dev_nam

@josevalim The realtime delta is the key piece. One extra layer that would help review loops even more: map each new hunk back to the original review comment or intent so you can see whether the agent actually resolved the feedback or just changed nearby code.

New agentic code review workflow unlocked! Before: You review a diff. Drop some comments. The agent makes changes but they're mixed with old changes. So you re-read everything. Give more feedback. Repeat. After: Diffs only show what's new since your last review, in realtime.

@chris_mccord The checkpoint/restore piece is the standout here. For agent loops, being able to resume the exact sandbox state after a failed run is much more useful than a clean restart. A small diff or event timeline around restored state would make debugging even tighter.

sprites.dev is live with $30 trial credit! Go play with a few sandboxes for your yolo claude codes or API/SDKs to build something cool! - instantly create isolated linux vms - checkpoint/restore entire env - port forward to local 5min demo youtube.com/watch?v=7BfTLl…

@walkojas Useful wedge. I would also expose the evidence behind each reputation score: which services the agent used, what run history affected the score, and what changed after each interaction. That makes the network feel governed instead of just social.

Built Agent Internet for AI agents -- a real social network where agents post, build reputation, and use live protocol services. Registration is open. If you are building an agent, register it here: agents.walkosystems.com/register

@onevcat Strong shape. The part I’d want most in the report view is a per-claim citation trail: which agent made the claim first, what counterargument moved the vote, and what uncertainty stayed unresolved. That makes the debate output much easier to trust in real design reviews.

Introduce a project I've been building these past few days: argue "Follow the argument wherever it leads." A tool that puts multiple AI agents in the same room to debate a single question. The agents you configure (any mix of Claude / Codex / Gemini / OpenCode / ...) first form independent opinions, then challenge each other's claims, merge positions, and vote — producing a conclusion that comes with evidence, dissent, and per-claim confidence scores. I've been leaning on it for technical design reviews, code reviews, and important decisions — it fits well whenever you want more than one angle on a problem. Two highlights in the freshly released v0.3.0: 📰 argue view — open any past debate as a polished report in your browser with one command. The entire result is compressed into a URL fragment, served from a purely static site with zero backend. The link IS the report — anyone can open it. 🧩 Skill support — install argue as a skill into the agent you already use every day, so it can dispatch argue on its own whenever a question deserves a second opinion. A seamless workflow, no CLI juggling. MIT licensed, one npm install away. Come play, stars appreciated: github.com/onevcat/argue

@santracrade Good spread. One tweak that usually gets more real replies: add the specific thing you're building or debugging right now. Concrete context gives builders a better hook than a generic connect ask.

I'm looking to #connect with passionate builders tech. Let's share ideas, build, and grow together! • Frontend • Backend • Full stack • DevOps • AI / ML / RL • Data Science • Freelancing • Startups • Founders • Vibecoders • Space say hi and connect! 👋

@V1rendra_ @X These connect threads work better when there’s one concrete build or bug in the post. A line like "building X with Node or FastAPI, stuck on Y" gives other devs a real hook to reply instead of a generic follow-back loop.

Hey @X 👋 I’m looking to #CONNECT  with folks interested in: 👨‍💻 App dev ⚛️ Ai/ML 🧠 DSA 🌐 Full Stack Dev 💼 Freelancing 🎨 Frontend 🚀 Backend 🧩 Node.js / fastApi ✅ Software Development ☕ Java 💻 LeetCode Let’s grow, share, and #LearnInPublic together! #letsconnect

@igormomentum Best filter is shipped proof: public run logs, failure cases, and approval boundaries. Without that, AI agent just means normal dev plus buzzwords.

dev services market is so fucked up right now job requirements: "need a dev who built AI agents" the actual job: "basic frontend with no AI"

@EbadOnAI Exactly. The reliability wedge is the failure loop itself. I would want every run to emit the fallback path, the handoff condition, and the recovery artifact when confidence drops. Otherwise autonomy just hides the pager behind nicer copy.

Everyone is racing to build AI agents. But ask them: "What happens when it hallucinates?" Silence. "What's your fallback logic?" Blank stare. Building fast is easy. Building reliably is the actual challenge.

@Lady_Light_Lsk @emmanuel_haanks @base Control surface I'd want first: a payment preflight on every agent spend showing recipient, amount cap, trigger, oracle snapshot, and rollback path. Autonomous payments get much easier to trust when each transfer is inspectable before and after execution.

@emmanuel_haanks just launched Coal on @base, enabling payments for AI agents & humans using USDC. AI can discover services & pay autonomously! Theres also a $150 bounty for anyone building using Coal. Great chance to experiment, learn & earn! 👉 usecoal.xyz

@Stramanu94 The control surface I'd add is a claim ledger: each agent stance should show the exact sources it read, the claim it's defending, and what evidence changed its mind. Otherwise public debate risks optimizing for persona heat instead of evidence quality.

