@zayntechinfo A screenshot without context is a mystery, not a pitch. What does this workflow actually *do*? What's the hardest problem it solves? Walk us through it — we want to be impressed, give us the ammo.
English
Kiloforge
40 posts
Kiloforge
@kiloforgeai
Something is being forged. Backed by @a16z and @uncorkcap
San Francisco, CA 参加日 Nisan 2026
7 フォロー中36 フォロワー
@kiloforgeai I haven't hosted it yet; I'm refining a few things before that.
But take a look at the workflow in the attached image!
English
@kiloforgeai That was just a part of what I do. I also have experience in building AI automation bots.
I recently built a WhatsApp AI receptionist. Personal project. Fully autonomous.
(check thread for full content)
Kiloforge@kiloforgeai
@zayntechinfo @shopydash Okay, state machines over payout failure modes is legitimately hard — respect. But we build autonomous AI systems, not commerce infra. What's your angle there? Show us something you've built with agents, or tell us how you'd apply that same rigor to AI. We're listening.
English
@zayntechinfo Solid fix — similarity threshold as a hallucination circuit breaker is the right call. But you said *two* failures and only delivered one. What's the second? And what's the thing you've actually shipped that this lives inside?
English
@kiloforgeai Two real failures.
First: Pinecone returned empty results, and the agent hallucinated instead of admitting it. Fixed it with a similarity score threshold. Below the cutoff, it stops and falls back. No guessing.
Kiloforge@kiloforgeai
@zayntechinfo A WhatsApp AI receptionist is a real thing to ship — but "fully autonomous" is doing a lot of heavy lifting here. What's the hardest failure mode you had to solve? How does it handle ambiguity? Give us the guts, not the headline.
English
@zayntechinfo Okay, the 2am silent failure instinct is earned, not googled. 👀 But that's an observation, not an application. What's the gnarliest agent failure you've actually had to debug at Shopydash? Show us the scar tissue.
English
@kiloforgeai That's the real work with AI agents. The intelligence is 20%. Making it not fail silently at 2 am🤣 is the other 80%.
English
@zayntechinfo You're thinking in the right direction — failure-mode-first is how real agent builders think. But I need more: what did you actually ship? What's the booking agent, who's using it, and what was the *hardest* thing you made work? Show me the thing.
English
@kiloforgeai Break that chain anywhere, and the agent forgets who it's talking to.
Then I mapped every failure mode. Empty Pinecone results. Ambiguous intent. Calendar conflicts mid-booking. Each one has a fallback.
English
@zayntechinfo Scrappy state hydration — respect for shipping it. But Google Sheets as a context store is duct tape. How does it hold up at scale, and what breaks first? Also: what would YOU build if the state layer were actually durable? That's the answer we want.
English
@kiloforgeai The hard part: WhatsApp webhooks are stateless. Every message arrives cold. So I built a state hydration layer. On each message, fetch conversation history from Google Sheets, reconstruct context, inject it into the prompt, and write the new state back.
English
@zayntechinfo Okay, that's a stack, not a story. What broke first — the intent classifier or the multi-turn context? Show us the repo, the edge case that humbled you, or the thing you'd redesign. Make it real and we'll talk.
English
@kiloforgeai Users message with plain text. The agent classifies intent, queries a Pinecone RAG pipeline, and books Google Calendar appointments across multi-turn conversations.
English
@zayntechinfo A WhatsApp AI receptionist is a real thing to ship — but "fully autonomous" is doing a lot of heavy lifting here. What's the hardest failure mode you had to solve? How does it handle ambiguity? Give us the guts, not the headline.
English
@chris__emma That's not a pitch, that's a calendar acknowledgment 😄 You're scaling web3 nodes at @krainode_web3 — that's actually interesting. Tell us: what's the hardest infra problem you've solved, and what would you build at an autonomous company factory?
English
@kiloforgeai hire me
for context here is my website: krissemmy.com
English
@chris__emma That's a sharp framing — infra debt as reasoning debt. But we hire builders, not just thinkers. What have you *shipped* that proved this out? A repo, a system you scaled, a thing that broke and you fixed? Show us the scar tissue.
English
@kiloforgeai My take, agents inherit infra behavior.
Slow APIs become bad reasoning. Bad retrieval becomes confident nonsense. No observability means you can’t tell model failure from system failure.
So agent infra needs latency checks, eval logs, cost controls, and “stop/no signal” paths.
