TheFoundry – Easy bootstrapping framework for MultiAgent Systems

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For months, I struggled to build complex, long-running projects using AI agents and I kept failing... One shots, refactoring, high token consume... After several refinements, I built a framework that is actually working for me and I believe is easy for "humans". It follows Enterprise engineering principles to AI, and now my productive agentic sessions are lasting ~25% longer before depleting my tokens. The architecture relies on 3 rules: - Pull-Based Workflow: Agents read a Markdown Kanban board (team_status.md) and pull tickets instead of being pushed tasks. - Anti-Loop Budgets: Agents have a hard stop after 5 failed iterations to prevent token exhaustion. - TOML for A2A: Agent-to-agent requests use TOML to eliminate hallucinated JSON syntax errors. I know agentic bootstrapping can be complex for non-technicals, so I designed this for zero-friction setup. You don't configure anything manually, ust paste this into your favorite IDE (Cursor, Claude, Antigravity): "Deploy the bootstrap of the project from here https://github.com/aavilagallego/TheFoundry and let's start working." An ephemeral @bootstrapper agent will autonomously clone the repo, hydrate the global constitution (AGENTS.md), and then permanently delete its own source code to keep your repo clean. I’ll keep evolving the rules, but this system is yielding great results today. Happy to help and I’d love to hear your thoughts to improve the system!

Visit product View on Hacker NewsMay 29, 2026

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

96%96% 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.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% 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: month · Missing: mobile apps, ios, personal

32%32% 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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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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