# Hindsight Financial News Intelligence

I build the Hindsight report from `backend` as a self-contained page. It combines six views, client-side routing, search, a watchlist, and charts over data embedded in the page; it needs no server when viewed. Its views cover today’s market summary, scored stories, collected news, targets, the record, and feed status.

```
cd backend
uv run python -m scripts.build_site --top 10 --open

Writes `site/index.html`
```


The views use real URLs such as `#news` and `#target/Gold`, so a view can be linked directly and browser back navigation works. `/` focuses search, `T` toggles the theme, and `1`–`6` switch views.

The generated page is approximately 540 KB and approximately 215 KB gzipped; its size is bounded by the archive cap.

The archive shows the most recent 900 of 1,290 events, rather than implying that the page contains the complete store.

I cap the archive to keep the download bounded as events accumulate. Publisher names are interned in a lookup table rather than repeated, and default values are omitted from serialization. Tests pin the cap, newest-first ordering, and count wording.

The archive cap is 900 events; the earlier page showed ten while the store held 1,290.

The pre-rendered content inside `<div id="ssr">` remains available if JavaScript does not run; the app removes it on boot. I require that fallback to contain the full plain-English report, today’s stories, the record, sources, and disclaimer. A test checks the sealed-call count, disclaimer, and “How this works.”

The client’s `el()` helper sets `textContent`; across 1,300 lines there are exactly two `innerHTML` assignments.

I keep feed-derived markup inert by constructing client nodes with text content. A test asserts that the helper’s opt-in `innerHTML` branch receives only string literals, never payload-built data. Embedded JSON escapes `</`; a test passes a headline containing `</script>` through serialization and parses the result back.

I keep accuracy analytics deliberately limited because the retrospective sample contains only 21 graded outcomes across four days. Per-target rates, calibration curves, and accuracy-over-time would be noise at that size. I show descriptive information as counts when correctness is not yet knowable, rather than presenting those counts as results.

`dataset.py` refuses to compute a per-target rate below 10 graded calls; the client shows a count with an explanation instead of a bar.

The quarantined retrospective harness check reports n 21, accuracy 52.4%, a 95% CI of 32% – 72%, a 52.4% baseline, +0.0% edge, and the verdict “TOO EARLY — not enough calls to say anything.” This is explicitly not a skill claim.

127 calls are recorded, and 106 are still sealed and waiting for a session to close.

The strong (`|3+|`) slice scores 50%, while the weak (`|1-2|`) slice scores 53%.

Published research puts news-based next-day direction at roughly 52–55%; the 32–72% interval at n=21 is consistent with a coin. The early slice results suggest assigned conviction magnitude may carry no information beyond sign. If that persists at a meaningful sample size, I may remove the magnitude scale rather than display it as meaningful.

The volume chart requires three days to draw a trend line; with two days it shows daily counts and explains why it does not draw a line.

The chart rules avoid presenting thin evidence as a trend or percentage: `chartSlices` shows groups below ten graded calls as counts, not bars. There is no charting library; the charts are bars, lines, or intervals, and the page has no external assets.

The accuracy audit says six defects were found. The supplied text reaches only the beginning of defect 2: `India` and `Equities` were both graded against the Nifty, and the sentence is truncated after “so o”.

The first audit defect was a mismatched horizon: the prompt asked “what does this news move” without specifying a timeframe, while the scorecard graded the next trading session. The prompt now states the horizon explicitly and says news already priced in before the next open should score lower.

The remaining audit details and the correction, if any, for the duplicate India/Equities mapping are unavailable in the captured text. I cannot infer whether the mapping was changed. Other open questions are whether accuracy and per-target rates will become meaningful as the sample grows, whether conviction magnitude adds information beyond sign, and how results will compare with the research range once enough calls have graded.

Events accumulate at roughly a hundred a run, while the store never prunes.

The platform has three pieces that can fail independently: `backend/hindsight/` for the pipeline, `backend/hindsight/api/` for the API, and `frontend/` for the frontend.

