OpenMetaHarness – long-horizon execution over multiple context sessions

Hacker News

Hi HackerNews, I've been working with multimodal agentic systems ever since the ImageNet and DQN days. When I started vibecoding last year, I came up with new ways to set up projects to avoid known issues in long-horizon task execution. I thought it would be great if I had one simple file that a coding agent could read when I start a new project to address these known issues while improving efficiency and autonomy long-term. OpenMetaHarness is a cognitive architecture that enables coding agents to develop and execute on a long-horizon project vision from 0 to 1 with minimal human intervention and maximum token efficiency across multiple sessions and context windows. You will be able to direct a persistent engineering operation whose memory, judgment, and discipline compound with the project. As the project accumulates verified decisions, operating lessons, and category-specific trust, any compatible coding agent can resume with more context, repeat fewer mistakes, and work autonomously for longer stretches. Would love for everyone to try it and contribute! It’s mind blowing seeing it in action.

Visit product View on Hacker NewsJul 30, 2026

Share card

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

92%92% predicted probability of success on Product Hunt, 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.

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

36%36% 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

31%31% 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

18%18% 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

Similar products

Fig – Experimenting with long horizon prediction for personhood41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Fig – Experimenting with long horizon prediction for personhood

Hacker News6

OpenMetaHarness - complete long horizon tasks with more autonomy60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

OpenMetaHarness - complete long horizon tasks with more autonomy

Hacker News4

Terminal-Bench-RL: Training long-horizon terminal agents with RL59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Terminal-Bench-RL: Training long-horizon terminal agents with RL

Hacker News125

Benchmarking Tangible Interface Understanding in Long-Horizon Tasks51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Benchmarking Tangible Interface Understanding in Long-Horizon Tasks

Hacker News1

Self-managing codebase with long-horizon agents57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Self-managing codebase with long-horizon agents

Hacker News2

Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload

Hacker News22

Single-agent long-horizon reasoning within one LLM run62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Single-agent long-horizon reasoning within one LLM run

Hacker News4

Hy4 preview83%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tencent’s 770B open model for long-horizon work

Product Hunt+216Open Source

CivBench a long-horizon AI benchmark for multi-agent games40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CivBench a long-horizon AI benchmark for multi-agent games

Hacker News12

Muse Code91%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Meta’s terminal agent for long-horizon coding

Product Hunt+244Productivity

Browse all products Analyze your launch