript framework that trains and deploys custom ML models on managed GPUs.
@soleil_colza_ · X
AI assistant that learns your workflow and routes coding tasks across 30+ frontier models.
@otakuaakash · X
Calculate GPU VRAM requirements for LLM fine-tuning and inference.
@FaiqAdzlan · X
Built a GPU VRAM calculator to try out Claude Code (#cursorfanboy). I wanted it as granular as possible so I based it off of LLMem) which really just covers fine-tuning tbh. Please tear it to shreds: GitHub: