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A Persistent Task List, Not Just an AI: How a 4B Model Was Fine-Tuned for ADHD

Actually pairs a fine-tuned language model with a persistent task list and personal forecasting. Its reported parser scores come from synthetic examples, not Zee’s real tasks.
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Arqam Waheed’s prototype Actually is built around a practical idea: if a task slips out of view, keep it visible until it is done. It turns free-form brain-dumps into structured tasks, carries unfinished items forward, and uses a person’s past task history to estimate how long work may take. The language model handles parsing; a separate forecasting model handles estimates. Waheed presents it as a planning aid, not a clinical intervention.

Why the persistent list matters more than the model

Waheed began the project for his friend Zee, who described forgetting people and tasks when they were not in front of them, and getting absorbed in one activity. Zee’s words, reproduced by Waheed, were: “can’t remember. if infront remember or else gone from brain. person or task”. That is a personal description, not a diagnosis.

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The initial idea focused on time blindness, but Waheed says the design changed after asking what actually went wrong. The central feature became persistent visibility: parsed tasks stay on a “Still on your plate” list until the user marks them complete. Unfinished tasks can carry over to tomorrow, and similar entries are deduplicated. The author says the app stores them in SQLite.

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That distinction is important. The model may convert a brain-dump into task fields, but the list is what keeps work from disappearing after that moment. As Waheed puts it, the “if it’s not in front of me, it’s gone” framing is a specification for surfacing tasks proactively, rather than simply producing better answers.

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What Actually does with a brain-dump

A user can enter loosely phrased items such as “email sir abt the attendance thing” or “call nani back i keep forgetting.” The parser turns the text into structured tasks. A separate forecasting step then asks, “How long will it actually take you?” using the person’s own completed-task history.

Parsing and forecasting are separate jobs

  • Parser: A fine-tuned Qwen3.5-4B model extracts tasks and related details from free-form text.
  • Duration forecast: TabPFN regression uses task information and personal history to estimate duration. The app reports 10th-, 50th- and 90th-percentile estimates rather than a single number.
  • Same-day estimate: A separate classifier estimates whether the user is likely to complete a task that day.

Reported forecasting features include task kind, the user’s own time estimate, start hour, weekday, whether the task is dreaded, whether it requires leaving home, and whether it has a deadline. The article describes a warning when a task’s 90th-percentile duration is at least half the available free time, intended as a cue that the plan may be too ambitious. These are design choices in a prototype, not validated rules for ADHD or a guarantee that a task will take a particular amount of time.

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What the parser benchmark does—and does not—show

Waheed reports building 450 synthetic brain-dumps, validating 448, then using 400 for training and 48 as a held-out test set. Large open models generated and labeled the examples; Zee’s real brain-dumps were not used. The results therefore show how closely the tested models matched synthetic labels produced by a larger model, not how accurately the app understands a person’s real needs.

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On those 48 held-out examples, run at temperature zero, Waheed reports these results:

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Parser setup Valid JSON Task F1 Kind accuracy Deadline accuracy Median latency
Qwen3.5-4B, zero-shot 98% 95% 78% 69% 3.19 seconds
Qwen3.5-4B, five-shot 96% 93% 82% 55% 3.28 seconds
Qwen3.5-9B, zero-shot 100% 94% 88% 61% 4.53 seconds
Qwen3.5-4B with LoRA fine-tuning 100% 97% 93% 76% 3.28 seconds

These are the author’s measurements on the synthetic test set, not general benchmarks. The fine-tuned model’s 97% task F1 is not the same as getting every detail right: Waheed reports that only 8% of test dumps had every field exactly correct. He says errors were concentrated in subjective fields such as whether a task felt dreaded and what its deadline was. High task detection can coexist with uncertainty about the details that determine a useful plan.

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How the fine-tune was made

Waheed reports fine-tuning Qwen3.5-4B with LoRA at rank 32, for 3 epochs and 75 steps. The 400-example training set and 48-example held-out set were drawn from the validated synthetic examples. He reports a total build cost of $2.81; that is the cost he states for this prototype, not a general estimate for building or operating a similar system.

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The article also reports that enabling TabPFN’s cached fit mode reduced prediction time from 2.43 seconds to 0.27 seconds. End-to-end planning time fell from 5.6 seconds to 3.5 seconds. Waheed attributes most of the remaining time to two network round trips to Tinker. Those timings describe his implementation; they should not be read as a universal performance expectation.

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“Local” applies to only part of the pipeline

Waheed says TabPFN runs locally on the CPU, while the parser can use Tinker’s sampling API. In that configuration, the whole application is not local: text sent to the parser may be processed through a remote service. The author explicitly cautions against describing the parser as local when it is served by Tinker.

Waheed says the implementation excludes prompts from Sentry and disables default personally identifiable information collection. Those are implementation claims from the project article, not the result of an independent privacy or security audit. Anyone considering a similar setup should identify which components run on-device, what text leaves the device, and what the service providers log before entering sensitive information.

What a reader can try today

Waheed’s article presents Actually as a prototype, not a hosted product with a public sign-up link. It points to a demo video and local setup instructions; the demo profile uses made-up data. The article does not establish clinical evaluation, improved ADHD outcomes, or reliable time estimates for real-world tasks. Its most concrete contribution is a design example: keep tasks visible and persistent, use personal history as an input to planning, and treat model predictions as fallible support rather than authority.

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