Device-test round 2: 4 in-app fixes + Matrix intake cleanup (v0.1.0:99)
Grant's real-phone testing surfaced seven items; this lands six (the seventh, in-app camera card intake, is planned in docs/handoffs/in-app-card-intake-plan.md). CRM half — ships in the s9pk (v0.1.0:99): - Intake fuzzy match no longer over-indexes on generic firm words. _name_similarity now compares DISTINCTIVE tokens only (generic descriptors — "Investment Group", "Capital", "Family Office" — stripped via _GENERIC_ORG_WORDS) for both the difflib ratio and the Jaccard, so "Fortitude Investment Group" stops surfacing Aether/Russell while "Aether Capital" still surfaces "Aether Investment Group". +2 regression cases. - Mobile grid "Last contact"/staleness sort is reversible. SortSheet gains opt-in dir/onToggleDir; other surfaces (Contacts/Pipeline) are untouched. - Mobile "Edit investor" prefills a contact's saved email. GET /api/fundraising/state heals a blank grid pill email from the linked classic contact (fundraising_contacts.contact_id -> contacts.email), fill-only, by pill order then name; the next one-row save persists it. +test_grid_email_heal.py. - Mobile quick-log pencil icon renders. iOS collapses a sole, centered, attribute-only -sized flex-child <svg>; .quicklog-btn svg now gets explicit CSS width/height + flex:none (the pattern the working bottom-tab/sort-pill icons use). The v97 fix only changed color. Matrix intake bot — ships on the Spark (bot-only, NOT the s9pk): - Approve/reject now redacts the whole intake thread (card + ack + main-timeline nudge + the user's own photo/note), mirroring the email-review room; redact_thread takes the room as an arg and matches replies by m.thread OR m.in_reply_to (so the nudge clears). No more in-Matrix confirmation after a commit (the thread vanishing is the ack). Needs the bot to hold a redact/moderator power level in the intake room. - New one-time backend/matrix_intake/redact_intake.py clears the room's pre-existing backlog (dry-run default; --apply). Tests 42/42 green; frontend render-smoke green. Frontend fixes are inspection + render -smoke verified (on-device confirm pending); the bot redaction is live-smoke only.
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Keysat
+86
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@@ -1305,14 +1305,40 @@ def _strip_legal_suffix(normalized_name):
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return " ".join(toks)
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# Generic firm-descriptor words that carry almost no identifying signal: nearly every firm name
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# contains one ("… Investment Group", "… Capital", "… Family Office"). Two names that overlap ONLY
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# on these are NOT duplicates — 'Fortitude Investment Group' is not 'Aether Investment Group'. We
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# compare on the DISTINCTIVE remainder so a shared descriptor can't inflate the score (the earlier
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# "Capital/Ventures/Partners are distinctive enough to keep" assumption produced false shortlists —
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# Grant, 2026-06-20). If a name is ALL descriptor ('Family Office'), we fall back to its full tokens
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# so there's still something to compare.
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_GENERIC_ORG_WORDS = frozenset({
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"investment", "investments", "investing", "investor", "investors",
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"capital", "ventures", "venture", "partners", "partner", "group",
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"fund", "funds", "management", "advisors", "advisers", "advisory",
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"asset", "assets", "holdings", "holding", "family", "office",
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"trust", "associates", "equity", "financial", "finance", "global",
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"international", "company", "enterprises", "wealth", "the", "and", "of",
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})
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def _distinctive_tokens(normalized_name):
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"""Tokens of a (legal-suffix-stripped) name with generic firm descriptors removed. Falls back to
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the full token list when the name is nothing but descriptors, so an all-generic name still compares."""
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toks = re.findall(r"[a-z0-9]+", normalized_name)
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keep = [t for t in toks if t not in _GENERIC_ORG_WORDS]
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return keep or toks
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def _name_similarity(a, b):
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"""0..1 fuzzy similarity between two investor names: the max of difflib's sequence ratio
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(catches near-spellings — 'Charlie'/'Charles') and token-set Jaccard overlap (catches
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word-order differences). Legal-entity suffixes are stripped first, so two names differing
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only by 'LLC'/'LP'/'Inc' score 1.0 (a near-certain duplicate to surface — find_intake_match
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won't have caught it, since it compares the full string). Favors recall: a shared common
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name-word ('… Capital') can lift unrelated firms into the 0.6–0.8 band — acceptable noise in
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a ranked, human-confirmed shortlist; semantic pruning is the deferred LLM-judge's job."""
