
Founder @holoweave. I mostly post about agents and a bit of investing. Built product orgs at CoinMarketCap, Binance, Chainlink and Zendesk.
Joined April 2012
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Replying to @SamE1311025
Looks like mine is back exactly to the 67% that I was at right before the issue. Looks like my guesses were right.
Well at least for this isolated incident, it seems they reverted the usage limit, it might still finish twice as fast as last time, but the way this happened, it seems more bug than policy change.
After testing Grok Bot, Muse, Hermes Agent, Hark, Instinct, and OpenAI Dots on real personal assistant work, shopping, and agent coordination through Holoweave Weave...
Hermes on its own computer/VM is S-tier once you customize it. For the best balance of capability, always-on access, and actually finishing jobs, Grok Bot is my top pick right now.
Give this to your agents now. I was mid talking to my bots about an issue and decided this explained issues better than I did. Hope it works!
how do you trust your agents' work more? copy this post/link and give it to your @bot or agent!
1. codebase improvements: this is just good software engineering - better architecture, stronger types and invariants, clear boundaries. agents love to copy what exists and take the shortest path, so make the easy thing the right thing. categorically eliminate problems through code and you won't need to think about it again.
2. static analysis: lint rules, tests, CI, verification (eg TLA+). very token efficient ways to catch issues ahead of time.
3. prose: comments, rules, AGENTS.md, skills - anything that can't be enforced and requires agent judgment. use it minimally, but do use /create-verification-skill.
4. human review: the hardest to scale. at the rate agents land code, it's pretty much impossible to thoroughly review every PR, so spend that effort on the highest leverage point, designing an environment where agents do the right thing by default.
this forms an inverse pyramid: you want most of your effort at the top, least at the bottom. codebase improvements are deterministic, making it more token efficient, and lets you sleep more soundly at night.
install github.com/cursor/plugins/tr… and create a new Project:
1. look through my slack/chat messages and code review comments where i've given feedback on PRs, code, architecture
2. use /recall on my past transcripts to learn how i steer agents
3. /poteto-mode use /correct and /architect on past commits to find the most common pitfalls agents fall into, then propose a plan to rearchitect the code i own so it's more agent friendly. answer open questions with prototyping rather than defer to me, and back the plan with real data.
4. before you begin, use /sharpen + /in-your-own-words and confirm this prompt with me
Here are some practical, real-world results after testing @MiaAI_lab optimizations on the 3 most popular models that can run on two sparks.
I am sticking with GLM because it is only about 20% to 25% more efficient at tool calling to beat the other models for my use cases.
Replying to @MiaAI_lab
My dudes are still chugging along fine. Had to swap the orchestrator to local but better than being down!