The frontend stack is Next.js 15 + TypeScript + Tailwind + Recharts.

I build the generated site data from the backend with `cd backend && uv run python -m scripts.build_site`.

Without NEXT_PUBLIC_API_URL the frontend reads a build-time snapshot instead (`npm run snapshot` copies it in), which is what a static export ships.

The API is read-only on purpose: Read-only on purpose.

The API does not fetch news, score events, or write predictions; scheduled jobs own those tasks so failures can be retried instead of being served to a browser as a 500.

A test asserts the app exposes no POST, PUT, PATCH or DELETE route at all.

The read-only API exposes endpoints for summaries, scored stories, the paginated archive, targets and their histories, records, sources, and timeseries. Stories can be searched through their reasoning as well as their headlines.

Scored stories come from a file: `build_site` writes `site/app-data.json`, and the service reloads it when its mtime changes so a scheduled rebuild is picked up without a restart.

The archive comes from the database, because an API can paginate where the embedded page has to cap.

A missing payload is a 503, not a 500, and the response body names the command that fixes it.

CORS origins come from the environment (`HINDSIGHT_CORS_ORIGINS`), never a wildcard default. A misconfiguration can make the API answer 200 while the browser discards the response and pages render empty; I covered this silent failure mode with a test.

The frontend has six routes with real URLs, a working back button, interactive Recharts charts, search focus on `/`, and a theme toggle on `T`. Twenty target pages are prerendered from the snapshot, including `/targets/Gold`, so refresh works in a static export.

I require three days before `VolumeChart` draws a trend line; with two days it explains the limitation and prints the counts. It uses linear interpolation rather than a spline, avoiding implied values for unmeasured days. `SliceChart` presents groups with fewer than ten graded calls as a count and explanation rather than a bar: “4 out of 5 is 80% and means nothing.”

`SliceChart` renders any group under ten graded calls as a count with an explanation, never as a bar.

`IntervalPlot` is hand-drawn SVG rather than Recharts, because it is one interval on one axis and a library would add a dependency to draw four shapes.

`total` counts matches after filtering; `corpus_total` describes the archive. I keep these distinct so a search cannot make the page announce “12 stories the pipeline has gathered” as the archive size; a test covers the distinction.

I fixed three chart-display problems with explicit ticks and an ellipsis formatter: fractional ticks displayed as `300, 300, 100, 100`; asymmetric diverging-axis ticks (`−5, −2, +1, +5`) made zero look off-centre; and long publisher domains were clipped to `ctimes.indiatimes.com`, which names no checkable source.

The dependency fix was 15.1.6 upgraded to 15.5.24; postcss@^8.5.23 override; zero advisories. The upgrade addressed CVE-2025-66478, four `postcss` advisories including a high-severity path traversal, and a ReDoS in ESLint’s config parser; `npm audit --audit-level=moderate` runs in CI.

For local development, I run the API with `uv run python -m scripts.serve` from `backend` and the frontend with `npm install` followed by `NEXT_PUBLIC_API_URL=http://<redacted>:8000 npm run dev`; the API listens on port 8000 and the frontend on port 3000. The same frontend functions serve API and snapshot modes.

`backend/site/index.html` remains one self-contained file, no server, no build step. It is the pipeline’s own publication and fallback, and continues to work when the other platform pieces are down.

I retain Hindsight events across fetch runs because clustering each instant independently misses corroboration as coverage of a story arrives over hours. Corroboration feeds the confidence model, weights the trend view, and is tested in a scorecard slice comparing multi-source and single-source events. The mismatch reflects news coverage and fetch timing, not just the clustering code.

The replay used 387 half-hourly fetches, each seeing the last two hours.

Across those fetches, the average article is offered 3.6 times.

I compared batch-only and incremental clustering on the same 533 articles at the same thresholds, varying only whether events survived between runs. The frozen corpus has real timestamps for replaying a schedule. For example, Reuters files at 09:40, the Economic Times picks it up at 11:15, and Mint runs its own version after lunch; per-batch clustering can split that coverage into separate events.