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won't have caught it, since it compares the full string). Both the ratio and the Jaccard run on
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the DISTINCTIVE tokens (generic descriptors like 'Investment Group'/'Capital' removed), so firms
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that share only a descriptor don't surface as look-alikes; 'Aether Capital' ~ 'Aether Capital
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Partners' still scores 1.0 on the distinctive 'aether'. Still recall-favoring on real overlap."""
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a = _normalize_text(a)
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b = _normalize_text(b)
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if not a or not b:
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@@ -1323,9 +1349,10 @@ def _name_similarity(a, b):
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sb = _strip_legal_suffix(b) or b
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if sa == sb:
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return 1.0
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ratio = difflib.SequenceMatcher(None, sa, sb).ratio()
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ta = set(re.findall(r"[a-z0-9]+", sa))
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tb = set(re.findall(r"[a-z0-9]+", sb))
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da = _distinctive_tokens(sa) # order-preserving for the sequence ratio
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db = _distinctive_tokens(sb)
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ratio = difflib.SequenceMatcher(None, " ".join(da), " ".join(db)).ratio()
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ta, tb = set(da), set(db)
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jaccard = len(ta & tb) / len(ta | tb) if (ta or tb) else 0.0
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return max(ratio, jaccard)
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@@ -1881,6 +1908,45 @@ def existing_investor_by_source_row(conn):
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return out
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def fundraising_contact_emails_by_row(conn):
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"""{ source_row_id: {'order': {sort_order: email}, 'name': {normalized_name: email}} } of the
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authoritative email per grid contact, for HEALING blank pill emails on read.
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The grid blob is canonical for the edit sheet, but an email can reach the linked classic
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contact (via email capture / a contact edit) without ever being written back into the blob
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pill — so the mobile "Edit investor" sheet shows an empty email for a contact the directory
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clearly has (Grant, 2026-06-20). We recover it from the relational mirror: prefer the synced
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fundraising_contacts.email, else the linked classic contacts.email (the source that actually
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holds the captured address). Keyed by sort_order (pills and fundraising_contacts share the
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blob order — the robust key) with a normalized-name fallback. Only non-blank emails are
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returned; filling is fill-only-when-blank in the handler, so it heals and converges (the next
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one-row save persists the recovered email into the blob)."""
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out = {}
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rows = conn.execute(
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"""
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SELECT fi.source_row_id AS srid, fc.sort_order AS so, fc.full_name AS name,
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COALESCE(NULLIF(TRIM(fc.email), ''), c.email) AS email
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FROM fundraising_investors fi
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JOIN fundraising_contacts fc ON fc.investor_id = fi.id
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LEFT JOIN contacts c ON c.id = fc.contact_id AND c.deleted_at IS NULL
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"""
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).fetchall()
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for r in rows:
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email = str(r['email'] or '').strip()
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if not email:
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continue
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srid = str(r['srid'] or '')
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if not srid:
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continue
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bucket = out.setdefault(srid, {'order': {}, 'name': {}})
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if r['so'] is not None:
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bucket['order'][int(r['so'])] = email
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nm = _normalize_text(r['name'])
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if nm:
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bucket['name'][nm] = email
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return out
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def contact_grid_signals(conn, contact_id=None):
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"""Return {contacts.id: {'committed': float, 'pipeline_stage': str|None, 'priority': bool}} for
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every classic contact linked to a fundraising-grid investor (via fundraising_contacts.contact_id,
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@@ -5830,6 +5896,7 @@ class CRMHandler(BaseHTTPRequestHandler):
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reminder_by_row = reminder_status_by_source_row(conn)
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existing_by_row = existing_investor_by_source_row(conn)
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recency_by_row = staleness_by_source_row(conn)
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emails_by_row = fundraising_contact_emails_by_row(conn)
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conn.close()
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try:
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@@ -5873,6 +5940,19 @@ class CRMHandler(BaseHTTPRequestHandler):
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last_activity, staleness = recency_by_row.get(srid, (None, ''))
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r['last_activity_at'] = last_activity
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r['staleness'] = staleness
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# Heal blank pill emails from the relational mirror (fill-only — never overwrite a value
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# already in the blob). Unlike the read-only columns above, email is a REAL blob field,
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# so this is a backfill, not a derived signal: it needs NO strip point, and the next
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# one-row save legitimately persists it. Match by pill order, then by name.
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heal = emails_by_row.get(srid)
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pills = r.get('contacts')
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if heal and isinstance(pills, list):
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for i, c in enumerate(pills):
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if not isinstance(c, dict) or str(c.get('email') or '').strip():
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continue
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found = heal['order'].get(i) or heal['name'].get(_normalize_text(c.get('name')))
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if found:
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c['email'] = found
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return self.send_json({
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"data": {
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