Zest – AI Agents for Slack

Hacker News

Hi HN, A few months ago, I started experimenting with building AI agents as Slack bots. What began as a fun weekend project has now evolved into a core part of how our team works every day. These bots live in Slack and handle real, non-trivial workflows such as: - When a new signup comes in, one of them enriches the lead via Apollo, drafts a personalized email, and drops it into my mailbox. - If someone mentions a new task in any channel, the bot jumps in, asks a few clarifying questions, and files a Linear ticket. - Collecting customer info from Stripe for transaction failures and bringing it into the slack thread automatically. - Scouring the web and drafting blog posts - sure you can do this with ChatGPT. But doing it collaboratively on Slack gives a different overall experience. - We even made them proactive. For example, if someone talks about sprint planning, the bot jumps into the thread and asks if it should help break things down into tasks. At first, this felt like "just automation." But what surprised us was how collaborative it became. Since the agents live in public Slack threads, teammates started observing how others interacted with them — how people phrased prompts, tagged bots, corrected outputs. It turned into a sort of lightweight, social learning system. Everyone got better at prompting and thinking in “agentic workflows” just by being part of the conversation. We also built a lightweight dashboard to help anyone on the team spin up and deploy new bots, manage access, customize behavior, and attach tools. Demo: Here’s a short video of one of our bots in action — it self-corrects, navigates tools like Linear and internal APIs, and finishes the task end-to-end. Stack: - Vercel AI SDK - axllm.dev(DSPy for typescript) for the agentic loop - Composio for tool integrations We are now running a closed beta for anyone interested in deploying Slack bots as agents. If interested, join our waitlist - https://heyzest.ai/

Visit product View on Hacker NewsJul 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video, month · Missing: mobile apps, ios, entrepreneurs

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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