SlothOps
An open-source engine for autonomous cognitive noise reduction and chore distillation. It runs in the background, compressing disorganized text into executable decision cards and automatically performing actions for low-risk items.
Timeline (last 90 days)
SlothOps positions itself as an "autonomous cognitive noise-reduction and chore-distillation engine": it does not proactively push or conduct conversations, it only runs in the background, ingesting messy text and compressing it into a one-line executable decision card, and directly executing actions for low-risk items.
SlothOps uses Thinking Machines' Tinker SDK to perform LoRA fine-tuning on meta-llama/Llama-3-8B-Instruct with rank 16 and alpha 32, applied to the four projection layers q/v/k/o.
The fine-tuned artifact sloth-distill-v1 outputs strict JSON containing an action enum (AUTO_DECLINE_WITH_SUMMARY, ONE_CLICK_APPROVE, SNOOZE_TO_FRIDAY, AUTO_ARCHIVE_NO_ACTION), a one-line summary and an automatic draft.
This benchmark used synthetic chore scenarios, and synthetic data may come from the same source as the training set, so the 97.8% accuracy should not be directly extrapolated to real calendar invitations or PR descriptions; however, the token savings and latency reductions brought by strict JSON output and the action enum are structural benefits that would usually still hold in real scenarios.
Render deploys two services via render.yaml as infrastructure as code: slothops-web (a FastAPI dashboard and REST API) and slothops-worker (a separate background process that asynchronously polls the chore queue).
Backboard.io provides stateful memory for SlothOps, using memory="Readonly" for semantic recall and memory="Auto" to automatically persist new habits.
The .tech domain provides SlothOps with the entry point slothops.shivysidinfo.tech, automatically uses Let's Encrypt TLS, and the app includes DomainVerifier for real-time monitoring of CNAME resolution and SSL propagation.
SlothOps' target users are engineers and team leads, and the author claims it recovers 5 to 10 hours of focus time per week, but this figure has no independent verification.
SlothOps processed more than 20 simulated tasks during the challenge week, and the author estimates about 6.5 hours were saved, but the sample size is very small and this is a self-reported experience that should not be treated as a product promise.
SlothOps is open-sourced in the satanic47/slothops repository on GitHub under the MIT license.
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All Facts (13)
SlothOps positions itself as an "autonomous cognitive noise-reduction and chore-distillation engine": it does not proactively push or conduct conversations, it only runs in the background, ingesting messy text and compressing it into a one-line executable decision card, and directly executing actions for low-risk items.
50%UnverifiedSlothOps uses Thinking Machines' Tinker SDK to perform LoRA fine-tuning on meta-llama/Llama-3-8B-Instruct with rank 16 and alpha 32, applied to the four projection layers q/v/k/o.
50%UnverifiedThe fine-tuned artifact sloth-distill-v1 outputs strict JSON containing an action enum (AUTO_DECLINE_WITH_SUMMARY, ONE_CLICK_APPROVE, SNOOZE_TO_FRIDAY, AUTO_ARCHIVE_NO_ACTION), a one-line summary and an automatic draft.
50%UnverifiedThis benchmark used synthetic chore scenarios, and synthetic data may come from the same source as the training set, so the 97.8% accuracy should not be directly extrapolated to real calendar invitations or PR descriptions; however, the token savings and latency reductions brought by strict JSON output and the action enum are structural benefits that would usually still hold in real scenarios.
50%UnverifiedRender deploys two services via render.yaml as infrastructure as code: slothops-web (a FastAPI dashboard and REST API) and slothops-worker (a separate background process that asynchronously polls the chore queue).
50%UnverifiedBackboard.io provides stateful memory for SlothOps, using memory="Readonly" for semantic recall and memory="Auto" to automatically persist new habits.
50%UnverifiedThe .tech domain provides SlothOps with the entry point slothops.shivysidinfo.tech, automatically uses Let's Encrypt TLS, and the app includes DomainVerifier for real-time monitoring of CNAME resolution and SSL propagation.
50%UnverifiedSlothOps' target users are engineers and team leads, and the author claims it recovers 5 to 10 hours of focus time per week, but this figure has no independent verification.
50%UnverifiedSlothOps processed more than 20 simulated tasks during the challenge week, and the author estimates about 6.5 hours were saved, but the sample size is very small and this is a self-reported experience that should not be treated as a product promise.
50%UnverifiedSlothOps is open-sourced in the satanic47/slothops repository on GitHub under the MIT license.
50%UnverifiedSlothOps' architectural scaffolding, memory bindings and Render Blueprint were completed by the author paired with Google Gemini.
50%UnverifiedSlothOps' code structure includes training/train_tinker.py (the full fine-tuning pipeline), training/evaluate_model.py (the benchmark suite) and app/core/ (the modular service layer).
50%UnverifiedOne of the core motivations for open-sourcing SlothOps is the data boundary: enterprise chore data (emails, calendar discussions, code diffs, internal memos) is intellectual property, and using open-weight models and the Backboard memory layer can prevent content from being sent into closed-source models for training.
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