Hiren Donda

AI coding agents often lose context. I built Debug-Ledger to fix this. Instead of letting AI guess, you set hardcore constraints it must respect. Total control for developers. github.com/hi0001234d/deb… #AI #ParseOS #BuildInPublic

@clawdb0t Connectors are just the plumbing; the real engineering challenge is building the sandboxing, permission gates, and rollback layers around them.

AI founders plugging MCP into business tools need to slow down. NSA Cyber just released security design considerations for AI-driven automation with MCP. My takeaway: connectors are not the product. The product is scoped tools, sandboxing, logs, human approval, and rollback.

@buildwithubaid Shifting the narrative from pure agent AI hype to actual modular infrastructure is exactly how we scale production-ready systems.

AI Agents are moving from hype to real infrastructure. • MCP (Model Context Protocol) • Single-Agent Architecture • Multi-Agent Systems • Skills & Tool Use • Agentic RAG • Memory Systems

founder loop i couldn't escape this month. wake up in product head. switch to marketing head by 11. by 2pm i'm trying to be product again and feeling 30% slower on whatever i open. the context-switch cost between dev and marketing is bigger than i used to think. days i picked one and stayed in it went better.

small mcp thing that quietly helped me. trimmed each server's tool list to only what the current project actually needs. tools you don't use still consume context tokens and shape the agent's first instinct. fewer tools, sharper sessions. fits the small-community ethos but easy to drift away from.

claude code thing that took me three projects to land on. my first CLAUDE.md was a wall of preferences. by project two it was a list of patterns from my own codebase. by project three it was mostly things to NOT do, harvested from where i'd seen the agent confidently go wrong. the don't list does more for me than the do list.

noticing a thing about myself this week. every time a new model version ships i spend half a day testing it instead of shipping the thing i was actually working on. the upgrade is real but the time tax of evaluating it is bigger than i admit. weeks where i ignore release notes i ship more.

agent design observation after a few months iterating. asking the same agent to grade its own output runs into the same blind spots that produced the bad output. a second agent doing the check, even a smaller cheaper one, catches stuff the original couldn't see. not about model quality. about who's holding which lens at which moment.

the part of solo building nobody warns you about: you become your own worst feedback loop. nobody pushes back on your reasoning so your bad ideas survive longer than they should. the only fix i've found is forcing myself to show half-finished work to one real person every week, even when it's awkward.

web dev thing i keep relearning the hard way: agent-built components work great at 1440px and silently break on iphone safari. responsive discipline lives entirely on me now. the agent doesn't see the small screen unless i force it into context. half my recent bugs are mobile-specific.

vibe coding rule that's becoming non-negotiable for me: spend the first five minutes specifying intent out loud before opening the agent. tight first 5 minutes = clean session. vague first 5 = drift for hours. the entry to the session sets the steering more than i thought it would.

the claude code habit i wish someone had told me about earlier: end each long session with a "what did we change" summary prompt. takes 30 seconds. catches half-edited files i would have missed until git diff caught them 2 hours later. cheap habit, weirdly load-bearing for solo work.

agent-pair lesson from a year that didn't show up in any benchmark: the agent's first answer matters less than its second. i write the prompt, get a draft, push back with one concrete edge case. the second response is where the real quality sits. first response is just the rough sketch.

@ingliguori Good point. Enterprise adoption will probably depend as much on control and observability as on autonomy.

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@AgenticEdgeAI @claudeai Good breakdown. I’d probably add observability and evaluation as well, they become important pretty quickly in real agent systems.

small ml thing that quietly mattered most this year for me: explicit data pipelines over clever one-liners. notebook one-liners are fun to write and impossible to debug six months later when the model starts behaving weird. boring stage-by-stage processing survives production longer than i expect every time.

the part of rag work that quietly mattered most this year for me: chunk quality. spent weeks tuning models, reranking, embeddings. the biggest single quality jump came from rewriting how we chunked source docs. boring to talk about, hard to optimize past. precision in, precision out.

remote-friendly thing AI quietly changed for me: onboarding. new hires used to need a senior dev's afternoon to get the lay of the codebase. now they ask claude code, get a tour, then ask the senior dev sharper questions. shorter check-ins, deeper conversations. better for both ends.

founder thing i underestimated: every shipped feature adds a quiet maintenance tax i didn't price in week one. bug reports, edge cases, support questions. by month four, half my time goes to maintaining stuff i shipped fast, not building new. shipping isn't the cost. carrying is.

weird trend i'm noticing in code review: human reviewers are catching different things than they used to. style and idiom comments dropped a lot. architecture and intent comments went up. ai-generated code is already syntactically clean, so reviewers naturally focus on what only humans can judge. shifted the value of review without anyone deciding to.

three months in with cursor, my use shifted in a way i didn't expect. tab-completion became the daily driver. agent mode i now reach for only when the change touches more than 4-5 files. somewhere along the line i stopped trusting agent mode on focused work. surprised by how that flipped.