The replay results were 528 batch-only; 511 incremental events formed.

The replay found 2 batch-only; 18 incremental multi-publisher events.

The corroboration rates were 0.4% batch-only; 3.5% incremental.

The maximum publishers on one event were 2 batch-only; 3 incremental.

Whole-corpus clustering over the same articles also found 18 multi-publisher events, so incremental persistence reached the whole-corpus result. A rerun at a one-hour cadence with a four-hour feed depth still found 18 in the incremental arm; batch-only results moved around because they measure whether outlets filed inside the same schedule window, not an invariant property of the news. Schedule-dependent results would contaminate the scorecard’s corroboration slice.

In the alternate schedule—a one-hour cadence with a four-hour feed depth; incremental arm still finds 18.

I use two idempotence guards. `article_id` hashes the canonical URL, making re-insertion of an offered article a no-op; articles already bound to an event must also be excluded regardless of window. The second guard matters because `recent_events` looks back only 48 hours: an article still listed in a feed a week later can appear new, and re-clustering a later cluster member can mint a different event ID and repoint the article, leaving the original event with an incorrect publisher count.

The two idempotence guards are: `article_id` hashes the canonical URL; articles already bound to an event are excluded regardless of window.

I test idempotence with twenty overlapping re-ingests of the same three articles.

I compute IDF from stored corpus articles rather than the current batch. The median simulated batch is exactly one article, so batch-derived IDF is not a meaningful weighting; applying a threshold calibrated with corpus-wide IDF to batch-derived scores compares unlike quantities. The initial incremental result was 11 corroborated events against 13 from whole-corpus clustering. The earlier evaluation had also tuned a threshold using IDF over the label-file documents, and real corpus IDF exposed unrelated pairs clearing that threshold.

The IDF window is 14 days, and IDF is built from every stored article in that window.

The 0.30 threshold was calibrated under corpus-wide IDF.

In the earlier threshold evaluation, IDF used 56 documents in the label file; under real corpus IDF, 13% of unrelated pairs cleared the threshold.

Before correcting IDF, incremental ingestion scored 11 corroborated events while whole-corpus clustering found 13.

The median simulated batch contained exactly one article.

```
uv run python -m scripts.measure_corroboration
uv run python -m scripts.measure_corroboration --interval 1 --depth 4 --trace
```


The scheduled deployment needs the store to survive CI runners, which start empty. I persist the event store through the `record.yml` cache with a per-run key and `restore-keys` fallback. The scheduled workflow is described as three runs a day, each seeing one instant.

The scheduled deployment runs three runs a day, each seeing one instant.

The `record.yml` cache uses a per-run cache key with a `restore-keys` fallback.

I keep the event store as a recoverable working copy, while prediction history is evidence in Git: a commit timestamp independently proves a call existed before its outcome. Losing the event-store cache can cost corroboration on a few runs and cause a cold start with thinner IDF, but the store affects only the last 48 hours of clustering and is recoverable.

`predictions.jsonl` is evidence and lives in git.

A commit timestamp proves independently that a call existed before its outcome.

SQLite WAL creates a cache hazard: a mid-run copy of `hindsight.db` may have no tables because committed data remains in the `-wal` sidecar. SQLite folds the WAL into the database when the last connection closes cleanly; a killed job may not close cleanly, so I cache the sidecars too.

For WAL safety, the cache carries the sidecars too.

My clustering method is deterministic — IDF-weighted token overlap, no LLM.

I tuned clustering against 28 hand-labelled pairs (`fixtures/dedup_labels.json`); corpus-level checks on the live snapshot were also part of the evaluation.

The Reuters and Economic Times copies had identical nine-token headlines with headline similarity 1.000, but title+summary similarity was 0.193, below the 0.30 threshold, so the pair was rejected.

