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Arqam Waheed’s prototype, Actually, is built around a simple idea: when tasks disappear from view, they can disappear from mind. It turns a free-form brain-dump into a visible list that stays put until items are marked done, then uses personal task history to estimate how long new tasks may take. A fine-tuned 4B language model handles the parsing; a separate forecasting model handles timing. Waheed presents it as a planning aid for a friend—not a clinical intervention—and its reported parser scores come from synthetic examples rather than the friend’s real brain-dumps.
What problem is Actually designed to solve?
Waheed describes building Actually for his friend Zee, who explained the difficulty in these words: “can’t remember. if infront remember or else gone from brain. person or task”. That is Zee’s reported description of an experience, not a diagnostic statement.
The design response is not simply to generate better answers. As Waheed puts it, “The ‘if it’s not in front of me, it’s gone’ framing is basically a spec for proactive surfacing rather than better answers.” In practice, tasks remain on a persistent “Still on your plate” list until the user clears them. The app stores them in SQLite, deduplicates similar entries, and allows unfinished tasks to carry over to tomorrow.
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This emphasis on visibility came from asking Zee what was actually going wrong. The initial idea focused on time blindness; the reported design shifted toward keeping tasks from vanishing out of sight. The list is therefore central to the concept, not just a place to display the language model’s output.
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How does the app turn a brain-dump into a plan?
Actually separates two jobs that are easy to conflate: understanding a task description and predicting how it may fit into someone’s day. For example, a brain-dump might include “email sir abt the attendance thing” or “call nani back i keep forgetting.” The parser structures such entries as tasks; the forecasting stage estimates duration and the chance of completing a task that day.
Parsing the words
Waheed fine-tuned Qwen3.5-4B with LoRA, reporting rank 32, three epochs, and 75 training steps. The parser is intended to extract structured task details from informal text. Its output can include subjective attributes such as whether a task is dreaded, as well as a deadline—fields that are harder to infer consistently than the task itself.
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Forecasting time and fit
TabPFN performs the forecasting, using the person’s completed-task history. Waheed says the regression predicts log duration and produces 10th-, 50th-, and 90th-percentile estimates; a separate classifier estimates whether the task will be completed that day. 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.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe range of duration estimates is more informative than a single precise-looking number: it can show that a task has meaningful uncertainty. The app also uses a cue when the 90th-percentile duration is at least half of the available free time, intended to warn that a task may crowd the day. Tasks that do not fit can remain visible and move to tomorrow rather than disappearing from the plan.
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What do the reported parser scores show—and what do they not show?
Waheed reports that large open models generated and labeled 450 synthetic brain-dumps, of which 448 were validated. He used 400 examples for training and held out 48 for evaluation. None of Zee’s real brain-dumps were used. The figures below are the author’s results on that synthetic held-out set, tested at temperature zero; they measure agreement with synthetic labels, not verified accuracy on Zee’s needs.
| Parser configuration | 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 |
Across the 48 held-out dumps, the fine-tuned model’s task F1 was 97% and its deadline accuracy was 76%. But only 8% of dumps had every field exactly right. Waheed reports that errors were concentrated in subjective “dreaded” and “deadline” fields. The high task F1 therefore should not be read as a claim that each complete plan is correct: finding tasks is different from getting every detail right.
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Because a larger model supplied the synthetic labels, this evaluation tests how well the smaller model reproduces that teacher’s annotations. It does not establish human-verified performance, reliable duration estimates for a real user, or improved ADHD outcomes.
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“Local” applies only to part of the pipeline in Waheed’s description. TabPFN runs locally on the CPU, but the parser can use Tinker’s sampling API. Waheed explicitly cautions against describing the parser as local when it is being served through Tinker. His article also says prompts are excluded from Sentry and default personally identifiable information collection is disabled. These are implementation claims from the author, not an independent privacy or security audit.
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What changed when the app was made faster?
Waheed says profiling led him to use TabPFN’s cached fit mode. In the author’s reported measurements, TabPFN prediction latency fell from 2.43 seconds to 0.27 seconds, while end-to-end planning time fell from 5.6 seconds to 3.5 seconds. He attributes most of the remaining delay to two network round trips to Tinker. These figures describe this prototype’s reported profiling, not a general performance guarantee for other machines or deployments.
Can you try Actually?
Waheed’s article points to a demo video and local setup instructions, but does not provide a hosted application link. The demo profile uses made-up data, so it should not be mistaken for Zee’s personal history or real task records. The author reports a total build cost of $2.81, without establishing that as a recurring operating cost or a cost readers should expect; the prototype uses software and services rather than a specified hardware purchase.
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