Platform to run agents on scale, first stable release

Hacker News

ExosphereHost 0.0.1, first stable release A few months ago this was a sketch. Today we shipped 0.0.1. The goal is simple. Make it easy for developers to build reliable, planet scale agent workflows without reinventing infra. Highlights: • Write agents in clean Python • Let execution branch at runtime with dynamic DAGs • Track every step with a durable state manager • Use built in retries, parallelism, fanout, and joins • Visualize the execution tree • Push the same graph from dev to production If you are exploring agents that plan, adapt, and complete work across huge datasets, I think you will like this release. I would love if you try it. DMs open for feedback and early design partners. As an example, we built WhatPeopleWant, an open source agent that analyzes Hacker News to surface validated problems for hackers. Built for devs by devs Some quick links: 1 - Repo: https://github.com/exospherehost/exospherehost 2 - Docs: https://docs.exosphere.host/ 3 - Example Repository: https://github.com/NiveditJain/WhatPeopleWant

Visit product View on Hacker NewsAug 26, 2025

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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