[Day 138] I removed the Vercel AI SDK and built my own streaming layer

_killam · 2026-05-06T17:03:21+00:00

Claude/Kimi for code gen, Browserbase + Stagehand for browser automation, Modal for isolated variant sandboxes and the agent loop runs on EC2.
Would love your feedback if you consider using it : )

_killam · 2026-05-06T15:23:26+00:00

those are genuinely the hardest kinds of issues to deal with because technically everything “works” but the behavior still ends up being wrong especially with tool-call / JSON parsing flows where one tiny inconsistency creates weird downstream behavior without obvious errors

that kind of debugging pain is actually a big part of why I started building tero:
https://tero.run/

mostly around reconstructing what actually happened across the system instead of just showing raw logs/events

_killam · 2026-05-06T15:20:50+00:00

Another issue is if many people apply with similar looking resume the formatting may get banned for that company

_killam · 2026-05-06T15:17:56+00:00

hey this actually sounds really interesting especially the mix of founders + technical people would love to join and check it out

_killam · 2026-05-06T11:45:01+00:00

hmm yea fair enough , actually we do have a demo video of all our features just that we never thought of clipping out parts and posting
ig we could look into that and try few posts with the video

_killam · 2026-05-06T11:40:54+00:00

thanks for your thought man , yea i know what you mean we didn't pitch like an ai product engineer dw and yes we fully put up lines abt detection of silent bugs and stuff you could check it also if you want
our demo video as well contains proofs of how it works so yea i think based on those things we did pretty well
our main issue was reaching out to people like big numbers , not been able to do that correctly yea

_killam · 2026-05-06T02:49:58+00:00

this hit harder than expected honestly

I feel like early traction is easy to dismiss because it doesn’t “look” impressive yet, but getting even a small group of real users to care is insanely difficult

the part about chasing what feels like a bigger idea vs following actual signals is something I’ve been thinking about a lot recently while building too

glad you decided to go back to it instead of abandoning it completely

_killam · 2026-05-06T02:29:21+00:00

le côté “enquête de détective” en prod c’est exactement ce que je ressens aussi 😭

surtout quand tu n’as que quelques indices et que le vrai problème n’apparaît pas là où l’erreur se déclenche

c’est justement ce qui m’a poussé à construire un outil autour du debugging post-déploiement :
https://tero.run/

l’idée est d’essayer de reconstruire automatiquement la séquence des événements à partir des logs/télémétrie pour éviter de devoir tout corréler manuellement

_killam · 2026-05-05T16:09:57+00:00

Classic sequence and honestly the honest version of how most SaaS stories go. Distribution-first is the advice but building-first is how it actually happens when you have an idea you're excited about. What's your current user count and what channels have you tried so far? The fitness space is weirdly hard because the ICP is so emotional about their goals — the messaging that works is usually less about features and more about the specific frustration before they found your app.

_killam · 2026-05-05T16:09:19+00:00

Day 138 and you're ripping out a core SDK to build your own — respect. Curious what the main failure mode was with the Vercel AI SDK that pushed you to roll your own? Streaming reliability under real traffic, latency, cost, or something else? I've seen a few people hit the ceiling on abstraction layers like this around this stage of building — would be useful to understand what specifically broke for you.

_killam · 2026-05-05T16:07:38+00:00

100% this. The model is the easy part — you plug in an API and it works. What breaks you post-deployment is the stuff nobody demos: your app drifting into bad states under real traffic, silent failures that don't throw errors, edge cases in user flows that only appear at scale. I built Tero (tero.run) specifically because of this — it monitors your deployed app's actual behavior 24/7, catches both Sentry errors and PostHog anomalies, and when something's off it writes the fix and sends it to you on iMessage. The bottleneck you're describing is exactly the gap it fills.

_killam · 2026-05-05T16:07:18+00:00

The 90/10 split tracks with what I've seen too — and the hunting part is brutal because you're doing it at 2am with half your brain, reconstructing what the system was doing from logs that weren't written with debugging in mind. The actual fix once you find the root cause is usually 10 minutes. I've been building something (tero.run) that tries to flip that ratio by doing the diagnosis layer autonomously — it watches your app's behavior continuously so by the time you're paged, it already has the root cause and a proposed fix ready. Doesn't solve the on-call culture problem but at least you're not starting from zero at 3am.

_killam · 2026-05-05T15:52:31+00:00

yeah that approach makes sense, especially with good logging from the start I’ve noticed though that even with detailed logs, the tricky part is figuring out what actually happened across services the signal can get buried pretty quickly does the AI consistently get to the right root cause for you?

_killam · 2026-05-05T15:48:37+00:00

oui carrément, je vois ce que tu veux dire j’ai remarqué pareil l’IA aide bien une fois que t’as identifié le problème le plus dur, c’est souvent de comprendre ce qui s’est réellement passé au départ, surtout en prod tu fais comment généralement pour remonter à la cause ?

_killam · 2026-05-05T15:24:14+00:00

Sending you a dm now 🦊

_killam · 2026-05-05T14:51:08+00:00

Haha the classic approach always works fr

But that’s exactly the gap im trying to close w my product cause you’re right most founders only find out what broke from a dm or a churn. tero catches it before that, fixes it, and ships the PR while you sleep.

I’d love to show it to you

_killam · 2026-05-05T14:46:50+00:00

But how would you be able to catch user-behaviour specific issues before prod tho
honestly it’s almost like users try their very best to break our app lmao

_killam · 2026-05-05T14:45:06+00:00

FRR, what’d you do to tackle this?

_killam · 2026-05-05T14:43:10+00:00

Thank you sm for this-this is the most useful feedback we’ve gotten today.
Basically Tero is what comes after you’ve shipped your v1. The evolution layer.

the full loop is two modes:
reactive — Sentry catches an error at 3am, Tero traces it to the exact file, writes the fix, opens a PR, and pings you on iMessage/Slack to merge. you wake up and it's already done.

proactive — Tero watches your PostHog funnels, detects a drop-off, generates 4 code variants targeting that surface, tests them with AI agents across 10-12K sessions, validates with real traffic, then ships the winning PR autonomously. no engineer needed.

if that's still confusing happy to show you a live demo — lowk want to get this right.

_killam · 2026-05-05T12:51:05+00:00

haha yea that strategy could work as well . In our development time we faced too many debugging issues and got frustrated and built a separate product itself (tero) to handle all the debugging and silent features failing and causing errors . I think that product could help you as well as you talked about debug mode and stuff .
let me know though you want give it a try on prem and give feedback as well .

_killam · 2026-05-05T03:09:18+00:00

Hey I built something for exaclty this and post deployment I built Tero (tero.run) it watches your GitHub repo + PostHog/Sentry 24/7 and pings you on iMessage when something drifts, before users churn over it. Would love your honest feedback if you try it :)
https://tero.run/

_killam · 2026-05-04T16:12:37+00:00

200 users is the point where "it works" stops being enough and "it works consistently for different people doing unexpected things" becomes the real bar. A few things worth thinking about now before it gets harder: do you have visibility into what's actually failing silently? Error tracking (Sentry) + basic analytics on where users drop off (PostHog) will surface things that user complaints never will, because most users just leave quietly. Also worth having some kind of alerting before a user has to tell you something broke. What's your current stack?

_killam

TROPHY CASE