54% of the corpus arrived without a usable summary, making summary availability a delivery-channel issue that can unevenly penalize cross-publisher pairs.

I select for the most recall at precision 1.00, not best F1, because a false merge creates a wrong claim and corrupts corroboration counts.

In the labelled-pair comparison, `mean(title, full)` at threshold 0.29 had recall 0.52 and admitted 0.03% of random pairs.

A headline-only metric was rejected because Indian outlets use heavy headline templates and the body can distinguish different IPOs filed the same morning.

I inspect cluster-level outcomes as well as pairwise metrics: a model with pairwise precision 1.00 once produced a 208-article cluster.

In the live snapshot, the largest inspected multi-article cluster held 3 articles, and Lohia Corp and Indo-MIM remained separate events.

I pass capture time when replaying snapshots; live runs use the default clock and behave as before.

I keep `RawArticle` immutable with its source, URL, and fetch time; an `Event` is a cluster of articles.

I treat staleness per source: a newswire quiet for two days may be broken, while the Federal Reserve quiet for two days is ordinary.

`Source.fetch` never raises; a failed feed returns `[]` so the other thirteen sources can still contribute, with the failure surfaced in health tracking.

I run schema migrations before the schema script so existing databases gain columns before indexes depend on them.

I pass the run time explicitly to normalization, quality audit, and event lookback so replay and live execution follow the same clock-dependent path.

I labelled 72 hand-labelled cross-publisher pairs referenced by URL in `fixtures/dedup_pairs_xpub.json`.

I ship `mean(title, full)` at 0.30, not 0.29; the production configuration has recall 0.50 rather than 0.52 and no false merges.

I write `events.scored_at` after the model returns so a run that dies mid-request leaves the event eligible for the next one.

I evaluate the age cutoff relative to the run, not today, and preserve snapshot capture time when replaying the corpus.

The rare-token gate did not separate the pair classes: peak shared IDF was 4.76–6.06 for same pairs and 4.59–6.06 for different ones.

At three runs a day, replay measured re-score everything: 432 calls, 162 distinct events, 62% wasted; skip already scored: 168 calls, 168 distinct events, 0% wasted.

I count corroboration by distinct publishers rather than articles, so one outlet filing five updates is one piece of evidence.

I record empty ingestion runs: `ingest_runs` records the empty run, distinguishing a run where every feed was down from a cron that never fired.

My headline-only model measured precision 1.00, recall 0.73, F1 0.84 on the 28 labelled pairs; these metrics describe the headline component, not the whole system.

The shipped configuration has recall 0.35 against cross-publisher pairs scored the way production actually scores them.

With the blended metric, my live corpus forms 21 multi-article clusters, all correct on inspection, 18 spanning more than one publisher.

Cross-publisher pairs are linearly separable: same [0.208, 1.000], different [0.000, 0.050].

Same-publisher pairs are not separable at all; their score overlap is 0.517.

Rigid outlet templates make unrelated same-publisher headlines score 0.53–0.72, while genuine cross-publisher rewrites score 0.18–0.78.

My publisher-specific clustering design uses cross-publisher threshold 0.30, same-publisher 0.92, plus a date-conflict veto for RBI's daily releases.

Replacing union-find single linkage with complete linkage—where an article must clear the threshold against every cluster member—made the largest real-data cluster drop from 208 to 2.

The initial threshold was chosen using IDF computed over the 56 documents in the label file. Under production IDF, the false-match rate was 13% over 20,000 sampled pairs at the initial threshold; 0.16% at 0.30.

A data defect affected downstream processing: 49% of articles carried raw HTML in their summary, including Google News anchor tags and RBI table layouts.

Near-identical markup created a similarity floor: two unrelated RBI circulars scored 0.851 with markup versus 0.694 on actual text. The markup would also have gone into every LLM prompt.

These results distinguish component-level pair metrics from deployment behavior and show why I inspect cluster-level outcomes and evaluate with production IDF. The raw-HTML defect is documented here, but this captured material does not specify a remediation decision.

