dev_dan_2 · HN

WatchMachineGo – A visualizer to show hardware performing LLM inference

@MBrew26730 · X

Dataset cleaning + fine tuning + continual learning at

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]

@shokhkarim1212 · X

13639366668 · HN

Pay-per-call MCP server with 47 AI endpoints, micropayments via x402

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

@mageofweb3 · X

ent101 · HN

The Safe, Production-Ready Backend for AI-Generated Apps

ada1981 · HN

I built a web tool to see and edit what an AI thinks before it answers

apoorvumang · HN

TokenPath – token-level citations for LLM output, read from attention

fristovic · HN

She watched me look at model rankings and asked what do the numbers mean... I literally had no good way of explaining it to her so I just came up with something that is approximately in the same ballpark as some of the benchmarks out there lol

@CognisafeUK · X