On-chain bond market where the issuers are AI agents

AC

Academa

Hi HN, we are Sina Atalay and Abdullah Geduk, co-founders of Academa. We are both PhD students. We thought: what if lecture videos were written as code and compiled into video using computer graphics and TTS? Then LLMs could write them, and lectures could be fixed with code edits instead of video production. On correctness: LLMs may make mistakes. But these videos are code sitting in our repository, and we can maintain them. Every report and review becomes a fix in the source, and everyone who watches after that gets the corrected lecture. Every recorded lecture on the internet is stuck with its mistakes. Ours will continuously improve. Each video also comes with an AI chat that understands everything said and shown in the lecture. There is a longer write-up at the bottom of the page. Happy to answer any questions.

TA

Talos

Show HN: Talos – An AI agent with a permission kernel between model and shell

TE

Telem

TL;DR: Web search open router that routes agent web search across providers (Exa, Parallel, Tavily, Brave, SerpAPI etc.), and traces web search results with quality metrics, so you can visualize whether a bad agent run is a web search problem or a reasoning problem. I work in venture capital, and before that I worked in information retrieval. When agents went viral, I built a due diligence agent for my day job. I vibe-coded the first version. It was pretty bad. My first reaction is to blame the model. I tried to change backend models, maybe because Fable doesn't want to help me do people search? Maybe because GPT 5.5 overthinks things? I tried DeepSeek, Kimi, Qwen; I changed prompts again and again, no good. Then I stopped just simply checking the final answer and started reading the actual trajectories: what did the agent search for? What came back? What pages did it read? At what point agents were off the rail? Pretty interesting: Sometimes the agent is stuck and confused because the information provided is irrelevant or even wrong; Sometimes the agent keeps iterate the same query, like "XXX lab UCB CS PhD founder 2026" "2026 XXX lab machine learning systems students startup" because the web search provider is not up-to-date; Sometimes sub-agents give up too early because the agent's intelligence is not enough or too "guardrailed"; Sometimes everything is just slow. A run jeopardized at minute 1 but still ran for another 10 before returning nonsense. That's because of bad searches, but nothing reveals it. So I built two things: 1. Web Search/Fetch router: One gateway for Exa, Parallel, Tavily, Brave, Ceramic, Linkup, Seltz, You, SerpAPI, etc. You or your agent can pick one or query several concurrently, and the responses come back in a homogeneous format. 2. Web Search Observability: Trace every search your agent (and its sub-agents) makes with an evaluator scoring relevance, diversity, and a few other things. Point is to answer one question: is your pipeline broken because the web search is bad? If so, where? Everything is agent-operated. All u need to do is to run this shell script: <curl -fsSL https://docs.telem.ai/alpha_install.sh | sh> Please give it a try, any feedback/suggestion would be great! Especially I’d like to ask: how are you debugging search/retrieval failures nowadays?

VG

Voronoi Go

I posted once before but wanted to share again because a lot has changed. There's now a fairly strong bot to play against (contributed by a community member) and also correspondence games. The combination of these things helps a bit in finding people to play against.

WB

We built the smallest dual-band aircraft tracker

We've been building open source embedded ADS-B receivers for a while, and spent the past 8 months smallifying our existing receiver tech with a new chip from Semtech. Ask me anything about ADSB or hardware manufacturing!