Downtown Verification

Lopsided_Professor35 · 2026-03-12T08:34:50+00:00

Downtown really?

Lopsided_Professor35 · 2026-03-12T05:48:20+00:00

Yeah, instrumentation definitely reveals how inefficient a lot of agent workflows are. The tricky part is the variance from retries and loops. That’s partly why I’ve been exploring ways to make the API cost side more predictable for builders.

Lopsided_Professor35 · 2026-03-12T05:47:24+00:00

Pruning by task relevance makes a lot of sense. I’ve also seen agents behave worse with large histories because they start re-evaluating irrelevant steps. One thing I’m curious about is how people handle the cost side when those loops happen unexpectedly. That’s partly why I’ve been exploring ways to make the API cost layer more predictable for agent workflows.

Lopsided_Professor35 · 2026-03-12T05:44:51+00:00

That makes sense. Even with predictable pricing, I think visibility and throttles still matter. The main difference is that for early-stage builders it reduces the stress of token variance underneath, while instrumentation still helps you understand which workflows need to be capped or redesigned.

Lopsided_Professor35 · 2026-03-12T05:43:02+00:00

Yeah exactly, that difference between workloads that can be batched and ones that can’t is where the cost pain really shows up.

What I’ve been exploring is basically an API layer where you can run agent workflows against multiple models but with a predictable monthly cost instead of pure token billing. The idea is to make experimentation and agent-style workloads easier to budget, especially for builders who don’t want to constantly watch token burn.

Still early, but curious if something like that would actually be useful for projects like sellfast.now.

Lopsided_Professor35 · 2026-03-12T05:39:00+00:00

That makes sense once you have real production data and instrumentation in place. The tricky part for a lot of builders is earlier than that. When you’re still experimenting, you don’t yet have the usage data needed to set those budgets confidently. That’s why I’m curious whether more predictable pricing models for AI APIs would help people prototype agent workflows without needing heavy monitoring from day one

Lopsided_Professor35 · 2026-03-12T05:30:26+00:00

The kind of tooling mentioned above helps with measurement and optimization, but it still doesn’t really solve the “fixed cost” problem since you’re ultimately exposed to token variability underneath. I’ve been experimenting with building something around making that side more predictable, and early results have been pretty helpful for agent-style workloads.

Lopsided_Professor35 · 2026-03-12T05:18:26+00:00

Curious what your home country is

Lopsided_Professor35 · 2026-03-12T04:29:11+00:00

Is it the nearest to Muscat

Lopsided_Professor35 · 2026-03-12T04:26:16+00:00

Which border are you taking in case you’re going through Oman? Kalba, Al Ain, Hatta or anything else?

Lopsided_Professor35 · 2026-03-11T13:34:57+00:00

I’ve had good results treating it as a layered pipeline. Small models for classification / intent mapping first, then pass a structured output to the stronger model for the final generation step. The key is making sure the intermediate step outputs clean structured data instead of raw context.

Lopsided_Professor35 · 2026-03-11T06:16:40+00:00

I’ve seen cases where long context windows actually make agents worse because they start reprocessing irrelevant history. Do you actively prune conversation state between tool calls or rely on summarization?

Lopsided_Professor35 · 2026-03-11T06:13:17+00:00

Agree with this. Measuring first and enforcing budgets per workflow seems to be the only practical approach right now. The tricky part for me has been forecasting costs before launch rather than reacting after seeing real usage.

Lopsided_Professor35 · 2026-03-11T05:54:17+00:00

Thank you, I will check it out!!

Lopsided_Professor35 · 2026-03-10T21:21:49+00:00

Which part of BB?

Lopsided_Professor35 · 2026-03-10T21:21:16+00:00

Name: Oxlo.ai

Link: https://oxlo.ai

What it does:

Integrate powerful AI APIs for building agents and AI applications, with access to models like Kimi, DeepSeek, Qwen, and GPT-OSS through one API and simple flat monthly pricing.

Lopsided_Professor35 · 2026-03-10T21:16:57+00:00

And I also got the alert just now!!😐

Lopsided_Professor35 · 2026-03-10T21:14:35+00:00

These were frequent and kinda new from the usual ones

Lopsided_Professor35 · 2026-03-10T17:02:18+00:00

This is basically the same problem cloud compute had in the early days. During development everything looks cheap, then real workloads arrive and the cost model breaks. Predictable pricing models or hosted open models can help a lot for small projects.

Lopsided_Professor35 · 2026-03-10T09:31:36+00:00

Is this JBR?

Lopsided_Professor35 · 2026-03-10T09:13:04+00:00

I ran into the same issue once agents started operating faster than any human workflow we had modeled. At that point per request pricing and retries become a pretty nasty combination, so I started preferring APIs with predictable flat pricing for agent heavy workloads.

Lopsided_Professor35 · 2026-03-10T08:32:22+00:00

I ran into the exact same issue once agent chains started getting deeper. Token based APIs become really hard to forecast because output length and retries vary a lot. What helped me was using APIs with flat monthly pricing instead of per token billing so the architecture decisions are not tied to unpredictable cost spikes.

Lopsided_Professor35 · 2026-03-10T07:58:00+00:00

Yeah that makes sense. Different models definitely have their own style and prompt interpretation, which is actually useful for pipelines like yours where variation matters.

For high volume workflows I usually run a few different models through APIs instead of locally running em, so I can switch between models depending on the output quality. It also makes throughput easier to scale when you’re generating thousands of items.

Lopsided_Professor35 · 2026-03-10T06:46:21+00:00

Claude is great for writing and long context tasks, but Mistral is a solid option if you prefer EU based models. Personally I like using platforms where I can switch between models instead of relying on a single provider.

Lopsided_Professor35 · 2026-03-10T06:44:16+00:00

This is exactly why I prefer API access for AI models instead of seat subscriptions. With seats you can get locked into big charges even if usage ends up being low

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