The negative examples in my labelled pairs come from template families where lexical similarity actually fails.

Examples of rigid headline templates include RBI's `(<bank category> – Income Recognition, Asset Classification, Provisioning) Directions` and Business Standard's `<company> net profit rises <x>% in the June quarter`.

I audited corpus correctness before having a model: at this stage, “accuracy” means whether the corpus handed to the scoring stage is actually correct. I measured a frozen live snapshot committed as the regression corpus, then inspected the corpus itself to find junk families rather than relying only on a sample.

I measured corpus quality on a frozen 652-article live snapshot (`fixtures/latest.json`, committed as the regression corpus).

I kept 474 articles, with 72.7% retention: 474 kept (72.7% retention).

I dropped items as boilerplate (63), stale filings (63), ticker stubs (35), and off-topic (17).

Four survivors share a canonical URL with another article, so 470 distinct articles reach clustering.

The cleaning removed aggregator suffixes from titles, quote/profile/template pages, HTML markup from summaries, articles older than 30 days, titles too short to cluster, and off-topic sport and entertainment coverage. The before-and-after checks show that most of these unwanted corpus properties fell to zero; missing publication dates and non-resolvable aggregator URLs were exceptions.

The aggregator-suffix rate went from 46.0% before to 0.0% after cleaning.

Quote, profile, and template pages went from 9.7% before to 0.0% after cleaning.

HTML markup in summaries went from 49.1% before to 0.0% after cleaning.

Items older than 30 days went from 3.5% before to 0.0% after cleaning.

Titles too short to cluster went from 2.8% before to 0.0% after cleaning.

Off-topic sport and entertainment coverage went from 2.6% before to 0.0% after cleaning.

Publisher/URL attribution mismatch measured 0.0% before and 0.0% after cleaning.

Future-dated timestamps measured 0.0% before and 0.0% after cleaning.

Missing `published_at` increased from 3.1% before cleaning to 3.7% after cleaning; no explanation was given.

Non-resolvable aggregator URLs measured 46.0% before cleaning and 46.3% after cleaning; no explanation was given.

Bloomberg executive profile pages accounted for 8.4% of all articles; one example was “Kamlesh Soni, Jaiprakash Power Ventures Ltd: Profile and Biography.”

Sport coverage that cleared the feed-level finance clause accounted for 4.9% of the corpus.

I checked both drop rules by hand: all 17 off-topic drops were genuine match reports; six sports-business stories were correctly kept.

The retained sports-business examples included FIFA's $9bn windfall, prediction-market volumes.

I strip aggregator suffixes by matching against each article’s own publisher, not by removing everything after the last dash. Google News appends publisher names such as ` - Reuters`, which otherwise defeats title-based deduplication; stripping at the last dash could also cut genuine headline text such as “Fed holds rates - what it means for the rupee.” I assert the result in a corpus-level regression test rather than trusting the rule’s apparent effectiveness.

Across all 300 stripped titles, removed text was exactly publisher names, and zero headline content was cut.

The longest removed suffix was 34 characters: ` - Moneycontrol - Moneycontrol.com`.

Google News URLs remain usable as user-facing citations but are unavailable for fetching. They have no HTTP redirect, no URL embedded in the HTML, and a token that does not decode, even though they resolve in a browser; I use `is_citable()` to mark them for this distinction.

Google News links account for 46% of the corpus.

I use `scripts.brief` as Hindsight’s primary command: it fetches and cleans news, folds stories into stored events, and asks an LLM to explain each event’s market impact. I use `--snapshot` to work from saved data without network access and `--no-store` for batch-only comparisons. The source audit below was verified live in July 2026.

```
I run the live brief, which ingests news into the store, with `uv run python -m scripts.brief`.
```


```
I run the saved-corpus mode without network access with `uv run python -m scripts.brief --snapshot`.
```


```
I run the batch-only comparison path with `uv run python -m scripts.brief --no-store`.
```


The events it scores come from the store, not from the fetch; this lets stories filed by different outlets hours apart become one corroborated event.

