It's a negative sum game (negative because binary prediction markets are zero sum, and polymarket takes a cut), so not surprising.

Are there cheaper, comparable RISC-V SBCs?

Yeah, I think it's overkill unless you have 20+ nodes. When I switched from just a few VPSs with containers deployed with docker-compose, to deploying and administering a highly-available k8s cluster of 9 nodes, I'm pretty sure our cumulative uptime actually went down. There's ironically more points of failure that can bring stuff down due to the extra complexity.

It's already pretty biased. For web projects it almost always recommends Supabase and Vercel.

market manipulation

Meh, disagree with a lot of this.

AI tools can generate functional, adequate, perfectly average code

Not in my experience.

The outsourcing era taught us that the expensive part of software was never writing it. It was understanding it well enough to change it safely, to debug it under pressure, to explain to the next person why a particular decision was made at 2 a.m. on a Tuesday.

Since AI is adequate, just have AI change, debug, and explain it. You don't even need devs running the AI. Have AI generate intent. Just have AI scrape Twitter for people complaining about applications they wish existed, and have the AI make them. Let AI do market research. It's supposedly perfectly adequate.

Guns are loud and draw attention. Also, in addition to Flock cameras, Flock also installs gunshot detectors all over the place that detect and triangulate gunshots.

They are dropping in poor countries too. Poor countries tend to have higher birth rates, but they are dropping.

Specifically, I’d love to hear your thoughts on how to better optimize the context window limits when dealing with massive raw outputs (like huge DNS dumps or nmap scans) before feeding them back into the LLM’s memory.

Launch a sub-agent that reports its findings back to the main orchestration agent. If it's even too long to fit in the sub-agent's context, you can chunk it up, have a sub-sub-agent per chunk report back to the sub-agent for a shorter summary, just the interesting lines per chunk, or "no relevant lines found" or whatever. Can get even fancier by allowing the sub agent to use tools like grep, head, tail, etc on the text to search it instead of reading the whole thing directly.

Surprised you're not using LangChain/LangGraph as it makes some of the things you're doing easier. But it looks like you're vibecoding this anyways, so it's just doing whatever Claude Code or whatever decides to do. My suggestion would be to code it yourself with minimal AI assistance, as this will just turn into an unmaintainable mess as time goes on, and eventually, the AI coding agent will get stuck and be unable to really progress.

Now that I think if it, you could probably get Claude Code or OpenCode to do everything this project can do by just installing all the tools needed in your environment, creating a new empty project, telling it what tools are available in your environment, allow it to download any other tools necessary, and prompting it to do the recon (may need to use an abliterate, heretic, or otherwise uncensored model to do some things).

TIL LibreWolf randomizes some fingerprinting targets.

I recently noticed that Postgres has "sounds-like" functions too, which I thought was neat: https://www.postgresql.org/docs/current/fuzzystrmatch.html.

Tons of people are getting fired because the owners think AI can replace them. Doesn't mean the AI can do the work properly, but they are getting fired anyway.

I know a person that works at an AI startup and they convinced a company to replace their HR department with AI a while back. Funnily enough, that startup's "AI" is largely "Actually Indians." Their service is an agent that writes its own "tools" to solve problems/complete tasks, but the tools often don't work, so they have a large team of devs in India rewrite them or do the tasks.