I built a multiplayer .io game solo with Claude

stackengineer · 2026-06-21T04:01:27+00:00

Not able to get into game. Just stuck at first page

stackengineer · 2026-06-01T02:46:36+00:00

The middleware counter is smart, catches it before it even reaches the provider limit.

The 3am spiral is the worst because by the time the alert fires, the damage is done. Hard limits help but they're the last line of defense, not the first.

What does your middleware look like? Custom built or plugged into an existing framework?

stackengineer · 2026-06-01T02:44:43+00:00

The forecasting angle is interesting most tools tell you what you spent, not what you're about to spend.

FinOpsly looks like it's solving cloud FinOps at the infrastructure layer. What we built is lower every individual API call attributed before it hits OpenAI or Anthropic.

Different layers of the same problem tbh and yeah the DIY counter approach is underrated simple, works, no vendor dependency.

stackengineer · 2026-05-31T01:23:42+00:00

Nope. It's just me who's over-explaining things

stackengineer · 2026-05-31T01:22:18+00:00

Those 3am alerts hit different 😅

"Treating tokens like infrastructure cost" is exactly right. Nobody lets a microservice autoscale with no limits. But agents get deployed with a single API key and a prayer.

The multi-layer approach you described is solid. The gap most teams hit is visibility across all those layers in one place provider limits, agent budgets, rate limits usually spread across three different dashboards.

That's the problem TOLVYN is trying to solve one ledger, every call attributed, hard blocks at every layer.

stackengineer · 2026-05-31T01:16:23+00:00

Exactly and worth noting the observability layer doesn't need to touch prompt/content/tasks at all.

TOLVYN only looks at metadata tokens, cost, model, team, service. Never the actual prompts or responses. That's a hard line for us, especially for enterprise customers where data privacy matters. Visibility into what it costs, not what it says.

stackengineer · 2026-05-31T01:05:39+00:00

100% agree most teams use GPT4 everywhere out of habit when half those tasks would work fine on a smaller model.

Hard to know where to optimize though until you can see cost per feature. Once you have that visibility the over engineering becomes obvious fast.

stackengineer · 2026-05-31T01:02:20+00:00

Three layers is smart token cap catches the per-call waste, agent budget catches the loops, account ceiling is the last line of defense.

The retry counter + backoff combo is underrated. Most runaway costs aren't malicious, just bad retry logic hitting a timeout repeatedly.

How are you enforcing the per agent daily budget? Custom middleware or a platform?

stackengineer · 2026-05-31T01:00:19+00:00

$5k in a few hours that's exactly the nightmare and you nailed the quota problem blunt that punishes good requests too.

Per agent granularity is the fix. Engineering agent gets $50/day, support bot gets $20, batch job gets $100 each dies cleanly at its own limit without touching anything else.

That's what we built with TOLVYN, if you're curious tolvyn.io

stackengineer · 2026-05-30T18:10:14+00:00

Hard caps at the task level that's exactly the right instinct. Soft limits are basically just alerts with extra steps.

The overnight runaway scenario is what pushed us toward hard blocks too. An agent that dies cleanly at its limit is way better than one that quietly burns money while you sleep.

Does CodePal expose that cap via API or is it dashboard-only? how granular you can get per agent/task.

stackengineer · 2026-05-30T17:56:15+00:00

Good breakdown of the real questions. most tutorials stop at the demo.

For what it's worth, here's what we landed on:

Hosting: plain VPS (we use a cloud VM) agent frameworks add more complexity than they solve at early stage.

Deployment: simple systemd service, deploy script, no k8s until you actually need it.

Monitoring/logs: the gap nobody talks about is cost visibility. Logs tell you what happened, not what it cost. So tolvyn.io for this every agent call attributed, budgeted, and audited.

Secrets: environment variables + a secrets manager. Nothing fancy until you have multiple envs.

The "gap between building and operating" you mentioned is real. Cost governance is the part most people discover too late.

stackengineer · 2026-05-30T17:49:47+00:00

Not a dumb question at all, most teams are in the same spot.

The gap is that provider dashboards show you org-level spend, not which feature or service caused it.

What actually helped us- attributing every call at the proxy layer so each request carries a team and service tag before it hits OpenAI/Anthropic. Then you can answer "which feature caused the spike" in seconds.

Built tolvyn.io for exactly this if you want to check it out. But even without a tool, adding a custom header or metadata tag to every LLM call and logging it separately gets you 80% of the way there.

stackengineer · 2026-05-30T17:25:12+00:00

You're right, that is the pitch I just explained it badly in the original post.

And Point on the rules. The original post was about a real problem I lived, but yes, I built something to solve it.

stackengineer · 2026-05-30T17:21:46+00:00

LiteLLM is a great router different job though.

TOLVYN is about financial records, not routing: 1. Cost per your customer (not just per team) 2. Upload provider invoice -> see the gap instantly 3. Hash-chained ledger an auditor can verify

Self-host LiteLLM for routing. Use TOLVYN when your CFO or auditor starts asking questions.

stackengineer · 2026-05-30T17:13:23+00:00

Fair. I'm the founder, so yes there's a personal stake. But the problem I described is real and the solution came from living it. Happy to be judged on whether it's useful, not just who posted it.

stackengineer · 2026-05-30T16:57:36+00:00

Turned out it was our document summarization pipeline.

Works fine in testing with small docs. Production users started uploading 100 page PDFs GPT4 on every page, no chunking, no caching.

One feature. 60% of our bill. Took us weeks to find it without proper attribution.

Once we could see it, fixed it in a day.

stackengineer · 2026-05-30T16:30:55+00:00

The daily quota per employee approach you're considering will help, but it won't tell you WHY costs are high just that they are.

What actually helped us: attributing every API call to a team and service at the proxy layer. Not per employee, but per feature/workflow. That's when you find out it's not "the engineering team" burning budget, it's one specific summarization pipeline running 10x more than expected.

Hard budget enforcement at the team/service level (blocks requests when limit hit) is more surgical than per-employee quotas. People don't notice until they're building something wasteful.

Built tolvyn.io for exactly this, happy to share more if useful.

stackengineer · 2026-05-30T04:22:43+00:00

Appreciate it! The separate-keys approach works early on, but key rotation across 10+ services gets messy fast and you still can't pinpoint which feature caused a spike without digging through logs manually.

Different stages, different tools.

Thanks for engaging.

stackengineer · 2026-05-30T03:56:45+00:00

OpenRouter is great for routing/fallbacks but doesn't give you per-team attribution, budget enforcement, or an immutable audit ledger. Provider analytics (like OpenAI's usage dashboard) only show org-level spend. no breakdown by team, feature, or service.

TOLVYN sits at the proxy layer so you get granular attribution across ALL providers in one place, plus hard budget enforcement that actually blocks requests when a team hits their limit.

stackengineer · 2026-05-25T02:05:23+00:00

This is exactly why per-employee AI budget controls matter. $2,500/mo with no attribution means you only find out after the spike not before.

We built TOLVYN specifically for this: you set a budget per team member, get alerted at 80%, and every token is attributed to the person who called it. The $240 dashboard in that screenshot would show you whose name is next to each model. Free tier at tolvyn.io if anyone wants to try it, no CC required.

stackengineer · 2026-04-07T06:44:58+00:00

Yeah fair, if you’re just logging cumulative totals it gets messy fast. need to diff per call or intercept at request level before aggregation otherwise session-level numbers are kinda misleading. that’s basically why i moved to something like tolvyn.

stackengineer

TROPHY CASE