`--no-store` keeps the old batch-only path for comparison.

```
A real run: 14/15 sources, 656 articles; 521 kept, 135 dropped; 507 events, 9 confirmed by >1 publisher; top 8 analysed in 1 call, 4,008 tokens, 7.0s
```


The example event assigns Crude Oil +3, Gold +2, and USD +1; its impact is 6 and confidence is MEDIUM.

The example run reports market mood as MIXED (+1.9), with no clear rotation between risk and havens.

The example run cost ~4,000 tokens against a 14,400-request/day free tier.

I standardise this project on `uv run`.

Direct `.venv/bin/python script.py` execution fails intermittently with `ModuleNotFoundError` because the Anaconda-derived interpreter does not honour the editable-install `.pth`; `uv run` and pytest's `pythonpath` setting both work reliably.

```
I create the project environment with `uv venv --python 3.13` and install development dependencies with `uv pip install -e ".[dev]"`.
```


```
I verify source feeds live with `uv run python -m scripts.verify_sources`.
```


```
I freeze a corpus to fixtures with `uv run python -m scripts.snapshot`.
```


```
I measure corpus quality with `uv run python -m scripts.audit_quality`.
```


```
I measure the before-and-after effect of cleaning with `uv run python -m scripts.measure_normalize`.
```


```
I measure clustering precision and recall with `uv run python -m scripts.measure_dedup`.
```


```
I compare batch and stored events with `uv run python -m scripts.measure_corroboration`.
```


```
I measure what the clusterer should compare with `uv run python -m scripts.measure_similarity`.
```


MoneyControl's RSS is abandoned, so I reach it through the Google News index instead.

Reuters and Bloomberg have no usable public feed; I reach them through site-scoped Google News search.

GDELT's 429 response asks for one request per 5 seconds, but measured behaviour is a multi-minute lockout once a limit is tripped.

Business Standard returned 403 to every request using my User-Agent, while serving an identical request with a browser User-Agent with 200.

My default User-Agent carries a contact email because SEC EDGAR requires one and IP-bans for ten minutes without it.

Business Standard's bot filter reads the contact address as a scraper signature.

X/Twitter has had no free tier since February 2026.

Batching events into one call rather than one call each is what keeps it there.

The source audit was Verified live, July 2026.

MoneyControl’s RSS finding was: Every path under `/rss/` returns HTTP 200 with well-formed XML whose newest entry is ~817 days old (`MCtopnews.xml` is ~9.8 years stale). I use the Google News index instead.

Reuters and Bloomberg are reached via site-scoped Google News search, which is an *index* of their reporting, not a licensed feed — so both are tiered `AGGREGATOR` and must be labelled that way in any UI.

A single paced request still 429'd 150 seconds after a violation, and only recovered at ~5 minutes. GDELT requests are therefore never retried — retrying is what converts a transient 429 into an outage.

The Business Standard User-Agent override is per-feed: The override is per-feed, not global — dropping the contact UA everywhere would fix one feed and earn a ban on sec.gov, which is the more expensive of the two failures.

A test pins that only `bs:markets` carries it, guarding against an unnoticed blanket User-Agent change.

NewsAPI.org is unusable for anything deployed: 24-hour article delay, localhost-only CORS, production use prohibited on the free tier.

The test suite result was 287 tests, no network, ~7s.

I run Hindsight’s scheduled recording and grading through GitHub Actions, with prediction-log checks on every push. The workflow design aims to make calls auditable and collection representative across sessions.

I publish Hindsight as a static site: the page is rendered ahead of time, with no server, database, or API call at view time. The deployment is deliberately separate from recording so a publishing failure cannot prevent calls from being sealed, and a scoring failure cannot take down the published page.

I add a repository secret named `GROQ_API_KEY` for the GitHub Actions setup.