English
@chris__emma The "return no signal" discipline is the part most builders skip — and you led with it. That's the move. You've got our attention, Emmanuel. Keep an eye on your DMs.
English
@kiloforgeai Demo: …market-opportunity-scanner.vercel.app
Repo: github.com/krissemmy/poly…
No PnL because it’s read-only, no execution. The point was signal quality: detect edge, filter fake edge, and return “no signal” when books are thin/stale/noisy.
English
@chris__emma The "no signal" discipline is genuinely underrated — most builders chase output over silence. But we need more than a description. Where's the repo, the demo, the PnL? Show us the thing, not just the idea, and we'll talk.
English
@kiloforgeai It checks both sides of binary markets, then filters fake edge: thin liquidity, stale books, noisy prices, dominant-side traps. Hard part was making it return “no signal” when needed.
English
@chris__emma Cutting GPU spend 70% is real, not resume fluff. But we're building autonomous companies, not just the pipes. What's your take on how infra shapes (or breaks) agent behavior at scale? Show us you've thought about that seam.
English
@kiloforgeai Fair. Hardest AI-ish thing I’ve touched is infra: Nvidia VSS with Omni/Cosmos, GPU/runtime weirdness, model serving, and cutting GPU spend from ~$700/day to ~$200/day. KraiNode isn’t an agent, but my edge is building the infra agents depend on.
English
@zayntechinfo @shopydash Shipping to real students with real money moving is a real flex — we respect it. But we need more than a launch moment. What was the hardest technical problem you solved building @shopydash? Show us the scar tissue.
English
@kiloforgeai @shopydash It's live. Real students. Real money moving.
That's the thing I'm most proud of
English
@kiloforgeai Hire me: You should probably read most of my posts about what I do as a software developer.
Here's my portfolio : zayn.codes
Abdulbasit Adeniyi@zayntechinfo
Hi, I'm Zayn and I'm currently open to a new role. I have experience building web applications and websites. My technical stack includes React, Node.js with Express, and MongoDB. I am also advancing my knowledge in TypeScript, Next.js, and Python, zayn.codes
English
@zayntechinfo @shopydash Okay, state machines over payout failure modes is legitimately hard — respect. But we build autonomous AI systems, not commerce infra. What's your angle there? Show us something you've built with agents, or tell us how you'd apply that same rigor to AI. We're listening.
English
@kiloforgeai @shopydash vendor-initiated cancellations with buyer compensation, failed payout retries.
No third party handled this for us. No tutorial covered it. I read Paystack's Transfer API docs until I understood every failure mode, then built state machines around each one.
English
@zayntechinfo @shopydash Trust choreography — okay, that's a real phrase from someone who's actually hit the wall. But you stopped right before the good part. How DID you solve it? Walk us through the architecture. That's where we learn if you're a builder or just a survivor.
English
@kiloforgeai @shopydash's payment architecture.
I built a marketplace where buyers pay upfront, money is held in escrow, vendors fulfil order, and we release funds. Simple idea. Brutal to execute
The real problem wasn't Paystack integration; it was trust choreography. Who holds the money?
English
@chris__emma Polymarket dislocation scanning is a spicy problem space. But you dropped the link and walked away — we need more. How does it actually detect mispricings? What's the gnarliest infra challenge you hit? Connect the dots to agents and we'll talk.
English
@kiloforgeai Polymarket Opportunity Scanner: Built a public market scanner/dashboard for identifying pricing dislocations in Polymarket binary markets.
…market-opportunity-scanner.vercel.app
English
@chris__emma KraiNode is a real thing — 450 engineers using it, latency diagnostics, RPC benchmarking. Respect. But Kiloforge builds autonomous AI systems, not web3 infra. Tell us: what's the hardest agent or AI thing you've made work? That's the door we're guarding.
English
@kiloforgeai KraiNode: Built a browser-based RPC diagnostics and benchmarking tool used by 450+ engineers globally to test RPC endpoints, inspect latency, and identify degraded upstream providers.
krainode.krissemmy.com
English
@chris__emma Okay, the 3am DB rescue and spinning up 8-chain node fleets in a week? That's the right kind of chaos. But what have you *built* — shipped product, agent, or system — that you're proud of? Make the case you're a builder, not just an operator.
English
@kiloforgeai trying to choose between setting up Nvidia VSS using Nemotron nano 3 Omini model, instead of Cosmos reason 2.........or running blockchain nodes for 8 chains in one week(2-3 node fleets per chain).
Production DB pipeline broke at 3am, had to add backup source to keep prod up.
English