The full gallery
Tech stack
21 projects
API providing token-level citations for LLM output grounded in attention analysis.
apoorvumang · HN
TokenPath – token-level citations for LLM output, read from attention
Paste messy AI/LLM output and get clean, valid, beautifully formatted JSON — with a plain-language explanation of every fix. Optional JSON Schema validation. Runs entirely in your
@SNSTRR88 · X
Recast: a browser tool that cleans messy LLM JSON and lists every fix. Nothing uploaded.
Estimate GPU memory, latency, TTFT, TPOT, and throughput for LLM inference.
popopanda · HN
LLM Inference Calculator – Estimate VRAM, Latency, and Throughput
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
Format-agnostic LLM hub. Bring your own provider keys and route across Anthropic, OpenAI, ChatGPT/Codex, Kimi, Alibaba DashScope, and AWS Bedrock — with unified observability and c
@0xxmemo · X
Compress prompts before LLM API calls to reduce token usage and costs.
@asgujjuasitgets · X
Track AI models used in your apps and receive warnings before they're deprecated.
taylorgt · HN
Find every AI model your code calls and warn before it's retired
Automatically route each prompt to the cheapest capable model to cut API costs.
u/ASDKING100 · Reddit
Launched an AI API router tonight, and found a bug hours in that would've taken real payments without ever upgrading the account Built LLMLite over the past few weeks — it classifies each prompt and routes it to the cheapest model that can actually handle it, instead of hitting GPT-4o for everything. Free tier, no card needed to try it. Tonight, right as I was about to launch, ran a real transaction to test the payment flow end to end. Paddle processed it, webhook fired, signature verified
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!
Tests LLM endpoints with adversarial cases and provides OWASP-mapped security audit reports.
@aryaan_sheth · X
- LLM security for small teams
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:
Compare latency and throughput performance across LLM API providers.
@QAInsights · X