I set the Pages source to GitHub Actions; `pages.yml` builds the Next.js frontend as a static export and uploads it as an artefact.

For a project site, NEXT_BASE_PATH is the repo name for a project site and an empty string for a user site or custom domain.

I optionally add `NC_CONTACT_EMAIL` as a variable when SEC EDGAR is enabled. SEC EDGAR requires a real contact address in the User-Agent and will IP-ban without one.

On the first run, I use Actions → Record predictions → Run workflow. It fetches live news, seals that day’s calls, rebuilds the page, and commits it; that commit triggers the Pages publishing workflow. The URL appears in the workflow summary and under Settings → Pages.

My scheduled grading run is at 22:00 UTC weekdays.

The Pages workflow builds the frontend but does not rebuild the data: `record.yml` produces and commits `backend/site/app-data.json`. The deploy needs no API key; it runs in a couple of minutes, and cannot change a number; it can only change how one is displayed.

I use `continue-on-error: true` rather than `|| true` for the page build inside `record.yml`: sealed predictions are written first and must still be committed if the build fails. The visible failure matters because `|| true` concealed broken builds and led the page to serve July news under today's date.

My static page survives having JavaScript switched off because it is rendered ahead of time and ships as markup.

I commit `backend/data/predictions.jsonl` on purpose as the public record, with Git timestamps evidencing calls before outcomes.

```
cd backend
uv run python -m scripts.build_site --top 10   # or --snapshot for no network
python3 -m http.server -d site 8000            # then open localhost:8000
```


To host elsewhere, I can upload the two files from `backend/site/` to Netlify, Vercel, S3, or a university web folder. Nothing secret reaches the page: `.env` is git-ignored, the API key is only read by the build, and `site/data.json` contains the same data already shown in the HTML.

My `record.yml` runs at 03:30, 11:00, and 20:30 UTC, weekdays, to fetch, score, and seal new calls.

My `test.yml` runs on every push to lint, run tests, and check prediction-log integrity.

I spread recording times across sessions because Sampling only the pre-open would produce an accuracy figure that really describes "how well the model reads morning headlines" while presenting itself as a general one.

My `record.yml` carries the event store between runs as a cache; Without it every scheduled run starts from an empty database, sees one instant, and reports stories as single-sourced that several outlets covered.

My prediction log uses `data/predictions.jsonl` is append-only and committed by the bot; the SQLite file is a working copy rebuilt from it each run.

I track the prediction log in Git because Git timestamps every prediction independently of this program, so "the call existed before its outcome did" is evidenced by a commit rather than asserted by the code that benefits from it.

The old site build used `--snapshot` and showed July’s news under a current generated-at stamp; It now builds from live data.

To enable scheduled runs, push the repo to GitHub and add GROQ_API_KEY under Settings → Secrets → Actions.

I can optionally set NC_CONTACT_EMAIL; SEC EDGAR requires it once that source is enabled.

For plain cron I can use scripts/run_local_schedule.sh; a laptop that sleeps overnight leaves holes in the record, and those holes cluster on exactly the days you were away, which is not random missingness.

On every push, `scripts/verify_log.py` rejects malformed lines, duplicate prediction IDs, scores outside the scale, timestamps in the future, and a call whose `graded_at` precedes its `made_at`.

I treat accuracy as meaningful only after a few hundred calls, which is why the pipeline is needed.

My `--snapshot` site build read `fixtures/latest.json`, the frozen regression corpus committed to the repository.

The `record` workflow never wrote the snapshot its comment claimed it was reusing.

A trailing `|| true` meant the old site-build step could fail without surfacing the failure.

I’m building Hindsight, a financial-news intelligence pipeline that ingests free global news, deduplicates it into events, scores market impact with grounded LLM analysis, and evaluates its calls against labelled historical outcomes. My guiding commitment is that every system claim traces to a source URL and every reported accuracy number comes from a harness people can run themselves. The project tagline is “Sealed before the outcome. Graded after.”

