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“Last time” has no single meaning until you define the clock it runs on. In one legal-practice memory system, the most recent upload was being returned as the most recent hearing. The fix was to give each memory the date of the event it describes, not the date the system received it. That one change turned a fluent but wrong answer into a correct one.
How the wrong answer happened
The system answered quickly, cited its sources, and still picked the wrong event. The example comes from the author’s own account of a small litigation practice: five cases and 79 uploads spanning April 2025 to September 2026. These are the author’s reported figures, not an independently audited data set, but the failure pattern is general.
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Uploads did not arrive in hearing order. Records were processed from a backlog, and some OCR work ran concurrently, so the order in which pages finished was unrelated to the order in which hearings took place. Because the system stamped each record with its ingestion time, every record looked as though it had happened when it was uploaded. A question about the latest hearing therefore returned whatever had finished processing last.
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Three different meanings of “last”
Before writing any code, decide which of three time axes the question depends on. They can all be valid timestamps for the same document, and they answer different questions.
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| Time axis | What it records | Question it answers | How Hindsight exposes it |
|---|---|---|---|
| Event time | When the hearing, incident, or transaction occurred | “What happened most recently?” | Supplied as timestamp at retain; returned as occurred_start and occurred_end at recall |
| Ingestion time | When the system received and processed the record | “What did we upload or process last?” | Used automatically when timestamp is omitted or null |
| Mention time | When a fact was retained or referenced | “When did we last record this fact?” | Returned as mentioned_at at recall |
A litigator asking “what happened last time” wants event time. Ingestion time is useful for auditing the pipeline, and mention time is useful for tracking when a fact entered the store. Keeping all three as separate fields avoids silently substituting one for another.
What Hindsight does with the timestamp
According to Hindsight’s official developer documentation for retaining memories, the timestamp field represents when the described event occurred. If it is omitted or null, the current time at ingestion is used instead. A real event timestamp is what allows temporal recall to work on the event rather than on the upload.
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On the recall side, the documentation describes temporal windows as a ranking aid, not a hard filter. Memories inside the requested window are ranked higher, but other retrieval methods can still return memories dated outside it. Correct timestamps therefore improve the ranking; they do not by themselves guarantee that the top result is the right event.
Assign the event date before the first write
The author’s implementation sets the event date on every hearing record before storage. The sequence below reflects that approach, and each step can be adapted to other event types.
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- Define the event. For hearing records, the event is the date the hearing took place. A “next date” written on a document is a scheduling note, not the event, and must be excluded.
- Extract the event date from each upload. If the date cannot be determined, store null. Do not invent a date to fill the field.
- Set the timestamp. The author uses the hearing date at 10:30 India Standard Time for every hearing. The stated reason is that district courts roughly begin sitting at that hour. This is the author’s convention, not a general requirement; use whatever time-of-day rule fits your event type, or a date-only value if your source has no time.
- Record the date in metadata. Store the hearing date as an explicit metadata field so it can be displayed independently of the timestamp.
- Include the date in the document ID. The author combines the hearing date with the upload and case identifiers so the ID stays stable for a given hearing.
- Retain the memory. Only after these fields are set should the record be written.
The author’s own summary of the principle is: “Decide what time a memory is about before you store the first one.” (Eeshaan, DEV Community, 2026.)
Date extraction is the harder problem
Setting the timestamp correctly depends on extracting the right date. The timestamp only carries whatever the extractor produced, so most of the reliability work happens upstream.
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Separating the hearing from other dates
A single court document can contain several dates: the hearing itself, the next hearing date, a filing date, and dates quoted inside the body of a deed or order. The extraction prompt has to tell these apart. Only the date of the hearing the document records should become the event time.
Filename fallback and confidence
When the extracted date is missing or invalid, the author falls back to the filename. Phone-camera names often include a date, which makes them a useful guess. Because a filename reflects when a photo was taken, not necessarily when the hearing happened, the author caps confidence for filename-derived dates at 0.75. The automatic-confirmation threshold is 0.8, so every filename-based date goes to human review rather than being confirmed automatically.
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Known failure cases
- Photos taken after the hearing. A photograph taken the day after a hearing can carry the wrong day in its filename.
- Pages photographed together. When several pages are photographed in one session and their printed dates are unreadable, they can inherit the photograph’s date rather than their own.
- Review limits. The author’s review step catches some of these errors but is not guaranteed to catch all of them.
Keeping provenance and repeat uploads consistent
The author has three goals for the event date: it should support temporal retrieval, it should appear next to the citation so a reader can check the source, and repeat uploads of the same hearing should not create duplicate memories. A stable document identifier that includes the hearing date and case identifiers serves all three. When a document with the same ID is ingested again, Hindsight’s retain documentation describes replacement behaviour, so the record is updated rather than duplicated.
What the timestamp does not fix
A correct event timestamp removes one class of error: the system no longer confuses upload order with event order. It does not correct OCR mistakes, misread dates, or wrong extractions. A confidently stamped record built from a misread date is still wrong, which is why the confidence cap and review step exist.
Scope of the example
The details below come from the author’s account and should be read as a single implementation, not as a benchmark or a verified court record:
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems- The sample practice (five cases, 79 uploads, April 2025 to September 2026) is the author’s own description.
- The pipeline uses Gemini on Vertex AI to read uploads, with Hindsight holding the hearing memories. These are implementation choices, and other models or stores would need their own validation.
- The author’s example identifies the latest hearing in one case, Seabreeze specific performance, as 24 September 2026, drawn from a typed note. It illustrates the method and is not an independently checked record.
The lesson does not depend on those specifics. Before storing a memory, decide which clock your question uses, store that clock explicitly, keep the other clocks as separate fields, and treat the extraction step as the place where errors enter.
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