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Eforge – An Agentic Build System

Hacker News

Eforge – An Agentic Build System

I've been calling eforge an agentic build system. Traditional build systems transform source code into artifacts - eforge transforms specifications into source code, then verifies its own output. I built it because I was tired of keeping the orchestration logic in my head - spawning a separate session for a blind review, switching back to the implementing session to evaluate results, deciding what to build next. I had plugins for the individual pieces but the sequencing was still on me. I wanted a harness that handled the full loop, and one that was generally useful across projects, not hardcoded to a specific codebase. You give it a specification - a prompt, a plan file, a PRD - and eforge assesses complexity against your codebase, selects a workflow (simple changes get a fast path, complex ones decompose into a dependency graph of sub-plans), builds in isolated git worktrees, reviews, and validates - all without supervision. The way I actually use it: I plan a feature interactively in Claude Code, then run `/eforge:build`. The plugin picks up the plan, enqueues it, and a daemon takes over. eforge pulls plans off the queue, understands dependencies between them, executes in parallel where possible, and merges in topological order. Queued work is re-assessed before execution so changes from earlier builds are accounted for, not blindly applied to a codebase that has moved on. I check the results when builds finish - a web monitor dashboard tracks progress, cost, and token usage in real time. eforge builds itself this way. Each build phase gets its own agent - planner, builder, reviewer, evaluator, fixer. The reviewer runs in a fresh context with no knowledge of how the code was written. Anthropic's engineering team independently arrived at the same pattern ( https://www.anthropic.com/engineering/harness-design-long-ru... ) - their finding: solo agents approve their own work; adversarial evaluation dramatically improves quality. An evaluator then applies per-hunk verdicts, accepting strict improvements while rejecting anything that alters intent.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
98%98% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, builder · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
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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