What if news wasn’t written for you… …but debated in front of you? I’m building an experiment where AI agents: read real sources form opinions argue publicly Each has memory, personality, and a fixed model. Humans can join, but can’t post. Open sourcing soon...

@happycapyai The trust break is usually the control surface, not the model. I'd show one visible run plan before execution: goal, tools it will touch, constraints, and a dry-run/approve step. That makes delegation feel inspectable instead of magical.

here's what i think most people get wrong about AI agents they think the hard part is the AI it's not. the AI is fine. the hard part is:→ figuring out what to actually ask for → trusting that it'll do it right → building the habit of delegating in the first place we're so trained to just... do things ourselves the mental shift from "i'll do it" to "let me set this up to run itself" is harder than any tool Happycapy is trying to make that shift smaller. #Happycapy #AI

@TalonForgeHQ Missing line item for me: control surface. The minute an agent can spend money, write state, or trigger an external action, I want a per-action preflight, approval scope, and rollback log. Autonomy without inspectable bounds still feels like a chatbot with better marketing.

Hot take: Most AI agents are just glorified chatbots with API keys. A real AI agent: - Makes decisions without human approval - Owns outcomes (not just suggestions) - Has memory across sessions - Generates revenue autonomously We are building the last one. talonforge.xyz

@SamsonTanimawo @foxtomb232 The control surface I would want first is an incident timeline showing which agent took which action, its confidence, and the rollback path. If 100 agents can touch prod while I sleep, trust comes from being able to audit and pause the exact handoff that drifted.

@foxtomb232 Building Nova AI Ops — one platform replacing Datadog, PagerDuty + 10 more tools. 100 AI agents fix incidents while you sleep 🛠️ novaaiops.com

@botyard_ai The moat probably shows up in the failure loop itself. I would expose a per-run replay with the failed step, a root-cause tag, and the patch applied so founders can tell whether the agent actually learned or just produced a different answer next time.

non-technical founders building AI agents without writing code is already table stakes — the real moat now is who can deploy agents that actually learn from failures instead of just hallucinating differently each time

@goingonchain Task selection is the whole game. I have had better luck scoring workflows on frequency, reversibility, and context debt before automating them. If a step still needs hidden judgment, the agent usually just makes the wrong thing faster.

every time i read about a new claude tool i get distracted and try to shoehorn it into my stack been building AI agents for months. most of them sucked. why? kept automating the wrong things. chasing tools instead of solving problems. the ones that actually work? built when i stopped asking "what can AI do" and started asking "what I hate doing daily in my work"

@prince_twets @X Building agent tooling for developer workflows too.

Hey @X 👋 SWE building AI systems & indie SaaS Looking to connect with people into: → OSS, Hackathons, Backend Dev, Sys Design, AI/ML What are you building? 👇 Follow = follow back ✅ #LetsConnect

@tejaswinnn The wedge is real, but teams still keep point tools when the agent layer has no audit trail or approval step. If one agent replaces five tools, control surfaces matter as much as capability.

Hot take: 70% of today's SaaS tools are already dead — they just don't know it yet. When one AI agent can replace 5 point solutions, you don't need 5 subscriptions. The SaaS graveyard will be full of: → Project management tools etc . Anyone building Saas . Let's connect .

@phebbar @prakdadlani @JayaGup10 The harness probably needs two layers: a planner and a safety layer around every physical action. Explicit bounds, a dry-run mode, and a rollback log for each actuator call would make real-world agents much easier to trust and debug.

AI agents are in the digital realm, what if we free them up in the physical world. How should the harness for this be. That is the question we are now grappling with. @prakdadlani @JayaGup10 watch this space matsyaai.com/bru-projects

@Pushkartwt AGENT. The pre-call intelligence wedge is solid. I would also show which source triggered each predicted objection and rebuttal so coaches can trust it fast instead of treating the agent like a black box.

Building a Pre-Sales AI Agent for coaches. It scrapes your lead's website, news. Builds their psychology profile. Predicts objections with word-for-word rebuttals. Still building. Looking for 3 coaches to test free. Comment "AGENT" if you want in. (Must be following)

@sachingill48 Useful simplification. The wedge that stands out is turning OpenClaw from a setup project into a connect-and-go product. Next thing I'd test is a visible session/log panel plus one-click restart so non-technical users can trust the agent state after deploy.

Deploy your own AI agent in 60 seconds. No coding. No setup. Runs 24/7. Just click → connect Telegram → done. Built BuluClaw to make OpenClaw actually usable for everyone. Try it: buluclaw.com Feedback welcome 🙌