u/CinderPillow

I have been looking at what kinds of agent ideas show up in interviews and reading more about how agents actually work. One topic I keep running into is multi-agent collaboration.

My current view is that the premise is still ahead of the underlying technology. An agent depends on a language model, and language models still hallucinate, forget context, lose capability under pressure, and occasionally make surprisingly weak decisions. Putting several agents on top of those failure modes can amplify them, especially when the agents use different models and have to hand work across a boundary.

The common designs I see are a shared workspace with restricted read/write access, plus a reviewer agent and some kind of circuit breaker. Those controls make sense, but they also look very similar to managing concurrent workers. The uncomfortable part is that the final safety check still depends on another agent.

The costs are obvious. Token usage multiplies, agents can lose track of ownership, and the orchestration becomes rigid and format-heavy. That last point feels the most damaging to me. We are supposed to be using the flexibility of an intelligent model, then we wrap it in so many fixed handoff formats that the system spends its time managing the workflow instead of solving the problem.

I am not sure a large amount of orchestration is the best answer. Maybe the better direction is to let the model decide when another agent is actually needed, with fewer predefined roles. ZenMux can serve as the API gateway when those calls need to cross model or provider boundaries, but the gateway does not solve the coordination problem itself.

So my current summary is that multi-agent systems are still more about exploring what might be possible, with a fair amount of demo value, than reliable production practice. The path to a useful deployment seems much harder than the diagrams suggest.

Do you have a real multi-agent workflow in production? Did it actually meet expectations, or did you eventually simplify it back to one agent and a few tools?

npm ci removes an existing node_modules before it installs. The docs say it plainly: if a node_modules is already present, it will be automatically removed before npm ci begins its install. What I had not thought about is that the gap between delete and reinstall is real wall clock time, and anything in that checkout resolving a module during it fails.

I started a test run in a second terminal while the install was still refilling the tree, and it died on Cannot find module for packages that were there a minute earlier.

It came up because I keep two lines of work in one checkout instead of branching every small change, and the other one was a verdent task pointed at the same working copy, so both shared one node_modules.

npm install at least does not start by deleting the tree, though it still writes into it while another process reads.

Is there a way to make an install land atomically, building the new tree beside the old one and swapping at the end, or is a separate checkout per line of work the only way around it?

Been cutting a sizzle reel for a client who has zero budget for a shoot but needs something visual to pitch investors. Normally I'd pull stock footage, but I wanted to test two AI video models that dropped at the end of July: Seedance 2.5 and MiniMax H3.

Short version: useful for scratch and previz. Not useful for anything you'd deliver without serious cleanup.

Seedance 2.5 can generate 30-second clips in a single pass at 4K, which caught me off guard. You feed it reference images and it keeps characters reasonably consistent across shots. I used it to rough out some product b-roll and a few talking-head style shots. First glance looked solid. Second glance, hands were doing the usual AI finger multiplication, hair moved like it was underwater, and anything with fast or complex motion just fell apart. Spent more time painting out artifacts in After Effects than I probably would have spent shooting on my phone.

MiniMax H3 is a different animal. Shorter clips (5-15 seconds, 2K) but it generates audio in the same pass and you can actually feed it audio to drive the video. That's genuinely interesting for scratch audio-visual sync. The motion felt slightly more natural on simple, slow shots but still broke on anything dynamic. I ended up generating the H3 clips through APOB AI since they're running it free and unlimited right now, so the raw material at least cost nothing while I burned through iterations figuring out what was usable.

The actual editing was still the actual work. Generated maybe 40 clips across both models, kept about 12 as starting points, then spent two full days in Premiere cutting around bad frames, color correcting to match, adding real audio design, and fixing compositing issues in AE. The client got a rough cut that works for a pitch deck and they're happy. I would not put any of this in front of someone who knows what they're looking at.

These tools are about where stock footage was 15 years ago. Useful in a pinch, obvious if you lean on it. The job is still the job.

My current AI signal wins more trades than a 20 and 50 candle moving average crossover. It also trades much more often. Once I charge both versions for the same execution, most of that advantage disappears. The idea came from a conversation at a crypto expo, though I did the testing later at home.

I keep the candles in chronological order and tune on the older data. The final 20 percent stays out of sight until I stop changing things. I used to check that block after every tweak, which quietly turned it into another tuning set. The model and the crossover use the same position size, and both enter at the next candle open after a signal.

For costs, I use the venue where I would place the trade. This test uses the fee schedule and funding history from BYDFi, with the same slippage model applied to both versions. Most of my clever versions finish close to the crossover after that. The result is dull, but it has kept a few bad ideas out of my live account.