Forcefield: A fast, lightweight local-first AI agent harness

Hacker News

I spent the last 3 months building my own AI agent harness, fully written in Go. It's called Forcefield, and I primarily built it because I had trouble using local AI models with agent harnesses like Claude Code. I found the configuration needed to get local models working frustrating, and I wanted something simpler. The main thing I've been optimizing for is the runtime itself. It needs to have low overhead, fast startup, and a simple enough system that users can run it locally without an account or telemetry. I'd be interested in feedback from people who use local AI coding tools. If you try Forcefield and find any bugs, please open an issue on GitHub. I'm also interested in feedback on the architecture and overall UX.

Visit product View on Hacker NewsSep 18, 2026

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Launch Intel predictions

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

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.

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

60%60% 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: io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

21%21% 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.

Correct prediction on native model

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