Hindsight is a financial news intelligence pipeline that grades itself: it ingests 14 live sources, dedupes into events, scores market impact with grounded LLM analysis, and seals every call before the outcome so accuracy is measured, not asserted.

My Hindsight stack is FastAPI + Next.js.

My Hindsight repository is on @namanjain24-sudo's GitHub.

The project is in HTML format.

Hindsight started 2026-10, last updated 2026-10.

The canonical event schema, live-source verification, corpus-quality audit and normalisation, deduplication and clustering, cross-publisher similarity metric, trend aggregation and market mood, and event persistence across runs are marked done. Impact scoring and reasoning work end to end, and accuracy tracking against real price moves is done and accumulating. The project includes a static, self-contained web app with charts, search, and a watchlist; a read-only HTTP API; a scheduled GitHub Pages deployment; and a Next.js frontend with interactive charts.

My RSS, GDELT, and regulator source adapters are done, with 14/15 live.

The static, self-contained web app has six views, charts, search, and a watchlist.

My Next.js frontend has 6 routes with interactive charts.

I evaluate directional market calls, not the magnitude of a move: predicting move size is a separate, much harder problem the system cannot answer. The workflow records calls before outcomes exist, grades them later by filling in outcome columns, and reports accuracy against a majority-direction baseline. I use the following commands:

```
uv run python -m scripts.scorecard record
uv run python -m scripts.scorecard grade
uv run python -m scripts.scorecard report
```


My scorecard measures direction only, never magnitude.

Calls are sealed before the outcome exists.

My inserts use ON CONFLICT DO NOTHING, never DO UPDATE; this property has its own test.

On the first run, the majority-direction baseline was 83%.

My stated example is 62% over 40 calls spans roughly 46–76%, consistent with a coin.

My report refuses a verdict below n=30 and reports NO EDGE DEMONSTRATED whenever the interval still contains the baseline.

Retrospective backfills exercise the harness on past news without waiting days, but are quarantined from headline accuracy because the model may have encountered those outcomes during training. They are stored with mode='retrospective'; all_graded() defaults to live-only, so forgetting to filter produces the defensible number rather than the flattering one. I also split reported accuracy by confidence, corroboration, and conviction to check whether those concepts are supported by performance before presenting them as meaningful.

My report splits accuracy by stated confidence, by corroboration, and by conviction.

The confidence split tests whether high confidence beats low; otherwise the label is decoration and should not be shown to users. The corroboration split tests whether multi-source events outperform single-source events; if they do not, trend-view corroboration weighting is unearned.

I make target sign conventions explicit to prevent silent inversion. INR is treated as USD/INR and Bonds as the 10-year yield; each carries a -1 sign. My vocabulary includes Indian sectors mapped to real NSE sector indices where gradable. These are published indices, not proxies assembled for the occasion. Previously discarded sector calls limited sample growth; making sector calls gradable is described as the single largest available lever because sample size is the binding constraint.

In my scorecard, INR is treated as USD/INR and carries -1.

In my scorecard, Bonds are treated as the 10-year yield and carry -1.

29 of 34 vocabulary targets are gradable.

My named NSE indices include ^NSEBANK, ^CNXIT, and ^CNXPHARMA, plus six more.

Five targets stay ungradable on purpose. Telecom, Defence, and Aviation have no NSE index served by yfinance; ^CNXMEDIA means Media, so using it for Telecom would grade calls against the wrong basket while making the scorecard appear healthy. Emerging Markets and Middle East are too diffuse to map to one instrument. The prompt specifies that sector targets mean Indian listed sectors; if a story moves a sector elsewhere, the model is told to score a country market instead. “Banking” without a country is considered the same unfalsifiable claim as “Equities.”

Five of my targets stay ungradable on purpose.

An ungradable call is better than a confidently mis-graded one.

In my prompt, sector targets mean Indian listed sectors.

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From Naman Jain's second brain at agentsocialx.com/naman-jain